Advanced Operation, Control, and Planning of Intelligent Energy Systems

Topic Information

Dear Colleagues,

We are pleased to announce a call for papers on our Topic “Advanced Operation, Control, and Planning of Intelligent Energy Systems”! As global energy systems are undergoing a transition toward decarbonization and digitalization, demands for intelligent energy systems with the more advanced operation, control, and planning are increasing. However, the operation, control, and planning of such intelligent systems pose a number of challenges that need to be addressed. Currently, the uncertainties, economic efficiencies, technical feasibilities, and environmental sustainability of energy systems all require more intelligent solutions. Therefore, researchers from both academia and industry are actively developing strategies and schemes to optimize the production, distribution, and consumption of energy in sustainable and efficient manners. This Topic provides a platform for researchers around the world to present their research related to the operation, control and planning of intelligent energy systems. Topics of interest include but are not limited to the following:

• Machine learning and computational intelligence for energy systems;
• Advanced methodology for energy system operation and control;
• Smart planning, market design, and regulatory frameworks for energy systems;
• Digitalization and management of urban energy systems;
• Power electronics for energy systems with renewables;
• Power electronics for power conversion, energy storage, and control in energy systems;
• Integration of other emerging technologies in the operation, control, and planning of energy systems.

Dr. Ziming Yan
Dr. Rui Wang
Dr. Chuan He
Dr. Tao Chen
Dr. Zhengmao Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	-	4.8	2020	27.9 Days	CHF 1000	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit
Resources resources	3.6	7.2	2012	26.1 Days	CHF 1600	Submit
World Electric Vehicle Journal wevj	2.6	4.5	2007	16.2 Days	CHF 1400	Submit
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.4 Days	CHF 2600	Submit

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Published Papers (67 papers)

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15 pages, 2064 KiB  

Open AccessArticle

Multi-Objective Day-Ahead Scheduling for Air Conditioning Load Considering Dynamic Carbon Emission Factor

by Kun Zhang, Zhengxun Guo, Ji Wang, Jianlin Tang and Xiaoshun Zhang

Electronics 2025, 14(8), 1550; https://doi.org/10.3390/electronics14081550 - 11 Apr 2025

Viewed by 212

Abstract

The traditional optimal scheduling of air conditioning load has traditionally focused on improving user comfort and reducing electricity costs. However, research on carbon emissions generated during the operation of air conditioning is still in the developmental stage. Currently used average carbon emission factors [...] Read more.

The traditional optimal scheduling of air conditioning load has traditionally focused on improving user comfort and reducing electricity costs. However, research on carbon emissions generated during the operation of air conditioning is still in the developmental stage. Currently used average carbon emission factors in carbon-emission studies face challenges such as delayed data updates and difficulty in reflecting spatiotemporal variations. These issues contribute to the inaccurate quantification of carbon emissions, creating a challenging situation, which does not meet the development needs of green power systems under the “dual carbon goals”. Therefore, this paper proposes a multi-objective scheduling method for cooling-dominant air conditioning load considering the dynamic carbon emission factor (CEF), in conjunction with real-time spatiotemporal data from the electricity grid model, generates the electric carbon factor for each moment throughout the day. Firstly, while considering user comfort, the dynamic CEF-based carbon-emission cost and electricity cost are fused into the user’s comprehensive electricity cost, and a multi-objective optimization model for day-ahead scheduling of air conditioning loads is established. In addition, the above model is solved by the NSGA-II algorithm to obtain the Pareto front composed of non-dominated solutions. Then, the best compromise solution is objectively selected through gray target decision (GTD) to provide scientific decision-making for day-ahead scheduling of cooling-dominant air conditioning loads. Finally, four users with different air conditioning loads and room temperature requirements are designed to verify the effectiveness of the proposed strategy. The simulation results illustrate that compared with single-objective optimization and simple multi-objective decision-making methods, the proposed strategy possesses a stronger trade-off ability, which can greatly reduce the comprehensive electricity cost while ensuring user comfort. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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24 pages, 1665 KiB  

Open AccessArticle

Quantum-Inspired Multi-Objective Optimization Framework for Dynamic Wireless Electric Vehicle Charging in Highway Networks Under Stochastic Traffic and Renewable Energy Variability

by Dong Hua, Chenzhang Chang, Suisheng Liu, Yiqing Liu, Dunhao Ma and Hua Hua

World Electr. Veh. J. 2025, 16(4), 221; https://doi.org/10.3390/wevj16040221 - 7 Apr 2025

Viewed by 287

Abstract

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in [...] Read more.

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in motion, offer a transformative approach to mitigating range anxiety and optimizing energy utilization. However, these systems face significant operational challenges, including dynamic traffic conditions, uncertain EV arrival patterns, energy transfer efficiency variations, and renewable energy intermittency. This paper proposes a novel quantum computing-assisted optimization framework for the modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. Specifically, we leverage Variational Quantum Algorithms (VQAs) to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The mathematical modeling framework captures critical aspects of the system, including power balance constraints, state-of-charge (SOC) dynamics, stochastic vehicle arrivals, and charging efficiency degradation due to vehicle misalignment and speed variations. The proposed methodology integrates quantum-inspired optimization techniques with classical distributionally robust optimization (DRO) principles, ensuring adaptability to system uncertainties while maintaining computational efficiency. A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day. The results demonstrate that the proposed quantum-assisted approach significantly enhances energy efficiency, reducing energy losses by up to 18% compared to classical optimization methods. Moreover, traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types. The framework also facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation. Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization. The findings contribute to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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22 pages, 3878 KiB  

Open AccessArticle

Precision Prediction Strategy for Renewable Energy Power in Power Systems—A Physical-Knowledge Integrated Model

by Shenbing Hua, Shuanglong Jin, Zongpeng Song, Xiaolin Liu, Shu Wang and Hengrui Ma

Processes 2025, 13(4), 1049; https://doi.org/10.3390/pr13041049 - 1 Apr 2025

Viewed by 368

Abstract

To promote energy transformation and upgrading and achieve the dual carbon strategic goals, the proportion of renewable energy generation in China has been increasing annually. Among them, wind and photovoltaic power generation play a significant role in renewable energy generation due to their [...] Read more.

To promote energy transformation and upgrading and achieve the dual carbon strategic goals, the proportion of renewable energy generation in China has been increasing annually. Among them, wind and photovoltaic power generation play a significant role in renewable energy generation due to their widely distributed energy sources, and their development has been particularly rapid. However, renewable energy generation exhibits strong volatility, and the integration of a large amount of renewable energy into the power grid poses a threat to the stable operation of the power system. Therefore, renewable energy power prediction is of great significance for the safe and stable operation of the power system. This paper proposes a physical-knowledge integrated renewable energy power prediction model. Firstly, the Fuzzy C-Means (FCM) method is used to handle missing data. Then, the variational mode decomposition (VMD) algorithm is applied to decompose the renewable energy power into high-frequency and low-frequency components. The high-frequency components are input into a Convolutional Neural Network (CNN) model, while the low-frequency components are input into a Long Short-Term Memory (LSTM) neural network for training and prediction. Finally, the effectiveness and feasibility of the proposed method in this paper are verified through real data from a certain actual power grid. After analysis, the proposed method demonstrates a prediction accuracy improvement of up to 5.71% compared to conventional approaches. Simultaneously, in high-noise interference environments, the prediction error can be reduced by up to 11.16%. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 1396 KiB  

Open AccessArticle

Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

by Melanie T. Bennett, J. Thomas Kirkpatrick and Yilu Liu

Electricity 2025, 6(1), 15; https://doi.org/10.3390/electricity6010015 - 14 Mar 2025

Viewed by 400

Abstract

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in [...] Read more.

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study considers a measurement-based approach to tuning both models. The load modeling study compares the response of the tuned models to generic candidate models using historical events. This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations. Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response. The sensitivity study covers the parameters that are tuned using event measurements. The process to estimate the parameters uses the particle-swarm optimization algorithm. Overall, the performance of the tuned model more accurately captures recovery voltage, delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 1450 KiB  

Open AccessArticle

Supply–Demand Dynamics Quantification and Distributionally Robust Scheduling for Renewable-Integrated Power Systems with Flexibility Constraints

by Jiaji Liang, Jinniu Miao, Lei Sun, Liqian Zhao, Jingyang Wu, Peng Du, Ge Cao and Wei Zhao

Energies 2025, 18(5), 1181; https://doi.org/10.3390/en18051181 - 28 Feb 2025

Viewed by 582

Abstract

The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study [...] Read more.

The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study introduces a data-driven distributionally robust optimization (DRO) framework for power system scheduling. The methodology comprises three key phases: First, a meteorologically aware uncertainty characterization model is developed using Copula theory, explicitly capturing spatiotemporal correlations in wind and PV power outputs. System flexibility requirements are quantified through integrated scenario-interval analysis, augmented by flexibility adjustment factors (FAFs) that mathematically describe heterogeneous resource participation in multi-scale flexibility provision. These innovations facilitate the formulation of physics-informed flexibility equilibrium constraints. Second, a two-stage DRO model is established, incorporating demand-side resources such as electric vehicle fleets as flexibility providers. The optimization objective aims to minimize total operational costs, encompassing resource activation expenses and flexibility deficit penalties. To strike a balance between robustness and reduced conservatism, polyhedral ambiguity sets bounded by generalized moment constraints are employed, leveraging Wasserstein metric-based probability density regularization to diminish the probabilities of extreme scenarios. Third, the bilevel optimization structure is transformed into a solvable mixed-integer programming problem using a zero-sum game equivalence. This problem is subsequently solved using an enhanced column-and-constraint generation (C&CG) algorithm with adaptive cut generation. Finally, simulation results demonstrate that the proposed model positively impacts the flexibility margin and economy of the power system, compared to traditional uncertainty models. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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36 pages, 20768 KiB  

Open AccessArticle

Cooperative and Hierarchical Optimization Design of Shipboard MVDC System for Adapting to Large, Pulsed Power Load

by Zhimeng Liu, Yongbao Liu, Youhong Yu and Rui Yang

J. Mar. Sci. Eng. 2025, 13(3), 434; https://doi.org/10.3390/jmse13030434 - 25 Feb 2025

Viewed by 309

Abstract

Supplying power to large, pulsed power loads in shipboard medium voltage direct current integrated power systems is challenging due to the limited dynamic power responsiveness of the gas turbine. The two main solutions to this problem are improving the gas turbine’s dynamic performance [...] Read more.

Supplying power to large, pulsed power loads in shipboard medium voltage direct current integrated power systems is challenging due to the limited dynamic power responsiveness of the gas turbine. The two main solutions to this problem are improving the gas turbine’s dynamic performance and using energy storage devices for transient power compensation. In this paper, these two approaches are combined to achieve optimal coordination between the gas turbine’s dynamic response and the system’s transient power sharing strategy, and a mechanical–electrical cooperative operation strategy and a hierarchical optimization method of the system are proposed. The hierarchical optimization model is designed with energy storage configuration and dynamic performance as the lower and upper objectives, and an efficient parallel neural network-based genetic algorithm is employed to solve this optimization. The proposed method is applied to determine the system optimal energy storage configuration and dynamic performance across multiple scenarios, including different propulsion conditions with various types of large, pulsed power loads. The results demonstrate that the proposed method effectively reduces energy storage requirements: fuel system optimization, IGV adjustment strategy, and bleeding strategy, respectively, lower the energy storage configuration optimization objective values by 10.6%, 20.1%, 2.4%, and 6.2%, 6.5%, 5.3%. The SVSDP scheme achieves reductions of 19.5%, 7.6%, and 49.6%, 39.7% compared to VRCD and PSO-FS. Furthermore, the method also enhances the system’s dynamic response: under the specified HESS configuration, fuel system optimization, IGV adjustment strategy, and bleeding strategy reduce the dynamic performance optimization objective values by 6.8%, 23.3%, 8.6%, and 9.2%, 21.5%, 6.8%. The SVSDP scheme results in reductions of 21.3%, 15.4%, and 66.2%, 26.0% compared to VRCD and PSO-FS. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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21 pages, 2351 KiB  

Open AccessArticle

Security-Constrained Multi-Stage Robust Dynamic Economic Dispatch with Bulk Storage

by Li Dai, Renshi Ye, Dahai You and Xianggen Yin

Energies 2025, 18(5), 1073; https://doi.org/10.3390/en18051073 - 22 Feb 2025

Viewed by 420

Abstract

As wind penetration rates continue to increase, the main challenge faced by operators is how to schedule flexible resources, such as traditional generation and storage, in the future to ensure the safe and stable operation of power grids under multiple uncertainties. In this [...] Read more.

As wind penetration rates continue to increase, the main challenge faced by operators is how to schedule flexible resources, such as traditional generation and storage, in the future to ensure the safe and stable operation of power grids under multiple uncertainties. In this paper, a security-constrained multi-stage robust dynamic economic dispatch model with storage (SMRDEDS) is proposed to address multiple uncertainties of wind power outputs and N-1 contingencies. Compared to the traditional two-stage robust dynamic economic dispatch model, the proposed multi-stage dispatch model yields sequential operation decisions with uncertainties revealed gradually over time. What is more, a combined two-stage Benders’ decomposition and relaxed approximation–robust dual dynamic programming (RA-RDDP) is proposed to handle the computational issue of multi-stage problems due to large-scale post-contingency constraints and the convergence issue of the stochastic dual dynamic programming (SDDP) algorithm. First, a two-stage Benders’ decomposition algorithm is applied to relax the SMRDEDS model into a master problem and sub-problem. The master problem determines the generator output and storage charge and discharge, and the sub-problem determines the total generation and storage reserve capacity to cover all the generator N-1 contingencies. Second, a relaxed approximation–RDDP algorithm is proposed to solve the multi-stage framework problem. Compared to the traditional SDDP algorithm and RDDP algorithm, the proposed RA-RDDP algorithm uses the inner relaxed approximation and outer approximation methods to approximate the upper and lower bounds of the future cost-to-go function, which overcomes the convergence issue of the traditional SDDP algorithm and solution efficiency of the RDDP algorithm. We tested the proposed algorithm on the IEEE-3 bus, IEEE-118 bus, and the German power system. The simulation results verify the effectiveness of the proposed model and proposed algorithm. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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25 pages, 1788 KiB  

Open AccessArticle

Improving Synchronization and Stability in Integrated Electricity, Gas, and Heating Networks via LSTM-Based Optimization

by Xiaoyu Wu, Yuchen Cao, Hengtian Wu, Shaokang Qi, Mengen Zhao, Yuan Feng and Qinyi Yu

Energies 2025, 18(3), 749; https://doi.org/10.3390/en18030749 - 6 Feb 2025

Viewed by 545

Abstract

This paper introduces an innovative optimization framework that integrates Long Short-Term Memory (LSTM) networks to enhance the synchronization and stability of urban integrated multi-energy systems (MESs), which include electricity, gas, and heating networks. The need for a holistic approach to manage these interconnected [...] Read more.

This paper introduces an innovative optimization framework that integrates Long Short-Term Memory (LSTM) networks to enhance the synchronization and stability of urban integrated multi-energy systems (MESs), which include electricity, gas, and heating networks. The need for a holistic approach to manage these interconnected systems is driven by the increasing complexity of urban energy demands and the imperative to adhere to stringent environmental standards. The proposed methodology leverages LSTM networks for dynamic state estimation, enabling real-time and accurate predictions of energy demands and operational states across the different energy networks. This approach allows for the optimization of energy flows by adapting to fluctuations in demand and supply with high precision, which traditional static models are unable to do. By comprehensively modeling the unique operational characteristics and interdependencies of the electricity, gas, and heating networks, the framework ensures that the integrated system operates efficiently, remains stable under varying loads, and meets regulatory compliance for emissions. A synthesized case study simulating the operation of an integrated MES—including the IEEE 123-bus system for electricity, a modeled Belgian high-caloric gas network, and a Danish district heating system—illustrates the effectiveness of the proposed model. The study results indicate significant improvements in operational efficiency, reductions in emissions, and enhanced system stability. Key contributions of this paper include the development of a multi-layered optimization framework that addresses the dynamics of MESs, integration of environmental and regulatory compliance within the operational strategy, and a robust validation of the LSTM-based model against simulated anomalies and real-world scenarios. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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28 pages, 5311 KiB  

Open AccessArticle

Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning

by Fumiya Matsushima, Mutsumi Aoki, Yuta Nakamura, Suresh Chand Verma, Katsuhisa Ueda and Yusuke Imanishi

Energies 2025, 18(3), 653; https://doi.org/10.3390/en18030653 - 30 Jan 2025

Viewed by 676

Abstract

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) [...] Read more.

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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26 pages, 3249 KiB  

Open AccessArticle

Optimal Scheduling of Multi-Energy Complementary Systems Based on an Improved Pelican Algorithm

by Hongbo Zou, Jiehao Chen, Fushuan Wen, Yuhong Luo, Jinlong Yang and Changhua Yang

Energies 2025, 18(2), 365; https://doi.org/10.3390/en18020365 - 16 Jan 2025

Viewed by 809

Abstract

In recent years, the global power industry has experienced rapid development, with significant advancements in the source, network, load sectors, and energy storage technologies. The secure, reliable, and economical operation of power systems is a critical challenge. Due to the stochastic nature of [...] Read more.

In recent years, the global power industry has experienced rapid development, with significant advancements in the source, network, load sectors, and energy storage technologies. The secure, reliable, and economical operation of power systems is a critical challenge. Due to the stochastic nature of intermittent renewable energy generation and the coupled time-series characteristics of energy storage systems, it is essential to simulate uncertain variables accurately and develop optimization algorithms that can effectively tackle multi-objective problems in economic dispatch models for microgrids. This paper proposes a pelican algorithm enhanced by multi-strategy improvements for optimal generation scheduling. We establish eight scenarios with and without pumped storage across four typical seasons—spring, summer, autumn, and winter—and conduct simulation analyses on a real-world case. The objective is to minimize the total system cost. The improved pelican optimization algorithm (IPOA) is compared with other leading algorithms, demonstrating the validity of our model and the superiority of IPOA in reducing costs and managing complex constraints in optimization. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 1026 KiB  

Open AccessArticle

Demand-Driven Resilient Control for Generation Unit of Local Power Plant Under Unreliable Communication

by Guizhou Cao, Dawei Xia, Bokang Liu, Kai Meng, Zhenlong Wu and Yuan-Cheng Sun

Energies 2025, 18(2), 300; https://doi.org/10.3390/en18020300 - 11 Jan 2025

Viewed by 526

Abstract

The resilient control issue for the generation unit (GU) in a local power plant with unreliable communication is addressed in this article, where the communication may be jammed by denial-of-service (DoS) attacks. Based on the GU model of voltage and current at the [...] Read more.

The resilient control issue for the generation unit (GU) in a local power plant with unreliable communication is addressed in this article, where the communication may be jammed by denial-of-service (DoS) attacks. Based on the GU model of voltage and current at the point of common coupling, a demand-driven network communication protocol is proposed to decrease the number of scheduling signal transmissions, and an observer-based prediction method is provided to replenish the lack of dispatching data during transmission intervals when the demand has not changed. The closed-loop performance is analyzed for the GU system in the input-to-state stable framework with or without attack. According to the DoS attack model, which is described by the assumptions of frequency and duration, the conservativeness of the tolerable DoS attack index is reduced by using the thought of robustness to the maximum disturbance-induced error. Simulation examples are provided to verify the effectiveness of the approach proposed in this article. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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28 pages, 2903 KiB  

Open AccessArticle

Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques

by Dong Hua, Peifeng Yan, Suisheng Liu, Qinglin Lin, Peiyi Cui and Qian Li

Energies 2025, 18(2), 297; https://doi.org/10.3390/en18020297 - 11 Jan 2025

Viewed by 1365

Abstract

This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties [...] Read more.

This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 7902 KiB  

Open AccessArticle

Multi-Objective Site Selection and Capacity Determination of Distribution Network Considering New Energy Uncertainties and Shared Energy Storage of Electric Vehicles

by Guodong Wang, Haiyang Li, Xiao Yang, Huayong Lu, Xiao Song, Zheng Li and Yi Wang

Electronics 2025, 14(1), 151; https://doi.org/10.3390/electronics14010151 - 2 Jan 2025

Viewed by 548

Abstract

In recent years, the share of renewable energy in the distribution network has been increasing. To deal with high renewable energy penetration, it is important to improve the energy efficiency and stability of the distribution network. In this paper, the optimal configuration of [...] Read more.

In recent years, the share of renewable energy in the distribution network has been increasing. To deal with high renewable energy penetration, it is important to improve the energy efficiency and stability of the distribution network. In this paper, the optimal configuration of a distribution network with a high proportion of new energy and electric vehicles is investigated. Firstly, based on the copula theory, the clustered new energy data are obtained by optimizing the wind and solar output scenarios. Secondly, the uncertainty of renewable energy output is fully considered in the planning stage of the distribution network. Subsequently, an improved multi-objective particle swarm optimization algorithm is adopted to determine the optimal capacity and location of charging stations. Finally, the IEEE 33-node distribution network is used for case analysis. Through the comparison of network loss, voltage change, and other related parameters, the advantages of shared energy storage characteristics of electric vehicles in smoothing the uncertainty of the high proportion of new energy are verified. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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13 pages, 4199 KiB  

Open AccessArticle

Enhanced Self-Supervised Transmission Inspection with Improved Region Prior and Scale Variation

by Wei Xie, Fei Wu, Chao Ouyang, Yan Yang, Jian Qian, Shuang Lin, Chenxi Zhou and Jun Zhang

Processes 2024, 12(12), 2913; https://doi.org/10.3390/pr12122913 - 19 Dec 2024

Viewed by 686

Abstract

As an important means to ensure the safety of power transmission, the inspection of overhead transmission lines requires high accuracy for detecting small objects on the transmission lines and relies heavily on the construction of large-scale datasets by using deep learning instead of [...] Read more.

As an important means to ensure the safety of power transmission, the inspection of overhead transmission lines requires high accuracy for detecting small objects on the transmission lines and relies heavily on the construction of large-scale datasets by using deep learning instead of manual inspection. However, transmission inspection data often involve some sensitive information and need to be labeled by professionals, so it is difficult to construct a large transmission inspection dataset. In order to solve the problem of how to effectively train only on a small amount of transmission line data and achieve high object detection accuracy considering the large-scale variation in transmission objects, we propose an enhanced self-supervised pre-training model for DETR-like models, which are innovative object detectors eliminating hand-crafted non-maximum suppression and manual anchor design compared to previous CNN-based detectors. This paper mainly covers the following two points: (i) We compare UP-DETR and DETReg, noting that UP-DETR’s random cropping method performs poorly on small datasets and affects DETR’s localization ability. To address this, we adopt DETReg’s approach, replacing Selective Search with Edge Boxes for better results. (ii) To tackle large-scale variations in transmission inspection datasets, we propose a multi-scale feature reconstruction task, aligning feature embeddings with multi-scale encoder embeddings, and enhancing multi-scale object detection. Our method surpasses UP-DETR DETReg with DETR variants when fine-tuning PASCAL VOC and PTL-AI Furnas for object detection. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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16 pages, 2425 KiB  

Open AccessArticle

A Time-Limited Adaptive Reclosing Method in Active Distribution Networks Considering Anti-Islanding Protection

by Fan Yang, Hechong Chen, Kaijun Fan, Bingyin Xu, Yu Chen, Yong Cai and Zhichun Yang

Processes 2024, 12(12), 2781; https://doi.org/10.3390/pr12122781 - 6 Dec 2024

Viewed by 779

Abstract

In active distribution networks (DNs), distributed energy resources (DERs) must be disconnected from the grid prior to automatic reclosing actions. Many scholars have proposed non-voltage checking reclosing methods, but a significant challenge arises; many substations lack line-side voltage transformers (LSVTs), making these schemes [...] Read more.

In active distribution networks (DNs), distributed energy resources (DERs) must be disconnected from the grid prior to automatic reclosing actions. Many scholars have proposed non-voltage checking reclosing methods, but a significant challenge arises; many substations lack line-side voltage transformers (LSVTs), making these schemes impractical. To address this, we introduce a time-limited adaptive automatic reclosing (TLAR) method that integrates DERs’ anti-islanding protection (AIP) with automatic reclosing. This method estimates the AIP action time using bus-side voltage measurements before the system-side protection (SSP) is tripped and adjusts the reclosing time accordingly to enhance power supply reliability. Simulations using PSCAD validate the method’s effectiveness. The TLAR method is well-suited for distribution lines without conditions for non-voltage checking, is cost-effective, easy to implement, and contributes to power system stability. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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40 pages, 12527 KiB  

Open AccessArticle

Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network

by Valbério Gonzaga de Araújo, Aziz Oloroun-Shola Bissiriou, Juan Moises Mauricio Villanueva, Elmer Rolando Llanos Villarreal, Andrés Ortiz Salazar, Rodrigo de Andrade Teixeira and Diego Antonio de Moura Fonsêca

Energies 2024, 17(18), 4609; https://doi.org/10.3390/en17184609 - 13 Sep 2024

Cited by 1 | Viewed by 1822

Abstract

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence [...] Read more.

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = $94.2$%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 4034 KiB  

Open AccessArticle

A Unified Model of a Virtual Synchronous Generator for Transient Stability Analysis

by Ming Li, Chengzhi Wei, Ruifeng Zhao, Jiangang Lu, Yizhe Chen and Wanli Yang

Electronics 2024, 13(17), 3560; https://doi.org/10.3390/electronics13173560 - 7 Sep 2024

Cited by 2 | Viewed by 1218

Abstract

A virtual synchronous generator (VSG) is prone to transient instability under a grid fault, which leads to the loss of synchronization between the new energy converter and grid, and threatens the operation safety of high-proportion new energy grids. There are a variety of [...] Read more.

A virtual synchronous generator (VSG) is prone to transient instability under a grid fault, which leads to the loss of synchronization between the new energy converter and grid, and threatens the operation safety of high-proportion new energy grids. There are a variety of control models in the existing VSG control, including active and reactive power models, which lead to their different transient stabilities. However, the evolution characteristics, correlation between different models of VSG, and the internal mechanism affecting transient stability have not been fully studied. To this effect, this paper analyzes their evolution characteristics based on the existing mainstream VSG control models and establishes a unified VSG model and its equivalent correspondence with other models. Then, the phase plane method is used to comprehensively analyze and compare the transient stability of the VSG unified model with other models. It is pointed out that the key factors affecting the transient stability of different models are three links of primary frequency regulation, reactive power regulation and reactive power tracking. Finally, the correctness of the established VSG unified model and the conclusion of transient stability analysis is verified by experiments. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 2743 KiB  

Open AccessArticle

Parametric Dueling DQN- and DDPG-Based Approach for Optimal Operation of Microgrids

by Wei Huang, Qing Li, Yuan Jiang and Xiaoya Lu

Processes 2024, 12(9), 1822; https://doi.org/10.3390/pr12091822 - 27 Aug 2024

Cited by 3 | Viewed by 1020

Abstract

This study is aimed at addressing the problem of optimizing microgrid operations to improve local renewable energy consumption and ensure the stability of multi-energy systems. Microgrids are localized power systems that integrate distributed energy sources, storage, and controllable loads to enhance energy efficiency [...] Read more.

This study is aimed at addressing the problem of optimizing microgrid operations to improve local renewable energy consumption and ensure the stability of multi-energy systems. Microgrids are localized power systems that integrate distributed energy sources, storage, and controllable loads to enhance energy efficiency and reliability. The proposed approach introduces a novel microgrid optimization method that leverages the parameterized Dueling Deep Q-Network (Dueling DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. The method employs a parametric hybrid action-space reinforcement learning technique, where the DDPG is utilized to convert discrete actions into continuous action values corresponding to each discrete action, while the Dueling DQN uses the current observation states and these continuous action values to predict the discrete actions that maximize Q-values. This integrated strategy is designed to tackle the co-scheduling challenge in microgrids, enabling them to dynamically select the most favorable control strategies based on their specific states and the actions of other intelligent entities. The ultimate objective is to minimize the overall operational costs of microgrids while ensuring the efficient local consumption of renewable energy and maintaining the stability of multi-energy systems. Simulation experiments were conducted to validate the efficacy and superiority of the proposed method in achieving the optimal microgrid operation, showcasing its potential to improve service quality and reduce operational expenses. Average rewards increased by 30% and 15% compared to the use of the Dueling DQN or DDPG only. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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16 pages, 2281 KiB  

Open AccessArticle

Performance Analysis of a 50 MW Solar PV Installation at BUI Power Authority: A Comparative Study between Sunny and Overcast Days

by Rahimat Oyiza Yakubu, Muzan Williams Ijeoma, Hammed Yusuf, Abdulazeez Alhaji Abdulazeez, Peter Acheampong and Michael Carbajales-Dale

Electricity 2024, 5(3), 546-561; https://doi.org/10.3390/electricity5030027 - 22 Aug 2024

Viewed by 2270

Abstract

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the [...] Read more.

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 4301 KiB  

Open AccessArticle

Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network

by Yuwen You, Zhonghua Wang, Zhihao Liu, Chunmei Guo and Bin Yang

Energies 2024, 17(16), 4190; https://doi.org/10.3390/en17164190 - 22 Aug 2024

Cited by 1 | Viewed by 878

Abstract

Cogeneration is an important means for heat supply enterprises to obtain heat, and accurate load prediction is particularly crucial. The heat load of a centralized heat supply system is influenced by various factors such as outdoor meteorological parameters, the building envelope structure, and [...] Read more.

Cogeneration is an important means for heat supply enterprises to obtain heat, and accurate load prediction is particularly crucial. The heat load of a centralized heat supply system is influenced by various factors such as outdoor meteorological parameters, the building envelope structure, and regulation control, which exhibit a strong coupling and nonlinearity. It is essential to identify the key variables affecting the heat load at different heating stages through data mining techniques and to use deep learning algorithms to precisely regulate the heating system based on load predictions. In this study, a heat station in a northern Chinese city is taken as the subject of research. We apply the Fuzzy Clustering based on Fourier distance (FCBD-FCM) algorithm to transform the factors influencing the long and short-term load prediction of heat supply from the time domain to the frequency domain. This transformation is used to analyze the degree of their impact on load changes and to extract factors with significant influence as the multifeatured input variables for the prediction model. Five neural network models for load prediction are established, namely, Backpropagation (BP), convolutional neural network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and CNN-BiLSTM. These models are compared and analyzed for their performance in long-term, short-term, and ultrashort-term heating load prediction. The findings indicate that the load prediction accuracy is high when multifeatured input variables are based on fuzzy clustering. Furthermore, the CNN-BiLSTM model notably enhances the prediction accuracy and generalization ability compared to other models, with the Mean Absolute Percentage Error (MAPE) averaging within 3%. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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12 pages, 1850 KiB  

Open AccessArticle

Compensation Mechanism of Controllable Load Shifting during Peak-Down Period Based on Revenue Balance Method

by Yalong Li, Wenlu Du, Chen Liang, Yuzhi Xu and Yaxin Li

Processes 2024, 12(8), 1692; https://doi.org/10.3390/pr12081692 - 13 Aug 2024

Viewed by 1124

Abstract

With the large-scale integration of new energy, the obstruction of new energy consumption is prone to occur often during peak-down periods with a low load and high output of new energy. It is urgent to mobilize controllable load shifting through compensation mechanisms to [...] Read more.

With the large-scale integration of new energy, the obstruction of new energy consumption is prone to occur often during peak-down periods with a low load and high output of new energy. It is urgent to mobilize controllable load shifting through compensation mechanisms to achieve the goals of peak shaving, valley filling, and promotion of new energy consumption. This study constructs a framework of auxiliary service market and compensation mechanism for power shift between new energy power generation enterprises and controllable load enterprises. Secondly, aiming to achieve the principle of revenue balance between new energy power generation enterprises and controllable load enterprises, a quantity and price compensation model based on the particle swarm optimization algorithm is proposed. Then, under the principle of determining the compensation order of different controllable load enterprises through comprehensive evaluation and formulating differentiated compensation prices one by one, a compensation method and process for controllable load enterprises to shift have been established. Finally, through a case analysis, compensation prices for five types of controllable loads were formulated, with values ranging from 99.36 to 197.41 CNY/MWh. This increased the compensation for controllable loads on the basis of the original peak-valley price, verifying the feasibility of the method described in this study. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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29 pages, 15394 KiB  

Open AccessArticle

Impact of the Exciter and Governor Parameters on Forced Oscillations

by Naga Lakshmi Thotakura, Christopher Ray Burge and Yilu Liu

Electronics 2024, 13(16), 3177; https://doi.org/10.3390/electronics13163177 - 11 Aug 2024

Viewed by 1946

Abstract

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances [...] Read more.

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS^®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 849 KiB  

Open AccessArticle

Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction

by Yinghong Zhou, Weihao Yang, Zhijing Yang and Ruihan Chen

Electronics 2024, 13(16), 3176; https://doi.org/10.3390/electronics13163176 - 11 Aug 2024

Viewed by 1300

Abstract

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from [...] Read more.

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 5887 KiB  

Open AccessArticle

A Fault Direction Criterion Based on Post-Fault Positive-Sequence Information for Inverter Interfaced Distributed Generators Multi-Point Grid-Connected System

by Fan Yang, Hechong Chen, Gang Han, Huiran Xu, Yang Lei, Wei Hu and Shuxian Fan

Processes 2024, 12(7), 1522; https://doi.org/10.3390/pr12071522 - 19 Jul 2024

Viewed by 1151

Abstract

In response to the poor reliability in identifying fault direction in distribution networks with Inverter Interfaced Distributed Generators (IIDGs), considering the control strategy of low-voltage ride-through, a fault direction criterion based on post-fault positive-sequence steady-state components is proposed. Firstly, the output steady-state characteristics [...] Read more.

In response to the poor reliability in identifying fault direction in distribution networks with Inverter Interfaced Distributed Generators (IIDGs), considering the control strategy of low-voltage ride-through, a fault direction criterion based on post-fault positive-sequence steady-state components is proposed. Firstly, the output steady-state characteristics of IIDGs considering the low-voltage ride-through capability are analyzed during grid failure, and the applicability of existing directional elements in a distribution network with IIDGs connected dispersively is demonstrated. Subsequently, for the typical structure of an active distribution grid operating under flexible modes, the positive-sequence voltage and current are examined in various fault scenarios, and a reliable direction criterion is suggested based on the difference in post-fault positive-sequence impedance angles on different sides of the lines that are suitable whether on the grid side or the IIDG side. Lastly, the reliability of the proposed direction criterion is verified by simulation and the results indicate that the fault direction can be correctly determined, whereas phase-to-phase and three-phase short circuit faults occur in different scenarios, independent of the penetration and grid-connected positions of IIDGs, fault location, and transition resistance. It is suitable for fault direction discrimination of an IIDGs multi-point grid-connected system under a flexible operation mode. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 2803 KiB  

Open AccessArticle

Research on Line Maintenance Strategies Considering Dynamic Island Partitioning in Distribution Areas under Adverse Weather Conditions

by Hao Chen, Yufeng Guo, Wei Xu, Linyao Zhang and Yifei Liu

Electronics 2024, 13(14), 2714; https://doi.org/10.3390/electronics13142714 - 11 Jul 2024

Viewed by 814

Abstract

As global climate change intensifies, extreme weather events are becoming more frequent, with ice disasters posing an increasingly significant threat to the stable operation of power distribution networks. Particularly during power outages for de-icing, multiple power islands may form within a distribution area, [...] Read more.

As global climate change intensifies, extreme weather events are becoming more frequent, with ice disasters posing an increasingly significant threat to the stable operation of power distribution networks. Particularly during power outages for de-icing, multiple power islands may form within a distribution area, increasing the complexity of grid operations. Existing research has not fully considered the comprehensive coordination of stable operation of these power islands and de-icing maintenance schedules. Therefore, for the potential multi-island operation of distribution networks caused by freezing disasters, this paper first establishes a dynamic island partitioning model based on distribution network reconfiguration technology. Secondly, based on the characteristics of the de-icing phase, a de-icing maintenance schedule model is established. Finally, dispatch optimization of the distribution network is coordinated with the line de-icing maintenance schedule. By adjusting the de-icing strategies and network structure, the aim is to minimize the risk of load loss. The relevant case analysis indicates that the collaborative optimization model established in this paper helps power distribution networks to reduce their economic losses when facing adverse weather conditions. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 7150 KiB  

Open AccessArticle

Optimization of Integrated Energy Systems Based on Two-Step Decoupling Method

by Linyang Zhang, Jianxiang Guo, Xinran Yu, Gang Hui, Na Liu, Dongdong Ren and Jijin Wang

Electronics 2024, 13(11), 2045; https://doi.org/10.3390/electronics13112045 - 24 May 2024

Cited by 1 | Viewed by 1000

Abstract

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very [...] Read more.

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO₂ and NO_X for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 2032 KiB  

Open AccessArticle

Coordinated Control of Proton Exchange Membrane Electrolyzers and Alkaline Electrolyzers for a Wind-to-Hydrogen Islanded Microgrid

by Zhanfei Li, Zhenghong Tu, Zhongkai Yi and Ying Xu

Energies 2024, 17(10), 2317; https://doi.org/10.3390/en17102317 - 11 May 2024

Cited by 7 | Viewed by 1665

Abstract

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development [...] Read more.

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 751 KiB  

Open AccessArticle

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

by Hesong Huang, Yang Li, Ge Song and Wendong Gai

Electronics 2024, 13(10), 1871; https://doi.org/10.3390/electronics13101871 - 10 May 2024

Cited by 3 | Viewed by 2226

Abstract

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path [...] Read more.

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 6047 KiB  

Open AccessArticle

Fuzzy PID Control Design of Mining Electric Locomotive Based on Permanent Magnet Synchronous Motor

by Chi Ma, Baosheng Huang, Md Khairul Basher, Md Abdur Rob and Yuqiang Jiang

Electronics 2024, 13(10), 1855; https://doi.org/10.3390/electronics13101855 - 10 May 2024

Cited by 6 | Viewed by 1364

Abstract

Achieving precise stopping of electric locomotives is crucial for the realization of intelligent and unmanned auxiliary transportation systems. Presently, human drivers play a central role in ensuring accurate stopping, presenting obstacles to automation and cargo location precision, especially within the coal mining sector. [...] Read more.

Achieving precise stopping of electric locomotives is crucial for the realization of intelligent and unmanned auxiliary transportation systems. Presently, human drivers play a central role in ensuring accurate stopping, presenting obstacles to automation and cargo location precision, especially within the coal mining sector. This article centers on achieving the precise stopping of electric locomotives under various conditions through the utilization of permanent magnet synchronous motor-driven locomotives. This approach introduces a novel stopping control method that integrates a fuzzy proportional–integral–derivative (F-PID) controller with a vector control model for permanent magnet synchronous motors (PMSM). Subsequently, we develop the F-PID controller using the PMSM technique, incorporating new fuzzy rules for each subsystem to enhance control accuracy and efficiency. Finally, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed to validate the effectiveness of the proposed control method. The results show that the method consistently achieves precise stopping under diverse working conditions, with an error of less than 0.3 m, confirming its robustness and reliability. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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16 pages, 4390 KiB  

Open AccessArticle

An Optimized Fault-Ride-Through Control Strategy of Hybrid MMC with Fewer FBSMs

by Yue Chen, Chenglin Ren, Junyi Sheng, Jinyu Wang, Yuebin Zhou, Wanyu Cao, Runtian Ding and Wujun Wang

Electronics 2024, 13(10), 1797; https://doi.org/10.3390/electronics13101797 - 7 May 2024

Cited by 2 | Viewed by 1326

Abstract

The modular multilevel converter (MMC) has many advantages of low switching losses, good harmonic performance and high modularity structure in state-of-the-art HVDC applications. The full-bridge submodules (FBSMs) of the hybrid MMC can inherently output negative voltage to absorb fault currents, and consequently the [...] Read more.

The modular multilevel converter (MMC) has many advantages of low switching losses, good harmonic performance and high modularity structure in state-of-the-art HVDC applications. The full-bridge submodules (FBSMs) of the hybrid MMC can inherently output negative voltage to absorb fault currents, and consequently the hybrid MMC can ride through severe DC faults without blocking. During the DC fault-ride-through process, the submodule capacitor voltage and arm current of the MMC will be temporarily increased. These characteristics limit the proportion of the FBSMs, which should not be too low and thus increase the cost and operating losses of the hybrid MMC. In this paper, an improved sorting algorithm of SM capacitor voltage is established, and a novel virtual damping control strategy is proposed that can effectively suppress the increase in submodule capacitor voltage and arm current of the hybrid MMC during the DC fault-ride-through process. By adopting this optimization control, the proportion of FBSMs can be reduced significantly without deteriorating the fault-ride-through capability or safety of the MMC. The effectiveness of the proposed control is verified by careful theoretical analysis and detailed simulation results. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 1722 KiB  

Open AccessArticle

Distribution System State Estimation Based on Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition

by Xiaomeng Chu and Jiangjun Wang

Processes 2024, 12(4), 823; https://doi.org/10.3390/pr12040823 - 19 Apr 2024

Cited by 1 | Viewed by 1164

Abstract

In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address this issue, this paper proposes an enhanced [...] Read more.

In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address this issue, this paper proposes an enhanced kernel ridge regression state estimation method based on ensemble empirical mode decomposition. Initially, ensemble empirical mode decomposition is employed to eliminate most of the noise data in the measurement information, ensuring the reliability of the data for subsequent filtering. Subsequently, the enhanced kernel ridge regression state estimation model is constructed to establish the mapping relationship between the measured data and the estimation residuals. By inputting the measured data, both estimation results and estimation residuals can be obtained. Finally, numerical simulations conducted on the standard IEEE-33 node system and a 78-node system in a specific city demonstrate that the proposed method exhibits high accuracy and robustness in the presence of strong non-Gaussian noise interference. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 7018 KiB  

Open AccessArticle

Non-Intrusive Load Monitoring Based on Multiscale Attention Mechanisms

by Lei Yao, Jinhao Wang and Chen Zhao

Energies 2024, 17(8), 1944; https://doi.org/10.3390/en17081944 - 19 Apr 2024

Cited by 2 | Viewed by 1782

Abstract

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to [...] Read more.

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 879 KiB  

Open AccessArticle

Fully Distributed Economic Dispatch with Random Wind Power Using Parallel and Finite-Step Consensus-Based ADMM

by Yuhang Zhang and Ming Ni

Electronics 2024, 13(8), 1437; https://doi.org/10.3390/electronics13081437 - 11 Apr 2024

Viewed by 959

Abstract

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated [...] Read more.

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 3820 KiB  

Open AccessArticle

A Unified Design Method for Multi-Source Complementary Heating Systems Based on a Transient Simulation

by Linyang Zhang, Jianxiang Guo, Jianan Li, Xinran Yu, Gang Hui, Jinqin Zhong, Na Liu, Dongdong Ren and Jijin Wang

Electronics 2024, 13(7), 1206; https://doi.org/10.3390/electronics13071206 - 25 Mar 2024

Viewed by 1205

Abstract

Combining various sources to create a complementary system plays a key role in utilizing clean energy sources economically and mitigating air pollution during the heating season in Northern China. However, there is a lack of unified and reasonable design methods for such systems, [...] Read more.

Combining various sources to create a complementary system plays a key role in utilizing clean energy sources economically and mitigating air pollution during the heating season in Northern China. However, there is a lack of unified and reasonable design methods for such systems, resulting in the excessive capacity of equipment and the waste of energy. In this work, a unified design method is proposed to solve this problem. A generalized structure and its mathematical model are firstly established, enabling transient simulations on the TRNSYS platform. Then, a preliminary screening criterion for the system composition a general operation strategy is proposed. Finally, the system configuration is optimized by using the genetic algorithm. The method is successfully applied in a demonstration project in China. The results show that the coupling system consisting of a biomass boiler (384 kW), an air-source heat pump (430 kW) and a ground-source heat pump (369 kW) is the most economical, and the annual cost is 26.7% lower than that of a single-equipment system. Additionally, the sensitive factors that strongly affect the optimization results are explored. The establishment of the generalized structure and its mathematical model enables the quick calculation and convenient comparison of various schemes, and simplifies the complicated optimization problem of the capacity optimization of each piece of equipment. The proposed design method can reduce the annual cost to a minimum value, and thus it provides a theoretical basis for the large-scale application of clean energy sources for heating. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 3537 KiB  

Open AccessArticle

Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty

by Yubo Wang, Xingang Zhao and Yujing Huang

Processes 2024, 12(4), 648; https://doi.org/10.3390/pr12040648 - 24 Mar 2024

Cited by 1 | Viewed by 1284

Abstract

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider [...] Read more.

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system’s carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 5236 KiB  

Open AccessArticle

Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm

by Xiaomeng Chu and Zhiji Zhou

Processes 2024, 12(4), 627; https://doi.org/10.3390/pr12040627 - 22 Mar 2024

Viewed by 1200

Abstract

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface [...] Read more.

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 3659 KiB  

Open AccessFeature PaperArticle

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

by Md. Ahasan Habib and M. J. Hossain

Energies 2024, 17(5), 1215; https://doi.org/10.3390/en17051215 - 3 Mar 2024

Cited by 4 | Viewed by 1602

Abstract

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make [...] Read more.

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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16 pages, 2703 KiB  

Open AccessArticle

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

by Jiawang Zhang, Xiaojing Ma, Zening Cheng and Xingchao Zhou

Processes 2024, 12(2), 422; https://doi.org/10.3390/pr12020422 - 19 Feb 2024

Cited by 3 | Viewed by 1604

Abstract

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the [...] Read more.

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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22 pages, 4160 KiB  

Open AccessArticle

Optimization of Topological Reconfiguration in Electric Power Systems Using Genetic Algorithm and Nonlinear Programming with Discontinuous Derivatives

by Giovanny Andrés Diaz Vargas, Darin Jairo Mosquera and Edwin Rivas Trujillo

Electronics 2024, 13(3), 616; https://doi.org/10.3390/electronics13030616 - 1 Feb 2024

Cited by 7 | Viewed by 1797

Abstract

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the [...] Read more.

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the network operator following disruptive events. The analysis is divided into two fundamental aspects. Firstly, it addresses the topological reconfiguration of the network, involving the addition of lines and distributed energy resources such as distributed generation. To determine the optimal topological reconfiguration, a genetic algorithm was developed and implemented. This approach aims to restore electrical service to the maximum load within the system. Secondly, an optimal energy dispatch was performed for each generator, considering the variation in load throughout the day. The system’s load curve is taken into account to determine the optimal energy distribution. Thus, the problem of economic losses is approached from two perspectives: the minimization of costs due to nonsupplied electrical energy and the determination of efficient energy dispatch for each generator after network reconfiguration. For the analysis and case studies, simulations were conducted on the IEEE 9- and 30-node test systems. The results demonstrate the effectiveness of the proposed solution, evaluated in terms of reduced load shedding and economic losses. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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25 pages, 44711 KiB  

Open AccessArticle

Low Energy Cost Synchronization Strategy for Markovian Switching Complex Systems/Networks: Multiple Perspectives Comparative Analysis

by Qian Xie, Haolan Xu, Jian Dang and Zhe Wang

Processes 2024, 12(1), 232; https://doi.org/10.3390/pr12010232 - 21 Jan 2024

Viewed by 1272

Abstract

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on [...] Read more.

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on the optimal node selection strategy that does not depend on the network coupling strength is improved, and a finite-time controller with a simpler structure is constructed. Secondly, based on the event-triggered control strategy an effective trigger event is designed to achieve the low energy cost synchronization of Markovian switching complex networks on the basis of reducing the information transmission and interaction between networks. Finally, the two control strategies mentioned in this paper are compared and analyzed from multiple perspectives through numerical simulations to better guide practical engineering. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 3820 KiB  

Open AccessArticle

Active Power Allocation Method of Doubly Fed Induction Generators Based on Rotor Speed

by Muxi Li and Fengting Li

Electronics 2024, 13(2), 279; https://doi.org/10.3390/electronics13020279 - 8 Jan 2024

Viewed by 1129

Abstract

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia [...] Read more.

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 2489 KiB  

Open AccessArticle

Multi-Objective Capacity Optimization of Grid-Connected Wind–Pumped Hydro Storage Hybrid Systems Considering Variable-Speed Operation

by Yang Li, Outing Li, Feng Wu, Shiyi Ma, Linjun Shi and Feilong Hong

Energies 2023, 16(24), 8113; https://doi.org/10.3390/en16248113 - 17 Dec 2023

Cited by 7 | Viewed by 1818

Abstract

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation [...] Read more.

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 1960 KiB  

Open AccessArticle

Reliability Evaluation of Cabled Active Distribution Network Considering Multiple Devices—A Generalized MILP Model

by Jiaxin Zhang, Bo Wang, Hengrui Ma, Yifan He, Yiwei Wang and Zichun Xue

Processes 2023, 11(12), 3404; https://doi.org/10.3390/pr11123404 - 11 Dec 2023

Cited by 3 | Viewed by 1257

Abstract

With the rapid development of power electronic equipment, the automation and intelligence level of active distribution networks (ADNs) continues to improve. Against this background, soft open points (SOPs) are gradually replacing traditional segmented switches and interconnection switches. The voltage support capability and fast [...] Read more.

With the rapid development of power electronic equipment, the automation and intelligence level of active distribution networks (ADNs) continues to improve. Against this background, soft open points (SOPs) are gradually replacing traditional segmented switches and interconnection switches. The voltage support capability and fast response characteristics of SOPs can shorten power outage time and expand load recovery range. However, the widespread integration of distributed renewable energy and new power electronic devices has made the fault characteristics of ADNs more complex, significantly increasing the computational complexity of ADN reliability assessment. At present, there are few studies that comprehensively consider ADNs with multiple devices. Therefore, this paper proposes a reliability evaluation method for ADNs that considers multiple devices. Firstly, the impact of circuit breakers, SOPs, and segmented switches on the load recovery process is analyzed. Secondly, an improved virtual fault flow model based on the action mechanisms of circuit breakers, SOPs, and segmented switches is established. The virtual fault flow is represented by logical variables to simulate post fault network reconstruction strategies that include circuit breaker tripping, SOP power supply recovery, and segmented switch isolation actions. Then, with the goal of minimizing the system average outage time after network reconstruction, a generalized mixed integer linear programming (MILP) model is given. Finally, taking the IEEE 33-node testing system as a case, the effectiveness and feasibility of the proposed method are demonstrated. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 4358 KiB  

Open AccessArticle

Multi-Objective Co-Operative Game-Based Optimization for Park-Level Integrated Energy System Based on Exergy-Economic Analysis

by Lili Mo, Zeyu Deng, Haoyong Chen and Junkun Lan

Energies 2023, 16(24), 7945; https://doi.org/10.3390/en16247945 - 7 Dec 2023

Cited by 3 | Viewed by 1108

Abstract

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of [...] Read more.

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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16 pages, 5724 KiB  

Open AccessArticle

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

by Mantas Plienis, Tomas Deveikis, Audrius Jonaitis, Saulius Gudžius, Inga Konstantinavičiūtė and Donata Putnaitė

Energies 2023, 16(23), 7837; https://doi.org/10.3390/en16237837 - 29 Nov 2023

Cited by 3 | Viewed by 2078

Abstract

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the [...] Read more.

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the increasing integration of renewable energy sources introduces heightened fluctuations, rendering the prediction and simulation of working modes more challenging. This paper presents an improved algorithm for calculating power transformer losses attributed to harmonics, with a comprehensive validation against simulation results obtained from the Power Factory application and real-world measurements. The advantages of the algorithm are that all evaluations are performed in real-time based on single-point measurements, and the algorithm was easy to implement in a Programmable Logic Controller (PLC). This allows us to receive the exchange of information to energy monitoring systems (EMSs) or with Power factor Correction Units (PFCUs) and control it. To facilitate a more intuitive understanding and visualization of potential hazardous scenarios related to resonance, an extra Dijkstra algorithm was implemented. This augmentation enables the identification of conditions, wherein certain branches exhibit lower resistance than the grid connection point, indicating a heightened risk of resonance and the presence of highly distorted currents. Recognizing that monitoring alone does not inherently contribute to increased energy efficiency, the algorithm was further expanded to assess transformer losses across a spectrum of Power Factory Correction Units power levels. Additionally, a command from a PLC to a PFCU can now be initiated to change the capacitance level and near-resonance working mode. These advancements collectively contribute to a more robust and versatile methodology for evaluating power transformer losses, offering enhanced accuracy and the ability to visualize potentially critical resonance scenarios. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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20 pages, 11202 KiB  

Open AccessArticle

Competitive Equilibrium Analysis of Power Generation Transaction Subjects Considering Tradable Green Certificates

by Jiaxing Wen, Rong Jia, Xin Gao, Ge Cao, Jian Dang, Wei Li and Peihang Li

Processes 2023, 11(10), 3008; https://doi.org/10.3390/pr11103008 - 19 Oct 2023

Cited by 2 | Viewed by 1258

Abstract

The implementation of renewable portfolio standards (RPS) and tradable green certificate schemes will exert significant influences on the market equilibrium outcomes and generation firms’ strategic behaviors. To quantitatively investigate these influences, firstly, considering the difference in power generation cost and the uncertainty of [...] Read more.

The implementation of renewable portfolio standards (RPS) and tradable green certificate schemes will exert significant influences on the market equilibrium outcomes and generation firms’ strategic behaviors. To quantitatively investigate these influences, firstly, considering the difference in power generation cost and the uncertainty of renewable energy power generation, the equilibrium model for various trade subjects in the electricity market is established. Secondly, the nondominated sorting genetic algorithm II is adopted for solving the equilibrium model to find well-distributed Pareto-optimal solutions. Finally, the grey relational projection method is used to calculate the priority membership of each decision-making scheme so as to determine the optimal compromise solution. The simulation focuses on analyzing the impact of RPS on the equilibrium results and market behavior of power generators and introduces the Lerner index to quantify the market power of generators. The results show that: (1) An increase in the quota ratio can effectively increase power generation in renewable energy generators. The game between thermal power generators and renewable energy generators raises the prices of both markets at the same time. (2) Improving the forecasting accuracy is conducive to alleviating the market power behavior of various power generators, thereby ensuring the healthy operation of the power market. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 2650 KiB  

Open AccessArticle

Pricing Mechanism and Trading Strategy Optimization for Microgrid Cluster Based on CVaR Theory

by Wengang Chen, Ying Zhang, Jiajia Chen and Bingyin Xu

Electronics 2023, 12(20), 4327; https://doi.org/10.3390/electronics12204327 - 18 Oct 2023

Cited by 7 | Viewed by 1362

Abstract

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading [...] Read more.

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading model for microgrid cluster based on price trading mechanism and Conditional value-at-risk (CVaR) theory. Firstly, the upper-layer establishes an objective to minimize the overall power fluctuation of the microgrid cluster using Demand response (DR) with a penalty mechanism. The microgrid cluster adopts an internal pricing mechanism and adjusts transaction prices based on internal supply-demand conditions to guide microgrids’ participation in intracluster trading, thereby encouraging the microgrid to use the flexible resources to reduce power fluctuation. Secondly, the lower-layer optimization establishes an optimization model with the objective of minimizing the comprehensive operating cost of the microgrid cluster. The model employs backward scenario reduction techniques to obtain multiple sets of typical scenarios for renewable energy generation, and the CVaR theory is introduced to quantify the potential risk of transaction default. Finally, the effectiveness of the proposed models is verified through case studies considering various application scenarios. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 8125 KiB  

Open AccessArticle

A Data-Driven Approach for the Ultra-Supercritical Boiler Combustion Optimization Considering Ambient Temperature Variation: A Case Study in China

by Zhi Wang, Guojia Yao, Wenyuan Xue, Shengxian Cao, Shiming Xu and Xianyong Peng

Processes 2023, 11(10), 2889; https://doi.org/10.3390/pr11102889 - 30 Sep 2023

Cited by 4 | Viewed by 1700

Abstract

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the [...] Read more.

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the time-consuming problems of boiler online combustion optimization systems. Firstly, A whole year’s worth of the historical operating data preprocessing procedure of a coal-fired boiler in a power station including data resampling, data cleaning, steady-state selection, and cluster analysis is performed. In order to meet the applicable conditions of the linear model, the historical operating data are divided into different sub-datasets (combination mode of coal mills, main steam flow, ambient temperature, lower heating value of coal). Secondly, the multi-objective optimization strategy of economical, carbon, and NOx emissions indexes is employed to select operating optimum data packets, and a new dataset is established that is better than the average value of the optimization target in each sub-dataset. On this basis, a stepwise regression algorithm (SRA) is used to select the specific manipulated variables (MVs) that are significant to the multiple optimization targets from 47 candidate MVs in each sub-dataset (different partitions have different types of MVs), and an MLR prediction model is developed. In order to further realize combustion optimization control, the MVs are optimized by employing the MLR model. According to the deviation between the optimal value and the real-time value of the MVs, a boiler combustion closed-loop control system is developed, which is connected with the DCS using the sum of the deviation signal and the corresponding original one. Then, a boiler combustion application test was carried out under some working conditions to verify the feasibility and effectiveness of the approach. The update time of the system signals running on industrial computers is less than 1 s and suitable for online applications. Finally, a full-scale test of the combustion optimization online control system (OCS) is executed. The results show that the boiler thermal efficiency increased by 0.39% based on standard coal, the NOx emissions reduced by 2.85% and the decarbonization effect is significant. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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25 pages, 2063 KiB  

Open AccessArticle

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

by Mark Kipngetich Kiptoo, Oludamilare Bode Adewuyi, Masahiro Furukakoi, Paras Mandal and Tomonobu Senjyu

Energies 2023, 16(19), 6838; https://doi.org/10.3390/en16196838 - 27 Sep 2023

Cited by 3 | Viewed by 1818

Abstract

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and [...] Read more.

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 1481 KiB  

Open AccessArticle

Economic Dispatch of AC/DC Power System Considering Thermal Dynamics

by Xiping Ma, Chen Liang, Xiaoyang Dong and Yaxin Li

Processes 2023, 11(9), 2522; https://doi.org/10.3390/pr11092522 - 23 Aug 2023

Viewed by 1336

Abstract

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a [...] Read more.

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a comprehensive characterization of the AC/DC power system considering multi-energy and renewable integration. Detailed models are elaborated for the AC/DC power grid, the district heating system (DHS), coupling units, and renewables to describe their inner interactions accurately. On this basis, an economic dispatch method is developed to minimize the operation cost and renewable’s abandonment. Simulations indicate that the interaction between the AC/DC power systems and multi-energy sources can enhance voltage levels and improve operational economy. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 5096 KiB  

Open AccessArticle

Counting Abalone with High Precision Using YOLOv3 and DeepSORT

by Duncan Kibet and Jong-Ho Shin

Processes 2023, 11(8), 2351; https://doi.org/10.3390/pr11082351 - 4 Aug 2023

Cited by 5 | Viewed by 1797

Abstract

In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras [...] Read more.

In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras used to detect abalones. Considering the computational effectiveness and improved detection algorithms, this proposal is promising compared to the previously proposed methods. Some of these methods have low effectiveness and accuracy, and they provide an incorrect counting rate because some of the abalones tend to entangle, resulting in counting two or more abalones as one. Conducting detection and tracking research is crucial to achieve modern solutions for small- and large-scale fishing industries that enable them to accomplish higher automation, non-invasiveness, and low cost. This study is based on the development and improvement of counting analysis tools for automation in the fishing industry. This enhances agility and generates more income without the cost created by inaccuracy. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 8636 KiB  

Open AccessArticle

Distributed Fixed-Time Secondary Control for MTDC Systems Using Event-Triggered Communication Scheme

by Xiaoyue Zhang, Xinghua Liu and Peng Wang

Processes 2023, 11(8), 2329; https://doi.org/10.3390/pr11082329 - 2 Aug 2023

Cited by 3 | Viewed by 1267

Abstract

Multi-terminal DC transmission (MTDC) systems have attracted much attention due to their significant advantages in long-distance and high-capacity transmission. To improve their reliability and operation performance, a distributed fixed-time secondary control of frequency restoration and active power sharing is proposed under event-triggered communication, [...] Read more.

Multi-terminal DC transmission (MTDC) systems have attracted much attention due to their significant advantages in long-distance and high-capacity transmission. To improve their reliability and operation performance, a distributed fixed-time secondary control of frequency restoration and active power sharing is proposed under event-triggered communication, which only depends on the states of each AC grid and its neighbors. By utilizing Lyapunov theory, we prove that the MTDC system with the fixed-time secondary control can be stable in a settling time, and the conditions of the settling time are established for fixed-time algorithms. In addition, we simulate a five-terminal MTDC system in Matlab/Simulink. Several cases of MTDC systems are exhibited to showcase how well the suggested controller works when dealing with load changes and attacks. The comparison of the number of event-triggered instants shows that the proposed control method can effectively reduce communication resources. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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64 pages, 13019 KiB  

Open AccessReview

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

by Haoming Liu, Muhammad Yasir Ali Khan and Xiaoling Yuan

Energies 2023, 16(15), 5665; https://doi.org/10.3390/en16155665 - 27 Jul 2023

Cited by 16 | Viewed by 3169

Abstract

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: [...] Read more.

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 6814 KiB  

Open AccessArticle

Disturbance-Suppression Method of Direct-Driven PMSG-Based Wind Power System in Microgrids

by Xiuqi Xu, Liancheng Xiu, Jingxuan He and Rongxin Gong

Processes 2023, 11(7), 2189; https://doi.org/10.3390/pr11072189 - 21 Jul 2023

Cited by 1 | Viewed by 1133

Abstract

In order to solve the current fluctuation problem in microgrids, a suppression method called the Direct-driven Permanent Magnet Synchronous Generator (DPMSG)-based Wind Power System (WPS) based on an adaptive enhanced moving average filter algorithm is proposed. Firstly, the mathematical model of the WPS [...] Read more.

In order to solve the current fluctuation problem in microgrids, a suppression method called the Direct-driven Permanent Magnet Synchronous Generator (DPMSG)-based Wind Power System (WPS) based on an adaptive enhanced moving average filter algorithm is proposed. Firstly, the mathematical model of the WPS is established. On this basis, the suppression method under unbalanced conditions is derived by the instantaneous power equation to ensure the stable operation of the microgrid. In order to improve the dynamic compensation capability of the DPMSG-based WPS, an enhanced moving average filtering algorithm with frequency adaptability is proposed. The positive and negative sequence components are obtained in the dq frame by this filtering algorithm. Subsequently, the angular frequency of the microgrid is obtained according to the changing phase, which realizes the high-performance control of the WPS and avoids the complicated parameter adjustment of traditional methods. The correctness of this method is verified by the simulation results. The DPMSG-based WPS with the proposed method can improve the stability of the microgrid. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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28 pages, 6659 KiB  

Open AccessArticle

A New Industry 4.0 Approach for Development of Manufacturing Firms Based on DFSS

by Alhadi Khlil, Zhanqun Shi, Abubakar Umar and Botong Ma

Processes 2023, 11(7), 2176; https://doi.org/10.3390/pr11072176 - 21 Jul 2023

Cited by 5 | Viewed by 2090

Abstract

The adoption of Industry 4.0 is attracting manufacturing companies, but the implementation barriers they expect to face, such as huge investment costs, and lack of skilled workers and infrastructure, make many of them hesitate to go through with implementation. The lack of a [...] Read more.

The adoption of Industry 4.0 is attracting manufacturing companies, but the implementation barriers they expect to face, such as huge investment costs, and lack of skilled workers and infrastructure, make many of them hesitate to go through with implementation. The lack of a standardization approach also adds more difficulties in the implementation of advanced key technologies. Based on the design for the six sigma (DFSS) method, a new decision-making and implementation approach (DM&I) is proposed to address some implementation barriers and provide strategic guidance to implement the Industry 4.0 advanced key technologies. In this study, a systematic literature review was conducted to determine the impact of the implementation barriers for Industry 4.0 adoption. The DM&I approach has been applied in a bearing ring production line as a real case. The proposed method consists of two steps: decision-making, which consists of the define phase in determining the exact system weak point in order to reduce the improvement risk and system operation disturbance; the evaluate phase, which is the determining of the level of use of key technologies; the visualize phase, which is the stage of designing and modeling the proposed system and creating the virtual environment to simulate the system in real-time in order to support the improved decision-making process and avoid the fear of high costs; and then the optimize phase, where the optimal level of use of key technologies is determined. Then, the implementation phase consists of the develop phase, which encompasses the stage of physical system construction and hardware software development, followed by the validate phase and the integrate phase. These phases support infrastructure improvement. However, the proposed approach can be used by manufacturing companies to improve production efficacy and competitiveness. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 2472 KiB  

Open AccessArticle

Multi-Agent Reinforcement Learning-Based Decentralized Controller for Battery Modular Multilevel Inverter Systems

by Ali Mashayekh, Sebastian Pohlmann, Julian Estaller, Manuel Kuder, Anton Lesnicar, Richard Eckerle and Thomas Weyh

Electricity 2023, 4(3), 235-252; https://doi.org/10.3390/electricity4030014 - 6 Jul 2023

Cited by 3 | Viewed by 2658

Abstract

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central [...] Read more.

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of $1.1\times {10}^{-5}$. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 1706 KiB  

Open AccessArticle

Merchant Energy Storage Investment Analysis Considering Multi-Energy Integration

by Long Wang

Energies 2023, 16(12), 4695; https://doi.org/10.3390/en16124695 - 14 Jun 2023

Cited by 2 | Viewed by 1464

Abstract

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology [...] Read more.

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology is a key means through which to deal with the instability of modern energy sources. One of the key development paths in the electricity market is the development by energy merchants of energy storage power plants in the distribution network to engage in a grid demand response. This research proposes a two-stage energy storage configuration approach for a cold-heat-power multi-energy complementary multi-microgrid system. Considering the future bulk connections of distributed power generation, the two most critical points of energy storage station construction are the power generation equipment and specific scenarios for serving the community, as well as the purchase and sale price of electricity for serving the community microgrid (which directly affects the investment revenue). Therefore, this paper focuses on analyzing the different impacts caused by these two issues; namely, the two most important concerns for the construction of energy storage configurations. First, the basic model enabling wholesale electricity traders to construct energy storage power plants is presented. Second, for a multi-microgrid system with a complementary cold-heat-power multi-energy scenario, a two-stage optimum allocation model is constructed, whereby the upper model calculates the energy storage allocation problem and the lower model calculates the optimal dispatch problem. The lower model’s dispatch computation validates the upper configuration model’s reasonableness. Finally, the two-layer model is converted to a single-layer model by the KKT condition, and the nonlinear problem is converted to a linear problem with the big-M method. The validity is proved via mathematical examples, and it is demonstrated that the planned energy storage plants by merchants may accomplish resource savings and mutual advantages for both users and wholesale power traders. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 3855 KiB  

Open AccessArticle

Optimal Scheduling Strategy for Multi-Energy Microgrid Considering Integrated Demand Response

by Long Wang

Energies 2023, 16(12), 4694; https://doi.org/10.3390/en16124694 - 14 Jun 2023

Cited by 8 | Viewed by 2012

Abstract

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. [...] Read more.

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. This study aims to contribute to the integrated energy virtual plant station research by exploring the relationship between the integrated energy electro-thermal coupling capacity, various forms of electro-thermal integrated energy response, and electro-thermal integrated energy storage. Analyzing the attributes of an integrated energy microgrid, including energy storage characteristics, time-of-use tariffs, and electric and thermal loads, is crucial. A grid-connected microgrid with cogeneration systems, electric boilers, fuel cells, and energy storage systems is used as an illustrative example. The dispatching method prioritizes multiple complementary energy sources while considering the integrated energy demand response. The study presents different models for the electricity demand and thermal energy demand response and introduces the design of a wholesale power trader involved in building energy storage facilities and participating in the demand response. To verify the feasibility and rationality of the integrated energy demand response scenario, three different schemes are compared, and an economic analysis is conducted. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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26 pages, 17941 KiB  

Open AccessArticle

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

by Dhan Lord B. Fortela and Ashley P. Mikolajczyk

Processes 2023, 11(6), 1747; https://doi.org/10.3390/pr11061747 - 7 Jun 2023

Cited by 2 | Viewed by 1758

Abstract

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to [...] Read more.

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to compute the net pairwise dynamic connectedness (NPDC), which originated as a volatility spillover index for financial markets studies in the field of econometrics. This work used the anomaly-detection benchmark Tennessee-Eastman chemical process (TEP) dataset, which consists of 41 measured variables and 11 manipulated variables subjected to various faulty operating conditions. The data analytics was performed using key functions from the R-package ‘ConnectednessApproach’ that implements connectedness computations based on time and frequency. The NPDC coefficient matrices were then transformed into network adjacency matrices for the rendering of the network topology of connectedness for TEP. The resulting network topologies allow a comprehensive analysis of oscillation effects across all plant-measured and manipulated variables. Analyzing the directed connectedness of the system dynamics at short-range, mid-range, and long-range frequencies showed how the oscillation effects of disturbances and faults propagate and dissipate in the short-term, mid-term, and long-term periods. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 3563 KiB  

Open AccessArticle

Data-Driven Operation of Flexible Distribution Networks with Charging Loads

by Guorui Wang, Zhenghao Qian, Xinyao Feng, Haowen Ren, Wang Zhou, Jinhe Wang, Haoran Ji and Peng Li

Processes 2023, 11(6), 1592; https://doi.org/10.3390/pr11061592 - 23 May 2023

Viewed by 1499

Abstract

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging [...] Read more.

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 2409 KiB  

Open AccessArticle

Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas

by Yanwen Wang, Yanying Sun, Yalong Li, Chen Feng and Peng Chen

Processes 2023, 11(5), 1530; https://doi.org/10.3390/pr11051530 - 17 May 2023

Cited by 6 | Viewed by 1705

Abstract

Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wind farms. To determine the aggregate output [...] Read more.

Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wind farms. To determine the aggregate output of multiple wind farms, this paper proposes an interval forecasting method based on long short-term memory (LSTM) networks and copula theory. The method uses LSTM networks for spot forecasting firstly and then uses the forecasting error data generated by LSTM networks to model the conditional joint probability distribution of the forecasting errors for multiple wind farms through the time-varying regular vine copula (TVRVC) model, so as to obtain the probability interval of aggregate output for multiple wind farms under different confidence levels. The proposed method is applied to three adjacent wind farms in Northwest China and the results show that the forecasting intervals generated by the proposed method have high reliability with narrow widths. Moreover, comparing the proposed method with other four methods, the results show that the proposed method has better forecasting performance due to the consideration of the time-varying correlations among multiple wind farms and the use of a spot forecasting model with smaller errors. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 6609 KiB  

Open AccessArticle

Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market

by Chen Zhao, Jiaqi Sun, Ping He, Shaohua Zhang and Yuqi Ji

Energies 2023, 16(8), 3339; https://doi.org/10.3390/en16083339 - 9 Apr 2023

Cited by 3 | Viewed by 1690

Abstract

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, [...] Read more.

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 236 KiB  

Open AccessArticle

Electricity Supply Unreliability and Technical Efficiency: Evidence from Listed Chinese Manufacturing Companies

by Presley K. Wesseh, Jr., Yuqing Zhong and Chin Hui Hao

Energies 2023, 16(7), 3283; https://doi.org/10.3390/en16073283 - 6 Apr 2023

Cited by 2 | Viewed by 1743

Abstract

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) [...] Read more.

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) is used to estimate the technical efficiency (TE) score of manufacturing companies. Subsequently, the TE score is used to evaluate the electricity shortage index and other factors that are postulated to affect enterprise productivity. Two estimation methods have been adopted including ordinary least squares (OLS), which is less robust to endogeneity and instrumental variable (IV) estimation, which turns out to be more robust to endogeneity in the data. The empirical results show that, under OLS estimation, electricity shortage has a significantly negative impact on the technical efficiency of the listed manufacturing companies. However, when IV regression is implemented to address endogeneity issues in the data, electricity shortages tend to have a significantly positive impact on the technical efficiency, underscoring the importance of capturing endogeneity in the data. Extending the baseline results, this study also finds that, while the size of an enterprise may have no bearing, state-owned companies are more likely to be negatively affected by electricity shortages compared to privately owned companies. These results have significant implications for industrial policy design in China in particular, and developing countries in general. Most importantly, the results of this study underscore the importance of policies and measures to promote a shift in the ownership structure towards the private sector. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

15 pages, 972 KiB  

Open AccessArticle

A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power

by Khalid A. Alnowibet, Ahmad M. Alshamrani and Adel F. Alrasheedi

Energies 2023, 16(7), 3256; https://doi.org/10.3390/en16073256 - 5 Apr 2023

Cited by 4 | Viewed by 1668

Abstract

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in [...] Read more.

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 1347 KiB  

Open AccessArticle

Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access

by Cailian Gu, Yibo Wang, Weisheng Wang and Yang Gao

Energies 2023, 16(7), 3093; https://doi.org/10.3390/en16073093 - 28 Mar 2023

Cited by 2 | Viewed by 1754

Abstract

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms [...] Read more.

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms of power supply and load in distribution networks are analyzed, the dynamic model of distribution networks based on complex network theory is established, and the risk index of voltage exceeding limits under the conditions of high permeability distributed power supply access is put forward. Secondly, the random power flow of distribution networks based on the Monte Carlo method is studied by sampling and analyzing the dynamic model of distribution networks. Then, the risk calculation and safety assessment of voltage exceeding limits are carried out on the currently extracted model, and the risk control strategy of distribution network operation is put forward. Finally, an improved IEEE30-node distribution network topology is proposed. Through simulation analysis, it is proven that the load situation awareness early warning method of distribution networks can effectively predict, improve the security of distribution networks, and provide timely early warning information for maintenance personnel. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 2087 KiB  

Open AccessArticle

Decision-Making Method for Mine Cable Insulation Monitoring and Grounding Fault Diagnosis

by Chen Feng, Pingfeng Ye, Yanying Sun, Jingrui Li, Xiangyu Zang and Chenhao Sun

Processes 2023, 11(3), 795; https://doi.org/10.3390/pr11030795 - 7 Mar 2023

Cited by 2 | Viewed by 1808

Abstract

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground [...] Read more.

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground coal mines to carry out large-scale calculations to diagnose the grounding fault of the cable. Additionally, there are many types of cable grounding faults, such as single grounding fault and two-phase ground fault. Therefore, how to determine the type of grounding fault quickly through effective calculations and alarms to select the grounding cable is always a difficult task. In this study, to reduce the complexity of cable insulation state classification, we develop a novel classification method based on the decision tree algorithm. Concerning the zero-sequence network under different insulation conditions, the first calculation’s positive and negative values were generated to identify whether the cable insulation was symmetrical. Then, the insulation degradation phase is identified by the relationship between the three-phase voltage phase angle and the current difference variation between the beginning and end of the line. By substituting the correlation quantities collected by a wide-area synchronous measurement system into different equations, the whole grid’s decision tree was constructed in different insulation states. Then, the insulation state of each line was evaluated according to the conductance value. The effectiveness of the proposed method was verified using a 35/6 kV mine power distribution system model based on the MATLAB/Simulink platform. The test results illustrate that the method can accurately diagnose the power cable insulation state based on the decision tree of whether the grid three-phase loads operate in an ungrounded mode or not. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 9065 KiB  

Open AccessArticle

Innovative Inertial Response Imitation and Rotor Speed Recovery Control Scheme for a DFIG

by Xiaocen Xue, Jiejie Huang and Shun Sang

Electronics 2023, 12(4), 1029; https://doi.org/10.3390/electronics12041029 - 18 Feb 2023

Cited by 1 | Viewed by 1535

Abstract

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric [...] Read more.

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric power grid. To achieve the first benefit, the coupling relationship between the control coefficient of DFIGs and the frequency deviation was established by using the exponential function so that the control coefficient becomes large with the increasing frequency deviations and sizes of disturbance. After supporting the system frequency, the exponential function was employed to schedule the dynamic control coefficient to alleviate the negative effects of RSR on the instantaneous system frequency. The benefits of the proposed IIR and RSR strategy were investigated in a test system under various scenarios of sizes of disturbance and wind speed conditions. Test results clearly demonstrate that the proposed IIR and RSR strategy is capable of boosting the maximum system frequency excursion and reducing the negative influences on the system frequency during the speed recovery period. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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