Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment

Topic Information

Dear Colleagues,

The rapid development of novel energy technologies and equipment, including renewable energy, energy storage, green hydrogen, energy production, and energy conversion and consumption devices, provides opportunities for smart grids to achieve the objectives of economic security, reliability, flexibility, and low carbon. Moreover, technological advancements cannot only control energy flow but also supply an energy load via alternative sources. However, it is difficult to adapt traditional methods to the increasingly complex and changing energy environment and ensure that they meet the requirements of rapid response and intelligent decision making. Therefore, this topic focuses on utilizing the latest innovative techniques and energy equipment to guarantee the intelligent and effective operation, control, and planning of smart grids. The goals of this Topic are as follows:

1) investigate accurate models of energy systems and equipment and explore the impact of energy equipment on energy systems;

2) coordinate the control of multiple types of energy equipment to achieve the safe, economical, reliable, flexible, and environmental operation of smart grids;

3) develop advanced energy management strategies and intelligent planning schemes to improve energy efficiency;

4) apply advanced optimization technologies and/ or artificial intelligence methods for the intelligent and effective operation, control, and planning of smart grids;

5) and realize synergy among multiple energy sources to improve the flexibility of smart grids.

Topics of interest include but are not limited to the following:

1. The advanced modeling of energy systems and equipment;
2. Efficient energy management strategies for smart grids;
3. The intelligent control of multiple types of equipment for the safe operation of smart grids;
4. The planning of multiple types of energy production, conversion, and consumption devices;
5. Advanced and effective methods for the operation, control, and planning of smart grids;
6. Machine learning and deep learning for the intelligent operation of smart grids;
7. Control strategies for intelligent switch and protection equipment, the design of renewable energy inverters, and power electronic topologies;
8. High-voltage transmission technology and the technological innovation of HVDC transmission;
9. Strategies for the safe and stable operation of smart grids under extreme weather.

Dr. Pengfei Zhao
Prof. Dr. Sheng Chen
Dr. Yunqi Wang
Dr. Liwei Ju
Dr. Zhengmao Li
Dr. Minglei Bao
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
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Forecasting forecasting	2.3	5.8	2019	18.5 Days	CHF 1800	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (31 papers)

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25 pages, 4133 KiB  

Open AccessReview

A Review of Carbon Reduction Pathways and Policy–Market Mechanisms in Integrated Energy Systems in China

by Yifeng Liu, Meng Chen, Pingfan Wang, Yingxiang Wang, Feng Li and Hui Hou

Sustainability 2025, 17(7), 2802; https://doi.org/10.3390/su17072802 - 21 Mar 2025

Viewed by 77

Abstract

Integrated energy systems are critical physical platforms for driving clean energy transitions and achieving carbon reduction targets. This paper systematically reviews carbon reduction pathways across generation, grid, load, and storage from the dual perspectives of technology and policy–market mechanisms. First, the review outlines [...] Read more.

Integrated energy systems are critical physical platforms for driving clean energy transitions and achieving carbon reduction targets. This paper systematically reviews carbon reduction pathways across generation, grid, load, and storage from the dual perspectives of technology and policy–market mechanisms. First, the review outlines a multi-tier integrated energy system architecture and evaluates crucial technologies, such as back-pressure modification, flexible direct current transmission, and virtual energy storage, in improving energy efficiency and carbon reduction. Second, it explores how policy–market mechanisms incentivize carbon reduction, focusing on green power, green certificates, and the carbon market to support integrated energy system transformation. This paper offers a comprehensive theoretical framework and practical basis for the low-carbon transition of integrated energy systems. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 7069 KiB  

Open AccessArticle

Abnormal Load Variation Forecasting in Urban Cities Based on Sample Augmentation and TimesNet

by Yiyan Li, Zizhuo Gao, Zhenghao Zhou, Yu Zhang, Zelin Guo and Zheng Yan

Smart Cities 2025, 8(2), 43; https://doi.org/10.3390/smartcities8020043 - 7 Mar 2025

Viewed by 474

Abstract

With the evolving urbanization process in modern cities, the tertiary industry load and residential load start to take up a major proportion of the total urban power load. These loads are more dependent on stochastic factors such as human behaviors and weather events, [...] Read more.

With the evolving urbanization process in modern cities, the tertiary industry load and residential load start to take up a major proportion of the total urban power load. These loads are more dependent on stochastic factors such as human behaviors and weather events, demonstrating frequent abnormal variations that deviate from the normal pattern and causing consequent large forecasting errors. In this paper, a hybrid forecasting framework is proposed focusing on improving the forecasting accuracy of the urban power load during abnormal load variation periods. First, a quantitative method is proposed to define and characterize the abnormal load variations based on the residual component decomposed from the original load series. Second, a sample augmentation method is established based on Generative Adversarial Nets to boost the limited abnormal samples to a larger quantity to assist the forecasting model’s training. Last, an advanced forecasting model, TimesNet, is introduced to capture the complex and nonlinear load patterns during abnormal load variation periods. Simulation results based on the actual load data of Chongqing, China demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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26 pages, 2506 KiB  

Open AccessArticle

Optimal Economic Dispatch of Hydrogen Storage-Based Integrated Energy System with Electricity and Heat

by Yu Zhu, Siyu Niu, Guang Dai, Yifan Li, Linnan Wang and Rong Jia

Sustainability 2025, 17(5), 1974; https://doi.org/10.3390/su17051974 - 25 Feb 2025

Viewed by 241

Abstract

To enhance the accommodation capacity of renewable energy and promote the coordinated development of multiple energy, this paper proposes a novel economic dispatch method for an integrated electricity–heat–hydrogen energy system on the basis of coupling three energy flows. Firstly, we develop a mathematical [...] Read more.

To enhance the accommodation capacity of renewable energy and promote the coordinated development of multiple energy, this paper proposes a novel economic dispatch method for an integrated electricity–heat–hydrogen energy system on the basis of coupling three energy flows. Firstly, we develop a mathematical model for the hydrogen energy system, including hydrogen production, storage, and hydrogen fuel cells. Additionally, a multi-device combined heat and power system is constructed, incorporating gas boilers, waste heat boilers, gas turbines, methanation reactors, thermal storage tanks, batteries, and gas storage tanks. Secondly, to further strengthen the carbon reduction advantages, the economic dispatch model incorporates the power-to-gas process and carbon trading mechanisms, giving rise to minimizing energy purchase costs, energy curtailment penalties, carbon trading costs, equipment operation, and maintenance costs. The model is linearized to ensure a global optimal solution. Finally, the experimental results validate the effectiveness and superiority of the proposed model. The integration of electricity–hydrogen coupling devices improves the utilization rate of renewable energy generation and reduces the total system operating costs and carbon trading costs. The use of a tiered carbon trading mechanism decreases natural gas consumption and carbon emissions, contributing to energy conservation and emission reduction. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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20 pages, 26102 KiB  

Open AccessArticle

Research on Power Coordination Control Strategy of Microgrid Based on Reconfigurable Energy Storage

by Xiaoxi Liu, Libo Jiang, Tianwen Zheng and Zhengwei Zhu

Energies 2025, 18(5), 1040; https://doi.org/10.3390/en18051040 - 21 Feb 2025

Viewed by 246

Abstract

Reconfigurable new energy storage can effectively address the security and limitation issues associated with traditional battery energy storage. To enhance the reliability of the microgrid system and ensure power balance among generation units, this paper proposes a power coordination control strategy based on [...] Read more.

Reconfigurable new energy storage can effectively address the security and limitation issues associated with traditional battery energy storage. To enhance the reliability of the microgrid system and ensure power balance among generation units, this paper proposes a power coordination control strategy based on reconfigurable energy storage. First, a new microgrid system incorporating reconfigurable energy storage, photovoltaic power generation, and a supercapacitor is introduced. By leveraging the structural advantages of reconfigurable energy storage, the potential safety hazards of traditional battery energy storage can be mitigated and the reliability of the microgrid system can be improved. Second, a novel control strategy for reconfigurable energy storage, photovoltaic units, and supercapacitors is proposed. The reconfigurable energy storage achieves constant current charge/discharge control through a DC-DC converter, while the supercapacitor maintains DC bus voltage stability via another DC–DC converter. Next, the power flow relationship within the microgrid system is analyzed. The dynamic reconfiguration characteristics of the reconfigurable energy storage, combined with the high power density of the supercapacitor, enable dynamic compensation of the photovoltaic power generation unit to meet the load’s power demand. Finally, a simulation model is developed in the MATLAB/Simulink environment to compare and analyze the power compensation effects of traditional energy storage and reconfigurable energy storage. The results demonstrate that the proposed control strategy achieves constant current charge/discharge control for reconfigurable energy storage, addressing the issue of battery life degradation caused by the continuous variation in charge/discharge current when traditional energy storage compensates for photovoltaic fluctuations. Additionally, the proposed control strategy can effectively and rapidly adjust the system’s power output, mitigating power fluctuations caused by variations in photovoltaic generation and load changes in the microgrid system, thereby improving the system’s reliability and stability. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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25 pages, 1778 KiB  

Open AccessArticle

Enhanced Dynamic Expansion Planning Model Incorporating Q-Learning and Distributionally Robust Optimization for Resilient and Cost-Efficient Distribution Networks

by Gang Lu, Bo Yuan, Baorui Nie, Peng Xia, Cong Wu and Guangzeng Sun

Energies 2025, 18(5), 1020; https://doi.org/10.3390/en18051020 - 20 Feb 2025

Viewed by 248

Abstract

The increasing integration of renewable energy-based distributed generation (DG) in modern distribution networks is essential for reducing reliance on fossil fuels. However, the unpredictability and intermittency of renewable sources such as wind and photovoltaic (PV) systems introduce significant challenges for distribution network planning. [...] Read more.

The increasing integration of renewable energy-based distributed generation (DG) in modern distribution networks is essential for reducing reliance on fossil fuels. However, the unpredictability and intermittency of renewable sources such as wind and photovoltaic (PV) systems introduce significant challenges for distribution network planning. To address these challenges, this paper proposes a Q-learning-based Distributionally Robust Optimization (DRO) model for expansion planning of distribution networks and generation units. The proposed model incorporates energy storage systems (ESSs), renewable DG, substations, and distribution lines while considering uncertainties such as renewable generation variability, load fluctuations, and system contingencies. Through a dynamic decision-making process using Q-learning, the model adapts to changing network conditions to minimize the total system cost while maintaining reliability. The Latin Hypercube Sampling (LHS) method is employed to generate multi-scenario data, and piecewise linearization is used to reduce the computational complexity of the AC power flow equations. Numerical results demonstrate that the model significantly improves system reliability and economic efficiency under multiple uncertainty scenarios. The results also highlight the crucial role of the ESS in mitigating the variability of renewable energy and reducing the expected energy not supplied (EENS). Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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18 pages, 2057 KiB  

Open AccessArticle

Cooperative Game Enabled Low-Carbon Energy Dispatching of Multi-Regional Integrated Energy Systems Considering Carbon Market

by Peiran Liang, Honghang Zhang and Rui Liang

Energies 2025, 18(4), 759; https://doi.org/10.3390/en18040759 - 7 Feb 2025

Viewed by 436

Abstract

With the growing global environmental concerns and the push for carbon neutrality, rural multi-regional integrated energy systems (IESs) face challenges related to low energy efficiency, high carbon emissions, and the transition to cleaner energy sources. This paper proposes a cooperative game-based low-carbon economic [...] Read more.

With the growing global environmental concerns and the push for carbon neutrality, rural multi-regional integrated energy systems (IESs) face challenges related to low energy efficiency, high carbon emissions, and the transition to cleaner energy sources. This paper proposes a cooperative game-based low-carbon economic dispatch strategy for rural IESs, integrating carbon trading mechanisms. A novel multi-regional IESs architecture is developed to exploit the synergy between photovoltaic (PV) and biomass energy systems. The proposed model described the anaerobic fermentation heat loads, incorporates variable-temperature fermentation, and employs a Nash bargaining model solved via the Alternating Direction Method of Multipliers (ADMM) to optimize cooperation while preserving stakeholder privacy. Simulation results show that the proposed strategy reduces total operating costs by 16.9% and carbon emissions by 7.5%, validating its effectiveness in enhancing efficiency and sustainability in rural energy systems. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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26 pages, 4448 KiB  

Open AccessArticle

Network and Energy Storage Joint Planning and Reconstruction Strategy for Improving Power Supply and Renewable Energy Acceptance Capacities

by Xianghao Kong, Liang Feng, Ke Peng, Guanyu Song and Chuanliang Xiao

Sustainability 2025, 17(3), 1292; https://doi.org/10.3390/su17031292 - 5 Feb 2025

Viewed by 565

Abstract

The integration of distributed generation (DG) into distribution networks has significantly increased the strong coupling between power supply capacity and renewable energy acceptance capacity. Addressing this strong coupling while enhancing both capacities presents a critical challenge in modern distribution network development. This study [...] Read more.

The integration of distributed generation (DG) into distribution networks has significantly increased the strong coupling between power supply capacity and renewable energy acceptance capacity. Addressing this strong coupling while enhancing both capacities presents a critical challenge in modern distribution network development. This study introduces an innovative joint planning and reconstruction strategy for network and energy storage, designed to simultaneously enhance power supply capacity and renewable energy acceptance capacity. The proposed approach employs a bi-level optimization model: the upper level focuses on minimizing economic costs by determining the optimal locations and capacities of energy storage systems and the layout of network lines, while the lower level aims to maximize power supply and renewable energy acceptance capacities by optimizing line switch states. Additionally, this research quantifies the coupling relationship between these two capacities under uncertainty, providing a deeper understanding of their dynamic interaction. Advanced computational techniques, including Monte Carlo simulations and particle swarm optimization (PSO), are utilized to solve the model efficiently. Case studies demonstrate that the proposed strategy effectively enhances both power supply and renewable energy acceptance capacities. Furthermore, exploring the strong coupling relationship between these two capacities under various conditions not only optimizes the utilization of renewable energy in the power system and prevents resource waste, but also helps avoid the volatility impacts of renewable energy uncertainty on the power system in actual planning. Additionally, the network and energy storage joint planning and reconstruction strategy proposed in this study achieves cost minimization under the constraint of limited resources and simultaneously enhanced both capacities. The strategy provides feasible solutions for power grid planning in actual applications. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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30 pages, 3108 KiB  

Open AccessArticle

Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks

by Wenjie Pan, Jun Han, Chao Cai, Haofei Chen, Hong Liu and Zhengyang Xu

Energies 2025, 18(3), 698; https://doi.org/10.3390/en18030698 - 3 Feb 2025

Viewed by 664

Abstract

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing [...] Read more.

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing uncertainties in renewable energy generation and load demand. By leveraging a dynamic interaction between sensitivity scores and temporal system conditions, the framework achieves efficient and resilient operation under extreme variability scenarios. Key methodological innovations include the incorporation of a social force model-based sensitivity mapping technique, a layered optimization approach balancing system-wide and localized decisions, and a robust uncertainty set to safeguard performance against distributional shifts. The framework is validated using a synthesized test system, incorporating realistic renewable generation profiles, load patterns, and energy storage dynamics. Results demonstrate a significant improvement in hosting capacity, with system-wide enhancements of up to 35% and a 50% reduction in renewable curtailment. Moreover, sensitivity-driven resource deployment ensures efficient utilization of flexibility resources, achieving a peak allocation efficiency of 90% during critical periods. This research provides a comprehensive tool for addressing the challenges of renewable integration and grid stability in modern power systems, offering actionable insights for resource allocation strategies under uncertainty. The proposed methodology not only advances the state-of-the-art in sensitivity-based optimization but also paves the way for scalable, resilient energy management solutions in high-renewable penetration scenarios. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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21 pages, 3508 KiB  

Open AccessArticle

Neural Network Based Power Meter Wiring Fault Recognition of Smart Grids Under Abnormal Reactive Power Compensation Scenarios

by Huizhe Zheng, Zhongshuo Lin, Huan Lin, Chaokai Huang, Xiaoqi Huang, Suna Ji and Xiaoshun Zhang

Energies 2025, 18(3), 545; https://doi.org/10.3390/en18030545 - 24 Jan 2025

Viewed by 512

Abstract

This paper explores the challenges of detecting wiring anomalies in three-phase, four-wire energy metering devices, especially when large amounts of reactive power compensation are involved. Traditional methods, such as the hexagon phasor diagram technique, perform well under standard loads, but struggle to adapt [...] Read more.

This paper explores the challenges of detecting wiring anomalies in three-phase, four-wire energy metering devices, especially when large amounts of reactive power compensation are involved. Traditional methods, such as the hexagon phasor diagram technique, perform well under standard loads, but struggle to adapt to new situations, such as over- or under-compensation. To overcome these limitations, this paper proposes a hybrid approach that combines mechanism-based knowledge with data-driven technologies, including backpropagation neural networks (BPNNs). This method improves the accuracy and efficiency of anomaly detection and can better adapt to a dynamic power environment. The result is improved universality of anomaly detection, which helps to achieve safer, more accurate, and more efficient smart grid operation in complex situations. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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18 pages, 2552 KiB  

Open AccessArticle

Short-Term Optimal Scheduling of a Cascade Hydro-Photovoltaic System for Maximizing the Expectation of Consumable Electricity

by Shuzhe Hu, Jinniu Miao, Jingyang Wu, Liqian Zhao, Yue Wang, Fanyan Meng, Chao Wei, Xiaoqin Zhang and Benrui Zhu

Processes 2025, 13(2), 328; https://doi.org/10.3390/pr13020328 - 24 Jan 2025

Viewed by 598

Abstract

Fully leveraging the regulatory role of cascade hydropower in river basins and realizing complementary joint power generation between cascade hydropower and photovoltaic (PV) systems is a crucial approach to promoting the consumption of clean energy. Given the uncertainty of PV outputs, this paper [...] Read more.

Fully leveraging the regulatory role of cascade hydropower in river basins and realizing complementary joint power generation between cascade hydropower and photovoltaic (PV) systems is a crucial approach to promoting the consumption of clean energy. Given the uncertainty of PV outputs, this paper introduces a short-term scheduling model for cascade hydropower–PV systems. The model aims to maximize electricity consumption by considering individual units, hydropower plant constraints, unit constraints, and grid constraints. By allocating loads among hydropower plants and periods, it optimizes hydropower’s dual roles, supporting grid power supplies and coordinating with PVs, thus boosting the overall system consumption. In terms of model solution, linearization methods and modeling techniques such as piecewise linear approximation, the introduction of 0–1 integer variables, and the discretization of generation headwater are employed to handle the nonlinear constraints in the original model, transforming it into a mixed-integer linear programming problem. Finally, taking a complementary system constructed by 15 units of 4 hydropower stations and 2 photovoltaic groups in a cascade in a river basin in Southwest China as an example, the results show that through the complementary coordination of cascade hydropower and photovoltaic power, under the same grid constraints, the expected value of the power consumption of the complementary system in the model of this paper increased by 863.2 MW·h, among which the power consumption of photovoltaic group 1 increased by 1035.7 MW·h, and the power consumption of photovoltaic group 2 decreased by 172.5 MW·h. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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20 pages, 4716 KiB  

Open AccessArticle

A Purely Real-Valued Fast Estimator of Dynamic Harmonics for Application in Embedded Monitoring Devices in Power-Electronic Grids

by Xiao Luo, Caihai Zou, Haoqiang Wu, Boyang Gao, Hongjian Sun and Zongshuai Jin

Processes 2025, 13(1), 227; https://doi.org/10.3390/pr13010227 - 15 Jan 2025

Viewed by 519

Abstract

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the [...] Read more.

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10⁻² Hz, 7 × 10⁻¹%, and 8 × 10⁻² degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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12 pages, 1554 KiB  

Open AccessArticle

Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

by Dong Hua, Fei Peng, Suisheng Liu, Qinglin Lin, Jiahui Fan and Qian Li

Energies 2025, 18(2), 333; https://doi.org/10.3390/en18020333 - 14 Jan 2025

Viewed by 787

Abstract

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR [...] Read more.

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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24 pages, 4514 KiB  

Open AccessArticle

Robust Trading Decision-Making Model for Demand-Side Resource Aggregators Considering Multi-Objective Cluster Aggregation Optimization

by Fei Liu, Shaokang Qi, Shibin Wang, Xu Tian, Liantao Liu and Xue Zhao

Energies 2025, 18(2), 236; https://doi.org/10.3390/en18020236 - 7 Jan 2025

Viewed by 643

Abstract

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and [...] Read more.

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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18 pages, 5394 KiB  

Open AccessArticle

Optimizing Charging and Discharging at Bus Battery Swap Stations Under Varying Environmental Temperatures

by Zihan Hu, Xiangjun Zeng, Chen Feng and Haoran Diao

Processes 2025, 13(1), 81; https://doi.org/10.3390/pr13010081 - 2 Jan 2025

Viewed by 899

Abstract

The grid ancillary service capability of bus swapping stations (BSSs) is significantly affected by environmental temperature fluctuations and the disorderly charging and discharging of batteries. This study addressed these challenges by developing a comprehensive optimization model for scheduling BSS operations under varying environmental [...] Read more.

The grid ancillary service capability of bus swapping stations (BSSs) is significantly affected by environmental temperature fluctuations and the disorderly charging and discharging of batteries. This study addressed these challenges by developing a comprehensive optimization model for scheduling BSS operations under varying environmental conditions. First, based on real-world energy consumption and temperature data, an energy consumption coefficient correction method was proposed to improve the accuracy of energy consumption calculations. Next, a greedy strategy was utilized to match vehicle battery swapping demands with available batteries in the BSS. A two-layer multi-objective optimization model was then constructed to optimize the charging and discharging power. The upper layer minimizes the total costs, including charging/discharging costs and battery degradation costs, while the lower layer minimizes the load peak-valley difference and maximizes the charging/discharging balance among all batteries. Numerical simulations of the proposed energy consumption correction coefficient improved the accuracy of electric bus energy consumption estimation. Additionally, the proposed optimization strategy reduced the operational cost of the bus battery swapping station by 53.09% and decreased the transformer’s maximum load rate by 28.28%. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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20 pages, 3614 KiB  

Open AccessArticle

Timing Optimization Method for Pumped Storage Plant Construction Considering Capital Expenditure Capacity Feedback

by Jie Jiao, Xiaoquan Lei, Puyu He, Qian Wang, Guangxiu Yu, Wenshi Ren and Shaokang Qi

Energies 2025, 18(1), 47; https://doi.org/10.3390/en18010047 - 27 Dec 2024

Viewed by 537

Abstract

With the extensive integration of renewable energy into the power grid, pumped storage power plants have become an essential component in the development of modern power systems due to their rapid response capabilities, advanced technology, and other beneficial features. However, high construction costs [...] Read more.

With the extensive integration of renewable energy into the power grid, pumped storage power plants have become an essential component in the development of modern power systems due to their rapid response capabilities, advanced technology, and other beneficial features. However, high construction costs and irrational capital expenditure and construction schedules have constrained the robust and sustainable growth of pumped storage plants. Therefore, this paper proposes a pumped storage plant construction timing optimization method considering capital expenditure capacity feedback. Initially, an analysis is conducted on the factors that influence the capital expenditure costs of pumped storage power plants throughout their lifecycle. Next, the value of investing in pumped storage plants is assessed across three different aspects: economics, environment, and reliability. Finally, according to the principle of dynamic planning combined with the actual needs and capital expenditure potential of pumped storage plants, the sum of the capital expenditure effectiveness values in each stage is used as the indicator function of each stage to construct the pumped storage plants project capital expenditure timing optimization model, and a simulation analysis is carried out with Province Z as an example to verify the validity and applicability of the proposed model. The findings indicate that the suggested model is effective in balancing the implementation time of individual projects to achieve the maximum cumulative capital expenditure performance over the entire planning period. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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22 pages, 1990 KiB  

Open AccessArticle

Hybrid Proximal Policy Optimization—Wasserstein Generative Adversarial Network Framework for Hosting Capacity Optimization in Renewable-Integrated Power Systems

by Jun Han, Chao Cai, Wenjie Pan, Hong Liu and Zhengyang Xu

Energies 2024, 17(24), 6234; https://doi.org/10.3390/en17246234 - 11 Dec 2024

Viewed by 548

Abstract

The rapid integration of distributed energy resources (DERs) such as photovoltaics (PV), wind turbines, and energy storage systems has transformed modern power systems, with hosting capacity optimization emerging as a critical challenge. This paper presents a novel Hybrid Proximal Policy Optimization-Wasserstein Generative Adversarial [...] Read more.

The rapid integration of distributed energy resources (DERs) such as photovoltaics (PV), wind turbines, and energy storage systems has transformed modern power systems, with hosting capacity optimization emerging as a critical challenge. This paper presents a novel Hybrid Proximal Policy Optimization-Wasserstein Generative Adversarial Network (PPO-WGAN) framework designed to address the temporal-spatial complexities and uncertainties inherent in renewable-integrated distribution networks. The proposed method combines Proximal Policy Optimization (PPO) for sequential decision-making with Wasserstein Generative Adversarial Networks (WGAN) for high-quality scenario generation, enabling robust hosting capacity enhancement and operational efficiency. Simulation results demonstrate a hosting capacity improvement of up to 128.6% in high-penetration scenarios (90% renewable), with average operational cost reductions of 22%. Voltage deviations are minimized to within ±5% of nominal levels, while energy losses are reduced by 18%. Scenario quality, evaluated using the Wasserstein metric, achieved convergence with an average score of 0.95 after 80 iterations, highlighting the WGAN’s ability to generate realistic and diverse scenarios. This study advances the state of the art in distribution network optimization by integrating machine learning techniques with robust mathematical modeling. The PPO-WGAN framework enhances scalability, ensures grid stability, and promotes efficient renewable integration, providing a robust foundation for future applications in modern power systems. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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34 pages, 4802 KiB  

Open AccessArticle

Integrated Energy Management in Small-Scale Smart Grids Considering the Emergency Load Conditions: A Combined Battery Energy Storage, Solar PV, and Power-to-Hydrogen System

by Hossein Jokar, Taher Niknam, Moslem Dehghani, Pierluigi Siano, Khmaies Ouahada and Mokhtar Aly

Smart Cities 2024, 7(6), 3764-3797; https://doi.org/10.3390/smartcities7060145 - 3 Dec 2024

Cited by 2 | Viewed by 1273

Abstract

This study introduces an advanced Mixed-Integer Linear Programming model tailored for comprehensive electrical and thermal energy management in small-scale smart grids, addressing emergency load shedding and overload situations. The model integrates combined heat and power sources, capable of simultaneous electricity and heat generation, [...] Read more.

This study introduces an advanced Mixed-Integer Linear Programming model tailored for comprehensive electrical and thermal energy management in small-scale smart grids, addressing emergency load shedding and overload situations. The model integrates combined heat and power sources, capable of simultaneous electricity and heat generation, alongside a mobile photovoltaic battery storage system, a wind resource, a thermal storage tank, and demand response programs (DRPs) for both electrical and thermal demands. Power-to-hydrogen systems are also incorporated to efficiently convert electrical energy into heat, enhancing network synergies. Utilizing the robust Gurobi solver, the model aims to minimize operating, fuel, and maintenance costs while mitigating environmental impact. Simulation results under various scenarios demonstrate the model’s superior performance. Compared to conventional evolutionary methods like particle swarm optimization, non-dominated sorting genetic algorithm III, and biogeography-based optimization, the proposed model exhibits remarkable improvements, outperforming them by 11.4%, 5.6%, and 11.6%, respectively. This study emphasizes the advantages of employing DRP and heat tank equations to balance electrical and thermal energy relationships, reduce heat losses, and enable the integration of larger photovoltaic systems to meet thermal constraints, thus broadening the problem’s feasible solution space. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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19 pages, 4079 KiB  

Open AccessArticle

Hierarchical Power System Scheduling and Energy Storage Planning Method Considering Heavy Load Rate

by Qiuyu Lu, Pingping Xie, Yingming Lin, Yang Liu, Yinguo Yang and Xu Lin

Processes 2024, 12(12), 2725; https://doi.org/10.3390/pr12122725 - 2 Dec 2024

Cited by 1 | Viewed by 746

Abstract

With the rise in the proportion of renewable energy and energy storage in modern power systems, the volatility of renewable energy and the increasing demand for loads pose a significant risk of congestion in transmission lines. Along with transmission congestion, prolonged heavy loads [...] Read more.

With the rise in the proportion of renewable energy and energy storage in modern power systems, the volatility of renewable energy and the increasing demand for loads pose a significant risk of congestion in transmission lines. Along with transmission congestion, prolonged heavy loads on transmission lines increase equipment failure rates, leading to a range of issues within the power system. This study proposes a scene clustering method for power system scheduling by leveraging the net load related with the load and renewable energy power outputs. Subsequently, a scheduling model and line load evaluation indexes were developed to analyze the line load rate of power systems with different renewable energy proportions. The simulation results indicate that the utilization rate of lines, the fluctuation rate of line load, the maximum line load, and heavy line load time increase as the installed proportion of renewable energy increases. Finally, a penalty term for heavy loads was incorporated into the objective function and methods of rescheduling and planning energy storage considering the heavy load penalty function are proposed. A case study validated the significant improvements in load management, achieving a reduction in heavy load time by approximately 30% and reducing transmission congestion by 20% under high-renewable-energy-penetration scenarios. These results illustrate the effectiveness of the heavy load cost function in enhancing system resilience and optimizing load distribution. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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26 pages, 2544 KiB  

Open AccessArticle

Two-Stage, Three-Layer Stochastic Robust Model and Solution for Multi-Energy Access System Based on Hybrid Game Theory

by Guodong Wu, Xiaohu Li, Jianhui Wang, Ruixiao Zhang and Guangqing Bao

Processes 2024, 12(12), 2656; https://doi.org/10.3390/pr12122656 - 25 Nov 2024

Viewed by 903

Abstract

This paper proposes a two-stage, three-layer stochastic robust model and its solution method for a multi-energy access system (MEAS) considering different weather scenarios which are described through scenario probabilities and output uncertainties. In the first stage, based on the principle of the master–slave [...] Read more.

This paper proposes a two-stage, three-layer stochastic robust model and its solution method for a multi-energy access system (MEAS) considering different weather scenarios which are described through scenario probabilities and output uncertainties. In the first stage, based on the principle of the master–slave game, the master–slave relationship between the grid dispatch department (GDD) and the MEAS is constructed and the master–slave game transaction mechanism is analyzed. The GDD establishes a stochastic pricing model that takes into account the uncertainty of wind power scenario probabilities. In the second stage, considering the impacts of wind power and photovoltaic scenario probability uncertainties and output uncertainties, a max–max–min three-layer structured stochastic robust model for the MEAS is established and its cooperation model is constructed based on the Nash bargaining principle. A variable alternating iteration algorithm combining Karush–Kuhn–Tucker conditions (KKT) is proposed to solve the stochastic robust model of the MEAS. The alternating direction method of multipliers (ADMM) is used to solve the cooperation model of the MEAS and a particle swarm algorithm (PSO) is employed to solve the non-convex two-stage model. Finally, the effectiveness of the proposed model and method is verified through case studies. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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20 pages, 1250 KiB  

Open AccessArticle

Developing China’s Electricity Financial Market: Strategic Design of Financial Derivatives for Risk Management and Market Stability

by Hao Feng, Yidi Zhang, Zhou Lan, Kun Wang, Yizheng Wang, Sheng Chen and Changsen Feng

Energies 2024, 17(23), 5854; https://doi.org/10.3390/en17235854 - 22 Nov 2024

Viewed by 672

Abstract

As China progresses with its electricity market reforms in pursuit of “carbon peak and carbon neutrality” objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental [...] Read more.

As China progresses with its electricity market reforms in pursuit of “carbon peak and carbon neutrality” objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental principles of electricity financial derivatives, assesses their applicability to the Chinese market through an analysis of international experiences from the United States, Nordic countries, and Australia, and highlights critical issues for the construction of a robust market framework. It offers strategic recommendations regarding the structural and developmental aspects of China’s electricity financial market and proposes derivative instruments tailored to China’s market to improve liquidity and risk management mechanisms, thereby facilitating the renewable energy transition. The study demonstrates that these derivatives are instrumental in mitigating price volatility, managing transmission congestion, and supporting the shift to renewable energy. This provides a pragmatic approach for the reform and advancement of China’s electricity financial market, aligning with global strategies and addressing the unique challenges of China’s energy transition. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 2599 KiB  

Open AccessArticle

Reinforcement Learning-Enhanced Adaptive Scheduling of Battery Energy Storage Systems in Energy Markets

by Yang Liu, Qiuyu Lu, Zhenfan Yu, Yue Chen and Yinguo Yang

Energies 2024, 17(21), 5425; https://doi.org/10.3390/en17215425 - 30 Oct 2024

Cited by 1 | Viewed by 905

Abstract

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. [...] Read more.

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 4381 KiB  

Open AccessArticle

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

by Chen Fu, Li Lan, Su Chen, Mingxing Guo, Xiaojing Jiang, Xiaoran Yin and Chuanbo Xu

Energies 2024, 17(20), 5203; https://doi.org/10.3390/en17205203 - 18 Oct 2024

Viewed by 1090

Abstract

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is [...] Read more.

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 2275 KiB  

Open AccessArticle

A Post-Disaster Fault Recovery Model for Distribution Networks Considering Road Damage and Dual Repair Teams

by Wei Liu, Qingshan Xu, Minglei Qin and Yongbiao Yang

Energies 2024, 17(20), 5020; https://doi.org/10.3390/en17205020 - 10 Oct 2024

Cited by 1 | Viewed by 855

Abstract

Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to [...] Read more.

Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to road damage or severe waterlogging, making some road sections impassable. Based on this, this paper first establishes a road network model to describe the dynamic changes in access performance and road damage. It provides the shortest time-consuming route suggestions for the traffic access of mobile class resources in the post-disaster recovery task of power distribution networks. Then, the model proposes a joint repair model with general repair crew (GRC) and senior repair crew (SRC) collaboration. Different types of faults match different functions of repair crews (RCs). Finally, the proposed scheme is simulated and analyzed in a road network and power grid extreme post-disaster recovery model, including a mobile energy storage system (MESS) and distributed power sources. The simulation finds that considering road damage and severe failures produces a significant difference in the progress and load loss of the recovery task. The model proposed in this paper is more suitable for the actual scenario requirements, and the simulation results and loss assessment obtained are more accurate and informative. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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15 pages, 1112 KiB  

Open AccessArticle

Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization

by Zhiming Gu, Tingzhe Pan, Bo Li, Xin Jin, Yaohua Liao, Junhao Feng, Shi Su and Xiaoxin Liu

Energies 2024, 17(19), 4801; https://doi.org/10.3390/en17194801 - 25 Sep 2024

Viewed by 1306

Abstract

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar [...] Read more.

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework’s capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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16 pages, 3090 KiB  

Open AccessArticle

Comprehensive Evaluation of a Pumped Storage Operation Effect Considering Multidimensional Benefits of a New Power System

by Yinguo Yang, Ying Yang, Qiuyu Lu, Dexu Liu, Pingping Xie, Mu Wang, Zhenfan Yu and Yang Liu

Energies 2024, 17(17), 4449; https://doi.org/10.3390/en17174449 - 5 Sep 2024

Viewed by 839

Abstract

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. [...] Read more.

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. The evaluation criteria are based on the values of indexes for pumped storage plants that have already been put into operation. Using this method, the operational effect of pumped storage plants with different installed capacities, regulation durations, and conversion efficiencies are comprehensively evaluated and analyzed. The calculation results show that the operation effect of a pumped storage plant with high regulation performance and high comprehensive conversion efficiency is better, indicating that the established index system and evaluation method can comprehensively and truly reflect the positive benefits brought by a pumped storage plant to a new power system. This study can provide a practical reference for the early planning and decision making of pumped storage in a new power system. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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21 pages, 2910 KiB  

Open AccessArticle

Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning

by Shruti Kalra, Ruby Beniwal, Vinay Singh and Narendra Singh Beniwal

Electricity 2024, 5(3), 585-605; https://doi.org/10.3390/electricity5030029 - 24 Aug 2024

Cited by 1 | Viewed by 2623

Abstract

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar [...] Read more.

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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18 pages, 3501 KiB  

Open AccessArticle

A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability

by Xiping Ma, Wenxi Zhen, Rui Xu, Xiaoyang Dong and Yaxin Li

Energies 2024, 17(16), 3910; https://doi.org/10.3390/en17163910 - 8 Aug 2024

Viewed by 1107

Abstract

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power [...] Read more.

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 4840 KiB  

Open AccessArticle

Cyber Insurance for Energy Economic Risks

by Alexis Pengfei Zhao, Faith Xue Fei and Mohannad Alhazmi

Smart Cities 2024, 7(4), 2042-2064; https://doi.org/10.3390/smartcities7040081 - 27 Jul 2024

Viewed by 1154

Abstract

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber [...] Read more.

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber intrusions in smart city infrastructures, this study introduces a two-stage hierarchical planning model for ICT-integrated multi-energy systems, emphasizing the economic role of cyber insurance. By adopting cyber insurance, smart city operators can mitigate the financial impact of unforeseen cyber incidents, transferring these economic risks to the insurance provider. The proposed two-stage optimization model strategically balances the economic implications of urban energy system operations with cyber insurance coverage. This approach allows city managers to make economically informed decisions about insurance procurement in the first stage and implement cost-effective defense strategies against potential cyberattacks in the second stage. Utilizing a distributionally robust approach, the study captures the emergent and uncertain nature of cyberattacks through a moment-based ambiguity set and resolves the reformulated linear problem using a dynamic cutting plane method. This work offers a distinct perspective on managing the economic risks of cyber incidents in smart cities and provides a valuable framework for decision making regarding cyber insurance procurement, ultimately aiming to enhance the financial stability of smart city energy operations. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 4387 KiB  

Open AccessArticle

A Stochastic Model Predictive Control Method for Tie-Line Power Smoothing under Uncertainty

by Molin An, Xueshan Han and Tianguang Lu

Energies 2024, 17(14), 3515; https://doi.org/10.3390/en17143515 - 17 Jul 2024

Cited by 1 | Viewed by 802

Abstract

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method [...] Read more.

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 1991 KiB  

Open AccessArticle

Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering

by Weijie Wu, Dongwei Li, Hui Sun, Yixin Li, Yining Zhang and Mingrui Zhao

Energies 2024, 17(14), 3439; https://doi.org/10.3390/en17143439 - 12 Jul 2024

Viewed by 1061

Abstract

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and [...] Read more.

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 3144 KiB  

Open AccessArticle

Coordinated Optimization of Hydrogen-Integrated Energy Hubs with Demand Response-Enabled Energy Sharing

by Tasawar Abbas, Sheng Chen, Xuan Zhang and Ziyan Wang

Processes 2024, 12(7), 1338; https://doi.org/10.3390/pr12071338 - 27 Jun 2024

Viewed by 1238

Abstract

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable [...] Read more.

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable multi-energy system operations. Therefore, our work aims to enhance multi-energy flexibility by coordinating various energy hubs within a hydrogen-based integrated system. This study focuses on a cost-effective, ecologically sound, and flexible tertiary hub (producer, prosumer, and consumer) with integrated demand response programs, demonstrating a 17.30% reduction in operation costs and a 13.14% decrease in emissions. Power-to-gas technology enhances coupling efficiency among gas turbines, boilers, heat pumps, and chillers. A mixed-integer nonlinear programming model using a GAMS BARON solver will achieve the optimal results of this study. The proposed model’s simulation results show reduced energy market costs, total emissions, and daily operation expenses. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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