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18 pages, 2944 KiB

Open AccessArticle

Optimal Strategy for Grid Loss Reduction Under Electricity Transmission and Distribution Reform Considering Low-Carbon Benefits

by Weiwu Li, Qing Xu, Xinying Wang, Zhengying Liu, Tianshou Li and Dandan Zhang

Processes 2025, 13(5), 1406; https://doi.org/10.3390/pr13051406 - 5 May 2025

Viewed by 390

Abstract

Selecting grid loss reduction strategies is crucial for energy-saving transformations, particularly in the context of electricity transmission and distribution pricing reforms. The optimization of strategic selection is not easy due to the vast number of grid devices, which leads to a multitude of [...] Read more.

Selecting grid loss reduction strategies is crucial for energy-saving transformations, particularly in the context of electricity transmission and distribution pricing reforms. The optimization of strategic selection is not easy due to the vast number of grid devices, which leads to a multitude of possible strategy combinations. This paper presents an optimal model for selecting loss reduction strategies, aiming to minimize the sum of comprehensive investment costs and energy loss costs over the life cycle of the strategies. The energy loss costs include both direct expenses due to energy loss and indirect costs, namely, carbon emission penalties. The constraints include allowable voltage deviations, branch power transmission, the number of loss reduction measures, loss rates, and total investment limits. The model comprehensively considers both economic benefits and the social benefits of reduced carbon emissions. It can help companies better adapt to electricity transmission and distribution pricing reforms, reduce operational costs, and contribute to low-carbon development. Finally, the model is validated using the data provided by one provincial power grid company in China. The results show that the loss reduction reaches 13.9 MW and the reduced carbon emission per hour is 10.425 t. The proposed method is also compared with the enumeration method, which demonstrates its effectiveness and efficiency. Further research will be conducted on establishing functional relationships between electricity sales prices and line losses to incentivize companies to apply loss reduction measures under different pricing functions. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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35 pages, 7694 KiB

Open AccessArticle

Optimized Dispatch of Integrated Energy Systems in Parks Considering P2G-CCS-CHP Synergy Under Renewable Energy Uncertainty

by Zhiyuan Zhang, Xiqin Li, Lu Zhang, Hu Zhao, Ziren Wang, Wei Li and Baosong Wang

Processes 2025, 13(3), 680; https://doi.org/10.3390/pr13030680 - 27 Feb 2025

Viewed by 454

Abstract

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and [...] Read more.

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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19 pages, 1807 KiB

Open AccessArticle

Reliability Evaluation Method for Underground Cables Based on Double Sequence Monte Carlo Simulation

by Jiaxing Zhang, Bo Wang, Hengrui Ma, Yifan He, Hongxia Wang and Hanqi Zhang

Processes 2025, 13(2), 505; https://doi.org/10.3390/pr13020505 - 12 Feb 2025

Viewed by 602

Abstract

With the increasing demand for electrical reliability, power grid companies often need to operate urban underground cable systems at flexible ratings during planned or emergency events, but the risk of failure caused by such loading operations has not yet been quantified. Based on [...] Read more.

With the increasing demand for electrical reliability, power grid companies often need to operate urban underground cable systems at flexible ratings during planned or emergency events, but the risk of failure caused by such loading operations has not yet been quantified. Based on this, this paper proposes a reliability assessment method for underground cables based on dual sequence Monte Carlo simulation, which can consider the cumulative risk impact caused by electrical faults and insulation aging of underground cables during emergency events. Firstly, the integration of the electrical and thermal design characteristics of the integrated underground cable system within the overall network reliability framework was introduced. A dual sequence Monte Carlo simulation loop was used to evaluate the performance of the cable and network while considering the thermal aging effects of emergency loads on the cable. Then, the proposed method is applied to improve the IEEE 14 bus network by introducing cable design risk factor α and aging risk acceptance factor β to achieve flexibility and operational risk under long-term and short-term high-load emergency rating scenarios. Finally, the research results of the IEEE 14 bus network show that the expected unserved energy of scenario 1 is 30.15 MWh/year, which is 59% lower than the basic scenario of 73.34 MWh/year. The expected network failure frequency remains almost unchanged. Scenario 2 and scenario 3 show a decrease in the gain of network performance due to emergency-rated operation after using α and β, demonstrating the accuracy of the proposed method in cable reliability assessment. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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16 pages, 2867 KiB

Open AccessArticle

Adaptive Scheduling Method of Heterogeneous Resources on Edge Side of Power System Collaboration Based on Cloud–Edge Security Dynamic Collaboration

by Li Li, Shanshan Lu, Haibo Sun and Runze Wu

Processes 2025, 13(2), 366; https://doi.org/10.3390/pr13020366 - 28 Jan 2025

Viewed by 794

Abstract

In recent years, the large-scale integration of new power distribution technologies such as distributed power generation, electric vehicles, and flexible load control has led to a sharp increase in the operating pressure of the power cloud master station. To this end, an adaptive [...] Read more.

In recent years, the large-scale integration of new power distribution technologies such as distributed power generation, electric vehicles, and flexible load control has led to a sharp increase in the operating pressure of the power cloud master station. To this end, an adaptive resource allocation method for edge-side general computing resources, which is used for cloud–edge collaborative security protection, is proposed. Firstly, considering the computing resources available to multiple edge substations, a Cloud–edge Collaborative Relay Business Security Protection Model (C2RBSPM) is constructed. Then, with the goal of minimizing the operating pressure of the maximum cloud master, the corresponding linear programming problem is established, and finally the Karush–Kuhn–Tucker (KKT) is used to solve it quickly. The simulation results show that the proposed method can reduce the expected operating pressure of the cloud master station by up to 35.19%. Therefore, reasonable mining of available computing resources on the edge side and relay security protection can effectively reduce the operating pressure of the cloud master station, and improve the operation efficiency of the system. This approach is of great significance for the flexible, intelligent, and digital transformation of the power distribution system in the future. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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17 pages, 3845 KiB

Open AccessArticle

A Fast Calculation Method for Economic Dispatch of Electro-Thermal Coupling System Considering the Dynamic Process of Heat Transfer

by Jingyan Chen, Qinting Lin, Zilong Yang, Qingming Liu and Hongbo Zou

Processes 2025, 13(1), 175; https://doi.org/10.3390/pr13010175 - 10 Jan 2025

Viewed by 623

Abstract

The dynamic spatial and temporal characteristics of heat transfer within heating network pipelines are important factors affecting the accuracy of economic dispatch decision-making results of electro-thermal coupling systems. However, the pipeline heat transmission process is described by partial differential equations, which makes it [...] Read more.

The dynamic spatial and temporal characteristics of heat transfer within heating network pipelines are important factors affecting the accuracy of economic dispatch decision-making results of electro-thermal coupling systems. However, the pipeline heat transmission process is described by partial differential equations, which makes it difficult to solve quickly. Therefore, this study introduces a model for calculating the economic dispatch of the electro-thermal coupling system (EDETCS) that takes into account the pipeline transmission process. Firstly, based on the implicit upwind difference method, a two-port model of branch heat transfer dynamics is established. Secondly, the general term formula of the two-port model is derived. Finally, the established two-port model is applied to the EDETCS. The findings from the example analysis indicate that, in contrast to the conventional calculation method, the proposed model improves the calculation speed while ensuring the accuracy of the solution. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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21 pages, 3614 KiB

Open AccessArticle

Power Quality Disturbance Identification Method Based on Improved CEEMDAN-HT-ELM Model

by Ke Liu, Jun Han, Song Chen, Liang Ruan, Yutong Liu and Yang Wang

Processes 2025, 13(1), 137; https://doi.org/10.3390/pr13010137 - 7 Jan 2025

Cited by 1 | Viewed by 848

Abstract

The issue of power quality disturbances in modern power systems has become increasingly complex and severe, with multiple disturbances occurring simultaneously, leading to a decrease in the recognition accuracy of traditional algorithms. This paper proposes a composite power quality disturbance identification method based [...] Read more.

The issue of power quality disturbances in modern power systems has become increasingly complex and severe, with multiple disturbances occurring simultaneously, leading to a decrease in the recognition accuracy of traditional algorithms. This paper proposes a composite power quality disturbance identification method based on the integration of improved Complementary Ensemble Empirical Mode Decomposition (CEEMDAN), Hilbert Transform (HT), and Extreme Learning Machine (ELM). Addressing the limitations of traditional signal processing techniques in handling nonlinear and non-stationary signals, this study first preprocesses the collected initial power quality signals using the improved CEEMDAN method to reduce modal aliasing and spurious components, thereby enabling a more precise decomposition of noisy signals into multiple Intrinsic Mode Functions (IMFs). Subsequently, the HT is utilized to conduct a thorough analysis of the reconstructed signals, extracting their time-amplitude information and instantaneous frequency characteristics. This feature information provides a rich data foundation for subsequent classification and identification. On this basis, an improved ELM is introduced as the classifier, leveraging its powerful nonlinear mapping capabilities and fast learning speed to perform pattern recognition on the extracted features, achieving accurate identification of composite power quality disturbances. To validate the effectiveness and practicality of the proposed method, a simulation experiment is designed. Upon examination, the approach introduced in this study retains a fault diagnosis accuracy exceeding 95%, even amidst significant noise disturbances. In contrast to conventional techniques, such as Convolutional Neural Network (CNN) and Support Vector Machine (SVM), this method achieves an accuracy enhancement of up to 5%. Following optimization via the Particle Swarm Optimization (PSO) algorithm, the model’s accuracy is boosted by 3.6%, showcasing its favorable adaptability. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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20 pages, 2907 KiB

Open AccessArticle

Robust Economic Management Strategy for Power Systems Considering the Participation of Virtual Power Plants

by Xin Li, Chen Zhang, Qianqian Yi and Jingwei Xu

Processes 2025, 13(1), 25; https://doi.org/10.3390/pr13010025 - 26 Dec 2024

Viewed by 570

Abstract

The rapid development of new energy technologies and the randomness and intermittency of renewable energy sources, coupled with the complexity of demand-side response, have posed higher requirements for the security and scheduling of power grids. The virtual power plant (VPP), as [...] Read more.

The rapid development of new energy technologies and the randomness and intermittency of renewable energy sources, coupled with the complexity of demand-side response, have posed higher requirements for the security and scheduling of power grids. The virtual power plant (VPP), as an innovative energy management model, effectively enhances the flexibility and economy of the power system by integrating distributed energy resources and demand-side response resources to participate in the power market and grid operation as a virtual entity. This paper proposes a robust economic management strategy for power systems considering the participation of VPPs. Firstly, the uncertainty of renewable energy output is characterized through prediction deviations. Subsequently, the economic dispatch and constraints of VPPs are established. Considering the nonlinear characteristics of the model, an improved ant lion optimizer (ALO) algorithm is adopted to ensure optimal solutions. Finally, the effectiveness and feasibility of the proposed method are validated through a case study, using an improved IEEE 30-bus test system. Compared to other methods, the economic income can be increased by up to 3.2%, and the calculation time is only 12.5% of that required for scenario screening-based stochastic optimization. After introducing the carbon trading mechanism, carbon emissions are reduced by 27.7%. The proposed method achieves the goal of maximizing economic benefits while ensuring the secure and stable operation of the power grid. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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20 pages, 2181 KiB

Open AccessArticle

Design Strategy of Electricity Purchase and Sale Combination Package Based on the Characteristics of Electricity Prosumers in Power System

by Xiaotian Wang, Chuang Liu, Binbin Wu, Wei Wang, Yi Sun, Jie Peng, Xinya Liu and Kai Zhang

Processes 2024, 12(12), 2836; https://doi.org/10.3390/pr12122836 - 11 Dec 2024

Viewed by 771

Abstract

With the progress in renewable energy and smart grid technologies, electricity users are evolving into prosumers, capable of both consuming and generating electricity through distributed photovoltaic (DPV) systems. Concurrently, the liberalization of the electricity retail market has prompted retailers to design customized electricity [...] Read more.

With the progress in renewable energy and smart grid technologies, electricity users are evolving into prosumers, capable of both consuming and generating electricity through distributed photovoltaic (DPV) systems. Concurrently, the liberalization of the electricity retail market has prompted retailers to design customized electricity packages based on users’ needs and preferences, aiming to enhance service quality, efficiency, and user retention. However, previous studies have not fully addressed the multidimensional characteristics and electricity consumption behaviors that influence package selection. This paper initially dissects user characteristics across three key dimensions: electricity demand preferences, price sensitivity, and risk tolerance. Therefore, leveraging utility functions and autonomous choice behavior models, we propose two innovative electricity purchase and sale combination packages: a fluctuating pricing package and a discount-based pricing package. Furthermore, we introduce the Self-Adaptive Weight and Reverse Learning Particle Swarm Optimization (SAW&RL-PSO) algorithm to address the complexities of these choices. Simulation results indicate that the methodologies presented significantly enhance user benefits and retailer revenues while also effectively managing electricity usage fluctuations and the challenges of integrating large-scale DPV systems into the electrical grid. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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18 pages, 11094 KiB

Open AccessArticle

Simulation and Experimental Design of Magnetic Fluid Seal Safety Valve for Pressure Vessel

by Zhenggui Li, Ziyue Wang, Changrong Shen, Wangxu Li, Yanxiong Jiao, Chuanshi Cheng, Jie Min and Yuanyuan Li

Processes 2024, 12(9), 2040; https://doi.org/10.3390/pr12092040 - 21 Sep 2024

Viewed by 1454

Abstract

This article focuses on the safety valve of pressure vessels, and a new ferrofluid sealing device for pressure vessel safety valves is developed based on a special magnetic circuit. A combined method of numerical calculation and experimental analysis is used to study the [...] Read more.

This article focuses on the safety valve of pressure vessels, and a new ferrofluid sealing device for pressure vessel safety valves is developed based on a special magnetic circuit. A combined method of numerical calculation and experimental analysis is used to study the relationship between seal clearance, number of seals, pole slot width, pole tooth height, pole tooth width, and the sealing pressure of the ferrofluid sealing device. The research results show that seal clearance and pole tooth width have a significant impact on the sealing performance, and as the dimensions increase, the sealing pressure decreases. As the number of seals, pole tooth height, and slot width increase, the sealing performance initially improves and then decreases. This phenomenon is attributed to the increase in magnetic reluctance in the magnetic circuit. In experimental studies, when the excitation current of the electromagnet is 240 mA and the coil turns number 30, the sealing capacity is 61.22 kPa. When the excitation current is 200 mA and the coil turns number 80, the sealing capacity is 168.24 kPa. The experiments demonstrate the compensating ability of magnetic fluid seals in combination with safety valve seals, confirming that combined seals have higher reliability compared to conventional mechanical seals. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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20 pages, 7636 KiB

Open AccessArticle

Primary-Side Indirect Control of the Battery Charging Current in a Wireless Power Transfer Charger Using Adaptive Hill-Climbing Control Technique

by Abdellah Lassioui, Marouane El Ancary, Zakariae El Idrissi, Hassan El Fadil, Kamal Rachid and Aziz Rachid

Processes 2024, 12(6), 1264; https://doi.org/10.3390/pr12061264 - 19 Jun 2024

Cited by 3 | Viewed by 1516

Abstract

This paper addresses the control task of a wireless power transfer (WPT) charger designed for electric vehicles (EVs). The challenge is to maintain a constant battery charging current when the WPT is controlled on the ground side. Indeed, the intermittent latency involved in [...] Read more.

This paper addresses the control task of a wireless power transfer (WPT) charger designed for electric vehicles (EVs). The challenge is to maintain a constant battery charging current when the WPT is controlled on the ground side. Indeed, the intermittent latency involved in the wireless data communication between the ground and vehicle sides leads to system instability. To overcome this issue, a new control approach has been proposed in this paper. The proposed technique ensures indirect control of the battery charging current through control of the current on the ground side. The control technique relies on an adaptive hill-climbing algorithm in conjunction with a PI-based controller. The adaptive parameter is adjusted online, during the operation of the charger, only when a new measure of the battery charging current is received on the primary side. This makes it possible to avoid the need for real-time wireless data communication. It should be noted that this aspect is crucial in ensuring the controller’s robustness and stability of the system regardless of potential delays in wireless communication and large misalignments between the coils. The validity of the proposed control technique has been confirmed through simulation. In addition, experimental validation, using a laboratory test bed, demonstrated satisfactory results. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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17 pages, 4465 KiB

Open AccessArticle

A Graphical User Interface for Calculating Exergy Destruction for Combustion Reactions

by M. Özgün Korukҫu

Processes 2024, 12(2), 294; https://doi.org/10.3390/pr12020294 - 30 Jan 2024

Viewed by 1525

Abstract

The combustion of fuels has been studied by many researchers as it is used in a wide range of engineering applications. The chemical equilibrium approach served as the foundation for the investigation of combustion reactions. This article presents a software application designed to [...] Read more.

The combustion of fuels has been studied by many researchers as it is used in a wide range of engineering applications. The chemical equilibrium approach served as the foundation for the investigation of combustion reactions. This article presents a software application designed to facilitate the calculation of combustion processes by calculating the combustion of 16 fuels among the common alkanes (C_nH_2n+2) and alcohols (C_nH_2n+1OH). The Ozan Combustion Calculator (OCC) offers a user-friendly and efficient graphical user interface (GUI) that allows users to easily input data and obtain results. The program was developed using MATLAB 2021a and LaTeX software, ensuring its reliability and accuracy. To perform these calculations, the program utilizes calculations of the thermophysical properties of fuels and water obtained from tables. The program consists of five modules, each serving a specific purpose. These modules calculate various parameters, such as the Adiabatic Flame Temperature, Exergy of Combustion with Dry Air, Exergy of Combustion with Moist Air, Energy of Combustion with Dry Air, and Energy of Combustion with Moist Air. Additionally, the program can be used to investigate the impact of relative humidity on the adiabatic flame temperature and exergy destruction. The results obtained from the calculations reveal that the adiabatic flame temperature exhibits a linear decrease as the relative humidity increases. On the other hand, exergy destruction demonstrates a quadratic increase with higher relative humidity values. The program derives mathematical relationships for the adiabatic flame temperature and exergy destruction with respect to relative humidity values, with a high regression coefficient (

r^{2} = 0.999

). The versatility of OCC makes it suitable for various applications. It can be utilized in university settings for both undergraduate- and graduate-level courses, providing students with a practical tool for studying combustion processes. Additionally, it finds applications in industrial settings for the design and optimization of combustors, gas turbines, and burners. The user-friendly interface and accurate calculations make OCC a valuable resource in the field of combustion engineering. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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13 pages, 8490 KiB

Open AccessArticle

The Influence of Blade Tip Clearance on the Flow Field Characteristics of the Gas–Liquid Multiphase Pump

by Yuxuan Deng, Yanna Li, Jing Xu, Chunyan Kuang and Yanli Zhang

Processes 2023, 11(11), 3170; https://doi.org/10.3390/pr11113170 - 7 Nov 2023

Cited by 3 | Viewed by 1193

Abstract

Gas–liquid multiphase pumps are critical transportation devices in the petroleum and chemical engineering industries, and improving their conveyance efficiency is crucial. This study investigates the influence of blade tip clearance variations on the flow characteristics within a multiphase pump. Numerical simulations were conducted [...] Read more.

Gas–liquid multiphase pumps are critical transportation devices in the petroleum and chemical engineering industries, and improving their conveyance efficiency is crucial. This study investigates the influence of blade tip clearance variations on the flow characteristics within a multiphase pump. Numerical simulations were conducted using Eulerian two-phase and SST k-ω turbulence models with four distinct tip clearance sizes (0 mm, 0.3 mm, 0.6 mm, and 0.9 mm). The performance curve, tip leakage flow (TLF), and internal gas distribution were subjected to analysis. The results indicate that the TLF is linearly related to the clearance size and traverses multiple flow passages, resulting in energy losses and a reduced pump head coefficient. Larger tip clearances (0.6 mm and 0.9 mm) exhibited a more uniform flow pattern, contrasting the irregularities seen with a 0.3 mm clearance. Compared to no tip clearance (0 mm), gas holdup within the impeller passages decreased by 18.39%, 39.62%, and 58.53% for clearances of 0.3 mm, 0.6 mm, and 0.9 mm, respectively, leading to decreased overall system efficiency. This study highlights the connection between tip clearance size and flow dynamics in multiphase pumps, offering insights for optimal tip clearance selection during multiphase pump design. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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17 pages, 3896 KiB

Open AccessArticle

Design and Simulation of a Feedback Controller for an Active Suspension System: A Simplified Approach

by Vasileios Provatas and Dimitris Ipsakis

Processes 2023, 11(9), 2715; https://doi.org/10.3390/pr11092715 - 11 Sep 2023

Cited by 7 | Viewed by 2326

Abstract

The concept of controlling vehicle comfort is a common problem that is faced in most under- and postgraduate courses in Engineering Schools. The aim of this study is to provide a simplified approach for the feedback control design and simulation of active suspension [...] Read more.

The concept of controlling vehicle comfort is a common problem that is faced in most under- and postgraduate courses in Engineering Schools. The aim of this study is to provide a simplified approach for the feedback control design and simulation of active suspension systems, which are applied in vehicles. Firstly, the mathematical model of an active suspension system (a quarter model of a car) which consists of a passive spring, a passive damper and an actuator is provided. In this study, we chose to design and compare the following controllers: (a) conventional P, PI and PID controllers that were tuned through two conventional methodologies (Ziegler–Nichols and Tyreus–Luyben); (b) an optimal PID controller that was tuned with a genetic algorithm (GA) optimization framework in terms of the minimization of certain performance criteria and (c) an internal model controller (IMC) based on the process transfer function. The controllers’ performance was assessed in a series of realistic scenarios that included set-point tracking with and without disturbances. In all cases, the IMC controller and the optimal PID showed superior performance. On the other hand, the P and PI controllers showed a rather insufficient behavior that involved persistent errors, overshoots and eventually, uncomfortable ride oscillations. Clearly, a step-by-step approach such as this, that includes modeling, control design and simulation scenarios can be applied to numerous other engineering examples, which we envisage to lead more students into the area of automatic control. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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16 pages, 3072 KiB

Open AccessArticle

Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

by Hugo de Oliveira Motta Serrano, Cleberton Reiz and Jonatas Boas Leite

Processes 2023, 11(8), 2464; https://doi.org/10.3390/pr11082464 - 16 Aug 2023

Viewed by 1353

Abstract

Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, [...] Read more.

Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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Review

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51 pages, 13880 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

by Rashed Kaiser, Chi-Yeong Ahn, Yun-Ho Kim and Jong-Chun Park

Processes 2024, 12(6), 1140; https://doi.org/10.3390/pr12061140 - 31 May 2024

Cited by 2 | Viewed by 2184

Abstract

For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict [...] Read more.

For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed. Full article

(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)

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