Pub Date : 2025-04-07DOI: 10.35833/MPCE.2024.000685
Yutong Li;Ningxuan Guo;Lili Wang;Jian Hou;Yinan Wang;Gangfeng Yan
Distributed secondary control has been proposed to maintain frequency/voltage synchronization and power sharing for distributed energy sources in AC microgrids (MGs). The cyber layer is susceptible to time delays and cyber failures and thus, a distributed resilient secondary control should be investigated. This paper proposes a distributed multi-scale attention and predictor-based control (DMAPC) strategy to address false data injection attacks and packet loss failures with time delays. The multi-scale attention mechanism enables the system to selectively focus on neighbors' states with higher confidence evaluated in different time scales, while the data-driven predictor compensates for lost neighbors' states in the nonlinear controller. The DMAPC does not impose strict limitations on the number of false communication links or upper bound for false data. Besides, the DMAPC is formulated as an uncertain system with time delays and is proven to be uniformly ultimately bounded. Extensive experiments on a hardware-in-the-loop MG testbed have validated the effectiveness of DMAPC, which successfully relaxes restrictions on cyber failures compared to existing strategies.
{"title":"Distributed Multi-Scale Attention and Predictor-Based Control for AC Microgrids with Time Delays and Cyber Failures","authors":"Yutong Li;Ningxuan Guo;Lili Wang;Jian Hou;Yinan Wang;Gangfeng Yan","doi":"10.35833/MPCE.2024.000685","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000685","url":null,"abstract":"Distributed secondary control has been proposed to maintain frequency/voltage synchronization and power sharing for distributed energy sources in AC microgrids (MGs). The cyber layer is susceptible to time delays and cyber failures and thus, a distributed resilient secondary control should be investigated. This paper proposes a distributed multi-scale attention and predictor-based control (DMAPC) strategy to address false data injection attacks and packet loss failures with time delays. The multi-scale attention mechanism enables the system to selectively focus on neighbors' states with higher confidence evaluated in different time scales, while the data-driven predictor compensates for lost neighbors' states in the nonlinear controller. The DMAPC does not impose strict limitations on the number of false communication links or upper bound for false data. Besides, the DMAPC is formulated as an uncertain system with time delays and is proven to be uniformly ultimately bounded. Extensive experiments on a hardware-in-the-loop MG testbed have validated the effectiveness of DMAPC, which successfully relaxes restrictions on cyber failures compared to existing strategies.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1800-1812"},"PeriodicalIF":6.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-27DOI: 10.35833/MPCE.2024.000211
Yingjun Wu;Runrun Chen;Yuyang Chen;Xuejie Chen;Jiangfan Yuan;Hengchao Mao;Juefei Wang
Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven levelset fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.
{"title":"A Joint Electricity-Reserve Trading Model for Virtual Power Plants to Mitigate Naked Selling","authors":"Yingjun Wu;Runrun Chen;Yuyang Chen;Xuejie Chen;Jiangfan Yuan;Hengchao Mao;Juefei Wang","doi":"10.35833/MPCE.2024.000211","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000211","url":null,"abstract":"Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven levelset fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1813-1822"},"PeriodicalIF":6.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stray currents from DC metro systems intrude into the grounded neutrals of large power transformers, posing a major threat to the differential relay protection of transformer. In this paper, the performance of harmonic blocking based differential relay protection considering neutral stray currents (NSCs) from DC metro systems is thoroughly investigated. The findings reveal that relays may fail to clear internal faults in some scenarios because they are blocked due to NSC-induced harmonic currents. To improve the reliability of differential relay protection, a method for preventing incorrect operation is proposed using a skewness-based criterion to detect the presence of NSCs. Then, the relay is unblocked when an internal fault is simultaneously detected by the novel internal fault detection block. The proposed method is resistant to current transformer saturation and accounts for NSC fluctuations. Various time-domain simulations conducted in PSCAD/EMTDC verify the effectiveness of the proposed method.
{"title":"Harmonic Blocking Based Differential Relay Protection Considering Neutral Stray Currents from DC Metro Systems","authors":"Haoran Fan;Sheng Lin;Aimin Wang;Qi Zhou;Hongbo Cheng","doi":"10.35833/MPCE.2024.000440","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000440","url":null,"abstract":"Stray currents from DC metro systems intrude into the grounded neutrals of large power transformers, posing a major threat to the differential relay protection of transformer. In this paper, the performance of harmonic blocking based differential relay protection considering neutral stray currents (NSCs) from DC metro systems is thoroughly investigated. The findings reveal that relays may fail to clear internal faults in some scenarios because they are blocked due to NSC-induced harmonic currents. To improve the reliability of differential relay protection, a method for preventing incorrect operation is proposed using a skewness-based criterion to detect the presence of NSCs. Then, the relay is unblocked when an internal fault is simultaneously detected by the novel internal fault detection block. The proposed method is resistant to current transformer saturation and accounts for NSC fluctuations. Various time-domain simulations conducted in PSCAD/EMTDC verify the effectiveness of the proposed method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1689-1700"},"PeriodicalIF":6.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of electric vehicles (EVs) introduces transformative opportunities and challenges for the stability of distribution networks. Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid, while regulated EV charging will alleviate such imbalance. To systematically address this challenge, this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators (EVAs). By constructing a coordinated framework that integrates the day-ahead and real-time markets, the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system, with a particular focus on dynamic compensation for deviations between scheduled and real-time operations. A bi-level Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance, with Ka-rush-Kuhn-Tucker (KKT) conditions and mixed-integer second-order cone programming (MISOCP) ensuring feasible solutions. The proposed coordinated framework is validated with different bidding modes includes independent bidding, full price acceptance, and cooperative bidding modes. The proposed two-stage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.
{"title":"Two-Stage Bidding Strategy with Dispatch Potential of Electric Vehicle Aggregators for Mitigating Three-Phase Imbalance","authors":"Chengwei Lou;Chen Li;Lu Zhang;Wei Tang;Jin Yang;Jake Cunningham","doi":"10.35833/MPCE.2024.001067","DOIUrl":"https://doi.org/10.35833/MPCE.2024.001067","url":null,"abstract":"The proliferation of electric vehicles (EVs) introduces transformative opportunities and challenges for the stability of distribution networks. Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid, while regulated EV charging will alleviate such imbalance. To systematically address this challenge, this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators (EVAs). By constructing a coordinated framework that integrates the day-ahead and real-time markets, the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system, with a particular focus on dynamic compensation for deviations between scheduled and real-time operations. A bi-level Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance, with Ka-rush-Kuhn-Tucker (KKT) conditions and mixed-integer second-order cone programming (MISOCP) ensuring feasible solutions. The proposed coordinated framework is validated with different bidding modes includes independent bidding, full price acceptance, and cooperative bidding modes. The proposed two-stage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1823-1835"},"PeriodicalIF":6.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a continuous control set model predictive control (CCS-MPC) algorithm of a modular multilevel matrix converter (M3C) for low-frequency AC transmission (LFAC), via which the offshore wind farm (OWF) is integrated. The M3C is operated with a 16.7 Hz frequency at the OWF side and a 50 Hz frequency at the onshore grid side. The balance of the capacitor voltages and the regulation of circulating currents in the M3C are performed using the proposed CCS-MPC algorithm, which is based on the online solution of a cost function with constraints. Simulation and experimental work (with a 5 kW M3C prototype) are provided, showing the performance of the LFAC system to operate with symmetrical and asymmetrical voltage dips, active and reactive power steps, and optimal limitation of currents and voltages using constraints. Unlike previous publications, the predictive control system in this paper allows seamless operation under balanced and unbalanced conditions, for instance, during asymmetrical voltage dips.
{"title":"Continuous Control Set Model Predictive Control of Modular Multilevel Matrix Converters for Low-frequency AC Transmission","authors":"Matias Uriarte;Roberto Cardenas-Dobson;Yeiner Arias-Esquivel;Matias Diaz;Oriol Gomis-Bellmunt","doi":"10.35833/MPCE.2024.000654","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000654","url":null,"abstract":"This paper proposes a continuous control set model predictive control (CCS-MPC) algorithm of a modular multilevel matrix converter (M3C) for low-frequency AC transmission (LFAC), via which the offshore wind farm (OWF) is integrated. The M3C is operated with a 16.7 Hz frequency at the OWF side and a 50 Hz frequency at the onshore grid side. The balance of the capacitor voltages and the regulation of circulating currents in the M3C are performed using the proposed CCS-MPC algorithm, which is based on the online solution of a cost function with constraints. Simulation and experimental work (with a 5 kW M3C prototype) are provided, showing the performance of the LFAC system to operate with symmetrical and asymmetrical voltage dips, active and reactive power steps, and optimal limitation of currents and voltages using constraints. Unlike previous publications, the predictive control system in this paper allows seamless operation under balanced and unbalanced conditions, for instance, during asymmetrical voltage dips.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1468-1480"},"PeriodicalIF":5.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Power system optimal dispatch with transient security constraints is commonly represented as transient security-constrained optimal power flow (TSC-OPF). Deep reinforcement learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converging DRL method for optimal dispatch of large-scale power systems under transient security constraints is proposed in this paper. The Markov decision process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved deep deterministic policy gradient algorithm with curriculum learning, parallel exploration, and ensemble decision-making (DDPG-CL-PE-ED) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.
{"title":"Fast-Converging Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems Under Transient Security Constraints","authors":"Tannan Xiao;Ying Chen;Han Diao;Shaowei Huang;Chen Shen","doi":"10.35833/MPCE.2024.000624","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000624","url":null,"abstract":"Power system optimal dispatch with transient security constraints is commonly represented as transient security-constrained optimal power flow (TSC-OPF). Deep reinforcement learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converging DRL method for optimal dispatch of large-scale power systems under transient security constraints is proposed in this paper. The Markov decision process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved deep deterministic policy gradient algorithm with curriculum learning, parallel exploration, and ensemble decision-making (DDPG-CL-PE-ED) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1495-1506"},"PeriodicalIF":6.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-27DOI: 10.35833/MPCE.2024.000768
Damià Gomila;Benjamín A. Carreras;José-Miguel Reynolds-Barredo;María Martínez-Barbeito;Pere Colet;Oriol Gomis-Bellmunt
The utilization of high-voltage direct current (HVDC) lines for the segmentation of the European power grid has been demonstrated to be a highly effective strategy for the mitigation of the risk of cascading blackouts. In this study, an accurate and efficient method for determining the optimal power flow through HVDC lines is presented, with the objective of minimizing load shedding. The proposed method is applied to two distinct scenarios: first, the segmentation of the power grid along the Pyrenees, with the objective of segmenting the Iberian Peninsula from the rest of Europe; and second, the segmentation of the power grid into Eastern and Western Europe, approximately in half. In both scenarios, the method effectively reduces the size of blackouts impacting both sides of the HVDC lines, resulting in a 46% and 67% reduction in total blackout risk, respectively. Furthermore, we have estimated the cost savings from risk reduction and the expenses associated with converting conventional lines to HVDC lines. Our findings indicate that segmenting the European power grid with HVDC lines is economically viable, particularly for segmenting the Iberian Peninsula, due to its favorable cost-risk reduction ratio.
{"title":"Reducing Blackout Risk by Segmenting European Power Grid with HVDC Lines","authors":"Damià Gomila;Benjamín A. Carreras;José-Miguel Reynolds-Barredo;María Martínez-Barbeito;Pere Colet;Oriol Gomis-Bellmunt","doi":"10.35833/MPCE.2024.000768","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000768","url":null,"abstract":"The utilization of high-voltage direct current (HVDC) lines for the segmentation of the European power grid has been demonstrated to be a highly effective strategy for the mitigation of the risk of cascading blackouts. In this study, an accurate and efficient method for determining the optimal power flow through HVDC lines is presented, with the objective of minimizing load shedding. The proposed method is applied to two distinct scenarios: first, the segmentation of the power grid along the Pyrenees, with the objective of segmenting the Iberian Peninsula from the rest of Europe; and second, the segmentation of the power grid into Eastern and Western Europe, approximately in half. In both scenarios, the method effectively reduces the size of blackouts impacting both sides of the HVDC lines, resulting in a 46% and 67% reduction in total blackout risk, respectively. Furthermore, we have estimated the cost savings from risk reduction and the expenses associated with converting conventional lines to HVDC lines. Our findings indicate that segmenting the European power grid with HVDC lines is economically viable, particularly for segmenting the Iberian Peninsula, due to its favorable cost-risk reduction ratio.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1556-1567"},"PeriodicalIF":6.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-25DOI: 10.35833/MPCE.2024.000521
Songmei Wu;Hui Guo;Fei Wang;Yuxin Zhu
Peer-to-peer (P2P) energy trading provides a promising solution for integrating distributed microgrids (MGs). However, most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator (DSO). Therefore, a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO. The interaction between DSO and MG is formulated as a Stackelberg game, in which the existence and uniqueness of optimal dynamic network usage fees are proven. Additionally, the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions. Furthermore, the underlying relationship among optimal dynamic network usage fees, electrical distance, and power flow is revealed, and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs. Finally, simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08% reduction in operation costs for MG while increasing DSO revenue by 15.36%.
{"title":"Balancing Benefits of Distribution System Operator in Peer-to-Peer Energy Trading Among Microgrids Based on Optimal Dynamic Network Usage Fees","authors":"Songmei Wu;Hui Guo;Fei Wang;Yuxin Zhu","doi":"10.35833/MPCE.2024.000521","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000521","url":null,"abstract":"Peer-to-peer (P2P) energy trading provides a promising solution for integrating distributed microgrids (MGs). However, most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator (DSO). Therefore, a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO. The interaction between DSO and MG is formulated as a Stackelberg game, in which the existence and uniqueness of optimal dynamic network usage fees are proven. Additionally, the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions. Furthermore, the underlying relationship among optimal dynamic network usage fees, electrical distance, and power flow is revealed, and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs. Finally, simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08% reduction in operation costs for MG while increasing DSO revenue by 15.36%.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 2","pages":"663-674"},"PeriodicalIF":5.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel planning tool for optimizing the placement of electric springs (ESs) in unbalanced distribution networks is introduced in this study. The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes. The power rating of the ES is adjusted on the basis of the available active power at the bus. And in the optimization problem, it is expressed as the power ratio of the non-critical load (NCL) and critical load (CL). The implemented ES model is flexible, which can be used on any bus and any phase. The model determines the output voltage from the parameters and operating conditions at the point of common coupling (PCC). These conditions are integrated using the backward/forward sweep method (BFSM) and are updated during power flow calculations. The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BF-SM-based genetic algorithm, which computes power flow and ES placement simultaneously. The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.
{"title":"Optimal Placement of Electric Springs in Unbalanced Distribution Networks Using Improved Backward/Forward Sweep Method Based Genetic Algorithm","authors":"Guillermo Tapia-Tinoco;Gerardo Humberto Valencia-Rivera;Martin Valtierra-Rodriguez;Arturo Garcia-Perez;David Granados-Lieberman","doi":"10.35833/MPCE.2024.000649","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000649","url":null,"abstract":"A novel planning tool for optimizing the placement of electric springs (ESs) in unbalanced distribution networks is introduced in this study. The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes. The power rating of the ES is adjusted on the basis of the available active power at the bus. And in the optimization problem, it is expressed as the power ratio of the non-critical load (NCL) and critical load (CL). The implemented ES model is flexible, which can be used on any bus and any phase. The model determines the output voltage from the parameters and operating conditions at the point of common coupling (PCC). These conditions are integrated using the backward/forward sweep method (BFSM) and are updated during power flow calculations. The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BF-SM-based genetic algorithm, which computes power flow and ES placement simultaneously. The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 3","pages":"940-952"},"PeriodicalIF":5.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Demand response (DR) is a practical solution to overcoming the challenges posed by the volatility and intermittency of the renewable generation in power systems. Industrial electricity demand is growing rapidly, which makes the DR potential estimation of industrial user critical for the DR implementation. In this paper, a unified model for estimating DR potential in the production processes of aluminum, cement, and steel is proposed on the basis of their unique operational characteristics. Firstly, considering the typical characteristic constraints of different industrial users, a DR potential estimation model is developed to capture typical industrial user response behavior under various operational and economic factors. The proposed estimation model is further refined to account for the uncertain and subjective factors present in the actual estimation environment. Secondly, a virtual data acquisition method is introduced to obtain the private virtual parameters required in the estimation process. Then, an industrial user participation threshold is presented to determine whether industrial users may participate in DR at a given time with consideration of their response characteristics. The industrial users may not always act with perfect rationality, and the response environment remains uncertain. In addition, the subjective factor in this paper includes the proposed threshold and the bounded rationality. Finally, an improved DR potential estimation model is proposed to reduce the difficulties in the actual estimation process. The simulation results validate the effectiveness of the proposed estimation model and the improved DR potential estimation model across multiple cases.
{"title":"Demand Response Potential Estimation Model for Typical Industrial Users Considering Uncertain and Subjective Factors","authors":"Tingyu Jiang;Chuan Qin;Yuzhong Gong;Ke Wang;Ping Ju;Chi Yung Chung","doi":"10.35833/MPCE.2024.000764","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000764","url":null,"abstract":"Demand response (DR) is a practical solution to overcoming the challenges posed by the volatility and intermittency of the renewable generation in power systems. Industrial electricity demand is growing rapidly, which makes the DR potential estimation of industrial user critical for the DR implementation. In this paper, a unified model for estimating DR potential in the production processes of aluminum, cement, and steel is proposed on the basis of their unique operational characteristics. Firstly, considering the typical characteristic constraints of different industrial users, a DR potential estimation model is developed to capture typical industrial user response behavior under various operational and economic factors. The proposed estimation model is further refined to account for the uncertain and subjective factors present in the actual estimation environment. Secondly, a virtual data acquisition method is introduced to obtain the private virtual parameters required in the estimation process. Then, an industrial user participation threshold is presented to determine whether industrial users may participate in DR at a given time with consideration of their response characteristics. The industrial users may not always act with perfect rationality, and the response environment remains uncertain. In addition, the subjective factor in this paper includes the proposed threshold and the bounded rationality. Finally, an improved DR potential estimation model is proposed to reduce the difficulties in the actual estimation process. The simulation results validate the effectiveness of the proposed estimation model and the improved DR potential estimation model across multiple cases.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1360-1372"},"PeriodicalIF":5.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：