Pub Date : 2025-01-20DOI: 10.1109/TSTE.2025.3531854
Haihan Ye;Wu Chen
This paper provides a generalized synchronization stabilization control method for offshore wind power transmission systems, which can be used to maintain synchronization during severe AC faults. The proposed method introduces the dynamics of phase-locked loop into the active current loop, so as to trigger the negative feedback between active current and power angle in the power circuit stage to stabilize the phase tracking of wind power plants under complex operating conditions, e.g., including dynamic coupling between multiple wind power plants and considering voltage-dependent current injection specified by the fault ride-through codes. Comparing with the classic Lyapunov methods and equal-area methods, the proposed method does not require either detailed analytical expressions of the entire system or real-time fault detection and high-speed communication, which fundamentally creates a novel idea for distributed synchronous stabilization control. Finally, the feasibility of the proposed method is demonstrated by Matlab/Simulink results.
{"title":"Generalized Synchronous Stabilization Control for Large-Scale Offshore Wind Power Plants During Severe AC Faults","authors":"Haihan Ye;Wu Chen","doi":"10.1109/TSTE.2025.3531854","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3531854","url":null,"abstract":"This paper provides a generalized synchronization stabilization control method for offshore wind power transmission systems, which can be used to maintain synchronization during severe AC faults. The proposed method introduces the dynamics of phase-locked loop into the active current loop, so as to trigger the negative feedback between active current and power angle in the power circuit stage to stabilize the phase tracking of wind power plants under complex operating conditions, e.g., including dynamic coupling between multiple wind power plants and considering voltage-dependent current injection specified by the fault ride-through codes. Comparing with the classic Lyapunov methods and equal-area methods, the proposed method does not require either detailed analytical expressions of the entire system or real-time fault detection and high-speed communication, which fundamentally creates a novel idea for distributed synchronous stabilization control. Finally, the feasibility of the proposed method is demonstrated by Matlab/Simulink results.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1425-1439"},"PeriodicalIF":8.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-20DOI: 10.1109/TSTE.2025.3529987
Yi Zhu;Huiqing Wen;Yong Yang;Caifeng Wen;Jianliang Mao;Pan Wang;Yihua Hu;Cristian Garcia;Jose Rodriguez
A hybrid microgrid system that includes photovoltaic (PV) panels, battery energy storages (BESs), and constant power loads (CPLs) is presented in this article, where three-phase four-leg three-level (3P4L3L) is utilized as the main power interface. As the penetration of CPLs increases significantly, the operational stability of PV-BES Microgrids has become one of the most challenging issues. To tackle this issue, this paper proposes virtual impedance compensation methods to prevent the instability and oscillations caused by CPLs. First, the small-signal model of main power interfaces, especially 3P4L3L converters and CPLs, is built. Then, the stability of the cascaded system is investigated using the Nyquist criterion. Two compensation strategies are proposed based on the derived small-signal model, and the two methods are analyzed and compared in terms of the stability margin. Experiments are performed to prove the feasibility of the proposed strategy, and the results show that the virtual impedance compensation can prevent instability in 3P4L3L PV-BES Microgrids with high penetration of CPLs.
{"title":"Novel Virtual Impedance Compensation Algorithm for Operation Stabilization of 3P4L3L PV-BES Microgrids With Constant Power Loads","authors":"Yi Zhu;Huiqing Wen;Yong Yang;Caifeng Wen;Jianliang Mao;Pan Wang;Yihua Hu;Cristian Garcia;Jose Rodriguez","doi":"10.1109/TSTE.2025.3529987","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529987","url":null,"abstract":"A hybrid microgrid system that includes photovoltaic (PV) panels, battery energy storages (BESs), and constant power loads (CPLs) is presented in this article, where three-phase four-leg three-level (3P4L3L) is utilized as the main power interface. As the penetration of CPLs increases significantly, the operational stability of PV-BES Microgrids has become one of the most challenging issues. To tackle this issue, this paper proposes virtual impedance compensation methods to prevent the instability and oscillations caused by CPLs. First, the small-signal model of main power interfaces, especially 3P4L3L converters and CPLs, is built. Then, the stability of the cascaded system is investigated using the Nyquist criterion. Two compensation strategies are proposed based on the derived small-signal model, and the two methods are analyzed and compared in terms of the stability margin. Experiments are performed to prove the feasibility of the proposed strategy, and the results show that the virtual impedance compensation can prevent instability in 3P4L3L PV-BES Microgrids with high penetration of CPLs.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1401-1413"},"PeriodicalIF":8.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a novel energy storage system (ESS) planning method for improving ESS emergency capability during hurricanes, as well as enhancing the integration of renewable power generation under normal weather simultaneously. First, a novel robust ESS planning (NREP) model is proposed that considers the uncertainties of wind power and transmission line faults, along with their correlation during hurricanes, thereby reducing load shedding losses and wind curtailment. Secondly, to improve both the modeling accuracy of line fault uncertainties and the solution efficiency, a spatio-temporal uncertainty set related to hurricane intensity is constructed through information fusion. Furthermore, an improved column-and-constraint generation (ICCG) algorithm, incorporating nonanticipativity constraints, is proposed to solve the NREP model. The ICCG is able to interrelate scenarios and identify generation-dependent worst-case scenarios, thereby improving the feasibility of multi-period generation decisions under nonanticipative uncertainty realization while reducing losses from wind curtailment and load shedding across all scenarios. Simulation results, obtained by comparisons to previous models and algorithms, validate the effectiveness and superiority of the proposed method.
{"title":"A Novel Robust Energy Storage Planning Method for Grids With Wind Power Integration Considering the Impact of Hurricanes","authors":"Huaizhi Yang;Cong Zhang;Jiayong Li;Lipeng Zhu;Ke Zhou","doi":"10.1109/TSTE.2025.3527448","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3527448","url":null,"abstract":"This paper proposes a novel energy storage system (ESS) planning method for improving ESS emergency capability during hurricanes, as well as enhancing the integration of renewable power generation under normal weather simultaneously. First, a novel robust ESS planning (NREP) model is proposed that considers the uncertainties of wind power and transmission line faults, along with their correlation during hurricanes, thereby reducing load shedding losses and wind curtailment. Secondly, to improve both the modeling accuracy of line fault uncertainties and the solution efficiency, a spatio-temporal uncertainty set related to hurricane intensity is constructed through information fusion. Furthermore, an improved column-and-constraint generation (ICCG) algorithm, incorporating nonanticipativity constraints, is proposed to solve the NREP model. The ICCG is able to interrelate scenarios and identify generation-dependent worst-case scenarios, thereby improving the feasibility of multi-period generation decisions under nonanticipative uncertainty realization while reducing losses from wind curtailment and load shedding across all scenarios. Simulation results, obtained by comparisons to previous models and algorithms, validate the effectiveness and superiority of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1388-1400"},"PeriodicalIF":8.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1109/TSTE.2025.3530485
Cong Luo;Shuhan Liao;Yajun Liu;Yandong Chen
Grid-forming (GFM) inverters have been widely used as the interface between renewable energy sources and power grid. During low voltage ride through (LVRT) period, to limit the fault current, GFM inverters will experience the switching of control strategy, which makes the transient stability of GFM inverters exhibit different features from that of synchronous generators (SGs). In this paper, considering the switching characteristics induced by virtual impedance (VI), the framework of predicting the transient stability of GFM inverters during the fault period and after fault clearance is established from the perspective of energy. To reduce the conservativeness of the stability region, a uniform energy function considering the damping dissipation and the dynamics of reactive power control loop is constructed for the transient stability analysis of GFM inverters. Compared with existing approaches, the stability region estimated by the proposed energy function can intuitively show the effect of damping, and effectively reduce the degree of conservatism in transient stability prediction. Finally, simulation and hardware-in-loop experiments are performed to verify the effectiveness and accuracy of the proposed method in the transient stability prediction of GFM inverters with switching characteristics.
{"title":"Estimation of Stability Region for Grid-Forming Inverters Considering Switching Characteristics via Constructing Damping-Reflected Energy Functions","authors":"Cong Luo;Shuhan Liao;Yajun Liu;Yandong Chen","doi":"10.1109/TSTE.2025.3530485","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3530485","url":null,"abstract":"Grid-forming (GFM) inverters have been widely used as the interface between renewable energy sources and power grid. During low voltage ride through (LVRT) period, to limit the fault current, GFM inverters will experience the switching of control strategy, which makes the transient stability of GFM inverters exhibit different features from that of synchronous generators (SGs). In this paper, considering the switching characteristics induced by virtual impedance (VI), the framework of predicting the transient stability of GFM inverters during the fault period and after fault clearance is established from the perspective of energy. To reduce the conservativeness of the stability region, a uniform energy function considering the damping dissipation and the dynamics of reactive power control loop is constructed for the transient stability analysis of GFM inverters. Compared with existing approaches, the stability region estimated by the proposed energy function can intuitively show the effect of damping, and effectively reduce the degree of conservatism in transient stability prediction. Finally, simulation and hardware-in-loop experiments are performed to verify the effectiveness and accuracy of the proposed method in the transient stability prediction of GFM inverters with switching characteristics.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1737-1748"},"PeriodicalIF":8.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1109/TSTE.2025.3529199
Weiye Song;Jie Yan;Shuang Han;Ning Zhang;Shihua Liu;Chang Ge;Yongqian Liu
As wind power is becoming a major energy source of power systems, the risk of power shortages due to its intermittent low power output is growing. Accurate forecasting of low wind power is crucial for mitigating these impacts. However, conventional methods struggle with few-sample issues due to the infrequent occurrence of low wind power, limiting accuracy improvements. To address this, a self-supervised pre-learning method is proposed to forecast low wind power occurrence period and output, leveraging the similarities and differences among low output samples to enhance forecasting accuracy. Low wind power output is decomposed into low wind power events (LWPE), which represent the occurrence timeframe, and low wind power processes (LWPP), which represent the power sequences. For LWPE forecasting, a siamese residual shrinkage network based on contrastive learning is introduced. This network pre-learns LWPE features from sample pairs to mitigate the impact of imbalanced sample distribution. For LWPP forecasting, a pattern recognition-based embedded forecasting framework is proposed, embedding typical LWPP fluctuations into the prediction network to improve fit under limited sample conditions. A case study on 3 wind farm clusters shows that this method improves LWPP forecasting accuracy from 84.99%-86.6% to 89.97%, outperforming traditional methods without pre-learning.
{"title":"A Self-Supervised Pre-Learning Method for Low Wind Power Forecasting","authors":"Weiye Song;Jie Yan;Shuang Han;Ning Zhang;Shihua Liu;Chang Ge;Yongqian Liu","doi":"10.1109/TSTE.2025.3529199","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529199","url":null,"abstract":"As wind power is becoming a major energy source of power systems, the risk of power shortages due to its intermittent low power output is growing. Accurate forecasting of low wind power is crucial for mitigating these impacts. However, conventional methods struggle with few-sample issues due to the infrequent occurrence of low wind power, limiting accuracy improvements. To address this, a self-supervised pre-learning method is proposed to forecast low wind power occurrence period and output, leveraging the similarities and differences among low output samples to enhance forecasting accuracy. Low wind power output is decomposed into low wind power events (LWPE), which represent the occurrence timeframe, and low wind power processes (LWPP), which represent the power sequences. For LWPE forecasting, a siamese residual shrinkage network based on contrastive learning is introduced. This network pre-learns LWPE features from sample pairs to mitigate the impact of imbalanced sample distribution. For LWPP forecasting, a pattern recognition-based embedded forecasting framework is proposed, embedding typical LWPP fluctuations into the prediction network to improve fit under limited sample conditions. A case study on 3 wind farm clusters shows that this method improves LWPP forecasting accuracy from 84.99%-86.6% to 89.97%, outperforming traditional methods without pre-learning.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1723-1736"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes an optimal scheduling algorithm for an integrated PV-ESS system to maximize the overall revenue from both system marginal price (SMP) and renewable energy certificate (REC), considering detailed settlement rules in South Korea. Furthermore, to prevent revenue losses caused by forecasting errors, robust optimization (RO) and receding horizon rescheduling (RHR) approaches, are exploited. The academic contributions of this work are: 1) the formulation of complex settlement rules as an optimization problem, and 2) the implementation of a mixed integer linear programming (MILP)-based RO that can be solved by non-commercial solvers. To verify the effectiveness of the proposed method, simulations and experiments were conducted using a commercial testbed. Compared to the rule-based algorithm which had been adopted in the testbed, the proposed algorithm achieved a 9.3% increase in revenue.
{"title":"Optimal Scheduling and Commercial Testbed-Based Verification of Integrated PV-ESS Systems Considering Settlement Rules in South Korea","authors":"Rae-Kyun Kim;Gyu-Sub Lee;Jae-Gyun Park;Hyoseop Lee;Seung-Il Moon;Jae-Won Chang","doi":"10.1109/TSTE.2025.3529693","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529693","url":null,"abstract":"This article proposes an optimal scheduling algorithm for an integrated PV-ESS system to maximize the overall revenue from both system marginal price (SMP) and renewable energy certificate (REC), considering detailed settlement rules in South Korea. Furthermore, to prevent revenue losses caused by forecasting errors, robust optimization (RO) and receding horizon rescheduling (RHR) approaches, are exploited. The academic contributions of this work are: 1) the formulation of complex settlement rules as an optimization problem, and 2) the implementation of a mixed integer linear programming (MILP)-based RO that can be solved by non-commercial solvers. To verify the effectiveness of the proposed method, simulations and experiments were conducted using a commercial testbed. Compared to the rule-based algorithm which had been adopted in the testbed, the proposed algorithm achieved a 9.3% increase in revenue.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1372-1387"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To fully utilize the potential of massive small-scale distributed photovoltaics (DPVs) for secondary frequency regulation (SFR), this article introduces a hierarchical coordination framework that incorporates the dynamic response characteristic (DRC) of DPV to automatic generation control (AGC) signals, thereby reflecting the dynamic flexibility of the aggregated DPVs (ADPVs). First, a reserved power feasible range is derived for scheduling the power reserve control (PRC) scheme considering the uncertainty in PV generation and the de-loaded margin base constraint. Second, a two-stage multi-cluster DRC aggregation method that considers the impact of the PRC scheme is developed to describe the equivalent DRC of the ADPVs. Last, the article constructs an integrated cost function (ICF) that reveals the interdependencies between SFR capacity, equivalent DRC and regulation cost, which enables the decoupled scheduling of the SFR indices and the PRC scheme. An event-triggered duty factor reassignment mechanism is further proposed to improve the reliability of SFR service deployment in case of unexpected events. Simulation results indicate that the framework is an efficient approach for quantifying, trading and realizing the dynamic flexibility of the ADPVs.
{"title":"Secondary Frequency Regulation From Aggregated Distributed Photovoltaics: A Dynamic Flexibility Aggregation Approach","authors":"Songyan Zhang;Peixuan Wu;Chao Lu;Huanhuan Yang;Tuo Jiang","doi":"10.1109/TSTE.2025.3529512","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529512","url":null,"abstract":"To fully utilize the potential of massive small-scale distributed photovoltaics (DPVs) for secondary frequency regulation (SFR), this article introduces a hierarchical coordination framework that incorporates the dynamic response characteristic (DRC) of DPV to automatic generation control (AGC) signals, thereby reflecting the dynamic flexibility of the aggregated DPVs (ADPVs). First, a reserved power feasible range is derived for scheduling the power reserve control (PRC) scheme considering the uncertainty in PV generation and the de-loaded margin base constraint. Second, a two-stage multi-cluster DRC aggregation method that considers the impact of the PRC scheme is developed to describe the equivalent DRC of the ADPVs. Last, the article constructs an integrated cost function (ICF) that reveals the interdependencies between SFR capacity, equivalent DRC and regulation cost, which enables the decoupled scheduling of the SFR indices and the PRC scheme. An event-triggered duty factor reassignment mechanism is further proposed to improve the reliability of SFR service deployment in case of unexpected events. Simulation results indicate that the framework is an efficient approach for quantifying, trading and realizing the dynamic flexibility of the ADPVs.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1356-1371"},"PeriodicalIF":8.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/TSTE.2025.3528948
Boyou Jiang;Chuangxin Guo;Zhe Chen
With the increasing penetration of wind power and gradual retirement of conventional generating units (CGUs), wind turbines (WTs) become promising resources to provide steady-state energy reserve (ER) and frequency support for the grid to facilitate supply-demand balance and frequency security. In this regard, a novel frequency constrained dispatch framework with ER and virtual inertia from WTs is proposed. Firstly, this paper establishes the WT model with both ER and virtual inertia, whose energy sources are WT's deloading and rotor kinetic energy, respectively. Secondly, the system frequency response and CGUs' power response are derived while considering WTs exiting inertia response at frequency nadir. Then, this paper develops a stochastic-optimization-based frequency constrained dispatch model, where both WTs' frequency regulation parameters and rotor speeds are decision variables, so that the coupling between WT's mechanical and electrical parts and the coupling between system's transient dynamics and steady-state operation can be fully reflected. Finally, convex hull relaxation, convex hull approximation and deep neural networks are used to transform the original nonlinear model into a mixed-integer second-order cone programming model. Case studies on the 118-bus system verify the effectiveness of the proposed models and methods.
{"title":"Frequency Constrained Dispatch With Energy Reserve and Virtual Inertia From Wind Turbines","authors":"Boyou Jiang;Chuangxin Guo;Zhe Chen","doi":"10.1109/TSTE.2025.3528948","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3528948","url":null,"abstract":"With the increasing penetration of wind power and gradual retirement of conventional generating units (CGUs), wind turbines (WTs) become promising resources to provide steady-state energy reserve (ER) and frequency support for the grid to facilitate supply-demand balance and frequency security. In this regard, a novel frequency constrained dispatch framework with ER and virtual inertia from WTs is proposed. Firstly, this paper establishes the WT model with both ER and virtual inertia, whose energy sources are WT's deloading and rotor kinetic energy, respectively. Secondly, the system frequency response and CGUs' power response are derived while considering WTs exiting inertia response at frequency nadir. Then, this paper develops a stochastic-optimization-based frequency constrained dispatch model, where both WTs' frequency regulation parameters and rotor speeds are decision variables, so that the coupling between WT's mechanical and electrical parts and the coupling between system's transient dynamics and steady-state operation can be fully reflected. Finally, convex hull relaxation, convex hull approximation and deep neural networks are used to transform the original nonlinear model into a mixed-integer second-order cone programming model. Case studies on the 118-bus system verify the effectiveness of the proposed models and methods.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1340-1355"},"PeriodicalIF":8.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/TSTE.2025.3528952
Chanjuan Zhao;Yunlong Li;Qian Zhang;Lina Ren
In this paper, an enhanced dueling double deep Q network algorithm with mixed penalty function (EN-D3QN-MPF) for microgrid energy management control is developed. First, a novel microgrid model including PV, wind turbine generator, electric storage system, electric vehicle charging station, thermostatically controlled loads, and residential price-responsive loads are proposed. Then, by combining the mixed penalty function method with D3QN reinforcement learning together, a mixed penalty function method is implemented to balance the reward weightings. Accordingly, an EN-D3QN-MPF algorithm is presented to achieve low-carbon economic and EV users' charging satisfaction operation of the microgrid. The effectiveness of the proposed method is verified by the dataset collected from eastern China in 2019. Simulation results validate that our proposed method has superior energy management performance over the genetic algorithm (GA), Particle Swarm Optimization (PSO), dueling deep Q network (dueling DQN), double DQN (DDQN), and D3QN algorithms.
本文提出了一种用于微电网能量管理控制的带有混合罚函数的增强决斗双深Q网络算法（EN-D3QN-MPF）。首先，提出了包括光伏发电、风力发电、储能系统、电动汽车充电站、恒温控制负荷和住宅价格响应负荷在内的新型微电网模型。然后，将混合惩罚函数法与D3QN强化学习相结合，实现混合惩罚函数法来平衡奖励权重。为此，提出EN-D3QN-MPF算法，实现微电网低碳经济和电动汽车用户充电满意运行。通过2019年中国东部地区的数据验证了该方法的有效性。仿真结果表明，该方法比遗传算法（GA）、粒子群算法（PSO）、dueling deep Q network （dueling DQN）、双DQN （DDQN）和D3QN算法具有更好的能量管理性能。
{"title":"Low Carbon Economic Energy Management Method in a Microgrid Based on Enhanced D3QN Algorithm With Mixed Penalty Function","authors":"Chanjuan Zhao;Yunlong Li;Qian Zhang;Lina Ren","doi":"10.1109/TSTE.2025.3528952","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3528952","url":null,"abstract":"In this paper, an enhanced dueling double deep Q network algorithm with mixed penalty function (EN-D3QN-MPF) for microgrid energy management control is developed. First, a novel microgrid model including PV, wind turbine generator, electric storage system, electric vehicle charging station, thermostatically controlled loads, and residential price-responsive loads are proposed. Then, by combining the mixed penalty function method with D3QN reinforcement learning together, a mixed penalty function method is implemented to balance the reward weightings. Accordingly, an EN-D3QN-MPF algorithm is presented to achieve low-carbon economic and EV users' charging satisfaction operation of the microgrid. The effectiveness of the proposed method is verified by the dataset collected from eastern China in 2019. Simulation results validate that our proposed method has superior energy management performance over the genetic algorithm (GA), Particle Swarm Optimization (PSO), dueling deep Q network (dueling DQN), double DQN (DDQN), and D3QN algorithms.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1686-1696"},"PeriodicalIF":8.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/TSTE.2025.3529254
Jianshu Yu;Pei Yong;Zhifang Yang;Juan Yu
The diversification of power system operation modes raises the necessity of incorporating dynamic characteristics into steady-state operation. Small-signal stability is a representative issue. Though, existing frameworks either ignore the uncertainties of renewables, or only focus on the worst case. In this regard, this paper establishes a small-signal stability constrained stochastic-robust optimal power flow (OPF) model, which aims to optimize the expected cost of scheduling results with respect to the probability distributions of uncertainties while ensuring the small-signal stability requirement even in extreme scenarios. However, the synergy of uncertainties and the complicated small-signal stability mechanism significantly increase the solving difficulty. This paper proposes a comprehensive framework to overcome this challenge. First, we solve the stochastic OPF without small-signal stability constraints. For those results that do not meet the stability requirements, we use them as initial points to locate the effective boundary of the OPF feasible region where the robust small-signal stability requirement is satisfied. The effective boundary location is realized in an iterative manner. Then, in the neighborhood of this effective boundary, we construct a linear surrogate expression to represent the original robust small-signal stability constraint with Markov-chain Monte Carlo (MCMC) sampling and sample weighted support vector machine (swSVM). Finally, we solve the OPF model with the surrogate constraint. Moreover, we further propose a constraint correction strategy to guarantee the stability requirement. Case studies verify that the proposed method can acquire economical operation schemes and meet the robust small-signal stability requirement at the same time.
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