Pub Date : 2024-03-05DOI: 10.35833/MPCE.2023.000744
Yuxuan Zhuang;Zhiyi Li;Qipeng Tan;Yongqi Li;Minhui Wan
The push for renewable energy emphasizes the need for energy storage systems (ESSs) to mitigate the unpre-dictability and variability of these sources, yet challenges such as high investment costs, sporadic utilization, and demand mismatch hinder their broader adoption. In response, shared energy storage systems (SESSs) offer a more cohesive and efficient use of ESS, providing more accessible and cost-effective energy storage solutions to overcome these obstacles. To enhance the profitability of SESSs, this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models. We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS. Subsequently, we design various long-term contracts from both capacity and energy perspectives, establishing associated models and real-time rental models. Lastly, multi-time-scale resource allocation based on the decomposition of user demand is proposed. Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction, effectively reducing battery degradation, and leveraging the aggregation effect for SESS can generate an additional increase of 10.7% in net revenue.
{"title":"Multi-Time-Scale Resource Allocation Based on Long-Term Contracts and Real-Time Rental Business Models for Shared Energy Storage Systems","authors":"Yuxuan Zhuang;Zhiyi Li;Qipeng Tan;Yongqi Li;Minhui Wan","doi":"10.35833/MPCE.2023.000744","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000744","url":null,"abstract":"The push for renewable energy emphasizes the need for energy storage systems (ESSs) to mitigate the unpre-dictability and variability of these sources, yet challenges such as high investment costs, sporadic utilization, and demand mismatch hinder their broader adoption. In response, shared energy storage systems (SESSs) offer a more cohesive and efficient use of ESS, providing more accessible and cost-effective energy storage solutions to overcome these obstacles. To enhance the profitability of SESSs, this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models. We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS. Subsequently, we design various long-term contracts from both capacity and energy perspectives, establishing associated models and real-time rental models. Lastly, multi-time-scale resource allocation based on the decomposition of user demand is proposed. Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction, effectively reducing battery degradation, and leveraging the aggregation effect for SESS can generate an additional increase of 10.7% in net revenue.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291021","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 : 2024-03-03DOI: 10.35833/MPCE.2023.000718
Ye Tang;Qiaozhu Zhai;Yuzhou Zhou
Energy storage (ES), as a fast response technology, creates an opportunity for microgrid (MG) to participate in the reserve market such that MG with ES can act as an independent reserve provider. However, the potential value of MG with ES in the reserve market has not been well realized. From the viewpoint of reserve provider, a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process, which differs from the model that MG with ES acts as a reserve consumer in most existing studies. Based on the proposed model, MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling. Considering that the proposed model is just in concept and cannot be directly solved, a multi-stage robust optimization reserve provision method is proposed, which leverages the structure of model constraints. Next, the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility. Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.
储能（ES）作为一种快速响应技术，为微电网（MG）参与储备市场创造了机会，使带有 ES 的微电网可以充当独立的储备供应商。然而，带 ES 的微电网在储备市场中的潜在价值尚未得到很好的体现。本文从储备提供者的角度出发，提出了一种综合考虑了实时调度过程影响的新型日前模型，该模型有别于现有大多数研究中将带 ES 的 MG 视为储备消费者的模型。基于提出的模型，带 ES 的 MG 可调度其内部资源为其他外部系统提供储备服务，并实现最优的自我调度。考虑到所提出的模型只是概念，无法直接求解，因此提出了一种多阶段鲁棒优化储备供应方法，该方法利用了模型约束的结构。接下来，原始模型可转换为混合整数线性规划问题，模型具有可操作性，并保证了求解的可行性。在现实世界中进行的数值测试证明了该方法的高效运行和经济效益。
{"title":"From Viewpoint of Reserve Provider: A Day-Ahead Multi-Stage Robust Optimization Reserve Provision Method for Microgrid with Energy Storage","authors":"Ye Tang;Qiaozhu Zhai;Yuzhou Zhou","doi":"10.35833/MPCE.2023.000718","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000718","url":null,"abstract":"Energy storage (ES), as a fast response technology, creates an opportunity for microgrid (MG) to participate in the reserve market such that MG with ES can act as an independent reserve provider. However, the potential value of MG with ES in the reserve market has not been well realized. From the viewpoint of reserve provider, a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process, which differs from the model that MG with ES acts as a reserve consumer in most existing studies. Based on the proposed model, MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling. Considering that the proposed model is just in concept and cannot be directly solved, a multi-stage robust optimization reserve provision method is proposed, which leverages the structure of model constraints. Next, the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility. Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324271","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}
Decarbonization in the power sector is one of the critical factors in achieving carbon neutrality, and the top-level design needs to be carried out from the perspective of power planning. A multi-stage provincial power expansion planning (PPEP) model is proposed to simulate the power expansion planning at different stages of the power systems rich in renewable energy generation. This model covers 16 types of power supply, considering macro-policy demands and micro-operation constraints. The stand-alone capacity aggregation model for coal-based units within the PPEP model allows for accurate construction and retirement with different stand-alone capacities. Moreover, the soft dynamic time warping (soft-DTW) based $K-text{medoids}$ technique is adopted to generate typical scenarios for balancing the model accuracy and solution efficiency. Additionally, a multi-market trading equilibrium (MMTE) mechanism is proposed to address the differences in the levelized cost of energy between the coal-based and renewable-based units by participating in energy and ancillary service markets. Since the coal-based units take on the task of providing ancillary services from renewable-based units in the ancillary service market, the MMTE mechanism can effectively equalize the profits of both by having renewable-based units purchase ancillary services from coal-based units and pay for them, thus improving the motivation of coal-based units. A case study in Xinjiang province, China, verifies the effectiveness of the planning results of the PPEP model and the profit equilibrium realization of the MMTE mechanism.
{"title":"Multi-Stage Provincial Power Expansion Planning and Multi-Market Trading Equilibrium","authors":"Guangsheng Pan;Zhongfan Gu;Yuanyuan Sun;Kaiqi Sun;Wei Gu","doi":"10.35833/MPCE.2024.000171","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000171","url":null,"abstract":"Decarbonization in the power sector is one of the critical factors in achieving carbon neutrality, and the top-level design needs to be carried out from the perspective of power planning. A multi-stage provincial power expansion planning (PPEP) model is proposed to simulate the power expansion planning at different stages of the power systems rich in renewable energy generation. This model covers 16 types of power supply, considering macro-policy demands and micro-operation constraints. The stand-alone capacity aggregation model for coal-based units within the PPEP model allows for accurate construction and retirement with different stand-alone capacities. Moreover, the soft dynamic time warping (soft-DTW) based \u0000<tex>$K-text{medoids}$</tex>\u0000 technique is adopted to generate typical scenarios for balancing the model accuracy and solution efficiency. Additionally, a multi-market trading equilibrium (MMTE) mechanism is proposed to address the differences in the levelized cost of energy between the coal-based and renewable-based units by participating in energy and ancillary service markets. Since the coal-based units take on the task of providing ancillary services from renewable-based units in the ancillary service market, the MMTE mechanism can effectively equalize the profits of both by having renewable-based units purchase ancillary services from coal-based units and pay for them, thus improving the motivation of coal-based units. A case study in Xinjiang province, China, verifies the effectiveness of the planning results of the PPEP model and the profit equilibrium realization of the MMTE mechanism.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328378","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 : 2024-02-28DOI: 10.35833/MPCE.2023.000742
Xiao Xu;Ziwen Qiu;Teng Zhang;Hui Gao
The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.
{"title":"Distributed Source-Load-Storage Cooperative Low-Carbon Scheduling Strategy Considering Vehicle-to-Grid Aggregators","authors":"Xiao Xu;Ziwen Qiu;Teng Zhang;Hui Gao","doi":"10.35833/MPCE.2023.000742","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000742","url":null,"abstract":"The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10453376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291201","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 : 2024-02-28DOI: 10.35833/MPCE.2023.000746
Carmen Bas Domenech;Antonella Maria De Corato;Pierluigi Mancarella
Community batteries (CBs) are emerging to support and even enable energy communities and generally help consumers, especially space-constrained ones, to access potential techno-economic benefits from storage and support local grid decarbonization. However, the economic viability of CB projects is often uncertain. In this regard, typical feasibility studies assess CB value for behind-the-meter (BTM) operation or whole-sale market participation, i.e., front-of-meter (FOM). This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTM and FOM benefits. A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.
{"title":"Co-Optimization of Behind-the-Meter and Front-of-Meter Value Streams in Community Batteries","authors":"Carmen Bas Domenech;Antonella Maria De Corato;Pierluigi Mancarella","doi":"10.35833/MPCE.2023.000746","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000746","url":null,"abstract":"Community batteries (CBs) are emerging to support and even enable energy communities and generally help consumers, especially space-constrained ones, to access potential techno-economic benefits from storage and support local grid decarbonization. However, the economic viability of CB projects is often uncertain. In this regard, typical feasibility studies assess CB value for behind-the-meter (BTM) operation or whole-sale market participation, i.e., front-of-meter (FOM). This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTM and FOM benefits. A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10453377","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291166","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 : 2024-02-26DOI: 10.35833/MPCE.2023.000512
Hongtao Ren;Chung-Li Tseng;Fushuan Wen;Chongyu Wang;Guoyan Chen;Xiao Li
Joint operation optimization for electric vehicles (EVs) and on-site or adjacent photovoltaic generation (PVG) are pivotal to maintaining the security and economics of the operation of the power system concerned. Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station (EVCS). Firstly, an optimization model for real-time EV charging strategy is proposed to address these challenges, which accounts for environmental uncertainties of an EVCS, encompassing EV arrivals, charging demands, PVG outputs, and the electricity price. Then, a scenario-based two-stage optimization approach is formulated. The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory (B-LSTM) network. Finally, numerical results substantiate the efficacy of the proposed optimization approach, and demonstrate superior profitability compared with prevalent approaches.
{"title":"Scenario-Based Optimal Real-Time Charging Strategy of Electric Vehicles with Bayesian Long Short-Term Memory Networks","authors":"Hongtao Ren;Chung-Li Tseng;Fushuan Wen;Chongyu Wang;Guoyan Chen;Xiao Li","doi":"10.35833/MPCE.2023.000512","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000512","url":null,"abstract":"Joint operation optimization for electric vehicles (EVs) and on-site or adjacent photovoltaic generation (PVG) are pivotal to maintaining the security and economics of the operation of the power system concerned. Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station (EVCS). Firstly, an optimization model for real-time EV charging strategy is proposed to address these challenges, which accounts for environmental uncertainties of an EVCS, encompassing EV arrivals, charging demands, PVG outputs, and the electricity price. Then, a scenario-based two-stage optimization approach is formulated. The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory (B-LSTM) network. Finally, numerical results substantiate the efficacy of the proposed optimization approach, and demonstrate superior profitability compared with prevalent approaches.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10445407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324365","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 : 2024-02-26DOI: 10.35833/MPCE.2023.000633
Shida Zhang;Shaoyun Ge;Hong Liu;Guocheng Hou;Chengshan Wang
To provide guidance for photovoltaic (PV) system integration in net-zero distribution systems (DSs), this paper proposes an analytical method for delineating the feasible region for PV integration capacities (PVICs), where the impact of battery energy storage system (BESS) flexibility is considered. First, we introduce distributionally robust chance constraints on network security and energy/carbon net-zero requirements, which form the upper and lower bounds of the feasible region. Then, the formulation and solution of the feasible region is proposed. The resulting analytical expression is a set of linear inequalities, illustrating that the feasible region is a polyhedron in a high-dimensional space. A procedure is designed to verify and adjust the feasible region, ensuring that it satisfies network loss constraints under alternating current (AC) power flow. Case studies on the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system verify the effectiveness of the proposed method. It is demonstrated that the proposed method fully captures the spatio-temporal coupling relationship among PVs, loads, and BESSs, while also quantifying the impact of this relationship on the boundaries of the feasible region.
{"title":"An Analytical Method for Delineating Feasible Region for PV Integration Capacities in Net-zero Distribution Systems Considering Battery Energy Storage System Flexibility","authors":"Shida Zhang;Shaoyun Ge;Hong Liu;Guocheng Hou;Chengshan Wang","doi":"10.35833/MPCE.2023.000633","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000633","url":null,"abstract":"To provide guidance for photovoltaic (PV) system integration in net-zero distribution systems (DSs), this paper proposes an analytical method for delineating the feasible region for PV integration capacities (PVICs), where the impact of battery energy storage system (BESS) flexibility is considered. First, we introduce distributionally robust chance constraints on network security and energy/carbon net-zero requirements, which form the upper and lower bounds of the feasible region. Then, the formulation and solution of the feasible region is proposed. The resulting analytical expression is a set of linear inequalities, illustrating that the feasible region is a polyhedron in a high-dimensional space. A procedure is designed to verify and adjust the feasible region, ensuring that it satisfies network loss constraints under alternating current (AC) power flow. Case studies on the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system verify the effectiveness of the proposed method. It is demonstrated that the proposed method fully captures the spatio-temporal coupling relationship among PVs, loads, and BESSs, while also quantifying the impact of this relationship on the boundaries of the feasible region.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10445408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291195","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 : 2024-02-12DOI: 10.35833/MPCE.2023.000909
Leijiao Ge;Tianshuo Du;Zhengyang Xu;Luyang Hou;Jun Yan;Yuanliang Li
The accurate identification of smart meter (SM) fault types is crucial for enhancing the efficiency of operation and maintenance (O&M) and the reliability of power collection systems. However, the intelligent classification of SM fault types faces significant challenges owing to the complexity of features and the imbalance between fault categories. To address these issues, this study presents a fault diagnosis method for SM incorporating three distinct modules. The first module employs a combination of standardization, data imputation, and feature extraction to enhance the data quality, thereby facilitating improved training and learning by the classifiers. To enhance the classification performance, the data imputation method considers feature correlation measurement and sequential imputation, and the feature extractor utilizes the discriminative enhanced sparse autoencoder. To tackle the interclass imbalance of data with discrete and continuous features, the second module introduces an assisted classifier generative adversarial network, which includes a discrete feature generation module. Finally, a novel Stacking ensemble classifier for SM fault diagnosis is developed. In contrast to previous studies, we construct a two-layer heuristic optimization framework to address the synchronous dynamic optimization problem of the combinations and hyper-parameters of the Stacking ensemble classifier, enabling better handling of complex classification tasks using SM data. The proposed fault diagnosis method for SM via two-layer stacking ensemble optimization and data augmentation is trained and validated using SM fault data collected from 2010 to 2018 in Zhejiang Province, China. Experimental results demonstrate the effectiveness of the proposed method in improving the accuracy of SM fault diagnosis, particularly for minority classes.
准确识别智能电表（SM）故障类型对于提高运行和维护（O&M）效率以及电力采集系统的可靠性至关重要。然而，由于特征的复杂性和故障类别之间的不平衡性，智能电表故障类型的智能分类面临着巨大挑战。为解决这些问题，本研究提出了一种包含三个不同模块的 SM 故障诊断方法。第一个模块采用标准化、数据估算和特征提取相结合的方法来提高数据质量，从而促进分类器的训练和学习。为了提高分类性能，数据估算方法考虑了特征相关性测量和顺序估算，而特征提取器则利用了判别增强型稀疏自动编码器。为了解决具有离散和连续特征的数据类间不平衡问题，第二个模块引入了辅助分类器生成对抗网络，其中包括离散特征生成模块。最后，我们开发了一种用于 SM 故障诊断的新型 Stacking 集合分类器。与以往研究不同的是，我们构建了一个双层启发式优化框架，以解决 Stacking 集合分类器的组合和超参数的同步动态优化问题，从而更好地处理使用 SM 数据的复杂分类任务。通过两层堆叠集合优化和数据增强提出的 SM 故障诊断方法，利用 2010 年至 2018 年在中国浙江省收集的 SM 故障数据进行了训练和验证。实验结果表明，所提出的方法能有效提高 SM 故障诊断的准确性，尤其是对少数类别的故障诊断。
{"title":"A Fault Diagnosis Method for Smart Meters via Two-Layer Stacking Ensemble Optimization and Data Augmentation","authors":"Leijiao Ge;Tianshuo Du;Zhengyang Xu;Luyang Hou;Jun Yan;Yuanliang Li","doi":"10.35833/MPCE.2023.000909","DOIUrl":"10.35833/MPCE.2023.000909","url":null,"abstract":"The accurate identification of smart meter (SM) fault types is crucial for enhancing the efficiency of operation and maintenance (O&M) and the reliability of power collection systems. However, the intelligent classification of SM fault types faces significant challenges owing to the complexity of features and the imbalance between fault categories. To address these issues, this study presents a fault diagnosis method for SM incorporating three distinct modules. The first module employs a combination of standardization, data imputation, and feature extraction to enhance the data quality, thereby facilitating improved training and learning by the classifiers. To enhance the classification performance, the data imputation method considers feature correlation measurement and sequential imputation, and the feature extractor utilizes the discriminative enhanced sparse autoencoder. To tackle the interclass imbalance of data with discrete and continuous features, the second module introduces an assisted classifier generative adversarial network, which includes a discrete feature generation module. Finally, a novel Stacking ensemble classifier for SM fault diagnosis is developed. In contrast to previous studies, we construct a two-layer heuristic optimization framework to address the synchronous dynamic optimization problem of the combinations and hyper-parameters of the Stacking ensemble classifier, enabling better handling of complex classification tasks using SM data. The proposed fault diagnosis method for SM via two-layer stacking ensemble optimization and data augmentation is trained and validated using SM fault data collected from 2010 to 2018 in Zhejiang Province, China. Experimental results demonstrate the effectiveness of the proposed method in improving the accuracy of SM fault diagnosis, particularly for minority classes.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769433","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}
In line commutated converter based high-voltage direct current (LCC-HVDC) transmission systems, the transformer saturation can induce harmonic instability, which poses a serious threat to the safe operation of the power system. However, the nonlinear characteristics of the power grids introduced by the transformer saturation considerably limit the application of the conventional analysis methods. To address the issue, this paper derives a linear model for the transformer saturation caused by the DC current due to the converter modulation. Afterwards, the nonlinear characteristics of power grids with the transformer saturation is described by a complex valued impedance matrix. Based on the derived impedance matrix, the system harmonic stability is analyzed and the mechanism of the transformer saturation induced harmonic instability is revealed. Finally, the sensitivity analysis is conducted to find the key factors that influence the system core saturation instability. The proposed impedance model is verified by the electromagnetic transient simulation, and the simulation results corroborate the effectiveness of the proposed impedance model.
{"title":"Impedance Model for Instability Analysis of LCC-HVDCs Considering Transformer Saturation","authors":"Qin Jiang;Ruiting Xu;Baohong Li;Xiang Chen;Yue Yin;Tianqi Liu;Frede Blaabjerg","doi":"10.35833/MPCE.2023.000340","DOIUrl":"10.35833/MPCE.2023.000340","url":null,"abstract":"In line commutated converter based high-voltage direct current (LCC-HVDC) transmission systems, the transformer saturation can induce harmonic instability, which poses a serious threat to the safe operation of the power system. However, the nonlinear characteristics of the power grids introduced by the transformer saturation considerably limit the application of the conventional analysis methods. To address the issue, this paper derives a linear model for the transformer saturation caused by the DC current due to the converter modulation. Afterwards, the nonlinear characteristics of power grids with the transformer saturation is described by a complex valued impedance matrix. Based on the derived impedance matrix, the system harmonic stability is analyzed and the mechanism of the transformer saturation induced harmonic instability is revealed. Finally, the sensitivity analysis is conducted to find the key factors that influence the system core saturation instability. The proposed impedance model is verified by the electromagnetic transient simulation, and the simulation results corroborate the effectiveness of the proposed impedance model.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433419","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769434","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 scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, where-in energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear re-construction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.
分布式能源规模不断扩大，但商业模式和价值传递机制不完善，导致利用率低、响应速度差。针对这一问题，提出了分布式储能（DES）清洁价值的概念，并从电能和清洁的角度探讨了时空分布机制。在此基础上，构建了分布式储能的环境效益评价体系，以平衡聚合器和电力系统运营商之间的利益。然后，构建了具有聚合 DES 的虚拟电厂（VPP）的最佳低碳调度，其中同时考虑了能量值和清洁度值。为实现这一目标，采用绿色属性标记法在配电网（DN）节点碳势与 DES 行为之间建立相关约束，但代价是带来多种非线性关系。随后，针对多变量非线性编程问题，提出了基于凸包络（CE）线性重构法的求解方法，从而提高了求解效率和可行性。最后，基于 IEEE 33 总线 DN 进行了仿真验证。仿真结果表明，DES 的多维价值认可激发了资源用户的响应意愿。同时，解决 DES 对节点碳势的影响可以有效缓解清洁度值的过度补偿。
{"title":"Low-Carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value","authors":"Hongchao Gao;Tai Jin;Guanxiong Wang;Qixin Chen;Chongqing Kang;Jingkai Zhu","doi":"10.35833/MPCE.2023.000762","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000762","url":null,"abstract":"The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, where-in energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear re-construction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291142","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：