Pub Date : 2024-03-31DOI: 10.35833/MPCE.2023.000743
Yumin Zhang;Pengkai Sun;Xingquan Ji;Fushuan Wen;Ming Yang;Pingfeng Ye
Carbon capture and storage (CCS) systems can provide sufficient carbon raw materials for power-to-gas (P2G) systems to reduce the carbon emission of traditional coal-fired units, which helps to achieve low-carbon dispatch of integrated energy systems (IESs). In this study, an extended carbon-emission flow model that integrates CCS-P2G coordinated operation and low-carbon characteristics of an energy storage system (ESS) is proposed. On the energy supply side, the coupling relationship between CCS and P2G systems is established to realize the low-carbon economic operation of P2G systems. On the energy storage side, the concept of “state of carbon” is introduced to describe the carbon emission characteristics of the ESS to exploit the potential of coordinated low-carbon dispatch in terms of both energy production and storage. In addition, a low-carbon economic dispatch model that considers multiple uncertainties, including wind power output, electricity price, and load demands, is established. To solve the model efficiently, a parallel multidimensional approximate dynamic programming algorithm is adopted, while the solution efficiency is significantly improved over that of stochastic optimization without losing solution accuracy under a multilayer parallel loop nesting framework. The low-carbon economic dispatch method of IESs is composed of the extended carbon emission flow model, low-carbon economic dispatch model, and the parallel multidimensional approximate dynamic programming algorithm. The effectiveness of the proposed method is verified on E14-B6-G6 and E57-B12-G12 systems.
碳捕集与封存（CCS）系统可以为P2G （power-to-gas）系统提供充足的碳原料，减少传统燃煤机组的碳排放，有助于实现综合能源系统（integrated energy system, ess）的低碳调度。本文提出了一种集成CCS-P2G协同运行和储能系统低碳特性的扩展碳排放流模型。在能源供给侧，建立CCS与P2G系统的耦合关系，实现P2G系统的低碳经济运行。在储能方面，引入“碳状态”的概念来描述ESS的碳排放特征，以挖掘能源生产和储能协调低碳调度的潜力。建立了考虑风电出力、电价、负荷需求等多种不确定因素的低碳经济调度模型。为了高效求解模型，采用并行多维近似动态规划算法，在多层并行循环嵌套框架下，求解效率显著提高，且不损失求解精度。该方法由扩展的碳排放流模型、低碳经济调度模型和并行多维近似动态规划算法组成。在E14-B6-G6和E57-B12-G12系统上验证了该方法的有效性。
{"title":"Low-Carbon Economic Dispatch of Integrated Energy Systems Considering Extended Carbon Emission Flow","authors":"Yumin Zhang;Pengkai Sun;Xingquan Ji;Fushuan Wen;Ming Yang;Pingfeng Ye","doi":"10.35833/MPCE.2023.000743","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000743","url":null,"abstract":"Carbon capture and storage (CCS) systems can provide sufficient carbon raw materials for power-to-gas (P2G) systems to reduce the carbon emission of traditional coal-fired units, which helps to achieve low-carbon dispatch of integrated energy systems (IESs). In this study, an extended carbon-emission flow model that integrates CCS-P2G coordinated operation and low-carbon characteristics of an energy storage system (ESS) is proposed. On the energy supply side, the coupling relationship between CCS and P2G systems is established to realize the low-carbon economic operation of P2G systems. On the energy storage side, the concept of “state of carbon” is introduced to describe the carbon emission characteristics of the ESS to exploit the potential of coordinated low-carbon dispatch in terms of both energy production and storage. In addition, a low-carbon economic dispatch model that considers multiple uncertainties, including wind power output, electricity price, and load demands, is established. To solve the model efficiently, a parallel multidimensional approximate dynamic programming algorithm is adopted, while the solution efficiency is significantly improved over that of stochastic optimization without losing solution accuracy under a multilayer parallel loop nesting framework. The low-carbon economic dispatch method of IESs is composed of the extended carbon emission flow model, low-carbon economic dispatch model, and the parallel multidimensional approximate dynamic programming algorithm. The effectiveness of the proposed method is verified on E14-B6-G6 and E57-B12-G12 systems.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1798-1809"},"PeriodicalIF":5.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844272","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 inter-regional electricity market is instrumental in enhancing the economic efficiency, reliability, and integration of renewable generation within interconnected power systems. As the market boundary expands, the complexity and solution difficulties of market clearing increase rapidly. The presence of hybrid alternating current (AC)/direct current (DC) interconnector networks further compounds challenges in modeling trading paths and transmission tariffs. To address these issues, this paper proposes a path-aware market-clearing (PAMC) model tailored for the inter-regional electricity market, which accommodates the hybrid AC/DC interconnector network. A variable aggregation strategy is proposed to reduce the problem scale while ensuring equivalent optimality. In addition, a novel redundancy elimination method is developed to expedite the solution of the market-clearing problem. This framework utilizes envelope approximations of residual demand curves to identify bidding blocks that will not affect the marginal price. Corresponding decision variables are then constrained to their bounds to remove redundant information. Comprehensive case studies across different power system scales validate the superiority of the proposed PAMC model in improving social welfare, and verify the effectiveness of the proposed redundancy elimination method in accelerating the solution of the market-clearing problem.
{"title":"Path-Aware Market Clearing Model for Inter-Regional Electricity Market via Redundancy Elimination","authors":"Shiyuan Tao;Zhenfei Tan;Chenxing Yang;Zheng Yan;Haihua Cheng","doi":"10.35833/MPCE.2023.000962","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000962","url":null,"abstract":"The inter-regional electricity market is instrumental in enhancing the economic efficiency, reliability, and integration of renewable generation within interconnected power systems. As the market boundary expands, the complexity and solution difficulties of market clearing increase rapidly. The presence of hybrid alternating current (AC)/direct current (DC) interconnector networks further compounds challenges in modeling trading paths and transmission tariffs. To address these issues, this paper proposes a path-aware market-clearing (PAMC) model tailored for the inter-regional electricity market, which accommodates the hybrid AC/DC interconnector network. A variable aggregation strategy is proposed to reduce the problem scale while ensuring equivalent optimality. In addition, a novel redundancy elimination method is developed to expedite the solution of the market-clearing problem. This framework utilizes envelope approximations of residual demand curves to identify bidding blocks that will not affect the marginal price. Corresponding decision variables are then constrained to their bounds to remove redundant information. Comprehensive case studies across different power system scales validate the superiority of the proposed PAMC model in improving social welfare, and verify the effectiveness of the proposed redundancy elimination method in accelerating the solution of the market-clearing problem.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1980-1992"},"PeriodicalIF":5.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543263","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844557","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 distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems (RIESs) with multiple stakeholders. A two-level Stackelberg game model is established, with the RIES operator as the leader and the users as the followers. It considers the interests of the RIES operator and demand response users in energy trading. The leader optimizes time-of-use (TOU) energy prices to minimize costs while users formulate response plans based on prices. A two-stage distributionally robust game model with comprehensive norm constraints, which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage, is constructed to manage wind power uncertainty. Karush-Kuhn-Tucker (KKT) conditions transform the two-level Stackelberg game model into a single-level robust optimization model, which is then solved using column and constraint generation (C&CG). Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders' interests and mitigating wind power risks.
{"title":"Distributionally Robust Scheduling for Benefit Allocation in Regional Integrated Energy System with Multiple Stakeholders","authors":"Qinglin Meng;Xiaolong Jin;Fengzhang Luo;Zhongguan Wang;Sheharyar Hussain","doi":"10.35833/MPCE.2023.000661","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000661","url":null,"abstract":"A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems (RIESs) with multiple stakeholders. A two-level Stackelberg game model is established, with the RIES operator as the leader and the users as the followers. It considers the interests of the RIES operator and demand response users in energy trading. The leader optimizes time-of-use (TOU) energy prices to minimize costs while users formulate response plans based on prices. A two-stage distributionally robust game model with comprehensive norm constraints, which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage, is constructed to manage wind power uncertainty. Karush-Kuhn-Tucker (KKT) conditions transform the two-level Stackelberg game model into a single-level robust optimization model, which is then solved using column and constraint generation (C&CG). Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders' interests and mitigating wind power risks.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1631-1642"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328383","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 : 2024-03-29DOI: 10.35833/MPCE.2023.000869
Kangshi Wang;Jieming Ma;Xiao Lu;Jingyi Wang;Ka Lok Man;Kaizhu Huang;Xiaowei Huang
The performance of photovoltaic (PV) systems is influenced by various factors, including atmospheric conditions, geographical locations, and spatial and temporal characteristics. Consequently, the optimization of PV systems relies heavily on the global maximum power point tracking (GMPPT) methods. In this paper, we adopt virtual reality (VR) technology to visualize PV entities and simulate their performances. The integration of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition (SPR) of PV systems, thereby enhancing their descriptive capabilities. Furthermore, we introduce an interactive GMPPT (IGMPPT) method based on VR technology. This method leverages interactive search techniques to narrow down search regions, thereby enhancing the search efficiency. Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improving the efficiency of GMPPT.
{"title":"Virtual Reality Based Shading Pattern Recognition and Interactive Global Maximum Power Point Tracking in Photovoltaic Systems","authors":"Kangshi Wang;Jieming Ma;Xiao Lu;Jingyi Wang;Ka Lok Man;Kaizhu Huang;Xiaowei Huang","doi":"10.35833/MPCE.2023.000869","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000869","url":null,"abstract":"The performance of photovoltaic (PV) systems is influenced by various factors, including atmospheric conditions, geographical locations, and spatial and temporal characteristics. Consequently, the optimization of PV systems relies heavily on the global maximum power point tracking (GMPPT) methods. In this paper, we adopt virtual reality (VR) technology to visualize PV entities and simulate their performances. The integration of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition (SPR) of PV systems, thereby enhancing their descriptive capabilities. Furthermore, we introduce an interactive GMPPT (IGMPPT) method based on VR technology. This method leverages interactive search techniques to narrow down search regions, thereby enhancing the search efficiency. Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improving the efficiency of GMPPT.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1849-1858"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844440","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 : 2024-03-29DOI: 10.35833/MPCE.2024.000090
Yi Yang;Peng Zhang;Can Wang;Zhuoli Zhao;Loi Lei Lai
The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system (IES). Therefore, this study proposes a state transition modeling method for an IES based on a cyber-physical system (CPS) to optimize the state transition of energy unit in the IES. This method uses the physical, integration, and optimization layers as a three-layer modeling framework. The physical layer is used to describe the physical models of energy units in the IES. In the integration layer, the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model, and the transition conditions between different states of the energy unit are given. The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state. Numerical simulations show that, compared with the traditional modeling method, the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle, which obtains an optimal state of the energy unit and further reduces the system operating costs.
{"title":"State Transition Modeling Method for Optimal Dispatching for Integrated Energy System Based on Cyber—Physical System","authors":"Yi Yang;Peng Zhang;Can Wang;Zhuoli Zhao;Loi Lei Lai","doi":"10.35833/MPCE.2024.000090","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000090","url":null,"abstract":"The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system (IES). Therefore, this study proposes a state transition modeling method for an IES based on a cyber-physical system (CPS) to optimize the state transition of energy unit in the IES. This method uses the physical, integration, and optimization layers as a three-layer modeling framework. The physical layer is used to describe the physical models of energy units in the IES. In the integration layer, the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model, and the transition conditions between different states of the energy unit are given. The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state. Numerical simulations show that, compared with the traditional modeling method, the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle, which obtains an optimal state of the energy unit and further reduces the system operating costs.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1617-1630"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324294","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 : 2024-03-29DOI: 10.35833/MPCE.2023.000765
Yang Fu;Zixu Ren;Shurong Wei;Lingling Huang;Fangxing Li;Yang Liu
The widespread adoption of renewable energy sources presents significant challenges for power system dispatching. This paper proposes a dynamic optimal power flow (DOPF) method based on reinforcement learning (RL) to address the dispatching challenges. The proposed method considers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling (PCCs). First, the operational area model of the offshore power grid at the PCCs is established by combining the prediction results and the transmission capacity limit of the offshore power grid. Built upon this, a dynamic optimization model of the power system and its RL environment are constructed with the consideration of offshore power dispatching constraints. Then, an improved algorithm based on the conditional generative adversarial network (CGAN) and the soft actor-critic (SAC) algorithm is proposed. By analyzing an improved IEEE 118-node example, the proposed method proves to have the advantage of economy over a longer timescale. The resulting strategy satisfies power system operation constraints, effectively addressing the constraint problem of action space of RL, and it has the added benefit of faster solution speeds.
{"title":"Dynamic Optimal Power Flow Method Based on Reinforcement Learning for Offshore Wind Farms Considering Multiple Points of Common Coupling","authors":"Yang Fu;Zixu Ren;Shurong Wei;Lingling Huang;Fangxing Li;Yang Liu","doi":"10.35833/MPCE.2023.000765","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000765","url":null,"abstract":"The widespread adoption of renewable energy sources presents significant challenges for power system dispatching. This paper proposes a dynamic optimal power flow (DOPF) method based on reinforcement learning (RL) to address the dispatching challenges. The proposed method considers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling (PCCs). First, the operational area model of the offshore power grid at the PCCs is established by combining the prediction results and the transmission capacity limit of the offshore power grid. Built upon this, a dynamic optimization model of the power system and its RL environment are constructed with the consideration of offshore power dispatching constraints. Then, an improved algorithm based on the conditional generative adversarial network (CGAN) and the soft actor-critic (SAC) algorithm is proposed. By analyzing an improved IEEE 118-node example, the proposed method proves to have the advantage of economy over a longer timescale. The resulting strategy satisfies power system operation constraints, effectively addressing the constraint problem of action space of RL, and it has the added benefit of faster solution speeds.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1749-1759"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844319","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 : 2024-03-28DOI: 10.35833/MPCE.2023.000321
Xiaokang Wu;Wei Xu;Feng Xue
Since the scale and uncertainty of the power system have been rapidly increasing, the computation efficiency of constructing the security region boundary (SRB) has become a prominent problem. Based on the topological features of historical operation data, a sample generation method for SRB identification is proposed to generate evenly distributed samples, which cover dominant security modes. The boundary sample pair (BSP) composed of a secure sample and an unsecure sample is defined to describe the feature of SRB. The resolution, sampling, and span indices are designed to evaluate the coverage degree of existing BSPs on the SRB and generate samples closer to the SRB. Based on the feature of flat distribution of BSPs over the SRB, the principal component analysis (PCA) is adopted to calculate the tangent vectors and normal vectors of SRB. Then, the sample distribution can be expanded along the tangent vector and corrected along the normal vector to cover different security modes. Finally, a sample set is randomly generated based on the IEEE standard example and another new sample set is generated by the proposed method. The results indicate that the new sample set is closer to the SRB and covers different security modes with a small calculation time cost.
{"title":"Sample Generation for Security Region Boundary Identification Based on Topological Features of Historical Operation Data","authors":"Xiaokang Wu;Wei Xu;Feng Xue","doi":"10.35833/MPCE.2023.000321","DOIUrl":"10.35833/MPCE.2023.000321","url":null,"abstract":"Since the scale and uncertainty of the power system have been rapidly increasing, the computation efficiency of constructing the security region boundary (SRB) has become a prominent problem. Based on the topological features of historical operation data, a sample generation method for SRB identification is proposed to generate evenly distributed samples, which cover dominant security modes. The boundary sample pair (BSP) composed of a secure sample and an unsecure sample is defined to describe the feature of SRB. The resolution, sampling, and span indices are designed to evaluate the coverage degree of existing BSPs on the SRB and generate samples closer to the SRB. Based on the feature of flat distribution of BSPs over the SRB, the principal component analysis (PCA) is adopted to calculate the tangent vectors and normal vectors of SRB. Then, the sample distribution can be expanded along the tangent vector and corrected along the normal vector to cover different security modes. Finally, a sample set is randomly generated based on the IEEE standard example and another new sample set is generated by the proposed method. The results indicate that the new sample set is closer to the SRB and covers different security modes with a small calculation time cost.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 4","pages":"1087-1095"},"PeriodicalIF":5.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10485267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769406","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}
Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strength-ened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.
{"title":"Towards Renewable-Dominated Energy Systems: Role of Green Hydrogen","authors":"Sheng Chen;Jingchun Zhang;Zhinong Wei;Hao Cheng;Si Lv","doi":"10.35833/MPCE.2023.000887","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000887","url":null,"abstract":"Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strength-ened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1697-1709"},"PeriodicalIF":5.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10485266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841993","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 recent years, the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network. However, due to the scarcity of historical data for these new consumers, it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods. This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering, deep learning, and transfer learning technologies to address this issue. To begin, this paper leverages the domain adversarial transfer network. It employs limited data as target domain data and more abundant data as source domain data, thus enabling the utilization of source domain insights for the forecasting task of the target domain. Moreover, a $boldsymbol{K}-mathbf{shape}$ clustering method is proposed, which effectively identifies source domain data that align optimally with the target domain, and enhances the forecasting accuracy. Subsequently, a composite architecture is devised, amalgamating attention mechanism, long short-term memory network, and seq2seq network. This composite structure is integrated into the domain adversarial transfer network, bolstering the performance of feature extractor and refining the forecasting capabilities. An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically. In the case study, the relative mean square error of the proposed method is within 30 MW, and the mean absolute percentage error is within 2%. A significant improvement in accuracy, compared with other comparative experimental results, underscores the reliability of the proposed method. The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecasting methods.
{"title":"Short-Term Residential Load Forecasting Based on $K-text{shape}$ Clustering and Domain Adversarial Transfer Network","authors":"Jizhong Zhu;Yuwang Miao;Hanjiang Dong;Shenglin Li;Ziyu Chen;Di Zhang","doi":"10.35833/MPCE.2023.000646","DOIUrl":"10.35833/MPCE.2023.000646","url":null,"abstract":"In recent years, the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network. However, due to the scarcity of historical data for these new consumers, it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods. This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering, deep learning, and transfer learning technologies to address this issue. To begin, this paper leverages the domain adversarial transfer network. It employs limited data as target domain data and more abundant data as source domain data, thus enabling the utilization of source domain insights for the forecasting task of the target domain. Moreover, a \u0000<tex>$boldsymbol{K}-mathbf{shape}$</tex>\u0000 clustering method is proposed, which effectively identifies source domain data that align optimally with the target domain, and enhances the forecasting accuracy. Subsequently, a composite architecture is devised, amalgamating attention mechanism, long short-term memory network, and seq2seq network. This composite structure is integrated into the domain adversarial transfer network, bolstering the performance of feature extractor and refining the forecasting capabilities. An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically. In the case study, the relative mean square error of the proposed method is within 30 MW, and the mean absolute percentage error is within 2%. A significant improvement in accuracy, compared with other comparative experimental results, underscores the reliability of the proposed method. The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecasting methods.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 4","pages":"1239-1249"},"PeriodicalIF":5.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769407","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 : 2024-03-27DOI: 10.35833/MPCE.2023.000824
Boyu Zhao;Hao Liu;Tianshu Bi;Sudi Xu
High-precision synchronized measurement data with short measurement latency are required for applications of phasor measurement units (PMUs). This paper proposes a synchrophasor measurement method based on cascaded infinite impulse response (IIR) and dual finite impulse response (FIR) filters, meeting the M-class and P-class requirements in the IEC/ IEEE 60255-118-1 standard. A low-group-delay IIR filter is designed to remove out-of-band interference components. Two FIR filters with different center frequencies are designed to filter out the fundamental negative frequency component and obtain synchrophasor estimates. The ratio of the amplitudes of the synchrophasor is used to calculate the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the FIR filters and the frequency. To shorten the response time introduced by IIR filter, a step identification and processing method based on the rate of change of frequency (RoCoF) is proposed and analyzed. The synchrophasor is accurately compensated based on the frequency and the frequency response of the IIR and FIR filters, achieving high-precision synchrophasor and frequency estimates with short measurement latency. Simulation and experiment tests demonstrate that the proposed method is superior to existing methods and can provide synchronized measurement data for M-class PMU applications with short measurement latency.
相位测量单元（PMU）的应用需要高精度同步测量数据和较短的测量延迟。本文提出了一种基于级联无限脉冲响应（IIR）和双有限脉冲响应（FIR）滤波器的同步相位测量方法，符合 IEC/ IEEE 60255-118-1 标准中的 M 级和 P 级要求。低群延迟 IIR 滤波器用于消除带外干扰成分。设计了两个中心频率不同的 FIR 滤波器，以滤除基频负分量，获得同步信号估计值。根据 FIR 滤波器的振幅频率响应比与频率之间的一一对应关系，利用同步信号的振幅比来计算频率。为了缩短 IIR 滤波器带来的响应时间，提出并分析了一种基于频率变化率（RoCoF）的阶跃识别和处理方法。根据 IIR 滤波器和 FIR 滤波器的频率和频率响应对同步相位进行精确补偿，从而以较短的测量延迟实现高精度的同步相位和频率估计。仿真和实验测试表明，所提出的方法优于现有方法，可为 M 级 PMU 应用提供同步测量数据，且测量延迟较短。
{"title":"Synchrophasor Measurement Method Based on Cascaded Infinite Impulse Response and Dual Finite Impulse Response Filters","authors":"Boyu Zhao;Hao Liu;Tianshu Bi;Sudi Xu","doi":"10.35833/MPCE.2023.000824","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000824","url":null,"abstract":"High-precision synchronized measurement data with short measurement latency are required for applications of phasor measurement units (PMUs). This paper proposes a synchrophasor measurement method based on cascaded infinite impulse response (IIR) and dual finite impulse response (FIR) filters, meeting the M-class and P-class requirements in the IEC/ IEEE 60255-118-1 standard. A low-group-delay IIR filter is designed to remove out-of-band interference components. Two FIR filters with different center frequencies are designed to filter out the fundamental negative frequency component and obtain synchrophasor estimates. The ratio of the amplitudes of the synchrophasor is used to calculate the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the FIR filters and the frequency. To shorten the response time introduced by IIR filter, a step identification and processing method based on the rate of change of frequency (RoCoF) is proposed and analyzed. The synchrophasor is accurately compensated based on the frequency and the frequency response of the IIR and FIR filters, achieving high-precision synchrophasor and frequency estimates with short measurement latency. Simulation and experiment tests demonstrate that the proposed method is superior to existing methods and can provide synchronized measurement data for M-class PMU applications with short measurement latency.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1345-1356"},"PeriodicalIF":5.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324326","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：