The increasing integration of renewable energy sources (RESs) presents significant challenges for the safe and economical operation of power grids. Addressing the critical need to assess the effect of RES uncertainties on optimal scheduling schemes (OSSs), this paper introduces a convex hull based economic operating region (CH-EOR) for power grids. The CH-EOR is mathematically defined to delineate the impact of RES uncertainties on power grid operations. We propose a novel approach for generating the CH-EOR, enhanced by a $mathbf{big}-boldsymbol{M}$ preprocessing method to improve the computational efficiency. Performed on four test systems, the proposed $mathbf{big}-boldsymbol{M}$ preprocessing method demonstrates notable advancements: a reduction in average operating costs by over 10% compared with the box-constrained operating region (BC-OR) derived from robust optimization. Furthermore, the CH-EOR occupies less than 11.79% of the generators' adjustable region (GAR). Most significantly, after applying the proposed $mathbf{big}-boldsymbol{M}$ preprocessing method, the computational efficiency is improved over 17 times compared with the traditional $mathbf{big}-boldsymbol{M}$ method.
{"title":"Convex Hull Based Economic Operating Region for Power Grids Considering Uncertainties of Renewable Energy Sources","authors":"Huating Xu;Bin Feng;Gang Huang;Mingyang Sun;Houbo Xiong;Chuangxin Guo","doi":"10.35833/MPCE.2023.000549","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000549","url":null,"abstract":"The increasing integration of renewable energy sources (RESs) presents significant challenges for the safe and economical operation of power grids. Addressing the critical need to assess the effect of RES uncertainties on optimal scheduling schemes (OSSs), this paper introduces a convex hull based economic operating region (CH-EOR) for power grids. The CH-EOR is mathematically defined to delineate the impact of RES uncertainties on power grid operations. We propose a novel approach for generating the CH-EOR, enhanced by a \u0000<tex>$mathbf{big}-boldsymbol{M}$</tex>\u0000 preprocessing method to improve the computational efficiency. Performed on four test systems, the proposed \u0000<tex>$mathbf{big}-boldsymbol{M}$</tex>\u0000 preprocessing method demonstrates notable advancements: a reduction in average operating costs by over 10% compared with the box-constrained operating region (BC-OR) derived from robust optimization. Furthermore, the CH-EOR occupies less than 11.79% of the generators' adjustable region (GAR). Most significantly, after applying the proposed \u0000<tex>$mathbf{big}-boldsymbol{M}$</tex>\u0000 preprocessing method, the computational efficiency is improved over 17 times compared with the traditional \u0000<tex>$mathbf{big}-boldsymbol{M}$</tex>\u0000 method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1419-1430"},"PeriodicalIF":5.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10477368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324344","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 this paper, we apply a model predictive control based scheme to the energy management of networked microgrid, which is reformulated based on column generation. Although column generation is effective in alleviating the computational intractability of large-scale optimization problems, it still suffers from slow convergence issues, which hinders the direct real-time online implementation. To this end, we propose a graph neural network based framework to accelerate the convergence of the column generation model. The acceleration is achieved by selecting promising columns according to certain stabilization method of the dual variables that can be customized according to the characteristics of the microgrid. Moreover, a rigorous energy management method based on the graph neural network accelerated column generation model is developed, which is able to guarantee the optimality and feasibility of the dispatch results. The computational efficiency of the method is also very high, which is promising for real-time implementation. We conduct case studies to demonstrate the effectiveness of the proposed energy management method.
{"title":"Graph Neural Network Based Column Generation for Energy Management in Networked Microgrid","authors":"Yuchong Huo;Zaiyu Chen;Qun Li;Qiang Li;Minghui Yin","doi":"10.35833/MPCE.2023.000385","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000385","url":null,"abstract":"In this paper, we apply a model predictive control based scheme to the energy management of networked microgrid, which is reformulated based on column generation. Although column generation is effective in alleviating the computational intractability of large-scale optimization problems, it still suffers from slow convergence issues, which hinders the direct real-time online implementation. To this end, we propose a graph neural network based framework to accelerate the convergence of the column generation model. The acceleration is achieved by selecting promising columns according to certain stabilization method of the dual variables that can be customized according to the characteristics of the microgrid. Moreover, a rigorous energy management method based on the graph neural network accelerated column generation model is developed, which is able to guarantee the optimality and feasibility of the dispatch results. The computational efficiency of the method is also very high, which is promising for real-time implementation. We conduct case studies to demonstrate the effectiveness of the proposed energy management method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1506-1519"},"PeriodicalIF":5.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10477373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324297","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 : 2024-03-20DOI: 10.35833/MPCE.2023.000760
Yaqi Sun;Wenchuan Wu;Yi Lin;Hai Huang;Hao Chen
The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained by specified reliability requirements. The reliability-constrained distribution network planning (RcDNP) problem can be cast as an instance of mixed-integer linear programming (MILP) which involves ultra-heavy computation burden especially for large-scale DNs. In this paper, we propose a parallel computing based solution method for the RcDNP problem. The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination. Then, a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems. The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem. Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition. Under mild conditions, the optimality and convergence of the proposed method are proven. Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-world problems.
{"title":"Parallel Computing Based Solution for Reliability-constrained Distribution Network Planning","authors":"Yaqi Sun;Wenchuan Wu;Yi Lin;Hai Huang;Hao Chen","doi":"10.35833/MPCE.2023.000760","DOIUrl":"10.35833/MPCE.2023.000760","url":null,"abstract":"The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained by specified reliability requirements. The reliability-constrained distribution network planning (RcDNP) problem can be cast as an instance of mixed-integer linear programming (MILP) which involves ultra-heavy computation burden especially for large-scale DNs. In this paper, we propose a parallel computing based solution method for the RcDNP problem. The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination. Then, a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems. The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem. Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition. Under mild conditions, the optimality and convergence of the proposed method are proven. Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-world problems.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 4","pages":"1147-1158"},"PeriodicalIF":5.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10477364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769432","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 : 2024-03-10DOI: 10.35833/MPCE.2024.000139
Jiawei Wang;Yujie Sheng;Huaichang Ge;Xiang Bai;Jia Su;Qinglai Guo;Hongbin Sun
Considering the enormous potential application of autonomous mobility-on-demand (AMoD) systems in future urban transportation, the charging behavior of AMoD fleets, as a key link connecting the power system and the transportation system, needs to be guided by a reasonable charging demand management method. This paper uses game theory to investigate charging pricing methods for the AMoD fleets. Firstly, an AMoD fleet scheduling model with appropriate scale and mathematical complexity is established to describe the spatio-temporal action patterns of the AMoD fleet. Subsequently, using Stackelberg game and Nash bargaining, two game frameworks, i. e., non-cooperative and cooperative, are designed for the charging station operator (CSO) and the AMoD fleet. Then, the interaction trends between the two entities and the mechanism of charging price formation are discussed, along with an analysis of the game implications for breaking the non-cooperative dilemma and moving towards cooperation. Finally, numerical experiments based on real-world city-scale data are provided to validate the designed game frameworks. The results show that the spatio-temporal distribution of charging prices can be captured and utilized by the AMoD fleet. The CSO can then use this action pattern to determine charging prices to optimize the profit. Based on this, negotiated bargaining improves the overall benefits for stakeholders in urban transportation.
{"title":"Charging Pricing for Autonomous Mobility-on-Demand Fleets Based on Game Theory","authors":"Jiawei Wang;Yujie Sheng;Huaichang Ge;Xiang Bai;Jia Su;Qinglai Guo;Hongbin Sun","doi":"10.35833/MPCE.2024.000139","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000139","url":null,"abstract":"Considering the enormous potential application of autonomous mobility-on-demand (AMoD) systems in future urban transportation, the charging behavior of AMoD fleets, as a key link connecting the power system and the transportation system, needs to be guided by a reasonable charging demand management method. This paper uses game theory to investigate charging pricing methods for the AMoD fleets. Firstly, an AMoD fleet scheduling model with appropriate scale and mathematical complexity is established to describe the spatio-temporal action patterns of the AMoD fleet. Subsequently, using Stackelberg game and Nash bargaining, two game frameworks, i. e., non-cooperative and cooperative, are designed for the charging station operator (CSO) and the AMoD fleet. Then, the interaction trends between the two entities and the mechanism of charging price formation are discussed, along with an analysis of the game implications for breaking the non-cooperative dilemma and moving towards cooperation. Finally, numerical experiments based on real-world city-scale data are provided to validate the designed game frameworks. The results show that the spatio-temporal distribution of charging prices can be captured and utilized by the AMoD fleet. The CSO can then use this action pattern to determine charging prices to optimize the profit. Based on this, negotiated bargaining improves the overall benefits for stakeholders in urban transportation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"2006-2018"},"PeriodicalIF":5.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844194","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 : 2024-03-10DOI: 10.35833/MPCE.2023.000871
Arif S. Malik;Majid A. Al Umairi
This paper presents a novel method for accurately estimating the cumulative capacity credit (CCC) of renewable energy (RE) projects. Leveraging data from the main interconnected system (MIS) of Oman for 2028, where a substantial increase in RE generation is anticipated, our novel method is introduced alongside the traditional effective load carrying capability (ELCC) method. To ensure its robustness, we compare CCC results with ELCC calculations using two distinct standards of reliability criteria: loss of load hours (LOLH) at 24 hour/year and 2.4 hour/year. Our method consistently gives accurate results, emphasizing its exceptional accuracy, efficiency, and simplicity. A notable feature of our method is its independence from loss of load probability (LOLP) calculations and the iterative procedures associated with analytic-based reliability methods. Instead, it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE plants. This innovation is of particular significance to prospective independent power producers (IPPs) in the RE sector, offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data, often restricted by privacy concerns.
{"title":"Cumulative Capacity Credit Estimation for Renewable Energy Projects","authors":"Arif S. Malik;Majid A. Al Umairi","doi":"10.35833/MPCE.2023.000871","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000871","url":null,"abstract":"This paper presents a novel method for accurately estimating the cumulative capacity credit (CCC) of renewable energy (RE) projects. Leveraging data from the main interconnected system (MIS) of Oman for 2028, where a substantial increase in RE generation is anticipated, our novel method is introduced alongside the traditional effective load carrying capability (ELCC) method. To ensure its robustness, we compare CCC results with ELCC calculations using two distinct standards of reliability criteria: loss of load hours (LOLH) at 24 hour/year and 2.4 hour/year. Our method consistently gives accurate results, emphasizing its exceptional accuracy, efficiency, and simplicity. A notable feature of our method is its independence from loss of load probability (LOLP) calculations and the iterative procedures associated with analytic-based reliability methods. Instead, it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE plants. This innovation is of particular significance to prospective independent power producers (IPPs) in the RE sector, offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data, often restricted by privacy concerns.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1643-1651"},"PeriodicalIF":5.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328377","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 : 2024-03-10DOI: 10.35833/MPCE.2023.000639
Shiying Ma;Liwen Zheng
Wind-thermal-bundled system has emerged as the predominant type of power system, incorporating a significant proportion of renewable energy. The dynamic interaction mechanism of the system is complex, and the issue of oscillation stability is significant. In this paper, the damping characteristics of the direct current (DC) capacitance oscillation mode are analyzed using the path analysis method (PAM). This method combines the transfer-function block diagram with the damping torque analysis (DTA). Firstly, the linear models of the permanent magnet synchronous generator (PMSG), the synchronous generator (SG), and the alternating current (AC) grid are established based on the transfer functions. The closed-loop transfer-function block diagram of the wind-thermal-bundled systems is derived. Secondly, the block diagram reveals the damping path and the dynamic interaction mechanism of the system. According to the superposition principle, the transfer-function block diagram is reconstructed to achieve the damping separation. The damping coefficient of the DTA is used to quantify the effect of the interaction between the subsystems on the damping characteristics of the oscillation mode. Then, the eigenvalue analysis is used to analyze the system stability. Finally, the damping characteristic analysis is validated by time-domain simulations.
{"title":"Damping Characteristic Analysis of Wind-Thermal-Bundled Systems","authors":"Shiying Ma;Liwen Zheng","doi":"10.35833/MPCE.2023.000639","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000639","url":null,"abstract":"Wind-thermal-bundled system has emerged as the predominant type of power system, incorporating a significant proportion of renewable energy. The dynamic interaction mechanism of the system is complex, and the issue of oscillation stability is significant. In this paper, the damping characteristics of the direct current (DC) capacitance oscillation mode are analyzed using the path analysis method (PAM). This method combines the transfer-function block diagram with the damping torque analysis (DTA). Firstly, the linear models of the permanent magnet synchronous generator (PMSG), the synchronous generator (SG), and the alternating current (AC) grid are established based on the transfer functions. The closed-loop transfer-function block diagram of the wind-thermal-bundled systems is derived. Secondly, the block diagram reveals the damping path and the dynamic interaction mechanism of the system. According to the superposition principle, the transfer-function block diagram is reconstructed to achieve the damping separation. The damping coefficient of the DTA is used to quantify the effect of the interaction between the subsystems on the damping characteristics of the oscillation mode. Then, the eigenvalue analysis is used to analyze the system stability. Finally, the damping characteristic analysis is validated by time-domain simulations.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1383-1395"},"PeriodicalIF":5.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324321","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}
Lately, the power demand of consumers is increasing in distribution networks, while renewable power generation keeps penetrating into the distribution networks. Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude. Hence, this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques. First, we formulate the short-term probabilistic residential load forecasting problem. Then, we propose a sequence-to-sequence (Seq2Seq) adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain (with massive consumption records of regular loads) to the target domain (with limited observations of new residential loads) and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem. For implementation, the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network, including the Seq2Seq recurrent neural networks (RNNs) composed of a long short-term memory (LSTM) encoder and an LSTM decoder, and quantile loss. Finally, this study conducts the case studies via multiple evaluation indices, comparative methods of classic machine learning and advanced deep learning, and various available data of the new residentical loads and other regular loads. The experimental results validate the effectiveness and stability of the proposed scheme.
{"title":"Probabilistic Residential Load Forecasting with Sequence-to-Sequence Adversarial Domain Adaptation Networks","authors":"Hanjiang Dong;Jizhong Zhu;Shenglin Li;Yuwang Miao;Chi Yung Chung;Ziyu Chen","doi":"10.35833/MPCE.2023.000841","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000841","url":null,"abstract":"Lately, the power demand of consumers is increasing in distribution networks, while renewable power generation keeps penetrating into the distribution networks. Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude. Hence, this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques. First, we formulate the short-term probabilistic residential load forecasting problem. Then, we propose a sequence-to-sequence (Seq2Seq) adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain (with massive consumption records of regular loads) to the target domain (with limited observations of new residential loads) and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem. For implementation, the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network, including the Seq2Seq recurrent neural networks (RNNs) composed of a long short-term memory (LSTM) encoder and an LSTM decoder, and quantile loss. Finally, this study conducts the case studies via multiple evaluation indices, comparative methods of classic machine learning and advanced deep learning, and various available data of the new residentical loads and other regular loads. The experimental results validate the effectiveness and stability of the proposed scheme.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1559-1571"},"PeriodicalIF":5.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324353","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 : 2024-03-10DOI: 10.35833/MPCE.2023.000874
Rui Zhang;Jilai Yu
Due to the effects of windless and sunless weather, new power systems dominated by renewable energy sources experience power supply shortages, which lead to severe electricity shortages. Because of the insufficient proportion of controllable thermal power in these systems, this problem must be addressed from the load side. This study proposes an orderly power utilization (OPU) method with load as the primary dispatching object to address the problem of severe electricity shortages. The principles and architecture of the new urban power grid (NUPG) OPU are proposed to complete the load curtailment task and minimize the effects on social production and daily life. A flexible load baseline division method is proposed that considers the effects of factors such as gross domestic product, pollutant emission, and carbon emission to increase the flexibility and applicability of the proposed method. In addition, an NUPG OPU model based on the load baseline is proposed, in which the electric quantity balance aggregator (EQBA) serves as a regular participant in the OPU and eliminates the need for other user involvement within its capacity range. The electric quantity reserve aggregator (EQRA) functions as a supplementary participant in the OPU and primarily performs the remaining tasks of the EQBA. The electric power balance aggregator primarily offsets the power fluctuations of the OPU. Case studies demonstrate the effectiveness and superiority of the proposed model in ensuring the completion of the load curtailment task, enhancing the flexibility and fairness of OPUs, and improving the applicability of the proposed method.
{"title":"An Orderly Power Utilization Method for New Urban Power Grids Facing Severe Electricity Shortages","authors":"Rui Zhang;Jilai Yu","doi":"10.35833/MPCE.2023.000874","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000874","url":null,"abstract":"Due to the effects of windless and sunless weather, new power systems dominated by renewable energy sources experience power supply shortages, which lead to severe electricity shortages. Because of the insufficient proportion of controllable thermal power in these systems, this problem must be addressed from the load side. This study proposes an orderly power utilization (OPU) method with load as the primary dispatching object to address the problem of severe electricity shortages. The principles and architecture of the new urban power grid (NUPG) OPU are proposed to complete the load curtailment task and minimize the effects on social production and daily life. A flexible load baseline division method is proposed that considers the effects of factors such as gross domestic product, pollutant emission, and carbon emission to increase the flexibility and applicability of the proposed method. In addition, an NUPG OPU model based on the load baseline is proposed, in which the electric quantity balance aggregator (EQBA) serves as a regular participant in the OPU and eliminates the need for other user involvement within its capacity range. The electric quantity reserve aggregator (EQRA) functions as a supplementary participant in the OPU and primarily performs the remaining tasks of the EQBA. The electric power balance aggregator primarily offsets the power fluctuations of the OPU. Case studies demonstrate the effectiveness and superiority of the proposed model in ensuring the completion of the load curtailment task, enhancing the flexibility and fairness of OPUs, and improving the applicability of the proposed method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1710-1723"},"PeriodicalIF":5.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844541","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}
To improve the economic efficiency of urban integrated energy systems (UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system (TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers (BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator (ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation (C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.
{"title":"Coordinated Dispatch Based on Distributed Robust Optimization for Interconnected Urban Integrated Energy and Transmission Systems","authors":"Wei Xu;Yufeng Guo;Tianhui Meng;Yingwei Wang;Jilai Yu","doi":"10.35833/MPCE.2023.000255","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000255","url":null,"abstract":"To improve the economic efficiency of urban integrated energy systems (UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system (TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers (BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator (ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation (C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 3","pages":"840-851"},"PeriodicalIF":6.3,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091146","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 : 2024-03-05DOI: 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.
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