Pub Date : 2024-11-26DOI: 10.1016/j.segan.2024.101577
Chika Maduabuchi , Mohana Alanazi , Ahmed Alzahmi
{"title":"Retraction notice to “Accurate prophecy of photovoltaic-segmented thermoelectric generator’s performance using a neural network that feeds on finite element-generated data” [Sustain. Energy Grids Netw. 32 (2022) 100905]","authors":"Chika Maduabuchi , Mohana Alanazi , Ahmed Alzahmi","doi":"10.1016/j.segan.2024.101577","DOIUrl":"10.1016/j.segan.2024.101577","url":null,"abstract":"","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101577"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.segan.2024.101572
Payam Mahmoudi-Nasr
A smart grid (SG) is based on integrated data from distributed information systems, and the common information model (CIM) provides standard data infrastructure. In the SG, a malicious insider operator can lead to widespread failures in the power system by disrupting the system processes. The severity of the attack increases when he/she can access integrated data with legal permissions and steal, delete or modify them. This paper proposes an authorization framework to mitigate data access permissions of an insider operator who does not perform its duties properly in a CIM-based SG. In the proposed method, the accessibility of a CIM class is determined based on the operator trust and the criticality level of the issued SQL command. The value of the operator trust is calculated using its performance periodically or when an anomaly is detected. The proposed method is also able to detect anomalies in operator performance.
{"title":"An authorization framework to mitigate insider threat in CIM-based smart grid","authors":"Payam Mahmoudi-Nasr","doi":"10.1016/j.segan.2024.101572","DOIUrl":"10.1016/j.segan.2024.101572","url":null,"abstract":"<div><div>A smart grid (SG) is based on integrated data from distributed information systems, and the common information model (CIM) provides standard data infrastructure. In the SG, a malicious insider operator can lead to widespread failures in the power system by disrupting the system processes. The severity of the attack increases when he/she can access integrated data with legal permissions and steal, delete or modify them. This paper proposes an authorization framework to mitigate data access permissions of an insider operator who does not perform its duties properly in a CIM-based SG. In the proposed method, the accessibility of a CIM class is determined based on the operator trust and the criticality level of the issued SQL command. The value of the operator trust is calculated using its performance periodically or when an anomaly is detected. The proposed method is also able to detect anomalies in operator performance.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101572"},"PeriodicalIF":4.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.segan.2024.101571
Elham Jamalinia, Javad Khazaei, Rick S. Blum
This paper proposes a novel data-driven modeling and dynamic state-estimation approach for nonlinear power and energy systems, highlighting the critical role of a known dynamic model for accurate state estimation in the face of uncertainty and complex models. The proposed framework consists of a two-phase approach: data-driven model identification and state-estimation. During the model identification phase, which spans a relatively short time interval, state feedback is collected to identify the dynamics of the nonlinear systems in the power grid using a novel density-guided sparse identification algorithm. Unlike conventional sparse regression, which relies on a large library of linear and nonlinear functions to fit data, our proposed algorithm iteratively updates a relatively small initial library by adding higher-order nonlinear functions if the coefficients of the current functions are dense. Following the identification of the model’s dynamics, the estimation phase addresses the challenge of incomplete state measurements. By implementing an unscented Kalman filter, the state variables of the system are dynamically estimated by measuring the noisy output. Finally, simulation results on an IEEE 30-bus system are presented to illustrate the effectiveness of the density-guided sparse regression unscented Kalman filter compared to a physics-based unscented Kalman filter with model uncertainty. This study contributes to the fields of data-driven modeling techniques, machine learning for power systems, and computational intelligence in smart grids. It emphasizes the use of advanced sparse regression and unscented Kalman filter methods for state estimation, enhancing the robustness and accuracy of monitoring and control in electrical and energy systems.
{"title":"Data-driven dynamic state estimation in power systems via sparse regression unscented Kalman filter","authors":"Elham Jamalinia, Javad Khazaei, Rick S. Blum","doi":"10.1016/j.segan.2024.101571","DOIUrl":"10.1016/j.segan.2024.101571","url":null,"abstract":"<div><div>This paper proposes a novel data-driven modeling and dynamic state-estimation approach for nonlinear power and energy systems, highlighting the critical role of a known dynamic model for accurate state estimation in the face of uncertainty and complex models. The proposed framework consists of a two-phase approach: <em>data-driven model identification</em> and <em>state-estimation</em>. During the model identification phase, which spans a relatively short time interval, state feedback is collected to identify the dynamics of the nonlinear systems in the power grid using a novel density-guided sparse identification algorithm. Unlike conventional sparse regression, which relies on a large library of linear and nonlinear functions to fit data, our proposed algorithm iteratively updates a relatively small initial library by adding higher-order nonlinear functions if the coefficients of the current functions are dense. Following the identification of the model’s dynamics, the estimation phase addresses the challenge of incomplete state measurements. By implementing an <em>unscented Kalman filter</em>, the state variables of the system are dynamically estimated by measuring the noisy output. Finally, simulation results on an IEEE 30-bus system are presented to illustrate the effectiveness of the density-guided sparse regression unscented Kalman filter compared to a physics-based unscented Kalman filter with model uncertainty. This study contributes to the fields of data-driven modeling techniques, machine learning for power systems, and computational intelligence in smart grids. It emphasizes the use of advanced sparse regression and unscented Kalman filter methods for state estimation, enhancing the robustness and accuracy of monitoring and control in electrical and energy systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101571"},"PeriodicalIF":4.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.segan.2024.101575
Lijun Yang , Xin Cui , Ying Qin
Natural disasters often lead to multi-point failures in urban active distribution networks (ADN), and the formulation of reasonable power supply plans and failure recovery strategies can reduce the time of power loss of critical loads. With the strengthening of electric-traffic network coupling, information sharing and resource interoperability between the two networks have been realized. Based on this tightly coupling characteristics, the emergency power supply scheme and fault repair strategy for ADN have been proposed. Firstly, the framework of electric-traffic synergy mechanism has been constructed which to ensure the coordination and synchronization between the two different systems during the restoration period; on this basis, we establish the optimal path solving model for emergency resources considering the dynamic traffic flow、and cross-cycle passage time to realize the rapid dispatch of maintenance personnel and emergency power vehicles; and then we developed a mathematical model of integrated power station(IPS) and formulated its power support strategy; after that, the two-stage strategy of emergency power supply and failure repair of the ADN is formulated, and the rolling optimization method is adopted to solve the topology reconfiguration scheme of the grid, the energy support strategy and the personnel and material dispatch plan after the disaster, so as to allocate the resources in an orderly manner and accelerate the recovery of the power supply. Finally, the example results verify the effectiveness of electric- transportation coordination in accelerating the recovery of ADN, and the proposed strategy can ensure the continuous power supply of important loads.
{"title":"Emergency power supply scheme and fault repair strategy for distribution networks considering electric -traffic synergy","authors":"Lijun Yang , Xin Cui , Ying Qin","doi":"10.1016/j.segan.2024.101575","DOIUrl":"10.1016/j.segan.2024.101575","url":null,"abstract":"<div><div>Natural disasters often lead to multi-point failures in urban active distribution networks (ADN), and the formulation of reasonable power supply plans and failure recovery strategies can reduce the time of power loss of critical loads. With the strengthening of electric-traffic network coupling, information sharing and resource interoperability between the two networks have been realized. Based on this tightly coupling characteristics, the emergency power supply scheme and fault repair strategy for ADN have been proposed. Firstly, the framework of electric-traffic synergy mechanism has been constructed which to ensure the coordination and synchronization between the two different systems during the restoration period; on this basis, we establish the optimal path solving model for emergency resources considering the dynamic traffic flow、and cross-cycle passage time to realize the rapid dispatch of maintenance personnel and emergency power vehicles; and then we developed a mathematical model of integrated power station(IPS) and formulated its power support strategy; after that, the two-stage strategy of emergency power supply and failure repair of the ADN is formulated, and the rolling optimization method is adopted to solve the topology reconfiguration scheme of the grid, the energy support strategy and the personnel and material dispatch plan after the disaster, so as to allocate the resources in an orderly manner and accelerate the recovery of the power supply. Finally, the example results verify the effectiveness of electric- transportation coordination in accelerating the recovery of ADN, and the proposed strategy can ensure the continuous power supply of important loads.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101575"},"PeriodicalIF":4.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1016/j.segan.2024.101550
Vikas K. Saini , Rajesh Kumar , Sujil A. , Ramesh C. Bansal , Chaouki Ghenai , Maamar Bettayeb , Vladimir Terzija , Elena Gryazina , Petr Vorobev
Smart grid can offer load sharing and utilize distributed energy resources to reduce energy consumption costs and potentially earn revenue through energy services. Information and communication technologies (ICT) in the smart grid have opened a lot of possibilities for developing residential Demand Response (DR), which is essential in smart grid applications. DR is a technique that enables customers to participate in the operation of the electricity grid either by shifting or reducing the loads during peak time in response to price signals. The DR program helps utilities ensure power balance and lower the cost of electricity in both wholesale and retail electricity markets. Multi-Agent System (MAS) is a distributed artificial intelligence technique that can be used for the implementation of DR programs in the electricity market. This paper aims to provide a comprehensive review of the MAS application for the implementation of DR programs in electricity markets. This paper highlights a review of 264 research papers that discusses MAS-based DR, MAS-based DR in the electricity market, and various platforms for the development of MAS-based DR. It also summarizes the potential of MAS in other applications of the smart grid along with the MAS research challenges, benefits, constraints for implementation and future research directions in this field.
智能电网可以提供负荷分担，利用分布式能源资源来降低能源消耗成本，并可能通过能源服务赚取收入。智能电网中的信息和通信技术（ICT）为开发住宅需求响应（DR）提供了很多可能性，而住宅需求响应在智能电网应用中至关重要。需求响应（DR）是一种使用户能够参与电网运行的技术，用户可以在用电高峰期根据价格信号转移或减少负荷。DR 计划有助于电力公司确保电力平衡，降低电力批发和零售市场的电力成本。多代理系统（MAS）是一种分布式人工智能技术，可用于在电力市场中实施 DR 计划。本文旨在全面综述 MAS 在电力市场实施 DR 计划中的应用。本文重点综述了 264 篇研究论文，其中讨论了基于 MAS 的电力需求评估、电力市场中基于 MAS 的电力需求评估以及开发基于 MAS 的电力需求评估的各种平台。本文还总结了 MAS 在智能电网其他应用中的潜力，以及 MAS 在该领域的研究挑战、优势、实施限制和未来研究方向。
{"title":"Multi agent framework for consumer demand response in electricity market: Applications and recent advancement","authors":"Vikas K. Saini , Rajesh Kumar , Sujil A. , Ramesh C. Bansal , Chaouki Ghenai , Maamar Bettayeb , Vladimir Terzija , Elena Gryazina , Petr Vorobev","doi":"10.1016/j.segan.2024.101550","DOIUrl":"10.1016/j.segan.2024.101550","url":null,"abstract":"<div><div>Smart grid can offer load sharing and utilize distributed energy resources to reduce energy consumption costs and potentially earn revenue through energy services. Information and communication technologies (ICT) in the smart grid have opened a lot of possibilities for developing residential Demand Response (DR), which is essential in smart grid applications. DR is a technique that enables customers to participate in the operation of the electricity grid either by shifting or reducing the loads during peak time in response to price signals. The DR program helps utilities ensure power balance and lower the cost of electricity in both wholesale and retail electricity markets. Multi-Agent System (MAS) is a distributed artificial intelligence technique that can be used for the implementation of DR programs in the electricity market. This paper aims to provide a comprehensive review of the MAS application for the implementation of DR programs in electricity markets. This paper highlights a review of 264 research papers that discusses MAS-based DR, MAS-based DR in the electricity market, and various platforms for the development of MAS-based DR. It also summarizes the potential of MAS in other applications of the smart grid along with the MAS research challenges, benefits, constraints for implementation and future research directions in this field.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101550"},"PeriodicalIF":4.8,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/j.segan.2024.101569
Ali Yazhari Kermani, Amir Abdollahi, Masoud Rashidinejad
The interconnection of local energy networks (LENs) enables efficient exchange of energy and flexibility among them, fostering the integration of distributed energy resources and demand-side management strategies. Thus, the interconnected local energy systems (ILEN) structure is a viable approach to electrical distribution systems’ operation and management. However, implementing distributed energy management structures such as ILEN entails a great amount of information transactions. Therefore, these structures are more vulnerable to cyber threats. Thus, the newly developed efficient and secure power systems’ operation methods should take digitalization-related security risks into account. As a result, this paper is focused on the development of a secure operation method, equipped with a hybrid algorithm to mitigate cyber threats in the context of ILEN. In this regard, this research proposes a novel hybrid XGBoost-based cyber threat mitigation (HXGBTM) method to cope with the vulnerabilities of the physical and information layers of the cyber-infrastructure. The proposed cyber threat mitigation method is built upon the classification and regression capabilities of the XGBoost ensemble of decision trees to identify and mitigate anomalies in the electrical consumption data. Therefore, in the first step, the ILEN’s multi-objective energy and flexibility scheduling problem considering demand response portfolio i.e., is developed that encompasses a bi-level optimization problem, in which the operator of the ILEN optimizes energy and flexibility trading in the upper level. While in the lower level, each LEN operator minimizes scheduling costs along with maximizing the local flexibility as well as providing a demand response portfolio as a negawatt resource. Here, the flexibility index, which is later maximized using the second objective function, is considered as the proportion between "the available ramping capacity" and "required ramping capacity". In this paper, direct load control, and interruptible/curtailable demand response comprehensive models are implemented as candidate programs for the suggested portfolio. Furthermore, a hybrid cyber threat is modeled considering the communication line intrinsic vulnerability, as a result of natural causes, wear, and aging of the infrastructure etc., as well as false data injection (FDI) attacks that target each LEN’s electrical consumption database. Finally, the proposed HXGBTM is employed to mitigate the above-mentioned cyber-vulnerabilities and achieve near real-world conditions.
{"title":"A hybrid machine learning-based cyber-threat mitigation in energy and flexibility scheduling of interconnected local energy networks considering a negawatt demand response portfolio","authors":"Ali Yazhari Kermani, Amir Abdollahi, Masoud Rashidinejad","doi":"10.1016/j.segan.2024.101569","DOIUrl":"10.1016/j.segan.2024.101569","url":null,"abstract":"<div><div>The interconnection of local energy networks (LENs) enables efficient exchange of energy and flexibility among them, fostering the integration of distributed energy resources and demand-side management strategies. Thus, the interconnected local energy systems (ILEN) structure is a viable approach to electrical distribution systems’ operation and management. However, implementing distributed energy management structures such as ILEN entails a great amount of information transactions. Therefore, these structures are more vulnerable to cyber threats. Thus, the newly developed efficient and secure power systems’ operation methods should take digitalization-related security risks into account. As a result, this paper is focused on the development of a secure operation method, equipped with a hybrid algorithm to mitigate cyber threats in the context of ILEN. In this regard, this research proposes a novel hybrid XGBoost-based cyber threat mitigation (HXGBTM) method to cope with the vulnerabilities of the physical and information layers of the cyber-infrastructure. The proposed cyber threat mitigation method is built upon the classification and regression capabilities of the XGBoost ensemble of decision trees to identify and mitigate anomalies in the electrical consumption data. Therefore, in the first step, the ILEN’s multi-objective energy and flexibility scheduling problem considering demand response portfolio i.e., <span><math><mrow><msubsup><mrow><mi>MOEFS</mi></mrow><mrow><mi>DRP</mi></mrow><mrow><mi>ILEN</mi></mrow></msubsup><mspace></mspace></mrow></math></span>is developed that encompasses a bi-level optimization problem, in which the operator of the ILEN optimizes energy and flexibility trading in the upper level. While in the lower level, each LEN operator minimizes scheduling costs along with maximizing the local flexibility as well as providing a demand response portfolio as a negawatt resource. Here, the flexibility index, which is later maximized using the second objective function, is considered as the proportion between \"the available ramping capacity\" and \"required ramping capacity\". In this paper, direct load control, and interruptible/curtailable demand response comprehensive models are implemented as candidate programs for the suggested portfolio. Furthermore, a hybrid cyber threat is modeled considering the communication line intrinsic vulnerability, as a result of natural causes, wear, and aging of the infrastructure etc., as well as false data injection (FDI) attacks that target each LEN’s electrical consumption database. Finally, the proposed HXGBTM is employed to mitigate the above-mentioned cyber-vulnerabilities and achieve near real-world conditions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101569"},"PeriodicalIF":4.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/j.segan.2024.101570
Yuqin Yi , Jiazhu Xu , Weiming Zhang
Under the requirement of improving energy utilization of the grid and the dual-carbon background, balancing the economic and environmental benefits of microgrids (MG) in the competition market has great research significance. To provide reasonable price signals for energy sharing among stakeholders, we propose a new low-carbon driven energy-sharing pricing mechanism based on supply and demand information. The mechanism can incentivize MG to actively participate in non-cooperative games for energy sharing. Specifically, on the one hand, the virtual energy sharing centre (VESC) generates price signals by analysing and integrating the supply-demand information of MG, and then releases them to each MG. On the other hand, each MG receives the latest price signals and accordingly optimizes its own operation. For each MG, a two-stage robust optimization (TSRO) model that considers the uncertainties of the source-load and aims at the economy and environmental friendliness of the MG is established. For the multi-microgrid system, a low-carbon driven energy sharing mechanism is proposed by introducing a low-carbon indicator. Finally, the alternating optimization procedure-looped CCG algorithm (AOP-Looped CCG) is adopted to solve the model effectively. The numerical examples validate that the energy complementarity is promoted and comprehensive benefits are enhanced of the proposed mechanism.
{"title":"A low-carbon driven price approach for energy transactions of multi-microgrids based on non-cooperative game model considering uncertainties","authors":"Yuqin Yi , Jiazhu Xu , Weiming Zhang","doi":"10.1016/j.segan.2024.101570","DOIUrl":"10.1016/j.segan.2024.101570","url":null,"abstract":"<div><div>Under the requirement of improving energy utilization of the grid and the dual-carbon background, balancing the economic and environmental benefits of microgrids (MG) in the competition market has great research significance. To provide reasonable price signals for energy sharing among stakeholders, we propose a new low-carbon driven energy-sharing pricing mechanism based on supply and demand information. The mechanism can incentivize MG to actively participate in non-cooperative games for energy sharing. Specifically, on the one hand, the virtual energy sharing centre (VESC) generates price signals by analysing and integrating the supply-demand information of MG, and then releases them to each MG. On the other hand, each MG receives the latest price signals and accordingly optimizes its own operation. For each MG, a two-stage robust optimization (TSRO) model that considers the uncertainties of the source-load and aims at the economy and environmental friendliness of the MG is established. For the multi-microgrid system, a low-carbon driven energy sharing mechanism is proposed by introducing a low-carbon indicator. Finally, the alternating optimization procedure-looped CCG algorithm (AOP-Looped CCG) is adopted to solve the model effectively. The numerical examples validate that the energy complementarity is promoted and comprehensive benefits are enhanced of the proposed mechanism.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101570"},"PeriodicalIF":4.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.segan.2024.101567
Marcos Tostado-Véliz , Yuekuan Zhou , Alaa Al Zetawi , Francisco Jurado
The deregulation of the electricity sector calls up for a more active participation of end-users and distributed energy resources. Distribution markets clear local marginal prices at distribution levels, guiding the consumption or flexible loads and providing bidding prices for distributed generators. This paper proposes a new distribution market model involving energy communities and grid-scale battery energy storage units. The new model is based on equilibrium rather than auction, optimization or leader-follower principles, thus resulting in a cooperative framework where all the agents partake as price-taker entities. Profit-oriented models of the distribution system operator, energy communities and battery systems are proposed, which are jointly solved through their equivalent first-order optimality conditions, thus recasting as an equilibrium problem. The final optimization model results in a tractable and easily implementable Mixed Integer Linear Programming. An illustrative 4-bus system serves to validate the new proposal, while further simulations in 33-, and 123-bus systems confirm that the new market model is implementable in large-scale distribution systems. The results obtained with the new proposal are compared with those from a conventional centralized model, demonstrating that the proposed distribution market inhibits distributed assets of high prices from wholesale market, thus enabling a better use of distributed resources and redounding in a more profitable result for communities and battery systems.
{"title":"An equilibrium-based distribution market model hosting energy communities and grid-scale battery energy storage","authors":"Marcos Tostado-Véliz , Yuekuan Zhou , Alaa Al Zetawi , Francisco Jurado","doi":"10.1016/j.segan.2024.101567","DOIUrl":"10.1016/j.segan.2024.101567","url":null,"abstract":"<div><div>The deregulation of the electricity sector calls up for a more active participation of end-users and distributed energy resources. Distribution markets clear local marginal prices at distribution levels, guiding the consumption or flexible loads and providing bidding prices for distributed generators. This paper proposes a new distribution market model involving energy communities and grid-scale battery energy storage units. The new model is based on equilibrium rather than auction, optimization or leader-follower principles, thus resulting in a cooperative framework where all the agents partake as price-taker entities. Profit-oriented models of the distribution system operator, energy communities and battery systems are proposed, which are jointly solved through their equivalent first-order optimality conditions, thus recasting as an equilibrium problem. The final optimization model results in a tractable and easily implementable Mixed Integer Linear Programming. An illustrative 4-bus system serves to validate the new proposal, while further simulations in 33-, and 123-bus systems confirm that the new market model is implementable in large-scale distribution systems. The results obtained with the new proposal are compared with those from a conventional centralized model, demonstrating that the proposed distribution market inhibits distributed assets of high prices from wholesale market, thus enabling a better use of distributed resources and redounding in a more profitable result for communities and battery systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101567"},"PeriodicalIF":4.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.segan.2024.101562
Abdurahman Yaldız, Tayfur Gökçek, Yavuz Ateş, Ozan Erdinç
The transition towards increased utilization of renewable energy and electric vehicles (EVs), along with the growing use of various other electrical devices, poses challenges to the stable and resilient operation of electric power systems (EPS), especially in the face of natural phenomena associated with climate change. This means that accurate topology and balanced EPS plays a key role to increase the capacity to respond quickly and in a coordinated manner to disaster situations such as cyber-attacks, earthquakes and floods. In this study, a new approach is presented to quickly and accurately detect topology attacks in EPS, thus contributing to making safer and more resilient. The proposed methods provide insights into maintaining uninterrupted electricity service by enabling EPS management through both post- and pre-event operational strategies. This approach is created by identifying faulty points with the obtained topology information and creating microgrid (MG) groups. Machine learning techniques have been integrated into the data intrusion attack detection (DIAD) system, enabling the detection of manipulated or faulty smart meters (SM). Concurrently, a topology identification (TI)-based graph learning algorithm is propounded to determine the exact fault locations before and after the event. For MV region restoration after determining the TI region, a mixed-integer linear programming (MILP) approach is employed to optimize the load restoration process in the MG regions. This approach aims to minimize losses and restore critical loads to their previous state as quickly as possible using flexible and emergency power balancing systems, including grid-support storage systems (GSSs), photovoltaic systems (PVs), electric vehicle charging stations (EVCS), and mobile generators. Moreover, a detailed compilation is presented under the topics of EPS topology, phase identification (PI) and its effect on power system resiliency (PSR), shedding light on the future development of EPS.
{"title":"Overview and advancement of power system topology addressing pre- and post-event strategies under abnormal operating conditions","authors":"Abdurahman Yaldız, Tayfur Gökçek, Yavuz Ateş, Ozan Erdinç","doi":"10.1016/j.segan.2024.101562","DOIUrl":"10.1016/j.segan.2024.101562","url":null,"abstract":"<div><div>The transition towards increased utilization of renewable energy and electric vehicles (EVs), along with the growing use of various other electrical devices, poses challenges to the stable and resilient operation of electric power systems (EPS), especially in the face of natural phenomena associated with climate change. This means that accurate topology and balanced EPS plays a key role to increase the capacity to respond quickly and in a coordinated manner to disaster situations such as cyber-attacks, earthquakes and floods. In this study, a new approach is presented to quickly and accurately detect topology attacks in EPS, thus contributing to making safer and more resilient. The proposed methods provide insights into maintaining uninterrupted electricity service by enabling EPS management through both post- and pre-event operational strategies. This approach is created by identifying faulty points with the obtained topology information and creating microgrid (MG) groups. Machine learning techniques have been integrated into the data intrusion attack detection (DIAD) system, enabling the detection of manipulated or faulty smart meters (SM). Concurrently, a topology identification (TI)-based graph learning algorithm is propounded to determine the exact fault locations before and after the event. For MV region restoration after determining the TI region, a mixed-integer linear programming (MILP) approach is employed to optimize the load restoration process in the MG regions. This approach aims to minimize losses and restore critical loads to their previous state as quickly as possible using flexible and emergency power balancing systems, including grid-support storage systems (GSSs), photovoltaic systems (PVs), electric vehicle charging stations (EVCS), and mobile generators. Moreover, a detailed compilation is presented under the topics of EPS topology, phase identification (PI) and its effect on power system resiliency (PSR), shedding light on the future development of EPS.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101562"},"PeriodicalIF":4.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the increase in penetration of renewable energy sources connected to the system reduces the inertia of power systems, the penetration of these sources leads to increase in the requirements of primary frequency control (PFC) services. Fortunately, with the expansion of network intelligence platforms, responsive loads (RL) can be effectively useful in ancillary services in the near future and can be used like traditional power plants. Since these equipment have a high rate of change of status, if they are visible in the market by aggregating (with virtual power plant (VPP)), they can compete with synchronous generations (SG). Because the response speed of the participants in the market can affect the decision independent system operator (ISO) in determining the winning units, therefore in this article, we have proposed a market framework to create competition between SGs and VPPs in providing ancillary services. In the proposed framework, ISO minimizes the weighted sum of power purchase costs from VPPs and SGs. The proposed weighting coefficients express the response speed of each unit. In fact, the desired objective function is affected by two terms, cost and speed. The presented model has been simulated on a test system including four SGs units and one VPP unit in matrix laboratory (MATLAB) software and checked under five different scenarios. The comparison of the obtained results indicates an increase in the possibility of accepting units with a smaller weighting factor and a higher response speed (the meaning of accepting units are market players, i.e. SGs and VPPs).
{"title":"The clearing strategy of primary frequency control ancillary services market from the point of view ISO in the presence of synchronous generations and virtual power plants based on responsive loads","authors":"Saeideh Ranginkaman, Elaheh Mashhour, Mohsen Saniei","doi":"10.1016/j.segan.2024.101566","DOIUrl":"10.1016/j.segan.2024.101566","url":null,"abstract":"<div><div>Since the increase in penetration of renewable energy sources connected to the system reduces the inertia of power systems, the penetration of these sources leads to increase in the requirements of primary frequency control (PFC) services. Fortunately, with the expansion of network intelligence platforms, responsive loads (RL) can be effectively useful in ancillary services in the near future and can be used like traditional power plants. Since these equipment have a high rate of change of status, if they are visible in the market by aggregating (with virtual power plant (VPP)), they can compete with synchronous generations (SG). Because the response speed of the participants in the market can affect the decision independent system operator (ISO) in determining the winning units, therefore in this article, we have proposed a market framework to create competition between SGs and VPPs in providing ancillary services. In the proposed framework, ISO minimizes the weighted sum of power purchase costs from VPPs and SGs. The proposed weighting coefficients express the response speed of each unit. In fact, the desired objective function is affected by two terms, cost and speed. The presented model has been simulated on a test system including four SGs units and one VPP unit in matrix laboratory (MATLAB) software and checked under five different scenarios. The comparison of the obtained results indicates an increase in the possibility of accepting units with a smaller weighting factor and a higher response speed (the meaning of accepting units are market players, i.e. SGs and VPPs).</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101566"},"PeriodicalIF":4.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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