Sustainable Energy Grids & Networks最新文献

英文中文

Contrastive learning for efficient anomaly detection in electricity load data

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-05 DOI: 10.1016/j.segan.2025.101639

Mohit Choubey, Rahul Kumar Chaurasiya, J.S. Yadav

Identifying irregularities in electricity load data is essential for maintaining dependable and effective power systems. Traditional approaches necessitate a significant amount of labeled data in order to achieve high accuracy, resulting in increased costs, and limited scalability. This paper introduces a feature extraction model based on contrastive learning, which greatly enhances the accuracy of anomaly detection for electricity load data. The model generates both positive and negative pairs after utilizing original input data sequences. This enables to learn complex similarities and differences. Through the utilization of a contrastive loss function, the aim is to minimize disparities between positive pairs and maximize the distances between negative pairs, resulting in the extraction of essential feature representations. The results demonstrate significant improvements enhancements such as accuracy rose from 69.85 % to 95.65 %, precision improved from 61.2 % to 96 %, recall increased from 74.5 % to 93 %, and the F1-score saw an improvement from 67.3 % to 94.6 %. The ROC-AUC score rose from 0.7286 to 0.9532, indicating better differentiation between normal and anomalous data. A paired t-test confirmed these gains with p-values well below 0.05, further validating the model’s effectiveness, while Cohen's d test validated the practical significance, indicating large effect sizes across all metrics. Furthermore, 95 % confidence intervals for the mean differences confirmed that the improvements are both statistically and practically meaningful. This approach not only improves detection accuracy but also reduces reliance on large labeled datasets, making it more scalable and cost-effective for real-world applications.

{"title":"Contrastive learning for efficient anomaly detection in electricity load data","authors":"Mohit Choubey, Rahul Kumar Chaurasiya, J.S. Yadav","doi":"10.1016/j.segan.2025.101639","DOIUrl":"10.1016/j.segan.2025.101639","url":null,"abstract":"<div><div>Identifying irregularities in electricity load data is essential for maintaining dependable and effective power systems. Traditional approaches necessitate a significant amount of labeled data in order to achieve high accuracy, resulting in increased costs, and limited scalability. This paper introduces a feature extraction model based on contrastive learning, which greatly enhances the accuracy of anomaly detection for electricity load data. The model generates both positive and negative pairs after utilizing original input data sequences. This enables to learn complex similarities and differences. Through the utilization of a contrastive loss function, the aim is to minimize disparities between positive pairs and maximize the distances between negative pairs, resulting in the extraction of essential feature representations. The results demonstrate significant improvements enhancements such as accuracy rose from 69.85 % to 95.65 %, precision improved from 61.2 % to 96 %, recall increased from 74.5 % to 93 %, and the F1-score saw an improvement from 67.3 % to 94.6 %. The ROC-AUC score rose from 0.7286 to 0.9532, indicating better differentiation between normal and anomalous data. A paired t-test confirmed these gains with p-values well below 0.05, further validating the model’s effectiveness, while Cohen's d test validated the practical significance, indicating large effect sizes across all metrics. Furthermore, 95 % confidence intervals for the mean differences confirmed that the improvements are both statistically and practically meaningful. This approach not only improves detection accuracy but also reduces reliance on large labeled datasets, making it more scalable and cost-effective for real-world applications.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101639"},"PeriodicalIF":4.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378244","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}

引用次数: 0

Optimal scheduling of combined air conditioner and fresh air system for price-based demand response

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-05 DOI: 10.1016/j.segan.2025.101637

Yu-Qing Bao , Xiao-Rui Song , Hui-Ling Su , Zhou-Chen Yu , Qing-He Sun , Zhong-Dong Wang

It is well known that energy management of air conditioners (ACs) is an important way of demand response (DR). The fresh air system (FAS), which is another common equipment in buildings, can effectively utilize cooling capacity of the outdoor air during the night to reduce indoor temperatures, achieving energy savings and efficiency. However, the combined scheduling of AC and FAS has received little attention. In this paper, a co-optimization scheduling method for combined AC and fresh air system (ACFAS) is proposed. Building upon the thermodynamic models of AC and FAS and considering the practical conditions, the main-power ON/OFF state, temperature set-point, and air volume level of the AC and FAS are selected as decision variables to establish a thermostatic control model. The thermostatic control model is then discretized and linearized to form a mixed-integer linear programming solvable format. Subsequently, taking into account electricity cost and comfort objectives, an optimization scheduling model is developed, allowing for the determination of the scheduling results of the main power ON/OFF state, temperature set-point, and air volume level. The testing results show that the co-optimization can utilize both AC and FAS for cooling the room temperature, and the energy efficiency can be improved.

{"title":"Optimal scheduling of combined air conditioner and fresh air system for price-based demand response","authors":"Yu-Qing Bao , Xiao-Rui Song , Hui-Ling Su , Zhou-Chen Yu , Qing-He Sun , Zhong-Dong Wang","doi":"10.1016/j.segan.2025.101637","DOIUrl":"10.1016/j.segan.2025.101637","url":null,"abstract":"<div><div>It is well known that energy management of air conditioners (ACs) is an important way of demand response (DR). The fresh air system (FAS), which is another common equipment in buildings, can effectively utilize cooling capacity of the outdoor air during the night to reduce indoor temperatures, achieving energy savings and efficiency. However, the combined scheduling of AC and FAS has received little attention. In this paper, a co-optimization scheduling method for combined AC and fresh air system (ACFAS) is proposed. Building upon the thermodynamic models of AC and FAS and considering the practical conditions, the main-power ON/OFF state, temperature set-point, and air volume level of the AC and FAS are selected as decision variables to establish a thermostatic control model. The thermostatic control model is then discretized and linearized to form a mixed-integer linear programming solvable format. Subsequently, taking into account electricity cost and comfort objectives, an optimization scheduling model is developed, allowing for the determination of the scheduling results of the main power ON/OFF state, temperature set-point, and air volume level. The testing results show that the co-optimization can utilize both AC and FAS for cooling the room temperature, and the energy efficiency can be improved.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101637"},"PeriodicalIF":4.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394796","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}

引用次数: 0

New tight expression of network radiality constraints using constant commodity flow equipped with the parent–child supply chain

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-04 DOI: 10.1016/j.segan.2025.101631

Ali Alizadeh , Moein Esfahani , Bo Cao , Innocent Kamwa , Minghui Xu

Preserving radiality is essential in distribution networks and Microgrid (MG) formation to ensure cost efficiency, reliability, and resiliency. However, maintaining radiality poses significant challenges due to the complexity of large-scale networks. Most existing models rely on Mixed-Integer Linear Programming (MILP) formulations, which suffer from low tightness, limiting their optimality and scalability. This paper addresses these limitations by introducing highly compact and tight radiality constraints designed to enhance computational performance and accuracy in reconfiguration and MG formation problems. The proposed approach is built on the novel Parent–Child Supply Chain (PCSC) framework, which, combined with a Constant Commodity Flow (CCF) model, ensures binary-like behavior for radiality variables without enforcing integer constraints. This innovation reduces the complexity of the problem, requiring binary variables only for line-switching decisions. Implementations of the model demonstrate significant improvements in computational performance, achieving a reduction of up to 72.61% in solution time and 14.7% in error margin compared to conventional MILP formulations. Moreover, the high tightness of the proposed constraints enables the use of second-order conic programming for highly accurate Distribution Power Flow (DistFlow) modeling. This advancement empowers operators to make realistic and informed decisions. The findings highlight the model’s potential to transform industry practices by offering a robust and scalable solution for network reconfiguration and MG formation.

{"title":"New tight expression of network radiality constraints using constant commodity flow equipped with the parent–child supply chain","authors":"Ali Alizadeh , Moein Esfahani , Bo Cao , Innocent Kamwa , Minghui Xu","doi":"10.1016/j.segan.2025.101631","DOIUrl":"10.1016/j.segan.2025.101631","url":null,"abstract":"<div><div>Preserving radiality is essential in distribution networks and Microgrid (MG) formation to ensure cost efficiency, reliability, and resiliency. However, maintaining radiality poses significant challenges due to the complexity of large-scale networks. Most existing models rely on Mixed-Integer Linear Programming (MILP) formulations, which suffer from low tightness, limiting their optimality and scalability. This paper addresses these limitations by introducing highly compact and tight radiality constraints designed to enhance computational performance and accuracy in reconfiguration and MG formation problems. The proposed approach is built on the novel Parent–Child Supply Chain (PCSC) framework, which, combined with a Constant Commodity Flow (CCF) model, ensures binary-like behavior for radiality variables without enforcing integer constraints. This innovation reduces the complexity of the problem, requiring binary variables only for line-switching decisions. Implementations of the model demonstrate significant improvements in computational performance, achieving a reduction of up to 72.61% in solution time and 14.7% in error margin compared to conventional MILP formulations. Moreover, the high tightness of the proposed constraints enables the use of second-order conic programming for highly accurate Distribution Power Flow (DistFlow) modeling. This advancement empowers operators to make realistic and informed decisions. The findings highlight the model’s potential to transform industry practices by offering a robust and scalable solution for network reconfiguration and MG formation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101631"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143239307","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}

引用次数: 0

Maximization of renewable generation hosting capacity in power transmission grids considering participation in energy and flexibility markets: A bilevel optimization model

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-04 DOI: 10.1016/j.segan.2025.101633

Milad Mousavi , Mahsa Azarnia , Jin Zhong , Sarah Rönnberg

Investment in renewable energy generation is integral to transitioning to sustainable power and energy systems. In this regard, the concept of hosting capacity (HC) is a useful tool for renewable generation investors and system operators to identify the maximum quantity of connected renewable resources without modification or strengthening of the grid. However, a considerable part of the extant research addresses the technical requirements of the problem in distribution systems while neglecting the transmission system and market constraints. Renewable generation uptake has reduced reliance on fossil fuel-based resources in the power sector, while also demonstrating capability to address the flexibility needs of the system. This paper proposes a market-based approach for maximizing renewable generation HC in transmission systems considering both energy and flexibility markets. To this end, a bilevel optimization problem is developed to study the profitability of maximizing renewable generation HC. In the upper-level problem, an HC maximization is developed with respect to the non-negative profitability of the new generation investment. The lower-level problem addresses social welfare maximization of energy and flexibility markets in which new renewable energy generation can participate. The formulations are transferred into a single-level mixed-integer linear programming (MILP) problem to avoid the nonlinearity of the bilevel model. The proposed model is applied to a 2-bus illustrative example and the IEEE 24-bus reliability test system (RTS). The results demonstrate that renewable generation units can improve their profitability by participating in the flexibility market and thereby increase the renewable generation HC from a market perspective.

{"title":"Maximization of renewable generation hosting capacity in power transmission grids considering participation in energy and flexibility markets: A bilevel optimization model","authors":"Milad Mousavi , Mahsa Azarnia , Jin Zhong , Sarah Rönnberg","doi":"10.1016/j.segan.2025.101633","DOIUrl":"10.1016/j.segan.2025.101633","url":null,"abstract":"<div><div>Investment in renewable energy generation is integral to transitioning to sustainable power and energy systems. In this regard, the concept of hosting capacity (HC) is a useful tool for renewable generation investors and system operators to identify the maximum quantity of connected renewable resources without modification or strengthening of the grid. However, a considerable part of the extant research addresses the technical requirements of the problem in distribution systems while neglecting the transmission system and market constraints. Renewable generation uptake has reduced reliance on fossil fuel-based resources in the power sector, while also demonstrating capability to address the flexibility needs of the system. This paper proposes a market-based approach for maximizing renewable generation HC in transmission systems considering both energy and flexibility markets. To this end, a bilevel optimization problem is developed to study the profitability of maximizing renewable generation HC. In the upper-level problem, an HC maximization is developed with respect to the non-negative profitability of the new generation investment. The lower-level problem addresses social welfare maximization of energy and flexibility markets in which new renewable energy generation can participate. The formulations are transferred into a single-level mixed-integer linear programming (MILP) problem to avoid the nonlinearity of the bilevel model. The proposed model is applied to a 2-bus illustrative example and the IEEE 24-bus reliability test system (RTS). The results demonstrate that renewable generation units can improve their profitability by participating in the flexibility market and thereby increase the renewable generation HC from a market perspective.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101633"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143239306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven and physically informed power grid dispatch decision-making method

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-04 DOI: 10.1016/j.segan.2025.101644

Kai Sun , Dahai Zhang , Jiye Wang , Wenbo Mao

This paper introduces an innovative approach, namely the Action Generation Network (AG-Net), designed for power system Security Constrained Economic Dispatch (SCED). In contrast to purely data-driven methodologies, our proposal incorporates a Physical Information Judgment Network (PIJ-Net), effectively integrating essential physical information into the model. This strategy simplifies the economic dispatch model's intricacies while facilitating the network's grasp of the model's underlying physical dynamics. The collaborative operation of these two networks is geared towards achieving highly accurate decision-making. Notably, experimental evaluations conducted on the SG-126 bus system demonstrate that our proposed method surpasses both model-based and neural network relaxed solutions. The results highlight the method's capacity to deliver more dependable and efficient dispatch decisions. This underscores the significance of marrying data-driven approaches with physical insights for enhanced performance in power system economic dispatch.

引用次数: 0

A generalized Nash-in-Nash bargaining solution to allocating energy loss and network usage cost of buildings in peer-to-peer trading market

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-01-30 DOI: 10.1016/j.segan.2025.101628

Yuanxing Xia , Yu Huang , Jicheng Fang

Although the peer-to-peer (P2P) energy market has emerged as a promising method to accommodate the distributed energy resources (DERs) on the demand side, the different stakeholders in the market make the energy loss allocation and network usage cost problems hard to solve. Considering the inconsistent interests of various market entities, we propose a generalized Nash-in-Nash bargaining (GNNB) model for the trading result, network usage cost, and energy loss allocation in the P2P market among buildings. We first establish the profit maximization models of distribution system operators (DSO) and building managers in P2P markets. A tripartite Nash bargaining model is developed to depict the negotiation among these entities. We then equivalently transform the Nash bargaining problem into two subproblems. A nested market-clearing algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the P2P energy market equilibrium with these bargaining results. We finally import two cases to verify the effectiveness of the GNNB solution. The heterogeneous network usage prices in the GNNB solution balance the interests of DSO and building managers. It can be concluded from the numerical results that the energy losses are allocated according to market participants’ trading amounts. Therefore, the negotiation result is fair. Our proposed model presents a fair framework to determine the network cost and energy loss allocation for the P2P energy market. It can be applied to optimize the trading result and energy loss in the local energy market project. All three parties will be satisfied with this welfare distribution.

{"title":"A generalized Nash-in-Nash bargaining solution to allocating energy loss and network usage cost of buildings in peer-to-peer trading market","authors":"Yuanxing Xia , Yu Huang , Jicheng Fang","doi":"10.1016/j.segan.2025.101628","DOIUrl":"10.1016/j.segan.2025.101628","url":null,"abstract":"<div><div>Although the peer-to-peer (P2P) energy market has emerged as a promising method to accommodate the distributed energy resources (DERs) on the demand side, the different stakeholders in the market make the energy loss allocation and network usage cost problems hard to solve. Considering the inconsistent interests of various market entities, we propose a generalized Nash-in-Nash bargaining (GNNB) model for the trading result, network usage cost, and energy loss allocation in the P2P market among buildings. We first establish the profit maximization models of distribution system operators (DSO) and building managers in P2P markets. A tripartite Nash bargaining model is developed to depict the negotiation among these entities. We then equivalently transform the Nash bargaining problem into two subproblems. A nested market-clearing algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the P2P energy market equilibrium with these bargaining results. We finally import two cases to verify the effectiveness of the GNNB solution. The heterogeneous network usage prices in the GNNB solution balance the interests of DSO and building managers. It can be concluded from the numerical results that the energy losses are allocated according to market participants’ trading amounts. Therefore, the negotiation result is fair. Our proposed model presents a fair framework to determine the network cost and energy loss allocation for the P2P energy market. It can be applied to optimize the trading result and energy loss in the local energy market project. All three parties will be satisfied with this welfare distribution.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101628"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143328124","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}

引用次数: 0

Distributed energy management coordinating energy scheduling and trading in transactive energy market

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-01-25 DOI: 10.1016/j.segan.2025.101629

Dewen Liu , Zhao Luo , Junkai Liang , Hua Wang , Jiahao Li , Yujun Yin , Yang Yu , Hesen Liang

The emergence of prosumers that is capable of both generating and consuming energy, coupled with advancements in communication technology, is driving the traditional energy market towards decentralization. Consequently, a novel transactive energy market (TEM) has evolved, enabling prosumers to trade energy in a distributed manner. The TEM allows prosumers to trade transactive energy in a distributed manner. To address the challenges associated with TEM, this study proposes a distributed energy management framework that coordinates energy scheduling and trading. In this model, energy scheduling and energy trading were coordinated in TEM. Specifically, a consensus-based energy supply and demand matching algorithm is proposed to balance local energy trading. Next, based on the matching results of the energy supply and demand in the TEM, a bilateral peer-to-peer energy trading algorithm based on the alternating direction method of multipliers (ADMM) is proposed to support sellers in selecting trading partners independently according to the bilateral location. This measure reduced TEM operating costs. To improve ADMM convergence, an iteration-based adaptive penalty parameter ADMM (IAPP-ADMM) algorithm is proposed. Numerical simulations based on a 14-node test system prove the effectiveness and rationality of the proposed framework.

{"title":"Distributed energy management coordinating energy scheduling and trading in transactive energy market","authors":"Dewen Liu , Zhao Luo , Junkai Liang , Hua Wang , Jiahao Li , Yujun Yin , Yang Yu , Hesen Liang","doi":"10.1016/j.segan.2025.101629","DOIUrl":"10.1016/j.segan.2025.101629","url":null,"abstract":"<div><div>The emergence of prosumers that is capable of both generating and consuming energy, coupled with advancements in communication technology, is driving the traditional energy market towards decentralization. Consequently, a novel transactive energy market (TEM) has evolved, enabling prosumers to trade energy in a distributed manner. The TEM allows prosumers to trade transactive energy in a distributed manner. To address the challenges associated with TEM, this study proposes a distributed energy management framework that coordinates energy scheduling and trading. In this model, energy scheduling and energy trading were coordinated in TEM. Specifically, a consensus-based energy supply and demand matching algorithm is proposed to balance local energy trading. Next, based on the matching results of the energy supply and demand in the TEM, a bilateral peer-to-peer energy trading algorithm based on the alternating direction method of multipliers (ADMM) is proposed to support sellers in selecting trading partners independently according to the bilateral location. This measure reduced TEM operating costs. To improve ADMM convergence, an iteration-based adaptive penalty parameter ADMM (IAPP-ADMM) algorithm is proposed. Numerical simulations based on a 14-node test system prove the effectiveness and rationality of the proposed framework.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101629"},"PeriodicalIF":4.8,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177822","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}

引用次数: 0

Increasing hosting capacity of low-voltage distribution network using smart charging based on local and dynamic capacity limits

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-01-22 DOI: 10.1016/j.segan.2025.101626

Marc Cañigueral , Rick Wolbertus , Joaquim Meléndez

While the Municipality of Amsterdam wants to expand the electric vehicle public charging infrastructure to reach carbon-neutral objectives, the Distribution System Operator cannot allow new charging stations where low-voltage transformers are reaching their maximum capacity. To solve this situation, a smart charging project called Flexpower is being tested in some districts. Charging power is limited during peak times to avoid grid congestion and, therefore, enable the expansion of charging infrastructure while deferring grid investments. This work simulates the implementation of the Flexpower strategy with high penetration of electric vehicles, considering dynamic and local power limits, to assess the impact on both the satisfaction of electric vehicle users and the business model of the Charging Point Operator. A stochastic approach, based on Gaussian Mixture Models, has been used to model different profiles of electric vehicle users using data from the Amsterdam public electric vehicle charging infrastructure. Several key performance indicators have been defined to assess the impact of such charging limitations on the different stakeholders. The results show that, while Amsterdam’s existing public charging infrastructure can host just twice the current electric vehicle demand, the application of Flexpower will enable the growth in charging stations without requiring grid upgrades. Even with 7 times more charging sessions, Flexpower could provide a power peak reduction of 57% while supplying 98% of the total energy required by electric vehicle users.

{"title":"Increasing hosting capacity of low-voltage distribution network using smart charging based on local and dynamic capacity limits","authors":"Marc Cañigueral , Rick Wolbertus , Joaquim Meléndez","doi":"10.1016/j.segan.2025.101626","DOIUrl":"10.1016/j.segan.2025.101626","url":null,"abstract":"<div><div>While the Municipality of Amsterdam wants to expand the electric vehicle public charging infrastructure to reach carbon-neutral objectives, the Distribution System Operator cannot allow new charging stations where low-voltage transformers are reaching their maximum capacity. To solve this situation, a smart charging project called Flexpower is being tested in some districts. Charging power is limited during peak times to avoid grid congestion and, therefore, enable the expansion of charging infrastructure while deferring grid investments. This work simulates the implementation of the Flexpower strategy with high penetration of electric vehicles, considering dynamic and local power limits, to assess the impact on both the satisfaction of electric vehicle users and the business model of the Charging Point Operator. A stochastic approach, based on Gaussian Mixture Models, has been used to model different profiles of electric vehicle users using data from the Amsterdam public electric vehicle charging infrastructure. Several key performance indicators have been defined to assess the impact of such charging limitations on the different stakeholders. The results show that, while Amsterdam’s existing public charging infrastructure can host just twice the current electric vehicle demand, the application of Flexpower will enable the growth in charging stations without requiring grid upgrades. Even with 7 times more charging sessions, Flexpower could provide a power peak reduction of 57% while supplying 98% of the total energy required by electric vehicle users.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101626"},"PeriodicalIF":4.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent reinforcement learning for decentralized control of shared battery energy storage system in residential community

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-01-22 DOI: 10.1016/j.segan.2025.101627

Amit Joshi , Massimo Tipaldi , Luigi Glielmo

This article proposes a data-driven decentralized control scheme for a battery energy storage system, shared among residential PV households characterized by their respective uncontrollable demand and PV generation. The households are connected to the grid via the point of common coupling and are accordingly billed by the utility company. We firstly translate the decentralized control objective into a multi-agent reinforcement learning (MARL) problem by modelling the interaction between the agents and their environment as a Markov Game. Thereafter, we present the novel Distributed Subgradient

Q -

learners (DSQL) algorithm based on the localization of the Hyper

- Q

function and the coordination among the learning agents connected via a communication network. The proposed algorithm holds merit in addressing the typical key-aspects of MARL algorithms, i.e., scalability, privacy and fairness. Finally, we perform numerical simulations by using real historical demand, PV generation and electricity tariff data and highlight the key advantages of the proposed algorithm w.r.t. the state-of-art, in terms of economic savings and key-performance indicators, such as peak-to-average ratio, valley-to-average ratio and root-mean-squared-deviation.

{"title":"Multi-agent reinforcement learning for decentralized control of shared battery energy storage system in residential community","authors":"Amit Joshi , Massimo Tipaldi , Luigi Glielmo","doi":"10.1016/j.segan.2025.101627","DOIUrl":"10.1016/j.segan.2025.101627","url":null,"abstract":"<div><div>This article proposes a data-driven decentralized control scheme for a battery energy storage system, shared among residential PV households characterized by their respective uncontrollable demand and PV generation. The households are connected to the grid via the point of common coupling and are accordingly billed by the utility company. We firstly translate the decentralized control objective into a multi-agent reinforcement learning (MARL) problem by modelling the interaction between the agents and their environment as a Markov Game. Thereafter, we present the novel Distributed Subgradient <span><math><mrow><mi>Q</mi><mo>−</mo></mrow></math></span>learners (DSQL) algorithm based on the localization of the Hyper<span><math><mrow><mo>−</mo><mi>Q</mi></mrow></math></span> function and the coordination among the learning agents connected via a communication network. The proposed algorithm holds merit in addressing the typical key-aspects of MARL algorithms, i.e., scalability, privacy and fairness. Finally, we perform numerical simulations by using real historical demand, PV generation and electricity tariff data and highlight the key advantages of the proposed algorithm w.r.t. the state-of-art, in terms of economic savings and key-performance indicators, such as peak-to-average ratio, valley-to-average ratio and root-mean-squared-deviation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101627"},"PeriodicalIF":4.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177812","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}

引用次数: 0

Efficient coordinated VAR planning for enhancing voltage stability of wind-penetrated power system using hybrid surrogate technique

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-01-22 DOI: 10.1016/j.segan.2025.101620

Qianggang Wang, Xinying Zheng, Yuan Chi, Jinsheng Guo, Xin Zhou

The rising integration of renewable power generation and the corresponding complex dynamics of the system pose challenges to the dynamic analysis of modern power systems. To reduce computational costs and enhance efficiency, a cost-effective hybrid surrogate model is adopted to approximate complex electromechanical transient (EMT) models for the improvement of voltage stability in a coordinated VAR planning problem. Firstly, the framework of an efficient planning model based on hybrid surrogate model is introduced. The hybrid model integrates the advantages of Polynomial Chaos Expansion and Kriging techniques. To further reduce the size of training samples required by the data-driven hybrid-surrogate-model-based approach without a compromise in the accuracy, and to improve the efficiency of solving the optimization problem, an efficient input selection strategy is proposed for the surrogate model and outputs are also designed to reduce the complexity. Finally, the proposed method is verified in a reactive power source deployment problem of wind-penetrated power systems to verify the effectiveness on an IEEE 39-bus system with wind farms.

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