Sustainable Energy Grids & Networks最新文献_第2页

Design of P2P coordinative transaction mechanism considering low-carbon preference of multiple prosumers in regional electricity market

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-20 DOI: 10.1016/j.segan.2025.101661

Yue Guan, Qiang Hou, Shiquan Wang

The market-oriented trading of distributed energy with the participation of multiple prosumers has gradually become a solution to promote the consumption of distributed energy. The transaction preferences of prosumers have a significant impact on market transaction efficiency. How to design a market transaction mechanism that considers the transaction preferences of prosumers to promote efficiency improvement has become a current research hotspot. In this paper: firstly, a regional electricity market trading framework with the participation of multiple prosumers at the distribution network level was established; Secondly, the internal resources of prosumers were analyzed, and the mathematical models of their output units and loads were constructed; Thirdly, a peer-to-peer (P2P) transaction mechanism for regional electricity market was designed based on the combinatorial double auction theory, which takes into account different energy demands and low-carbon preferences of prosumers, among them, with the goal of minimizing operating costs during the scheduling cycle of prosumers, the internal resources were coordinated to achieve balance and their energy supply - demand plans were obtained (as their bidding electricity quantity), and on the basis of considering the low-carbon preferences, a Supply Function Equilibrium (SFE) model was adopted to construct their bidding price strategy (as their bidding electricity price). Third party auctioneer was used to coordinate P2P transactions between prosumers, and different bidding types were introduced to accurately express prosumers´ different energy demands during the trading process. Finally, the effectiveness and feasibility of the proposed P2P transaction mechanism were verified through numerical examples.

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引用次数: 0

Critical component analysis of cyber-physical power systems in cascading failures using graph convolutional networks: An energy-based approach

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-19 DOI: 10.1016/j.segan.2025.101653

Sajedeh Soleimani, Ahmad Afshar, Hajar Atrianfar

Power systems, with increasing integration into communication networks, have evolved to become complex and interdependent cyber–physical power systems that are highly vulnerable to cascading failures. These failures, due to their propagation through the cyber and physical networks, often lead to severe disruptions. We employ improved percolation theory to model cascading failures triggered by malware cyber-attacks. Addressing the vulnerability of CPPS requires a comprehensive analysis that spans both the structural and functional dimensions of CPPS. This paper introduces a novel framework for vulnerability assessment in CPPS using Graph Convolutional Networks (GCN). Our approach captures the topological complexities and dynamic characteristics of CPPS, incorporating the entropy of potential energy of power system as a new feature to predict and analyze failure propagation. Through Layer-wise Relevance Propagation (LRP), we subsequently quantify the influence of potential energy on system vulnerabilities. Critical components are identified by using LRP scores and an entropy weighting method (EWM). Simulation results based on the cyber–physical IEEE 39-bus and IEEE RTS-96 power systems as test cases, demonstrate the model’s efficacy in identifying vulnerable nodes and branches and highlight the significant role of potential energy in cascading failures. This framework provides a comprehensive approach for real-time vulnerability assessments and resilience enhancement in CPPS.

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引用次数: 0

Optimizing electric vehicle battery health monitoring: A resilient ensemble learning approach for state-of-health prediction

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-19 DOI: 10.1016/j.segan.2025.101655

Vankamamidi S. Naresh, P.N.S. Gayathri, P.Baby Tejaswi, P. Induja, Ch Rohith Reddy, Y.Sai Sudheer

State of Health (SoH) prediction is critical for optimizing electric vehicle (EV) battery performance and longevity. This study proposes an Ensemble of Ensemble Models (EEMs) framework to enhance SoH prediction accuracy by combining ensemble learning methods—Random Forests, Gradient Boosting, and AdaBoost—using a stacking-based meta-learning approach. The method captures complex patterns in key input features such as voltage, temperature, and charge-discharge cycles. The approach was tested using a Li-ion battery dataset, with evaluation metrics including MSE, RMSE and R-squared. Results demonstrate that EEMs with 99.9 accuracy and nearly error-free predictions (RMSE of 0.00000025), validate the importance of advanced ensemble techniques in optimizing SoH prediction and outperform individual and conventional ensemble models, providing accurate and reliable SoH estimates. This framework offers practical implications for improving battery management, extending battery lifespan, and promoting energy sustainability in EV systems.

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引用次数: 0

Coordinated routing optimization and charging scheduling in a multiple-charging station system: A strategic bilevel multi-objective programming

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-19 DOI: 10.1016/j.segan.2025.101659

Pegah Alaee , Junyan Shao , Július Bemš , Josep M. Guerrero

Effectively managing EV charging queues not only alleviates traffic congestion in high-demand areas but also improves user satisfaction by minimizing waiting times. This framework enhances overall system efficiency by better distributing the concentration of EVs during peak periods. This research investigates collaborative mechanisms from the perspectives of various stakeholders, including charging stations (CSs) and EVs, to optimize the charging process. A route optimization model is employed to direct EVs toward the most suitable CSs, followed by the introduction of two scheduling models: (1) a social welfare maximization model and (2) a game-theoretic iterative framework. These models aim to optimize EV charging locations while increasing CS profitability. EVs scheduling is performed using a mixed-integer non-linear programming (MINLP) approach, offering critical insights into its applicability across different scenarios. The numerical results demonstrate that coordinated EV scheduling substantially enhances the operational efficiency of E-mobility systems in both centralized and decentralized configurations. Compared to uncoordinated scheduling, total profits for CSs are 42 % higher for Test System 1 and 39 % higher for Test System 2. EV owners’ costs decrease by 47 % in the social welfare model and 32 % in the game-based model for Test System 1. In Test System 2, cost reductions are 12 % and 7 % for the social welfare and game-based models, respectively. Although power transactions with the market are slightly higher in the social welfare model, the game-based model demonstrates a more efficient distribution of EVs across charging stations, especially in Test System 2, resulting in a more balanced system and optimized resource allocation.

有效管理电动汽车充电队列不仅能缓解高需求地区的交通拥堵，还能通过尽量缩短等待时间提高用户满意度。这一框架通过在高峰期更好地分配电动汽车的集中，提高了整体系统效率。本研究从充电站（CS）和电动汽车等各利益相关方的角度出发，研究了优化充电过程的协作机制。研究采用了一个路线优化模型来引导电动汽车驶向最合适的 CS，随后引入了两个调度模型：（1）社会福利最大化模型和（2）博弈论迭代框架。这些模型旨在优化电动汽车充电地点，同时提高 CS 的盈利能力。电动汽车调度采用混合整数非线性编程（MINLP）方法，为其在不同场景下的适用性提供了重要见解。数值结果表明，无论是集中式还是分散式配置，协调的电动汽车调度都能大幅提高电动汽车系统的运行效率。与非协调调度相比，CS 的总利润在测试系统 1 中提高了 42%，在测试系统 2 中提高了 39%。在社会福利模型中，电动汽车所有者的成本在测试系统 1 中降低了 47%，在基于游戏的模型中降低了 32%。在测试系统 2 中，社会福利模式和游戏模式的成本分别降低了 12% 和 7%。虽然在社会福利模型中，与市场的电力交易略高，但基于博弈的模型表明，电动汽车在充电站的分布更有效，尤其是在测试系统 2 中，从而使系统更平衡，资源配置更优化。

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引用次数: 0

Integrated stochastic reserve estimation and MILP energy planning for high renewable penetration: Application to 2050 South African energy system

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-18 DOI: 10.1016/j.segan.2025.101650

Enrico Giglio , Davide Fioriti , Munyaradzi Justice Chihota , Davide Poli , Bernard Bekker , Giuliana Mattiazzo

The energy transition imposes a shift towards renewable energy sources, and the integration of variable ones introduces significant risks to power system stability. Variable renewable energy sources are mostly unpredictable and can provide limited spare capacity to compensate for imbalance in demand and supply. To meet system adequacy and reliability requirements, the power system is operated with different types of reserve margins to ensure the availability of spare capacity at various time scales. However, despite existing guidelines to operate the current system, limited methodologies have been proposed to estimate reserve requirements for future power systems with high penetration of renewables, including their integration into planning tools. In this study, a comprehensive methodology is proposed to estimate the least-cost power system design which include an endogenous stochastic model for estimating reserve requirements interfaced to a Mixed-Integer Linear Programming model. The proposed stochastic reserve estimation model incorporates generator tripping events, renewable energy variability, and ramping characteristics of the residual demand, extending ENTSO-E guidelines to accommodate future scenarios with high penetration of renewable energy sources. Furthermore, a non-linear parametric function is trained to represent the results of the stochastic reserve estimation model and then integrated into an optimization model to plan future power systems, using an iterative approach. The methodology is validated on the current South African power system. The results indicate the model’s effectiveness in optimizing reserve requirements, showing substantial benefits in including storage and other renewable energy technologies to meet future energy demands, while reducing carbon emissions and enhancing grid reliability.

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引用次数: 0

DSO-prosumers cooperative scheduling approach considering multi-timescale peer-to-peer transactions of electricity and flexibility resources

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-17 DOI: 10.1016/j.segan.2025.101632

Kang Wang , Chengfu Wang , Jianwei Dai , Shuang Dong , Yongling Cui , Guoying Wang

The rapid evolution of peer-to-peer (P2P) transaction mechanisms has facilitated end-use prosumers in energy sharing to enhance energy utilization and address uncertainties in renewable energy sources (RES). However, the lack of coordination across different timescales and heterogeneous distributed resources results in potential economic losses. To this end, a cooperative scheduling approach considering multi-timescale P2P transactions is proposed. Firstly, multi-timescale P2P transactions mechanism is proposed to coordinate the heterogeneous resources between prosumers. In day-ahead stage, prosumers engage in electricity transactions using expected output of RES. In intraday stage, flexible resources are traded among prosumers to mitigate prediction deviation of RES. Meanwhile, the Nash bargaining theory is introduced to allocate the interests. Then, to determine reasonable flexibility requirement in intraday stage, a two-side chance constrained economic dispatch (TS-CCED) model is proposed, in which DSO can set the reference requirement interval at given confidence level to balance the economy and safety of system operation. Finally, to reduce the computational complexity, the Gaussian mixture model is applied to convert the TS-CCED model into a convex optimization problem with guaranteed accuracy. Case study based on the IEEE 33-bus system and IEEE-123 bus system verifies the effectiveness of the proposed method.

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引用次数: 0

Enhanced microgrid reliability through software-defined networking and extended horizon predictive control

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-14 DOI: 10.1016/j.segan.2025.101635

Ricardo Pérez , Marco Rivera , Baldomero Araya , Juan S. Gómez , Yamisleydi Salgueiro , Carlos Restrepo , Patrick Wheeler , Minglei You , Mark Sumner

The dynamic nature of power systems combined with the need for low-latency and loss-tolerant communications, presents significant challenges to maintaining system reliability and resiliency. This paper proposes a novel integration of Finite Control Set Model-based Predictive Control with an extended prediction horizon and Software Defined Networked to address the resiliency problem and voltage/frequency deviations associated with traditional hierarchical microgrid. The communication framework integrates Software Defined Networked as a set of microservices distributed across local controllers and improved system reliability under communication constraints. The secondary control considers the variability of communication latency and packet loss to adjust the shared reference based on the spatial and temporal correlation. The microgrid is subjected to four test scenarios to analyze the impact of communications on distributed generation, plug-and-play capacity and load variations. The proposed control framework significantly improves system performance, achieving a 0.2–0.3 s recovery time, 0.05 s communication latency, and maintaining stability with up to 60% packet loss. Compared to hierarchical methods, it reduces recovery time by up to 90%, frequency deviation by up to 80%, and enhances power sharing and coordination between distributed generators. This method addresses the problem of low dynamic response of control strategies during disturbances, allowing the implementation of new, reliable and resilient hierarchical microgrids.

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引用次数: 0

Local market-aware optimal allocation of energy storage systems considering price fairness in power distribution networks

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-13 DOI: 10.1016/j.segan.2025.101648

Lu Wang, Matthieu Jacobs, Pål Forr Austnes, Mario Paolone

The increasing integration of Distributed Energy Resources (DERs) within power distribution grids introduces uncertainties that can result in control and operation issues such as line congestions, reduced voltage quality and increased grid imbalance cost. Existing research tackles these issues through topology reconfiguration or reinforcement of existing assets, such as lines and transformers, as well as allocation of new assets. However, these strategies may lead to an inefficient allocation of financial and human resources, since they solve specific optimization problems without a holistic view of the infrastructure and stakeholders involved. In this respect, this paper presents a stochastic two-stage local market-aware Energy Storage Systems (ESSs) allocation model which aims at optimally siting and sizing ESSs within a fair local market environment, thereby enhancing the effective activation of local resource flexibility. First, price fairness is defined in terms of quality of experience (QoE), taking into account the characteristics of the local distribution network. Under the proposed price fairness condition, the site and size of ESSs are determined by a planning stage. With this optimal allocation of ESSs, the operation stage makes use of a local market cleared based on the Augmented Relaxed Optimal Power Flow (AROPF) and primal–dual method to maximize social welfare. To solve the resulting Mixed-Integer Second-Order Cone Programming (MISOCP) problem, the Benders decomposition approach is applied. Case studies conducted on the IEEE 33-bus and Lausanne 193-bus networks validate the model’s effectiveness of investment utilization, local flexible resources activation and local market fairness. Compared to the business-as-usual model, the proposed approach achieves cost reductions of up to 23.6%. Additionally, the ABD algorithm exhibits strong scalability, solving the 193-bus system in just four times the duration required for the IEEE 33-bus case.

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引用次数: 0

Low-carbon scheduling of mobile energy storage in distribution networks based on an equivalent reconfiguration method

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-12 DOI: 10.1016/j.segan.2025.101649

Weiqing Sun, Yingzhi Peng, Haibing Wang, Yankun Qiao

Under the context of low-carbon power systems, the integration of high-penetration renewable energy and mobile energy storage systems (MESS) presents new challenges for distribution network scheduling, primarily in the coupling of power and transportation networks and the complexity of allocating users' carbon emission responsibilities. To address these challenges, this study proposes a bi-level optimization model that combines demand response mechanisms and carbon flow theory for the low-carbon scheduling of MESS. The upper-level model optimizes the global scheduling strategy of distribution network operators, while the lower-level model captures the dynamic demand response behavior of users, achieving collaborative optimization among multiple stakeholders. Using an equivalent reconfiguration method, the coupling issue between the power grid and transportation network is transformed into a pure distribution network problem. Furthermore, carbon flow theory and the Shapley value method are employed to analyze carbon emission distribution and allocate carbon responsibility on the load side. Simulation results based on the IEEE-33 node distribution network and a simple transportation network show that the proposed model can reduce system carbon emissions by 21.14 %, lower user costs by 5.2 %, and increase operator revenue by 10.21 %. These findings validate the model’s ability to balance economic benefits and low-carbon operational goals, providing a practical and effective solution for the optimal scheduling of distribution networks with high renewable energy penetration.

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引用次数: 0

Distributed control of flexible assets in distribution networks considering personal usage plans

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-02-11 DOI: 10.1016/j.segan.2025.101638

Tongmao Zhang, Alessandra Parisio

In this article, a distributed Mixed-Integer Linear Programming (MILP)-based control scheme is proposed to coordinate flexible assets as a Virtual Storage Plant (VSP) for providing flexibility services to distribution networks. The VSP aggregates flexible assets, such as Heating, Ventilation, and Air Conditioning (HVAC) systems and battery storage systems, while considering their individual needs. It tracks a time-varying signal instructed by the Distribution System Operator (DSO) within the required time to support the host network. HVAC systems are treated as virtual batteries, considering indoor temperature comfort, while battery storage systems are managed according to users’ usage plans. To accurately model the flexible assets, binary variables are required, thus formulating an MILP problem. The MILP problem is then incorporated into a Model Predictive Control (MPC) scheme to manage system constraints. The formulated MILP-based MPC problem is finally solved using an accelerated primal decomposition method in a distributed fashion. Unlike existing distributed algorithms commonly proposed in the literature, the algorithm presented in this article is specifically developed for large-scale MILP problems, with guarantees of constraint satisfaction. The effectiveness of the proposed control scheme is evaluated through several case studies, which demonstrate that it ensures acceptable tracking precision. Furthermore, it supports plug-and-play functionality and enhances scalability.

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引用次数: 0