Sustainable Energy Grids & Networks最新文献_第4页

P2P modeling formation coalitions and prosumers participation based on dynamic pricing algorithm and line congestion consideration 基于动态定价算法和考虑线路拥塞的P2P建模形成联盟和产消参与

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-08 DOI: 10.1016/j.segan.2025.102099

Zhen Ji, Wei Sun, Bo Yan, BoHao Sun

The rapid proliferation of distributed energy resources such as photovoltaic systems, wind turbines, battery energy storage systems, and electric vehicles has transformed residential microgrids into active, transactive energy communities. However, realizing fair, efficient, and scalable peer-to-peer energy sharing under stochastic household demand, dynamic pricing, and network constraints remains a major challenge. This study develops a hybrid centralized-decentralized peer-to-peer energy-sharing framework that models heterogeneous household prosumers five distinct types equipped with photovoltaic, wind turbine, battery energy storage, and electric vehicles within a demand-supply environment. The model integrates a home energy management system with dynamic pricing derived from the balance between Feed-in Tariff and Real-Time Pricing, augmented by congestion and degradation costs to ensure market fairness. A heuristic battery control algorithm and a two-level robust optimization based on the MILP and column-and-constraint generation method are implemented to coordinate energy exchanges between prosumers and the grid. Electric vehicles are treated as active market agents capable of bidirectional energy trading to enhance grid flexibility. Case studies involving 30, 120, and 240 households simulated using MATLAB to compare three operational scenarios without P2P trading, hybrid centralized-decentralized peer to peer trading, and large-scale community participation. The findings indicate that the proposed framework increases household self-consumption rates by 64.22 %, decreases grid energy imports by 52.5 %, and elevates prosumer revenue by 41.6 %, while preserving network stability and fairness. Hybrid market structure efficiently reduces peak energy costs, ensures strong local balance, and offers scalable basis for resilient, consumer-driven energy communities.

分布式能源的迅速扩散，如光伏系统、风力涡轮机、电池储能系统和电动汽车，已经将住宅微电网转变为活跃的、可交互的能源社区。然而，在随机家庭需求、动态定价和网络约束下实现公平、高效和可扩展的点对点能源共享仍然是一个重大挑战。本研究开发了一个混合的集中式-分散式点对点能源共享框架，该框架模拟了不同类型的家庭产消者，在供需环境中配备了光伏、风力涡轮机、电池储能和电动汽车。该模型集成了一个家庭能源管理系统，其动态定价来源于上网电价和实时定价之间的平衡，并通过拥堵和退化成本来增强，以确保市场公平。采用启发式电池控制算法和基于MILP和列约束生成法的两级鲁棒优化算法来协调产消者与电网之间的能量交换。电动汽车被视为活跃的市场主体，能够进行双向能源交易，以增强电网的灵活性。使用MATLAB模拟了涉及30户、120户和240户家庭的案例研究，比较了没有P2P交易、集中式和分散式混合点对点交易和大规模社区参与的三种操作场景。研究结果表明，该框架在保持电网稳定性和公平性的前提下，提高了家庭自消费率64.22% %，减少了电网能源进口52.5 %，提高了产消收入41.6 %。混合市场结构有效地降低了峰值能源成本，确保了强大的本地平衡，并为弹性、消费者驱动的能源社区提供了可扩展的基础。

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引用次数: 0

Reinforcement learning-based optimal scheduling strategy for charging and discharging of electric vehicle clusters 基于强化学习的电动汽车集群充放电最优调度策略

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-08 DOI: 10.1016/j.segan.2025.102087

Baoqiang Lao , Xu Zhang , Didi Liu , Yanli Zou

The increasing integration of clustered electric vehicles (EVs) and intermittent renewable energy sources (RES) into power systems presents significant operational challenges to smart grids, notably heightened load fluctuations and reduced grid stability. This paper proposes an intelligent charging-discharging optimization model for EV clusters by leveraging their dual load-storage and spatial transfer characteristics, with EV aggregators (EVAs) acting as the coordinating entity. The model incorporates dynamic electricity pricing, the stochastic nature of RES, the temporal coupling of EV charging constraints, and battery aging effects. To address this stochastic optimization problem, a model-free reinforcement learning-based approximate state Q-learning algorithm is proposed. Through environmental interactions and reward feedback mechanisms, this algorithm enables EVAs to intelligently control the charging and discharging behaviors of EV clusters to dynamically respond to real-time electricity price fluctuations and RES output uncertainties, and ultimately mitigate operational stress on the power grid. While ensuring that the charging demands of EV owners are met, the proposed method achieves coordinated operation among the smart grid, EVAs, and end-users through optimized power scheduling strategies. Finally, comparative experiments with existing algorithms verify that the proposed method has significant advantages in reducing the charging costs of EV users and improving the operational profits of EVAs. Simulation results demonstrate that the proposed algorithm exhibits superior performance: under this algorithm, the monthly service profit of the EVA increases by 9.68 % compared with the unidirectional scheduling algorithm and by 22.97 % compared with the greedy algorithm.

集束式电动汽车（ev）和间歇性可再生能源（RES）日益融入电力系统，给智能电网带来了重大的运营挑战，特别是负荷波动加剧和电网稳定性降低。本文以电动汽车集散器为协调主体，利用电动汽车集群的双重负荷存储和空间转移特性，提出了一种电动汽车集群充放电智能优化模型。该模型考虑了动态电价、可再生能源的随机性、电动汽车充电约束的时间耦合以及电池老化效应。为了解决这一随机优化问题，提出了一种基于无模型强化学习的近似状态q学习算法。该算法通过环境交互和奖励反馈机制，实现电动汽车集群充放电行为的智能控制，以动态响应实时电价波动和可再生能源输出的不确定性，最终缓解电网的运行压力。该方法在保证电动汽车车主充电需求的同时，通过优化的电力调度策略，实现智能电网、EV和终端用户之间的协调运行。最后，通过与现有算法的对比实验，验证了该方法在降低电动汽车用户充电成本和提高电动汽车运营利润方面具有显著优势。仿真结果表明，该算法具有较好的性能，EVA的月服务利润比单向调度算法提高9.68%，比贪婪调度算法提高22.97%。

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引用次数: 0

Assessing the role of flexible technologies in the Greek wholesale electricity market under National Energy and Climate Plan targets 根据国家能源和气候计划目标，评估灵活技术在希腊批发电力市场中的作用

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-08 DOI: 10.1016/j.segan.2025.102090

Panagiota T. Kyrimlidou , Christos K. Simoglou , Pandelis N. Biskas

This paper investigates the impact that the penetration of flexible resources, such as battery energy storage systems, cross-border capacity and the application of load shifting, may have on the Greek wholesale electricity market operation under the main provisions of the recent National Energy and Climate Plan (NECP). A thorough scenario-based analysis of the Greek day-ahead and real-time balancing markets for the year 2030 has been conducted using a specialized market simulation software under finest time granularity to evaluate critical market indicators, including the electricity generation mix, RES curtailments, wholesale market prices, revenues/profits of market participants and CO₂ emissions. Simulation results underscore the significant role that the adopted flexibility resources are expected to bring in the Greek electricity market and power system operation, since they are expected to effectively reduce RES curtailments up to 50 %, reduce conventional gas-fired units’ generation volumes up to 8 % and increase average day-ahead market clearing prices up to 6 %. The combined deployment of all examined flexibility options may improve the environmental footprint of the Greek power system by reducing the annual CO₂ emissions up to 2.9–3.8 %. The findings of this study also highlight the strategic importance of developing balanced flexibility portfolios that combine domestic flexibility resources with regional interconnection upgrades, while providing targeted financial support for newly invested, capital-intensive assets whose market revenues alone cannot ensure their economic viability.

本文研究了在最近的国家能源和气候计划（NECP）的主要规定下，电池储能系统、跨境容量和负荷转移应用等灵活资源的渗透可能对希腊批发电力市场运营产生的影响。对希腊2030年的日前和实时平衡市场进行了全面的基于场景的分析，使用专业的市场模拟软件，在最精细的时间粒度下评估关键市场指标，包括发电组合、可再生能源削减、批发市场价格、市场参与者的收入/利润和二氧化碳排放。模拟结果强调了所采用的灵活性资源有望在希腊电力市场和电力系统运行中发挥的重要作用，因为它们有望有效减少高达50% %的可再生能源削减，将传统燃气发电机组的发电量减少高达8% %，并将平均日前市场结算价格提高高达6% %。所有被检查的灵活性选项的联合部署可能会通过减少每年2.9 - 3.8% %的二氧化碳排放量来改善希腊电力系统的环境足迹。本研究的结果还强调了发展平衡的灵活性投资组合的战略重要性，将国内灵活性资源与区域互联互通升级相结合，同时为新投资的资本密集型资产提供有针对性的金融支持，这些资产仅靠市场收入无法确保其经济可行性。

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引用次数: 0

Robust optimization of electric bus charging-operation scheduling considering charging discrepancy 考虑充电差异的电动客车充电调度鲁棒优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102084

Zhouzuo Wang , Xinghua Hu , Jiahao Zhao , Fang Liu , Lanping Si

Optimizing electric bus (EB) scheduling is crucial for advancing urban bus systems and reducing carbon emissions. In this study, we establish an EB scheduling model using a robust optimization paradigm to address the challenges associated with charging demand uncertainty during the operation period. To model the charging process of electric buses (EBs), we adopted a piecewise linear function to handle the nonlinear charging function. This approach improves the practicality of the model while ensuring basic realism. This study introduced a mixed-integer programming model to maximize the profit of the EB system, including the weighted delay time. The main constraints include the departure time window and the charging process. To account for the impact of multiple vehicle types on the scheduling of EBs, a distributed robust optimization model is established for the uncertainty of the EB operation. An instantiated analysis is conducted to schedule an EB line in a Chinese city. The results demonstrate that the distributed robust optimization model enhances the expected profit by approximately 27.27 %-54.24 % compared with the deterministic model. Additionally, the robust optimization model exhibits a steeper increase in expected profit as the uncertainty level increases. Furthermore, the mixed scheduling strategies with multiple vehicle types in the robust optimization model enhance the profit compared to the model relying solely on a single vehicle type. The results demonstrate the applicability and effectiveness of the proposed model for EB scheduling.

优化电动公交调度对于推进城市公交系统建设和减少碳排放至关重要。在本研究中，我们使用鲁棒优化范式建立了一个EB调度模型，以解决与运营期间充电需求不确定性相关的挑战。为了模拟电动公交车的充电过程，我们采用分段线性函数来处理非线性充电函数。这种方法在保证基本真实感的同时提高了模型的实用性。本文引入了一个混合整数规划模型，使EB系统的利润最大化，并考虑了加权延迟时间。主要的约束条件包括出发时间窗口和收费过程。为了考虑多种车辆类型对电动汽车调度的影响，针对电动汽车运行的不确定性，建立了分布式鲁棒优化模型。以中国某城市的EB线调度为例进行了实例分析。结果表明，与确定性优化模型相比，分布式鲁棒优化模型的预期利润提高了27.27 % ~ 54.24 %。此外，鲁棒优化模型显示，随着不确定性水平的增加，期望利润的增加幅度更大。此外，在鲁棒优化模型中，多车型混合调度策略比单一车型混合调度策略的收益更高。结果表明，该模型在电子商务调度中的适用性和有效性。

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引用次数: 0

Multi-objective reinforcement learning for electric vehicle charging 电动汽车充电的多目标强化学习

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102083

Maximiliano Trimboli, Luis Avila

The transportation sector is a significant contributor to global greenhouse gas emissions, and Electric Vehicles (EVs) have emerged as a promising solution to mitigate this impact by reducing emissions and integrating renewable energy sources. However, battery charging remains a major obstacle to widespread EV adoption, as charging speed is constrained by battery specifications, C-rate limits, and the need to prevent degradation due to thermal and electrochemical stress. To address these challenges, this work proposes a Multi-Objective Reinforcement Learning (MORL) approach for optimal EV battery charging. Unlike traditional methods that rely on hand-crafted scalar rewards, MORL enables the agent to learn control policies that dynamically balance multiple, often conflicting, objectives—such as fast charging and thermal safety—based on user-defined preferences. Leveraging the architecture of a Deep RL agent, the proposed method adapts its charging strategy in real-time, applying high currents when thermal conditions are favorable and reducing them near critical thresholds. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.

交通运输部门是全球温室气体排放的重要贡献者，电动汽车（ev）已经成为一种有希望的解决方案，通过减少排放和整合可再生能源来减轻这种影响。然而，电池充电仍然是电动汽车广泛采用的主要障碍，因为充电速度受到电池规格、c -速率限制以及防止热应力和电化学应力导致的退化的需要的限制。为了解决这些挑战，本研究提出了一种多目标强化学习（MORL）方法来优化电动汽车电池充电。与依赖手工制作的标量奖励的传统方法不同，MORL使智能体能够根据用户定义的偏好学习动态平衡多个经常相互冲突的目标（例如快速充电和热安全）的控制策略。利用Deep RL代理的架构，所提出的方法可以实时调整其充电策略，在热条件有利时施加大电流，并在临界阈值附近降低电流。实验结果表明，该策略具有较强的适应性：当温度约束较宽松时，充电速度较快；而当电池寿命优先考虑时，充电曲线较为保守。这凸显了MORL在提高电动汽车充电安全性和效率方面的潜力。

{"title":"Multi-objective reinforcement learning for electric vehicle charging","authors":"Maximiliano Trimboli, Luis Avila","doi":"10.1016/j.segan.2025.102083","DOIUrl":"10.1016/j.segan.2025.102083","url":null,"abstract":"<div><div>The transportation sector is a significant contributor to global greenhouse gas emissions, and Electric Vehicles (EVs) have emerged as a promising solution to mitigate this impact by reducing emissions and integrating renewable energy sources. However, battery charging remains a major obstacle to widespread EV adoption, as charging speed is constrained by battery specifications, C-rate limits, and the need to prevent degradation due to thermal and electrochemical stress. To address these challenges, this work proposes a Multi-Objective Reinforcement Learning (MORL) approach for optimal EV battery charging. Unlike traditional methods that rely on hand-crafted scalar rewards, MORL enables the agent to learn control policies that dynamically balance multiple, often conflicting, objectives—such as fast charging and thermal safety—based on user-defined preferences. Leveraging the architecture of a Deep RL agent, the proposed method adapts its charging strategy in real-time, applying high currents when thermal conditions are favorable and reducing them near critical thresholds. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102083"},"PeriodicalIF":5.6,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738304","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

Applying two-stage risk-based market structures for energy hub-based plug-in electric vehicles using information decision gap theory and a hybrid recurrent convolutional network 基于信息决策缺口理论和混合递归卷积网络的两阶段风险型插电式电动汽车市场结构研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102085

A. Heidari , R.C. Bansal , R. Bo

This paper investigates the optimal operation of an energy hub engaged in both day-ahead and real-time trading. A two-stage optimization framework Information Gap Decision Theory (IGDT) for day-ahead bidding and stochastic programming with Monte Carlo scenarios for real-time recourse is applied. Risk-neutral, risk-averse, and risk-taking strategies are considered to capture different risk preferences. The hub integrates combined heat and power, renewable energy, plug-in electric vehicles, and vehicle-to-grid and grid-to-vehicle technologies. Price and load forecasts are generated using a hybrid recurrent convolutional network (HRCN). Results highlight the trade-off between risk management and economic performance: costs are 16.5 % higher in the risk-averse mode than in the risk-neutral mode, and 55.6 % higher than in the risk-taking mode. Natural gas accounts for the most in the risk-taking case, at ∼33 % of the total cost. Under the tested conditions, the proposed IGDT–stochastic–HRCN framework improves expected costs relative to baselines, though outcomes may vary under different market rules, fuel prices, or volatility regimes.

本文研究了一个能源枢纽同时进行日前交易和实时交易的最优运行问题。将信息缺口决策理论（IGDT）应用于蒙特卡罗情景下的日前竞价和随机规划的两阶段优化框架。风险中性、风险厌恶和风险承担策略被认为可以捕获不同的风险偏好。该中心集成了热电联产、可再生能源、插电式电动汽车、车对网和网对车技术。价格和负荷预测使用混合循环卷积网络（HRCN）生成。结果强调了风险管理和经济绩效之间的权衡：风险厌恶模式的成本比风险中性模式高16.5% %，比风险承担模式高55.6% %。在风险承担情况下，天然气占最大，约占总成本的33% %。在测试条件下，拟议的igdt - random - hrcn框架提高了相对于基线的预期成本，尽管结果可能因不同的市场规则、燃料价格或波动机制而异。

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引用次数: 0

Nonlinear integrated energy market optimization based on smoothing approaches 基于平滑方法的非线性综合能源市场优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102089

Jian Jia, Weifeng Chen

To address the computational complexity of the mixed-integer programming (MIP) model in integrated energy system (IES) optimization, a smooth nonlinear programming (NLP) method based on a bi-level optimization model is proposed. In this approach, the upper-level model maximizes the profit of the energy hub (EH) by coordinating supply and demand decisions with the lower-level system. Integer variables are replaced with continuous variables through a smoothing method, which reduces computational complexity while preserving operational equivalence. Relaxed complementarity constraints are incorporated into the KKT conditions to ensure that the smoothed nonlinear model can be effectively solved. Furthermore, incorporating the full nonlinear power flow (NLPF) model in the optimization allows a more accurate representation of the system’s intrinsic characteristics. This approach also helps prevent potential safety risks associated with constraint violations in linear power flow (LPF) models. The case study results demonstrate that the smooth NLP model produces results comparable to the mixed-integer linear programming (MILP) model, and demonstrate its good applicability in handling nonlinear problems.

针对综合能源系统优化中混合整数规划（MIP）模型的计算复杂性，提出了一种基于双层优化模型的光滑非线性规划（NLP）方法。在这种方法中，上层模型通过与下层系统协调供需决策，使能源枢纽（EH）的利润最大化。通过平滑方法将整型变量替换为连续型变量，在保持运算等价的同时降低了计算复杂度。在KKT条件中加入了松弛互补约束，保证了光滑非线性模型的有效求解。此外，在优化中加入全非线性潮流（NLPF）模型可以更准确地表示系统的内在特性。这种方法还有助于防止线性潮流（LPF）模型中与约束违规相关的潜在安全风险。算例研究结果表明，光滑NLP模型的计算结果与混合整数线性规划（MILP）模型相当，在处理非线性问题时具有良好的适用性。

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引用次数: 0

Dynamic pricing strategies for electric vehicle charging: Enhancing cost-reflectivity and revenue stability 电动汽车充电动态定价策略：提高成本反射性和收益稳定性

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-05 DOI: 10.1016/j.segan.2025.102082

Toni Simolin , Tim Unterluggauer , Mattia Secchi , Francesco Pastorelli , Mattia Marinelli , Pertti Järventausta

Public charging infrastructure is essential for accelerating electric vehicle (EV) adoption. Currently, in Europe, customers are often offered fixed charging prices, while the costs incurred by charging site owners (CSOs) vary significantly due to factors such as electricity prices and power grid tariffs. This paper proposes alternative pricing solutions to improve cost-reflectivity based on an analysis of the current pricing landscape and related scientific literature. Simulations are carried out, using Danish and Finnish charging session data of multiple locations and electricity price databases, to assess the impact of the proposed pricing solutions on CSO revenues and their potential implications for the charging service business model. The findings indicate that dynamic cost-reflective pricing enhances the stability of CSO revenues and allows users to optimise their charging decisions by providing transparency through precise hourly charging costs. Furthermore, the results show that the proposed dynamic pricing schemes provide a competitive economic advantage for the CSO over the competitors using the present pricing schemes. Additionally, the proposed pricing schemes lead to lower charging costs for 53–64 % of the users even if they do not alter their charging behaviour.

公共充电基础设施对于加速电动汽车的普及至关重要。目前，在欧洲，客户通常获得固定的充电价格，而充电站点所有者（cso）所产生的成本由于电价和电网关税等因素而差异很大。本文在分析当前定价格局和相关科学文献的基础上，提出了提高成本反射率的替代定价方案。利用丹麦和芬兰多个地点的充电时段数据和电价数据库进行了模拟，以评估拟议的定价解决方案对CSO收入的影响及其对充电服务商业模式的潜在影响。研究结果表明，动态成本反射定价提高了CSO收入的稳定性，并允许用户通过精确的小时收费成本提供透明度来优化他们的收费决策。此外，结果表明，所提出的动态定价方案为CSO提供了比使用现有定价方案的竞争对手更具竞争力的经济优势。此外，拟议的定价方案导致53-64 %的用户的收费成本降低，即使他们不改变他们的收费行为。

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引用次数: 0

Optimal management of green hydrogen production in renewable energy systems using deep reinforcement learning methods 利用深度强化学习方法优化可再生能源系统中绿色制氢的管理

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-05 DOI: 10.1016/j.segan.2025.102075

Donguk Yang , Junki Shim , Jinkun Lee , Seongim Choi

This research focuses on developing a deep reinforcement learning (DRL) framework to optimize green hydrogen production within renewable energy systems. By integrating a DRL-based model, the study aims to enhance real-time management of energy supply, storage, and distribution, involving an electrolyzer and balancing energy flows from photovoltaic (PV) sources, an energy storage system (ESS) and grid power. Utilizing real-world data, the DRL model adapts dynamically to fluctuations in renewable energy output and market prices, thereby optimizing operational efficiency. The study compares various DRL algorithms, including proximal policy optimization (PPO), soft actor-critic (SAC), and advantage actor-critic (A2C), assessing their performance in maximizing predefined reward functions. The findings demonstrate the robustness of the PPO algorithm, demonstrating significant reward accumulation and adaptability in managing dynamic environments. This validation is supported by empirical data and learning curves, confirming the DRL model’s proficiency in optimizing energy use and enhancing operational performance in green hydrogen systems. The integration of DRL with the framework for green hydrogen and renewable energy suggests a comprehensive solution that improves energy efficiency, operational costs, and sustainability initiatives. The research highlights the potential of advanced machine learning techniques for enhanced operational efficiency of renewable energy systems.

本研究的重点是开发一个深度强化学习（DRL）框架，以优化可再生能源系统中的绿色氢气生产。通过集成基于drl的模型，该研究旨在增强能源供应、存储和分配的实时管理，包括电解槽和平衡来自光伏（PV）源、储能系统（ESS）和电网的能量流。DRL模型利用实际数据，动态适应可再生能源产量和市场价格的波动，从而优化运行效率。该研究比较了各种DRL算法，包括近端策略优化（PPO）、软行为者批评（SAC）和优势行为者批评（A2C），评估了它们在最大化预定义奖励函数方面的表现。研究结果证明了PPO算法的鲁棒性，在管理动态环境中展示了显著的奖励积累和适应性。这一验证得到了经验数据和学习曲线的支持，证实了DRL模型在优化能源使用和提高绿色氢系统运行性能方面的熟练程度。DRL与绿色氢和可再生能源框架的整合提出了一个全面的解决方案，可以提高能源效率、运营成本和可持续性举措。该研究强调了先进的机器学习技术在提高可再生能源系统运行效率方面的潜力。

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引用次数: 0

Unified peer-to-peer energy and frequency response reserve trading in isolated multi-microgrid systems 孤立多微电网系统中统一点对点能量和频率响应储备交易

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-05 DOI: 10.1016/j.segan.2025.102080

Chao Sun , Yun Liu , Ziyu Chen , Jizhong Zhu

Peer-to-peer (P2P) energy trading in a multi-microgrid (MMG) system can incentivize energy sharing and reduce the overall operational cost. However, the MMG system operating in isolated mode may face a reduction in system frequency response reserves, especially the inertia and primary frequency response (IPFR) reserve due to the growing integration of renewable energy resources (RESs) via power electronic inverters. Therefore, the current P2P trading framework ignoring the component of IPFR reserve could lead to frequency insecurity. To overcome these limitations, this paper proposes a two-stage P2P energy and IPFR reserve trading mechanism while considering the participation of synchronous generators (SGs) and inverter-based RES (IBRs) in a MMG system. In the first stage, a frequency-constrained unit commitment (UC) problem is formulated, where the unified transfer function structure is implemented in SGs and IBRs to analyze the frequency dynamic processes. In the second stage, each microgrid autonomously negotiates optimal energy and IPFR reserve trading based on the determined UC results through a fully decentralized ADMM based iterative algorithm, clearly reflecting the costs and prices involved. Case studies on 4-MG and 10-MG systems demonstrate that the proposed scheme ensures frequency-secure operation with good scalability. Results show that an additional cost of 995 CNY per day can avoid an economic loss of 1599 CNY per minute during frequency collapse events, confirming the economic efficiency and frequency-security benefits of the proposed approach.

多微电网（MMG）系统中的点对点（P2P）能源交易可以激励能源共享，降低整体运营成本。然而，在隔离模式下运行的MMG系统可能会面临系统频率响应储备的减少，特别是由于可再生能源（RESs）通过电力电子逆变器的日益整合，惯性和一次频率响应（IPFR）储备的减少。因此，目前的P2P交易框架忽略了IPFR储备的组成部分，可能导致频率不安全。为了克服这些限制，本文提出了一种考虑同步发电机（SGs）和基于逆变器的RES （IBRs）在MMG系统中的参与的两阶段P2P能源和IPFR储备交易机制。首先，提出频率约束单元承诺（UC）问题，在SGs和IBRs中实现统一的传递函数结构，分析频率动态过程；在第二阶段，每个微电网通过完全分散的基于ADMM的迭代算法，根据确定的UC结果自主协商最优能源和IPFR储备交易，清楚地反映所涉及的成本和价格。对4-MG和10-MG系统的实例研究表明，该方案保证了频率安全运行，具有良好的可扩展性。结果表明，每天995元的额外费用可以避免频率崩溃事件中每分钟1599元的经济损失，证实了该方法的经济效率和频率安全效益。

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引用次数: 0