Sustainable Energy Grids & Networks最新文献_第6页

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 : 2026-03-01 Epub 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

Integration of thermal energy harvesting in smart energy systems 智能能源系统中热能收集的集成

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.segan.2025.102098

Amir Karimdoost Yasuri

The rising global demand for energy efficiency and the urgency of climate change mitigation have intensified interest in waste-heat utilization. Thermal Energy Harvesting (TEH) offers a scalable pathway to recover otherwise lost thermal energy and integrate it into Smart Energy Systems (SES). In this study, a unified analytical framework is developed that combines quantitative modeling, literature-derived performance data, and predictive optimization to evaluate TEH performance across industrial, residential, and transportation sectors. Results show that thermoelectric generators achieve efficiencies of 5–8 % under moderate gradients, while organic Rankine cycles reach up to 20 % at higher temperatures. Integrating TEH within SES can enhance overall energy utilization by 10–15 % and reduce CO₂ emissions by approximately 9 %. The analysis identifies that system-level integration—linking material properties, thermodynamic design, and control intelligence—is more decisive for practical performance than isolated device improvements. The paper concludes by outlining research and policy priorities to advance hybridized, intelligent TEH solutions for sustainable and resilient energy infrastructures.

全球对能源效率的需求日益增加，以及缓解气候变化的紧迫性，加强了人们对废热利用的兴趣。热能收集（TEH）提供了一种可扩展的途径来回收原本损失的热能，并将其集成到智能能源系统（SES）中。在本研究中，开发了一个统一的分析框架，将定量建模、文献导出的性能数据和预测优化相结合，以评估工业、住宅和交通部门的TEH绩效。结果表明，热电发电机在中等梯度下的效率为5-8 %，而有机朗肯循环在较高温度下的效率可达20 %。在SES中整合TEH可以提高总体能源利用率10 - 15% %，减少二氧化碳排放量约9% %。分析表明，系统级集成——连接材料特性、热力学设计和控制智能——比孤立的设备改进对实际性能更具决定性。论文最后概述了研究和政策重点，以推进可持续和弹性能源基础设施的混合智能TEH解决方案。

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引用次数: 0

Consensus clustering-based electric vehicle charging considering inaccurate user preferences and efficient charging operation zone 考虑不准确用户偏好和高效充电操作区域的共识聚类电动车充电

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.segan.2025.102112

Shicong Zhang , Klaas Thoelen , Mohamed Yasko , Geert Deconinck

The rapid adoption of electric vehicles (EVs) necessitates smart charging solutions to prevent distribution grid overload. However, existing optimization frameworks often overlook critical real-world factors: (1) behavioral uncertainties from inaccurate user preferences (e.g., energy requirements), and (2) non-negligible energy losses during charging operations. This paper addresses critical inefficiencies in EV charging optimization through data-driven behavioral analysis and operational innovation. Using two real-world datasets—from the EnergyVille smart charging platform and the public ACN dataset at the Caltech campus—we quantify critical estimation gaps in user-provided preferences, such as energy demand and departure time. These genuine behavioral inputs are typically missing from synthetic data. Building on these insights, we develop a consensus-clustering forecasting framework that enhances preference prediction accuracy by 18 % (EnergyVille) and 85 % (ACN) versus user inputs. Furthermore, we propose an efficient charging operation zone (ECOZ), a dynamic constraint model that adapts to nonlinear charging efficiency characteristics. Integrated within a mixed-integer linear programming (MILP) optimization formulation, ECOZ maintains 85 % energy conversion efficiency during power allocation. Through simulation, we demonstrate the effectiveness of the proposed method on real-world data and achieve a 5 % reduction in daily EV charging energy losses compared to unconstrained scheduling approaches.

电动汽车的快速普及需要智能充电解决方案来防止配电网过载。然而，现有的优化框架往往忽略了现实世界的关键因素：(1)不准确的用户偏好（例如，能量需求）带来的行为不确定性；(2)充电过程中不可忽略的能量损失。本文通过数据驱动的行为分析和操作创新解决了电动汽车充电优化中的关键低效问题。使用两个真实世界的数据集——来自EnergyVille智能充电平台和加州理工学院校园的公共ACN数据集——我们量化了用户提供的偏好中的关键估计差距，比如能源需求和出发时间。这些真实的行为输入通常在合成数据中缺失。基于这些见解，我们开发了一个共识聚类预测框架，与用户输入相比，该框架将偏好预测精度提高了18% （EnergyVille）和85% （ACN）。在此基础上，提出了一种适应非线性充电效率特征的动态约束模型——高效充电操作区（ECOZ）。ECOZ集成在混合整数线性规划（MILP）优化配方中，在功率分配期间保持85%的能量转换效率。通过仿真，我们证明了该方法在实际数据上的有效性，与无约束调度方法相比，每日电动汽车充电能量损失减少了5%。

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引用次数: 0

Capacity allocation of pumped hydro storage under marketization process: A transitional strategy 市场化进程下抽水蓄能容量配置：一种过渡性策略

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-25 DOI: 10.1016/j.segan.2025.102107

Yizhou Feng , Zhi Wu , Chen Chen , Liang Ma , Wei Gu , Suyang Zhou

To address the challenges posed by renewable energy integration in power systems, China is advancing the development of Pumped Hydro Storage (PHS). However, the rapid growth of PHS installations, coupled with strict regulations and a high reliance on capacity compensation, has led to increasing financial burdens on other utilities. One solution is to reduce PHS’s capacity compensation through its marketization. To this end, a ‘partial-regulated dispatch’ mechanism is proposed as a transitional strategy for gradual marketization. Also, an operational policy analysis framework is proposed based on evaluating dispatch mechanisms and business models. The dispatch mechanism evaluates the capacity support PHS provides to the power system, while the business models focus on enhancing PHS profitability to reduce the dependency on capacity compensation while ensuring long-term economic sustainability. Furthermore, the flexibility of PHS is introduced into the capacity compensation to incentivize PHS to support the power system during transitional stages. This flexibility is mathematically defined using the discrete Minkowski sum, considering both the vibration characteristics of individual units and the unit-commitment of PHS as a whole. The case study shows that through partial-regulated dispatch, PHS can reduce its reliance on capacity compensation by nearly 50 % while ensuring its regulatory service via flexibility compensation. This policy effectively balances economic viability with system support capabilities. Moreover, flexibility compensation provides PHS operators with a risk mitigation strategy in the complex power market environment. Under an appropriate operational strategy and policy incentives, flexibility can be enhanced by nearly 30 % in a fully marketized scenario, thereby contributing to both system stability and operational efficiency.

为了应对可再生能源在电力系统中的整合所带来的挑战，中国正在推进抽水蓄能（PHS）的发展。然而，小灵通安装的快速增长，加上严格的法规和对容量补偿的高度依赖，导致其他公用事业的财务负担不断增加。解决方案之一是通过市场化降低小灵通的容量补偿。为此，建议建立“部分管制调度”机制，作为逐步市场化的过渡战略。同时，提出了基于调度机制和业务模型评估的操作策略分析框架。调度机制评估小灵通为电力系统提供的容量支持，商业模式侧重于提高小灵通的盈利能力，以减少对容量补偿的依赖，同时确保长期的经济可持续性。此外，在容量补偿中引入小灵通的灵活性，以激励小灵通在过渡阶段支持电力系统。这种灵活性在数学上是用离散闵可夫斯基和来定义的，同时考虑了单个单元的振动特性和小灵通作为一个整体的单元承诺。案例研究表明，通过部分调节调度，小灵通在保证灵活性补偿的同时，可将其对容量补偿的依赖程度降低近50%。这一政策有效地平衡了经济可行性和系统支持能力。此外，灵活性补偿为小灵通运营商在复杂的电力市场环境中提供了一种降低风险的策略。在适当的运营战略和政策激励下，在完全市场化的情况下，灵活性可以提高近30%，从而有助于系统稳定性和运营效率。

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引用次数: 0

Dynamic security region modeling via conditional style transfer and causal representation learning 基于条件风格迁移和因果表征学习的动态安全区域建模

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.segan.2026.102119

Zixuan Zheng , Yuan Zeng , Junzhi Ren , Chao Qin , Changgang Li , Wei Xu , Weilun Ni

The increasing complexity of power system operations poses challenges for traditional dynamic security region (DSR) modeling, particularly in critical sample transferability, boundary accuracy, and interpretability. This paper proposes a unified framework integrating conditional style transfer learning (CSTL), deep modeling, and causal inference to enhance DSR characterization across varying scenarios. First, a perturbation-style transfer method using conditional diffusion effectively generalizes critical samples across operating scenarios. Second, a deep feedforward neural network (DFNN) is employed to model complex nonlinear DSR boundaries, aided by a stability margin index for quantitative state assessment. The model achieves high accuracy, low latency, and strong noise robustness, supporting online deployment. Finally, to improve the physical interpretability, we incorporate a causal representation learning mechanism combining inverse probability weighting, doubly robust estimation, conditional shapley values and counterfactual inference. This enables causal attribution of perturbations to boundary variations and supports counterfactual analysis. Validation on the IEEE 39-bus and IEEE 145-bus systems confirms the effectiveness and scalability of the proposed approach.

电力系统运行的复杂性日益增加，对传统的动态安全区域（DSR）建模提出了挑战，特别是在关键样本可转移性、边界精度和可解释性方面。本文提出了一个整合条件风格迁移学习（CSTL）、深度建模和因果推理的统一框架，以增强不同场景下的DSR表征。首先，使用条件扩散的微扰式传递方法有效地概括了不同操作场景下的关键样本。其次，采用深度前馈神经网络（DFNN）对复杂非线性DSR边界进行建模，并辅以稳定裕度指标进行状态定量评估；该模型精度高、时延低、噪声鲁棒性强，支持在线部署。最后，为了提高物理可解释性，我们引入了一种结合反概率加权、双鲁棒估计、条件shapley值和反事实推理的因果表示学习机制。这使得扰动的因果归因到边界变化，并支持反事实分析。在IEEE 39总线和IEEE 145总线系统上的验证证实了该方法的有效性和可扩展性。

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引用次数: 0

An optimal peer-to-peer market in energy communities: A game-theoretic approach with replicator dynamics 能源社区中最优点对点市场：具有复制因子动力学的博弈论方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-31 DOI: 10.1016/j.segan.2025.102114

Sofía Chacón , Katerine Guerrero , Germán Obando , Andrés Pantoja

Energy communities (ECs) enable prosumers, consumers, and distributed energy resources (DERs) to jointly manage energy in a coordinated and economically efficient manner. In this work, we propose an energy management system (EMS) for ECs that integrates a demand response (DR) program with a peer-to-peer (P2P) market based on sealed-bid auctions and continuous Stackelberg dynamics. The buyers determine prices according to their energy demand and risk aversion, and generators decide on the amount of energy to sell based on the rewards received and their associated costs. Methodologically, we develop three algorithms to maximize the welfare of the community. The first algorithm incorporates a DR program and generation constraints to keep the EC competitive with grid prices over time. The second and third algorithms use replicator dynamics (RD) to find equilibria that optimize the system’s welfare, using Lagrangian relaxation (LR) to handle the model constraints. We integrate the models for sellers and buyers via a system of differential equations that simulate a Stackelberg game. Additionally, a filtering mechanism is employed to improve convergence and reduce computation time. We validate the EMS in a case study, showing that the proposed approach achieves greater self-sufficiency compared to a system without demand response and enables better resource management, enhanced fairness, and a more equitable distribution of benefits compared to a non-hierarchical and decoupled model.

能源共同体（ec）使产消者、消费者和分布式能源者（der）能够以协调和经济高效的方式共同管理能源。在这项工作中，我们为ec提出了一种能源管理系统（EMS），该系统将需求响应（DR）计划与基于密封投标拍卖和连续Stackelberg动态的点对点（P2P）市场集成在一起。买家根据他们的能源需求和风险厌恶来决定价格，而发电商则根据获得的回报和相关成本来决定出售的能源数量。在方法上，我们开发了三种算法来最大化社区的福利。第一种算法结合了DR程序和发电限制，以保持EC随着时间的推移与电网价格具有竞争力。第二和第三种算法使用复制因子动力学（RD）来找到优化系统福利的平衡点，使用拉格朗日松弛（LR）来处理模型约束。我们通过一个模拟Stackelberg博弈的微分方程系统来整合卖方和买方的模型。此外，采用滤波机制提高收敛性，减少计算时间。我们在一个案例研究中验证了EMS，表明与没有需求响应的系统相比，所提出的方法实现了更大的自给自足，并且与非分层和解耦模型相比，可以实现更好的资源管理，增强公平性和更公平的利益分配。

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引用次数: 0

Exploring dimensional distinctions of residential heat load profiles using an unsupervised machine learning clustering framework 使用无监督机器学习聚类框架探索住宅热负荷分布的维度差异

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.segan.2025.102117

Vasilis Michalakopoulos , Elissaios Sarmas , Viktor Daropoulos , Giannis Kazdaridis , Stratos Keranidis , Vangelis Marinakis , Dimitris Askounis

Decarbonizing the heating sector is central to achieving the energy transition, as heating systems provide essential space heating and hot water in residential and industrial environments. A major challenge lies in effectively profiling large clusters of buildings to improve demand estimation and enable efficient Demand Response schemes. This paper addresses this challenge by introducing a novel unsupervised machine learning framework for clustering residential heating load profiles, focusing on natural gas space heating and hot water preparation boilers, while analyzing five different dimensions: boiler usage, heating demand, weather conditions, building characteristics, and user behavior. Three distance metrics, Euclidean Distance, Dynamic Time Warping, and Derivative Dynamic Time Warping, are applied and evaluated using established clustering indices. The proposed method is assessed considering 29 residential buildings in Greece equipped with smart heating controllers throughout a calendar heating season (i.e. 210 days). This study demonstrates that Dynamic Time Warping is demonstrably the most suitable metric. A subsequent correlation analysis of the clustering results from each dimension reveals strong, time-dependent relationships between boiler usage, heat demand and temperature, identifying them as the most important and correlated directions. These findings shed light on heating load behavior, establishing a solid foundation for developing more targeted and effective demand response programs.

供暖部门脱碳是实现能源转型的核心，因为供暖系统为住宅和工业环境提供必要的空间供暖和热水。一个主要的挑战在于有效地分析大型建筑群，以改善需求估计和实现有效的需求响应计划。本文通过引入一种新的无监督机器学习框架来解决这一挑战，该框架用于聚类住宅供暖负荷概况，重点关注天然气空间供暖和热水准备锅炉，同时分析五个不同的维度：锅炉使用、供暖需求、天气条件、建筑特征和用户行为。三种距离度量，欧几里得距离，动态时间翘曲，和导数动态时间翘曲，应用和评估使用已建立的聚类指标。在整个日历采暖季节（即210天）中，对希腊29座配备智能供暖控制器的住宅建筑进行了评估。研究表明，动态时间翘曲是最合适的度量。随后对每个维度的聚类结果进行相关性分析，揭示了锅炉使用、热需求和温度之间强烈的时间依赖关系，将它们确定为最重要和相关的方向。这些发现揭示了热负荷行为，为制定更有针对性和更有效的需求响应计划奠定了坚实的基础。

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引用次数: 0

A novel bilevel model for service restoration in distribution systems integrating technical constraints and the energy market environment 考虑技术约束和能源市场环境的配电系统服务恢复新二层模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.segan.2025.102092

Etiane O.P. Carvalho , Wandry R. Faria , Leonardo H. Macedo , Gregorio Muñoz-Delgado , Javier Contreras , Benvindo R. Pereira Junior , João Bosco A. London Junior

This paper introduces a bilevel programming model for service restoration in distribution systems, integrating private distributed generations (DGs) and market strategies. The upper-level problem minimizes costs associated with unsupplied loads and voltage regulator parameters, while the lower-level problem maximizes the profits of DG owners. By incorporating realistic market-based pricing to incentivize privately owned DGs during contingencies, the model addresses the gap in current literature, where DG ownership and production costs are often overlooked. Validation using a 53-node test system under multiple fault scenarios demonstrates the model’s effectiveness in achieving cost-efficient restoration and providing fair compensation to DG owners. This approach ultimately enhances the resilience and reliability of distribution systems.

本文提出了一种结合私有分布式代（dg）和市场策略的配电系统服务恢复双层规划模型。上层问题最小化与未供电负载和稳压器参数相关的成本，而下层问题最大化DG所有者的利润。通过结合现实的市场定价来激励突发事件中的私有DG，该模型解决了当前文献中的空白，即DG所有权和生产成本经常被忽视。在多个故障场景下使用53节点测试系统验证了该模型在实现成本效益恢复和为DG所有者提供公平补偿方面的有效性。这种方法最终提高了配电系统的弹性和可靠性。

引用次数: 0

Validation of a data-driven multi-ancillary services framework for photovoltaic/Storage units via power hardware-in-the-loop testing 通过电力硬件在环测试验证数据驱动的光伏/存储单元多辅助服务框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.segan.2026.102133

Kalliopi D. Pippi , Georgios C. Kryonidis , Theofilos A. Papadopoulos , Zhiwang Feng , Mazheruddin H. Syed , Graeme M. Burt

This paper presents the experimental validation of a unified multi-ancillary services (AS) architecture for distributed photovoltaic–battery energy storage (PV-DBESS) systems using power hardware-in-the-loop (PHiL) testing. The focus is on voltage regulation (VR) and voltage unbalance mitigation (VUM), with full decoupling between the two control schemes to ensure interference-free operation. Experiments are conducted on a small-scale distribution network using a hybrid PHiL setup that combines real-time simulation with physical hardware, bridging the simulation-to-practice gap. A detailed three-phase four-leg converter model with embedded VR and VUM algorithms is implemented in RSCAD, and simplified controllable current source models are also evaluated to balance computational efficiency with accuracy. The results demonstrate that the proposed control strategies effectively mitigate overvoltage and unbalance events across varying operating conditions and network characteristics. The VUM scheme leverages reactive power through virtual damping susceptances, while the VR scheme coordinates active and reactive power to regulate the positive-sequence voltage. Interoperability with conventional constant-power converters is also verified. The study confirms that the proposed AS architecture provides a robust and practical solution for enhancing reliability and hosting capacity in active distribution networks, while indicating its potential suitability for future large-scale real-time studies and integration with advanced grid operation systems, particularly through the use of simplified converter models.

本文提出了分布式光伏电池储能（PV-DBESS）系统统一多辅助服务（AS）架构的实验验证，并采用电源硬件在环（PHiL）测试。重点是电压调节（VR）和电压不平衡缓解（VUM），两种控制方案之间完全解耦，以确保无干扰运行。实验在一个小规模的配电网络上进行，使用混合PHiL设置，将实时仿真与物理硬件相结合，弥合了模拟到实践的差距。在RSCAD中实现了嵌入式VR和VUM算法的三相四脚转换器的详细模型，并对简化的可控电流源模型进行了评估，以平衡计算效率和精度。结果表明，所提出的控制策略可以有效地缓解不同运行条件和网络特性下的过电压和不平衡事件。VUM方案通过虚拟阻尼电纳来利用无功功率，VR方案通过协调有功和无功功率来调节正序电压。还验证了与传统恒功率转换器的互操作性。该研究证实，拟议的AS架构为提高主动配电网络的可靠性和承载能力提供了一个强大而实用的解决方案，同时表明其潜在的适用性，特别是通过使用简化的转换器模型，用于未来大规模的实时研究和与先进电网运行系统的集成。

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引用次数: 0

An advanced predictive battery control strategy for plus energy building flexibility: Simulation-based assessment and laboratory experimental setup 一种先进的预测电池控制策略：基于仿真的评估和实验室实验设置

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.segan.2026.102127

Enrico Dalla Maria, Francesco Turrin, Annamaria Belleri, Grazia Barchi

Buildings account for 40% of energy use making them central to achieving climate neutrality goals. In this context, energy building flexibility emerges as a key enabler, particularly when combined with the Plus Energy Building (PEB) concept, where buildings generate more renewable energy than they consume annually to achieve climate-neutrality goals. As an energy system, the building can offer demand-side flexibility by responding to external penalty signals such as price, CO₂ emissions or grid congestion, thus enabling system operators to dynamically influence consumption patterns. In this work, we present a predictive advanced PV-battery management strategy in year-long simulations across different scenarios, obtained by combining a reference building archetype, various representative European geo-clusters, and electrical consumption resulting from tailored controls of the thermal assets. The heterogeneous results across geo-clusters underscore the influence of climate, culture, and system sizing on predictive control performance, with findings from the Mediterranean cluster (with expected best case flexibility improvement in the range of 14%). These outcomes motivate the implementation of a laboratory-scale setup to port the proposed control strategies to commercially available devices under real working conditions. We report observations of a year-long monitoring of such a laboratory setup, recording the ability to shift battery stored energy toward high-priced periods, with non-standard induced inverter operations observed 10% of the time, highlighting the system’s responsiveness under real-world conditions.

建筑占能源使用的40%，是实现气候中和目标的核心。在这种背景下，能源建筑的灵活性成为一个关键的推动因素，特别是当与“+能源建筑”（PEB）概念相结合时，建筑每年产生的可再生能源比其消耗的要多，以实现气候中和目标。作为一个能源系统，建筑可以通过响应外部惩罚信号（如价格、二氧化碳排放或电网拥堵）来提供需求侧灵活性，从而使系统运营商能够动态影响消费模式。在这项工作中，我们通过结合参考建筑原型、各种具有代表性的欧洲地理集群以及热资产定制控制导致的电力消耗，在长达一年的不同场景模拟中提出了一种预测性高级pv电池管理策略。不同地理集群的不同结果强调了气候、文化和系统规模对预测控制性能的影响，地中海集群的研究结果（预期最佳情况下灵活性提高在14%的范围内）。这些结果激发了实验室规模设置的实施，将所提出的控制策略移植到实际工作条件下的商用设备上。我们报告了对这种实验室设置进行为期一年的监测的观察结果，记录了将电池存储的能量转移到高价时段的能力，观察了10%的非标准诱导逆变器操作，突出了系统在现实条件下的响应性。

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引用次数: 0