Sustainable Energy Grids & Networks最新文献_第8页

Analysis on improvement of photovoltaic hosting capacity through the flexible connection policy 柔性接入政策对光伏装机容量的提升分析

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-01 DOI: 10.1016/j.segan.2025.102071

Jae Hyeon Shin , Jin Hyeok Kim , Seung Wan Kim , Dam Kim

The rapid integration of renewable energy sources, including photovoltaics (PV), presents operational challenges for distribution networks, such as reverse power flow, voltage fluctuations, and network congestion. In industrial parks, growing demand for on-site and shared renewables has spurred interest in deploying microgrids, where the concentration of variable generation creates hosting capacity constraints at feeder and substation. Conventional firm connection policies impose strict capacity limits based on worst-case scenarios, delaying interconnection and underutilization of the grid. To address these limitations, this study introduces a time-series bi-level optimization framework for evaluating flexible connection policies that allow controlled PV curtailment. A linearized power flow-based hosting capacity optimization model is developed and applied to evaluate maximum hosting capacity and optimize the siting of PV systems under firm and flexible connection cases. A case study on an IEEE 40-bus networked microgrid system demonstrates that allowing modest annual PV curtailment (1–11 %) can significantly enhance the hosting capacity of the network—up to 45 % greater than that achieved under firm connection approaches—while maintaining or even increasing the total annual renewable generation. Furthermore, an economic analysis reveals that although curtailment may slightly reduce developer profitability, significant savings from deferred grid upgrades provide substantial benefits to both microgrid and distribution system operators. Therefore, we establish a cost-effective pathway for large-scale renewable energy integration by proposing practical incentive mechanisms, such as net present value and benefit-cost ratio-based compensation. These findings emphasize the importance of strategically flexible connection policies in enabling efficient, economical, and high-capacity renewable energy integration into future power grids.

包括光伏（PV）在内的可再生能源的快速整合给配电网带来了运营挑战，如反向潮流、电压波动和网络拥塞。在工业园区，对现场可再生能源和共享可再生能源的需求不断增长，激发了人们对部署微电网的兴趣，在微电网中，可变发电的集中造成了支线和变电站的托管容量限制。传统的企业接入政策根据最坏的情况施加了严格的容量限制，延迟了电网的互联和未充分利用。为了解决这些限制，本研究引入了一个时间序列双级优化框架，用于评估允许可控光伏弃风的灵活连接策略。建立了基于线性潮流的托管容量优化模型，并将其应用于光伏系统在刚性和柔性连接情况下的最大托管容量评估和系统选址优化。对IEEE 40总线网络微电网系统的案例研究表明，允许适度的年度光伏削减（1 - 11% %）可以显着提高网络的承载能力-比固定连接方法实现的能力高出45% % -同时保持甚至增加年度可再生能源发电总量。此外，一项经济分析显示，尽管弃风可能会略微降低开发商的盈利能力，但推迟电网升级带来的大量节省为微电网和配电系统运营商提供了实质性的好处。因此，我们通过提出切实可行的激励机制，如净现值和基于收益成本比率的补偿，为大规模可再生能源整合建立了一条具有成本效益的途径。这些发现强调了战略上灵活的连接政策在实现高效、经济和高容量可再生能源整合到未来电网中的重要性。

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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 : 2026-03-01 Epub 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 : 2026-03-01 Epub 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

Uncertainty quantification in load profiles with rising EV and PV adoption: The case of residential, industrial, and office buildings 随着电动汽车和光伏采用的增加，负载剖面的不确定性量化：以住宅、工业和办公楼为例

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.segan.2025.102078

Aiko Fias, Md Umar Hashmi , Geert Deconinck

The integration of photovoltaic (PV) generation and electric vehicle (EV) charging introduces significant uncertainty in electricity consumption patterns, particularly at the distribution level. This paper presents a comparative study for selecting metrics for uncertainty quantification (UQ) for net load profiles of residential, industrial, and office buildings under increased DER penetration. A variety of statistical metrics is evaluated for their usefulness in quantifying uncertainty, including, but not limited to, standard deviation, entropy, ramps, and distance metrics. The proposed metrics are classified into baseline-free, with baseline and error-based. These UQ metrics are evaluated for increased penetration of EV and PV. The results highlight suitable metrics to quantify uncertainty per consumer type and demonstrate how net load uncertainty is affected by EV and PV adoption. Additionally, it is observed that joint consideration of EV and PV can reduce overall uncertainty due to compensatory effects of EV charging and PV generation resulting from temporal alignment during the day. Uncertainty reduction is observed across all datasets and is most pronounced for the office building dataset.

光伏（PV）发电和电动汽车（EV）充电的整合在电力消费模式中引入了重大的不确定性，特别是在配电层面。本文提出了一项比较研究，以选择不确定性量化指标（UQ）的住宅，工业和办公建筑的净负荷剖面在增加的渗透。评估各种统计度量在量化不确定性方面的有用性，包括但不限于标准偏差、熵、斜坡和距离度量。建议的度量被分为无基线、有基线和基于错误。对这些UQ指标进行评估，以增加EV和PV的渗透率。结果强调了量化每种消费者类型不确定性的合适指标，并展示了电动汽车和光伏采用对净负荷不确定性的影响。此外，研究还发现，由于电动汽车充电和光伏发电在白天的时间排列造成的补偿效应，联合考虑电动汽车和光伏发电可以降低总体不确定性。在所有数据集中都观察到不确定性的降低，其中办公楼数据集的不确定性降低最为明显。

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引用次数: 0

Uncertainty interval assessment method for joint output of renewable power stations considering time-varying volatility correlations 考虑时变波动相关性的可再生电站联合出力不确定区间评估方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-14 DOI: 10.1016/j.segan.2026.102157

Xilong Li , Feng Zheng , Jianjian Zhao , Cheng Tang , Lingfeng Zhou , Ziwen Liu , Zhenghua Chen

The construction of wind-solar renewable power stations exhibits regional clustering characteristics. Compared with individual stations, the time-varying volatility correlations among wind and solar resources amplify the uncertainties in their joint output, leading to reduced accuracy in forecasting results. To address this, this paper proposes an uncertainty assessment method for the joint output of wind-solar stations based on the Long Short Term Memory-Generalized Auto Regressive Conditional Heteroskedasticity-Difference (LSTM-GARCH-D) model and R-vine Copula. First, a probability distribution function for wind-solar unit output uncertainty is constructed by fitting residuals using LSTM-GARCH-D. Specifically, the difference series of wind-solar point forecasts is introduced as a correction term to the traditional GARCH model to enhance residual fitting accuracy. Second, R-vine Copula functions are employed to establish interdependencies among multiple residual variables, effectively capturing time-varying volatility correlations in multi-dimensional wind-solar output. This approach resolves the challenge of jointly characterizing multi-dimensional uncertain variables and enables uncertainty assessment for coordinated wind-solar output. Finally, case studies using operational data from a regional wind-solar cluster validate the effectiveness and superiority of the proposed method. Assessing wind–solar joint output uncertainty helps select more reliable forecasts and improves dispatch decision credibility under high renewable penetration. By revealing worst-case scenarios within a reasonable prediction error range, the proposed uncertainty assessment quantitatively supports reserve capacity configuration—avoiding excessive conservatism from blind reserve expansion. It also enables dispatch optimization, reducing wind and solar curtailment and improving power system economic performance.

风能-太阳能可再生电站建设呈现区域集聚特征。与单个台站相比，风能和太阳能资源之间的时变波动相关性放大了它们联合输出的不确定性，导致预测结果的准确性降低。针对这一问题，本文提出了一种基于长短期记忆-广义自回归条件异方差-差（LSTM-GARCH-D）模型和R-vine Copula的风能-太阳能联合输出不确定性评估方法。首先，利用LSTM-GARCH-D方法拟合残差，构造了风电机组输出不确定性的概率分布函数。在传统GARCH模型中引入了风-日点预报差序列作为校正项，提高了残差拟合精度。其次，利用R-vine Copula函数建立多个残差变量之间的相互依赖关系，有效捕获多维风能-太阳能输出的时变波动相关性。该方法解决了多维不确定变量联合表征的挑战，实现了对协调的风光输出的不确定性评估。最后，利用区域风能-太阳能集群的运行数据进行了实例研究，验证了该方法的有效性和优越性。评估风能-太阳能联合输出的不确定性有助于选择更可靠的预测，提高可再生能源高渗透率下调度决策的可信度。提出的不确定性评估通过在合理的预测误差范围内揭示最坏情况，定量支持储备容量配置，避免了盲目储备扩张带来的过度保守性。它还可以优化调度，减少风能和太阳能弃电，提高电力系统的经济性能。

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引用次数: 0

A transformer and CNN-based hybrid model for localization detection of false data injection attacks in smart grids 基于变压器和cnn的智能电网假数据注入攻击定位检测混合模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.segan.2026.102150

Huan Pan , Hang Yang , Chunning Na , Jiayi Jin

False data injection attacks (FDIAs) pose a serious threat to the secure and economic operation of smart grids, particularly in medium- and large-scale networks where attacks may occur at multiple locations. Failure to detect and localize FDIAs in a timely manner prevents grid operators from isolating compromised buses, thereby hindering effective loss mitigation. To address this challenge, this paper proposes a deep learning-based FDIA localization detection model. The proposed model consists of three main components: coordinate attention (CA), a convolutional neural network (CNN), and a Transformer. The CA mechanism enhances the feature representation capability of the network, while the CNN and Transformer extract local and global characteristics of the input tensor, respectively. Using the IEEE-14 and IEEE-39 bus systems as test cases, attacked measurement data are generated with PYPOWER, and the proposed Transformer+CNN-based model is evaluated against several benchmark methods. Experimental results demonstrate that the proposed hybrid model achieves superior FDIA localization performance.

虚假数据注入攻击（FDIAs）对智能电网的安全和经济运行构成严重威胁，特别是在中大型网络中，攻击可能发生在多个位置。如果不能及时检测和定位fdi，电网运营商就无法隔离受损的总线，从而阻碍了有效的损失缓解。为了解决这一问题，本文提出了一种基于深度学习的FDIA定位检测模型。该模型由三个主要组成部分组成：协调注意（CA）、卷积神经网络（CNN）和Transformer。CA机制增强了网络的特征表示能力，而CNN和Transformer分别提取输入张量的局部和全局特征。以IEEE-14和IEEE-39总线系统为测试用例，利用PYPOWER生成受攻击的测量数据，并对所提出的基于Transformer+ cnn的模型进行了几种基准测试方法的评估。实验结果表明，该混合模型具有较好的FDIA定位性能。

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引用次数: 0

Hierarchical multi-spatial-temporal scale coordinated optimization of power-transportation interconnected system based on dynamic user equilibrium with point queue 基于点队列动态用户均衡的电力运输互联系统分层多时空尺度协同优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.segan.2026.102146

Min Hou , Xinrui Liu , Ruohan Fu , Rui Wang , Yushuai Li , Zhengmao Li , Qiuye Sun

The rapid development of electric vehicles (EVs) has promoted the deep coupling of power distribution network (PDN) and traffic network (TN), which has brought great uncertainty and time coupling problems to the power-transportation interconnected system (PTIS). A hierarchical dynamic interaction optimization of PTIS considering both economic factors and uncertainty is constructed. First, in view of the time scale difference between PDN and TN, a hierarchical multi-spatial-temporal scale coordinated optimal framework of PTIS is constructed to coordinate the path selection of EVs and the dispatching strategy of PDN considering the uncertainty of renewable energy. Then, dynamic differential equations are adopted to describe the dynamic transmission process of traffic flow, and based on the queue problem within fast charging stations, a model considering the spatial-temporal dynamic traffic flow distribution based on dynamic user equilibrium with point queue is established. Furthermore, to address the issue of power distribution for charging of EVs under the same charging station, a model predictive control strategy is adopted to redistribute the power to meet the charging and discharging requirements of EVs with different demands. Finally, a simulation analysis is conducted to verify the effectiveness of the method proposed in this paper. Compared with SUE, the proposed dynamic queue update of FCS and DUE reduced the congestion degree of TN by 3.65% and the TN cost by 570,000 CNY.

电动汽车的快速发展促进了配电网（PDN）与交通网络（TN）的深度耦合，这给电-运互联系统（PTIS）带来了很大的不确定性和时间耦合问题。构造了考虑经济因素和不确定性的分层动态交互优化模型。首先，针对PDN与TN在时间尺度上的差异，构建了层次化的PTIS多时空尺度协调优化框架，以协调电动汽车的路径选择和考虑可再生能源不确定性的PDN调度策略。然后，采用动态微分方程描述交通流的动态传输过程，并基于快速充电站内部的排队问题，建立了基于动态用户均衡的考虑点排队的时空动态交通流分布模型。针对同一充电站下电动汽车充电功率分配问题，采用模型预测控制策略对充电功率进行重新分配，以满足不同充电需求电动汽车的充放电需求。最后，通过仿真分析验证了本文方法的有效性。与SUE相比，提出的FCS和DUE动态队列更新使TN拥塞程度降低了3.65%，TN成本降低了57万元。

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引用次数: 0

Practical sensitivity-based optimization technique to solve the hosting capacity problem in unbalanced low voltage networks 求解不平衡低压电网承载容量问题的实用灵敏度优化技术

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.segan.2026.102140

Rubén Carmona-Pardo , Rafael Morán-Corbacho , Álvaro Rodríguez del Nozal , Esther Romero-Ramos

This work addresses the problem of computing the hosting capacity of distributed energy resources, both generation and demand, in a three-phase four-wire low voltage network. A new methodology, based on the use of voltage and current sensitivity coefficients to model the system, allows defining a second-order cone programming formulation that guarantees the convexity of the problem. This approach results in a very practical and accurate tool capable of solving the hosting capacity problem, for generation and demand hosting capacity, in large unbalanced distribution networks, regardless of the initial operating conditions or its radial or meshed topology, and considering the limiting constraints on voltages, currents, reverse power flows and voltage unbalances. Tests on numerous different real low-voltage networks demonstrate the practical usefulness of the tool, highlighting the accuracy of the results obtained. For the largest tested distribution network, a comparison has been included between the results obtained with the proposed methodology and those derived from using a Monte Carlo-based probabilistic approach, demonstrating the computational advantage of the new method and the good accuracy of the optimum obtained. With the new hosting capacity computation tool, it has been possible to identify technically safe scenarios that allow for the accurate quantification and localization of the nodes and phases to which the new generation/demand must be connected, reaching penetration levels of up to 32%/21% with respect to the transformer’s rated power.

这项工作解决了在三相四线制低压网络中计算分布式能源的发电和需求的承载能力的问题。一种基于使用电压和电流灵敏度系数对系统建模的新方法，允许定义二阶锥规划公式，以保证问题的凸性。这种方法产生了一种非常实用和准确的工具，能够解决大型不平衡配电网络中的发电和需求托管容量问题，无论其初始运行条件或其径向或网状拓扑如何，并考虑电压、电流、反向潮流和电压不平衡的限制约束。在许多不同的实际低压网络上的测试表明了该工具的实用性，突出了所获得结果的准确性。对一个最大的配电网进行了测试，并将所提出的方法与基于蒙特卡罗的概率方法的结果进行了比较，证明了新方法的计算优势和所获得的最佳结果的良好准确性。借助新的托管容量计算工具，可以确定技术上安全的方案，从而对新一代/需求必须连接的节点和阶段进行准确的量化和定位，达到变压器额定功率的32%/21%的渗透水平。

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引用次数: 0

A new convex Stackelberg game theory oriented optimization model for resilient day-ahead planning of distribution network by optimal distributed generation pricing and incentive-based demand response program 基于最优分布式发电定价和基于激励的需求响应方案的配电网弹性日前规划新凸Stackelberg博弈优化模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1016/j.segan.2026.102134

Saeed Behzadi , Mehdi Naserian

Following severe disasters in distribution systems, distribution network operators (DNOs) should employ various methods to minimize load-shedding. A highly effective strategy is offering incentive-oriented rewards to consumers to reduce load in critical conditions. In this paper, two case studies have been compared under low-probability and high-impact (LPHI) outage conditions to indicate the impact of incentive-based demand response program (IBDRP) on load restoration. In the best case, the offered optimal incentive reward price to the consumers is determined based on the optimal pricing of distributed generation (DG) in critical conditions. These proposed prices have been obtained by taking into account the optimal benefit view of both consumers and DNOs. To reach an optimal solution for day-ahead pricing in resilient distribution system planning according to this point of view, the Stackelberg game theory (SGT) is utilized. On the other side, accurate day-ahead network load forecasting is obtained by using machine learning and classical methods. In addition, all the formulations have been convexified and implemented in GAMS software and tested in the IEEE 33-bus system. Finally, the Pareto optimization scenarios have been considered, and the optimal solution is reached by the fuzzy satisfying method.

在配电网发生重大灾害后，配电网运营商应采取各种措施将负荷减少到最小。一个非常有效的策略是向消费者提供以激励为导向的奖励，以减少关键条件下的负荷。本文比较了低概率和高影响（LPHI）停电条件下的两个案例，以表明基于激励的需求响应计划（IBDRP）对负荷恢复的影响。在最优情况下，根据临界条件下分布式发电的最优价格确定向消费者提供的最优激励奖励价格。这些建议的价格是在考虑了消费者和dno的最佳利益观点后得出的。根据这一观点，利用Stackelberg博弈论（SGT）求解弹性配电系统规划中日前电价问题的最优解。另一方面，利用机器学习和经典方法，获得了准确的日前网络负荷预测。此外，所有的公式都在GAMS软件中进行了凸化和实现，并在IEEE 33总线系统中进行了测试。最后，考虑了Pareto优化方案，并采用模糊满足法得到了最优解。

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引用次数: 0

Prosumers' peer-to-peer multi-energy transactions considering distributed energy resources and demand response 考虑分布式能源和需求响应的产消点多能源交易

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.segan.2025.102113

Mohammad Bagher Moradi , Mohammad Hassan Nazari , Hamed Nafisi , Hossein Askarian Abyaneh , Seyed Hossein Hosseinian , Marco Merlo

Smart grids support the integration of renewable resources and enable demand response, transforming consumers into prosumers who both generate and use energy. Prosumers can improve their economic outcomes by selling surplus energy through peer‑to‑peer (P2P) transactions instead of relying solely on the upstream grid. In residential microgrids, this can reduce energy costs while increasing revenues from surplus energy sales. This study investigates P2P energy sharing as a mechanism for energy exchange among prosumers and examines how different optimization objectives affect individual benefits in smart grid environments. Two primary objectives are considered: maximizing revenues from energy sales and minimizing energy procurement costs. The approach determines transactive energy through prosumer self‑scheduling and formulates a P2P model that maximizes social welfare. The objective functions are assessed under three scenarios: P2P energy sharing within a resource‑constrained smart grid, an expanded‑resource setting that evaluates the influence of additional prosumer capacity, and a multi‑energy hub in which participants can trade both electrical and thermal energy. Mixed‑integer linear programming simulations are carried out under two pricing schemes, with and without differentiation between renewable and conventional energy prices, and are complemented by a demand sensitivity analysis. The results indicate that prosumers prioritizing cost minimization achieve substantially lower energy expenses, with reductions between 2.2 % and 67.8 % compared with prosumers focused on revenue maximization. Furthermore, increased prosumer resources and energy price variations significantly affect profitability under each objective. Appropriate adjustments to resources and prices can enhance profits when energy sales are prioritized over cost reduction, as confirmed by sensitivity analysis and comparison with prior work.

智能电网支持可再生资源的整合，实现需求响应，将消费者转变为生产和使用能源的生产消费者。生产消费者可以通过点对点（P2P）交易出售剩余能源，而不是仅仅依赖上游电网，从而提高经济效益。在住宅微电网中，这可以降低能源成本，同时增加剩余能源销售的收入。本研究探讨了P2P能源共享作为产消者之间能源交换的机制，并考察了智能电网环境中不同的优化目标如何影响个人利益。考虑两个主要目标：能源销售收入最大化和能源采购成本最小化。该方法通过产消自我调度来确定交易能量，并构建了社会福利最大化的P2P模型。目标函数在三种情况下进行评估：资源受限的智能电网中的P2P能源共享，评估额外产消能力影响的扩展资源设置，以及参与者可以交易电能和热能的多能中心。混合整数线性规划模拟是在两种定价方案下进行的，可再生能源和传统能源价格有和没有差别，并辅以需求敏感性分析。结果表明，优先考虑成本最小化的生产消费者实现了显著降低的能源支出，与专注于收入最大化的生产消费者相比，减少了2.2% %至67.8% %。此外，生产消费者资源的增加和能源价格的变化显著影响了每个目标下的盈利能力。敏感度分析和与之前工作的比较证实，当能源销售优先于降低成本时，适当调整资源和价格可以提高利润。

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引用次数: 0