发表机构
College of Electrical Engineering, Sichuan University（四川大学电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对DSO与FRA协调缓解三相不平衡的激励难题，提出基于广义纳什议价的机会约束模型，通过定制议价能力实现公平分配，并采用分布式算法求解，数值验证了其有效性与鲁棒性。
AI 中文摘要
尽管以电动汽车聚合商（EVA）和负荷聚合商（LA）为代表的灵活资源聚合商（FRA）具有缓解三相不平衡的内在灵活性，但关键挑战在于如何有效激励它们积极参与不平衡缓解。为此，本文提出了一种基于广义纳什议价（GNB）的机会约束协调运行模型，用于配电系统运营商（DSO）与灵活资源聚合商（FRA）之间的协同。在该框架中，DSO与FRA合作缓解三相不平衡，其中FRA提供灵活性以降低DSO的不平衡缓解成本，而DSO则向FRA提供经济激励作为回报。议价能力根据各参与方对不平衡缓解的贡献进行定制，以确保公平的利润分配。此外，模型集成了基于场景的机会约束公式，以处理光伏（PV）出力不确定性。为求解该模型，针对独立模型引入了分布式近端分解算法（PDA），而协调模型则被分解为社会福利最大化子问题和支付议价子问题，并开发了一种改进的双线性Benders分解算法来求解前者。数值结果验证了所提方法在缓解不平衡、确保公平利润分配以及增强对不确定性鲁棒性方面的有效性。
英文摘要
Although flexible resource aggregators (FRAs), represented by electric vehicle aggregators (EVAs) and load aggregators (LAs), possess inherent flexibility to mitigate three-phase unbalance, the key challenge lies in how to effectively incentivize them to actively participate in unbalance mitigation. To address this, this paper proposes a generalized Nash bargaining (GNB)-based chance-constrained coordinated operation model for the distribution system operator (DSO) and FRAs. In this framework, the DSO and FRAs cooperate to mitigate three-phase unbalance, where FRAs provide flexibility to reduce the DSO's unbalance mitigation costs, and the DSO offers economic incentives to FRAs in return. Bargaining power is tailored according to each participant's contribution to unbalance mitigation to ensure fair profit allocation. Moreover, a scenario-based chance-constrained formulation is integrated to handle photovoltaic (PV) output uncertainties. To solve the model, a distributed proximal decomposition algorithm (PDA) is introduced for the independent model, while the coordinated model is decomposed into a social welfare maximization subproblem and a payment bargaining subproblem, with an improved bilinear Benders decomposition algorithm developed to solve the former. Numerical results validate the effectiveness of the proposed method in mitigating unbalance, ensuring fair profit distribution, and enhancing robustness against uncertainties.
