在极端灾害发生后电动汽车(electric vehicle,EV)可作为移动电源给负荷供电,从而支撑配电网韧性提升。针对配电网故障发生不可预见、正常态和故障态调度模型切换导致调度指令不稳定问题,文章研究了运行状态平滑切换的EV参与配电网韧性提升策略。基于EV状态和连接方式的二进制变量建立线性化EV时空动态模型,能适应EV灵活调度算力大的需求。考虑EV和交通网不确定性因素,建立基于交通网和配电网信息实时滚动更新的正常态和故障态统一调度模型,正常态以经济性和韧性最优为目标,故障态以韧性最优为目标,通过引入状态表征参数实现两种状态间平滑切换。算例仿真表明,该策略在不影响精度的情况下显著降低了计算量,有助于实现配电网最优韧性恢复和状态平稳过渡。

In the aftermath of extreme disasters, electric vehicles (EV) can serve as mobile power sources, bolstering the resilience of distribution networks by supplying power to critical loads. To address the challenge of dispatching instruction instability stemming from unforeseeable faults, alongside the need for seamless switching between normal and fault state scheduling models in distribution networks, this study explores the integration of EVs into resilience enhancement strategies, with smooth switching of the running state. Based on the normalization of binary variables representing the EV state and connection mode, a linearized EV spatiotemporal dynamic model is developed, meeting the power requirements for large flexible EV scheduling computations. Considering the uncertainty factors inherent in EVs and transportation networks, a unified scheduling model encompassing both normal and fault states is formulated. This model relies on real-time rolling updates of information from both the transportation and distribution networks. In the normal state, the objective is to optimize economy and resilience, while the fault state aims at solely optimizing resilience. The seamless switching between these two states is realized through the introduction of state characterization parameters. The simulation results demonstrate that the proposed strategy significantly reduces computational overhead without compromising accuracy, thereby facilitating optimal resilience recovery and smooth transitions within the distribution network.

为了充分利用电动汽车（electric vehicle，EV）大规模的储能优势与代理聚合商在电力市场灵活购售电优势，以此弥补虚拟电厂（virtual power plant，VPP）内部供需不平衡情况，构建电动汽车参与的虚拟电厂双层博弈模型，对虚拟电厂同时进行内外部优化。首先，构建上层代理聚合商虚拟电厂完全信息动态博弈模型进行虚拟电厂外部优化；其次，构建虚拟电厂电动汽车聚合商合作博弈模型进行虚拟电厂内部优化，并利用改进的Shapley值分配虚拟电厂与电动汽车聚合商的合作收益；最后，以集成风电机组、可控负荷、储能电池、用户、电动汽车的虚拟电厂进行算例分析，采取协同免疫量子粒子群优化（coevolutionary immune quantum partical swarm optimization，CIQPSO）算法搜寻最优解。算例结果表明，电动汽车参与虚拟电厂能够同时提高两者的经济效益，提高虚拟电厂内部供需平衡能力。

In order to make full use of the large-scale energy-storage advantages of electric vehicles （EVs） and the advantages of agent aggregators to flexibly purchase and sell electricity in the power market， to make up for the imbalance between supply and demand in virtual power plant （VPP）， this paper constructs a two-layer game model of a virtual power plant in which electric vehicles participate， to perform internal and external optimization of the virtual power plant at the same time. On this basis， firstly， a fully dynamic game model of the upper-level agent aggregator - virtual power plant is constructed to perform external optimization of the virtual power plant. Secondly， a virtual power plant - electric vehicle aggregator cooperation game model is built to optimize the virtual power plant internals， and the improved Shapley value is used to allocate the cooperation revenue between the virtual power plant and the electric vehicle aggregator. Finally， a virtual power plant with integrated wind turbines， controllable loads， energy storage batteries， users， and electric vehicles is used to analyze the example. The coevolutionary immune quantum partical swarm optimization （CIQPSO） algorithm is used to search for the optimal solution. The results of a numerical example show that the participation of electric vehicles in a virtual power plant can simultaneously improve the both economic benefits  and increase the supply-demand balance capability of the virtual power plant.

伴随着电力市场售电侧的日益开放化,虚拟电厂(virtual power plant, VPP)内不同利益体可通过电能交易提高经济效益。首先,文章针对包含分布式电源的运营商(distributed generation operator,DGO)、云储能运营商(cloud energy storage operator,CESO)以及产消者聚合商(prosumer aggregator,PA)等多种运营主体的虚拟电厂,提出基于合作博弈的多运营主体间电能交易机制,实现VPP总运行成本最小。其次,以各运营主体单独与配电网交易的运行成本作为谈判破裂点,利用纳什议价方法求解各运营主体间的电能交易量与收益转移,维持各运营主体参与合作的积极性。考虑到纳什议价模型的非凸性与各运营主体的隐私安全,将议价均衡问题转换为两个凸的子问题,并采用交替方向乘子法(alternating direction method of multipliers,ADMM)进行求解。最后,通过算例仿真进一步验证了所提方法能有效减少各运营主体的运行成本,为VPP内电能交易机制的设计提供了参考方案。

With the power market sales side increasingly open, different stakeholders in virtual power plants (VPP) can increase economic benefits through electricity trading. Firstly, for a type of VPP that includes multiple operating entities such as distributed new energy operator, energy storage operator, and aggregator of prosumers, a cooperative game-based energy trading mechanism among multiple operating entities is proposed in this paper, and the total operating cost of VPP is minimized. Secondly, taking the operating cost of each operating entity’s separate transaction with the distribution network as the breakdown point of the negotiation, the Nash bargaining method is used to solve the electricity transaction volume and revenue transfer among various operating entities, so as to maintain the enthusiasm of each operating entity to participate in the cooperation. Taking into account the non-convexity of the Nash bargaining model and the privacy security of each operating entity, the bargaining equilibrium problem is converted into two convex sub-problems, and the ADMM algorithm is used to solve them. Finally, a simulation example further verifies that the proposed method can effectively reduce the operating cost of each operating entity, and provides a reference scheme for the design of the electricity transaction mechanism in VPP.

以包含高比例光伏和规模化电动汽车(electric vehicle,EV)的配电网规划为研究对象,首先对电动汽车虚拟电厂进行灵活性量化,然后建立了综合考虑电动汽车虚拟电厂灵活性与高比例光伏接入的配电网规划模型。模型以配电网线路年综合投资成本最小为目标,同时兼顾新能源消纳、储能系统投资成本和电动汽车虚拟电厂灵活性补偿成本,以期在提升配电网规划经济性的同时实现电力系统“削峰填谷”,并提高光伏出力消纳率。最后以IEEE RTS-24节点配电网系统为例进行仿真验证,算例表明,所提规划模型利用储能系统和电动汽车灵活性降低了系统的规划运行成本,并提高了配电网内部光伏电站的消纳率,能够对未来包含高比例可再生能源和虚拟电厂灵活性资源的电力系统规划提供借鉴和参考。

This paper studies the distribution network planning problem which considers high proportion of photovoltaic power and large-scale electric vehicles (EVs). Firstly, the paper quantifies the flexibility of the EV virtual power plant, and then establishes a distribution network planning model that comprehensively considers the flexibility of the EV virtual power plant and the high proportion of photovoltaic access. The model aims to minimize the annual investment cost of distribution network lines, while taking into account renewable energy consumption, investment costs of energy storage system and flexibility compensation costs of EV virtual power plant, in order to improve the economy of distribution network planning and realize peak shaving and valley filling of power system and improve the photovoltaic output consumption rate. Finally, the IEEE 24-node distribution network system is taken as an example for simulation verification. The example shows that the planning model proposed in this paper uses the flexibility of the energy storage system and electric vehicles to reduce the planning and operation cost of the system and improves the consumption rate of the photovoltaic power station in the distribution network. The model can provide reference for future power system planning including high penetration of renewable energy and virtual power plant flexibility resources.