计及配电网运行约束的多虚拟电厂合作博弈策略

Cooperative Game Strategy of Multiple Virtual Power Plants Considering the Operational Constraints of Distribution Network

虚拟电厂(virtual power plant,VPP)作为一种有效的可再生能源聚合利用手段,近年来得到迅速发展,随着VPP并网规模的扩大,多个互联VPP交易博弈问题日益凸显。针对多个VPP间的交易博弈问题,考虑物理网络特性,提出了计及配电网运行约束的多VPP合作博弈策略。首先,考虑配电网运行约束,对VPP内部资源进行整合和建模,建立了VPP能量管理模型。其次,通过引入点对点(peer to peer,P2P)能量交易,实现多VPP系统自主能量管理与协同定价,能够在不损害各方利益的情况下达成P2P能量交易。同时考虑到用电负荷和可再生能源出力的不确定性,利用典型场景生成算法构造了不确定性变量的概率分布模糊集。针对多主体交易产生的隐私性问题,采用列和约束生成算法联合交替方向乘子法对模型进行求解。最后,在IEEE 123节点测试系统上进行算例仿真,仿真结果验证了所提模型和算法的有效性。

In recent years,virtual power plant(VPP)has been developing rapidly as an effective means of aggregate utilization of renew-able energy.With the expansion of VPP grid-connected scale,the problem of multiple interconnected VPP transactions has become increasingly prominent.Aiming at the transaction game between multiple VPPs,considering the characteristics of the physical network,cooperative game strategy of multiple VPPs considering the operational constraints of distribution network is proposed in this paper.Firstly,considering the operation constraints of the distribution network,the internal resources of the VPP are integrated and modeled,and then the VPP energy management model is established.Secondly,through the introduction of peer-to-peer(P2P)energy transac-tions,the autonomous energy management and collaborative pricing of multiple VPPs systems can be realized,and P2P energy transac-tions can be achieved without harming the interests of all parties.At the same time,considering the uncertainty of electricity load and renewable energy output,a typical scenario generation algorithm is used to construct a fuzzy set of probability distribution of uncertain variables.Aiming at the privacy problem arising from multi-agent transactions,column constraint generation algorithm combined with alternating direction multiplier method are adopted to solve the model in this paper.Finally,a numerical example is simulated on the IEEE 123 nodes test system,and the simulation results verify the effectiveness of the proposed model and algorithm.