含共享储能的微电网群分布鲁棒博弈优化调度方法

共享储能作为一种新兴的储能方案,有助于微电网内部新能源的消纳并降低运行成本,释放微网作为独立的利益相关者的资源共享潜力。而传统的共享储能和微网间的互联忽略了各主体交易的信息隐私问题,且合作策略往往不能实现合理的利益分配。为此,提出了一种含有共享储能的微电网群分布鲁棒博弈优化调度方法。首先,建立了具有多种能量形式的微电网模型以及共享储能模型。其次,为降低风光出力不确定性对系统经济性的影响,采用分布鲁棒优化理论对其进行处理,求解最恶劣概率分布下的运行策略。最后,基于纳什谈判理论,建立了共享储能与微电网系统的联合运行模型,并利用具有良好收敛性与私密性的交替方向乘子法将模型分解为联合系统运行成本最小化问题和系统内部电能交易谈判问题进行求解。通过合作前后对比分析,所提方法使得微电网运行成本分别降低了2.99%、4.90%和4.27%,说明所提方法能够在有效应对风光出力不确定性的同时降低各主体的运行成本,使系统兼具灵活性与经济性。

A robust game optimization scheduling method for shared energy storage micro electric network group distribution

Shared energy storage, as an emerging energy storage solution, helps to integrate renewable energy sources within microgrids and reduce operational costs, unleashing the potential for resource sharing among independent stakeholders in the microgrid. However, traditional approaches to shared energy storage and interconnection between microgrids overlook the issue of information privacy in transactions among entities, and cooperative strategies often fail to achieve fair benefit allocation. To address this, a distributed robust game-theoretic optimization scheduling method is proposed for microgrid clusters with shared energy storage. First, a microgrid model with multiple energy forms and a shared energy storage model are established. Second, to mitigate the impact of uncertain wind and solar power outputs on system economics, robust optimization theory is applied to handle uncertainty and solve for the worst-case probability distribution of operational strategies. Finally, based on the Nash bargaining theory, a joint operation model for shared energy storage and microgrid systems is developed, and the model is decomposed into two sub-problems: minimizing the joint system operational cost and negotiating internal electricity transactions within the system, using the alternating direction method of multipliers with good convergence and privacy properties. Through comparative analysis before and after cooperation, the proposed method reduces microgrid operational costs by 2.99%, 4.90%, and 4.27%, respectively, demonstrating its effectiveness in addressing wind and solar power uncertainty while reducing operational costs for all stakeholders, achieving both flexibility and economic efficiency in the system.