Distributed Resource Aggregation and Optimal Scheduling Based on Zonotopes

As electricity demand increases with social development, the inadequate upgrade of distribution network infrastructure fails to meet peak demand. To address this issue, introducing source-load distributed resources to enhance power system flexibility has become a development trend that reduces distribution equipment retrofitting costs while satisfying peak grid demand. To this end, this paper proposes an optimal aggregation and scheduling strategy for distributed resources based on zonotopes. First, a Monte Carlo simulation-based scenario generation model is developed to supplement the scenario set for uncertain photovoltaic output. Second, a distributed resource aggregation method using zonotopes is proposed to determine the adjustable range of distributed resource clusters. Finally, an industrial park case study is conducted to validate the superiority of the proposed strategy in coordinating source-load distributed resources for peak-load shaving, achieving a peak shaving rate of 40.09% during peak demand periods.