Energy and Reserve Sharing Considering Uncertainty and Communication Resources

Abstract

In this paper, we study the joint energy and reserve sharing problem considering renewable generation uncertainty and limited communication resources. We propose a data-driven distributionally robust energy and reserve sharing model among different agents in electricity markets. We put forward data-driven distributionally robust chance constraints (DRCC) to determine the reserve capacity, which cannot be directly solved. The inner approximation is employed to convert the DRCC into tractable linear constraints. Taking into account the agents in the Internet of Things exchange information by a resource-limited communication network, we develop a communication-censored consensus alternating direction method of multipliers (ADMM) to utilize the limited communication resources and solve the sharing problem in a fully decentralized manner. We analyze the convergence of the proposed algorithm, and propose an adaptive penalty parameter method to speed up the convergence. Extensive simulations are conducted to verify the effectiveness of the proposed model and theoretical results.