LAUSR

The stochastic nature of the renewable generators and price-responsive loads, as well as the high computational burden and violation of the generators’ and load aggregators’ privacy can make the centralized energy market management a big challenge for distribution network operators. In this paper, we first formulate the centralized energy trading as a bilevel optimization problem, which is nonconvex and includes the entities’ optimal strategy to the price signals. We tackle the uncertainty issues by proposing a probabilistic load model and studying the down-side risk of renewable generation shortage. To address the nonconvexity of the centralized problem, we apply convex relaxation techniques and design proper price signals that guarantee zero relaxation gap. It enables us to address the privacy issue by developing a decentralized energy trading algorithm. For the sake of comparison, we use the dual decomposition and proximal Jacobian alternating direction method of multipliers for the algorithm design. Extensive simulations are performed on different standard test feeders to compare the CPU time of the proposed algorithm with the centralized approach and evaluate its performance in increasing the load aggregators’ and generators’ profit. Finally, we compare the impact of load and generation uncertainties on the optimality of the results.