Shapley Value Method and Stochastic Dantzig-Wolfe Decomposition for Decentralized Scheduling of Multimicrogrid

Abstract

The decentralized economic scheduling of multimicrogrid is an important aspect in the operational planning of microgrids (MGs). This article proposes an approach to maximize economic benefit among MGs through cooperative scheduling. The cooperative scheduling is achieved via price signals so that MGs are encouraged to share power among themselves for economic benefit. An MG operator generates a time-variable tariff based on energy trading status so that the parking lot operator and distributed battery energy storage system aggregator participate with flexibility in the MG's energy management. The Shapley value method is used for generating fair price signals. The stochastic Dantzig-Wolfe decomposition is used to solve the resulting optimization problem in a decentralized manner. The uncertainties related to load demand and renewable energy sources are captured using scenario-based methods, whereas the uncertainty associated with plug-in hybrid electric vehicles is modeled using copula theory based estimation. The simulation studies and comparison with the existing methods establish that the proposed approach effectively reduces the total energy cost in a decentralized manner with the minimum amount of information exchange.