Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Abstract

To facilitate energy system integration, it is imperative that a multienergy system produce and deliver in a coordinated way the energy in its different component forms. In particular, a small‐scale integrated energy system must accommodate renewable energy resources, flexible loads, and energy coupling technologies, which creates new challenges to the interactions between the energy vectors. Hence, an energy management model for a microenergy system in grid‐connected mode under uncertainties is proposed to perform the decision‐making for shifting energy modes for all energy sources and end‐use applications with the aim of optimally scheduling controllable energy resources in the system and minimizing the net management cost under uncertainty. To address the stochastic nature of the price of electricity, a data‐driven robust optimization approach is introduced. It uses the available sample data to design the appropriate uncertainty set using statistical hypothesis tests, and a combination of conditional value‐at‐risk and a worst‐case optimization problem is used to formulate the energy management problem under uncertainty. We explore a computationally tractable robust counterpart of the original optimization problem. The optimal energy scheduling solution obtained from the proposed approach is immune against any worst‐case realizations. Numerical results demonstrate the effectiveness of the proposed approach and its performance of less conservation. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.