Stochastic-Weighted Robust Optimization Based Bilayer Operation of a Multi-Energy Building Microgrid Considering Practical Thermal Loads and Battery Degradation

TLDR

The study proposes a bilayer coordinated operation scheme for a multi‑energy building microgrid that addresses comprehensive uncertainty sources. The authors develop a bilayer model that first applies stochastic‑weighted robust optimization for day‑ahead dispatch of storage, demand response, and CCHP plants, then sequentially finalizes hourly power‑to‑thermal conversion, CCHP operation, and grid transactions using uncertainty realizations. Case studies show the approach achieves economical operation, high computational efficiency, and robustness against uncertainties.

Abstract

This paper discusses a bilayer coordinated operation scheme for the multi-energy building microgrid (MEBM) with comprehensive uncertainty sources. First, a building model considering the battery degradation, practical/detailed thermal loads, and various operating tasks of residential appliances is presented. Second, to alleviate the adverse effects from diverse uncertainties of electricity demands, cooling water temperature, outdoor temperature, occupants’ metabolism as well as solar irradiance, a bilayer model is applied which makes full utilization of historical data and manages the solution conservativeness. The first layer is the stochastic-weighted robust optimization-based day-ahead operation. It determines the dispatch of heterogeneous energy storage assets, multi-energy demand response, and on-off status of combined cooling, heat and power (CCHP) plants on an hourly basis for the entire day. The second layer sequentially finalizes the operation of the power-to-thermal conversion unit, CCHP plant, and electricity transactions between the MEBM and utility grid hourly with uncertainty realizations. Numerical case studies demonstrate the effectiveness of the proposed approach in obtaining the economic MEBM operation with the high comput-ational performance and immunizing against uncertainties.