Optimizing the Operation and Coordination of Multi-Carrier Energy Systems in Smart Microgrids Using a Stochastic Approach

Energy hubs (EHs) have become essential to facilitate the coupling of the various energy carriers in smart microgrids characterized by high penetration levels of various renewable energy sources (RES), such as photovoltaics and wind power. Optimal operation and coordination of these microgrid resources are crucial for satisfying electrical and thermal demands with minimal cost and achieving eco-friendly operation. To this end, this paper proposes a stochastic multi-objective optimization approach for optimal operation and coordination of RES, EH systems, and plug-in electric vehicles (PEVs). The EH includes compressed air energy storage, battery energy storage, and thermal energy storage. The objective functions to be minimized are operating costs and emissions. The proposed approach considers the uncertainties of RES, electrical and thermal demands, electrical prices during seasonal-based horizons, the stochastic nature of PEV's owners' driving habits, and various microgrid operational constraints. Furthermore, a price-based demand response program is employed considering the end-user's discomfort. The multi-objective grey wolf optimizer is employed to solve the proposed optimization problem and obtain the Pareto-optimal solutions. Different case studies are performed to demonstrate the proposed approach's effectiveness. The simulation results show that the proposed approach can reduce the operation cost and emissions by 64.1% and 57.6%, respectively.

Automated summary

This paper proposes a stochastic multi-objective optimization approach for optimal operation and coordination of renewable energy sources (RES), energy hub systems, and plug-in electric vehicles (PEVs). The proposed approach considers the uncertainties of RES, electrical and thermal demands, electrical prices during seasonal-based horizons, the stochastic nature of PEV's owners' driving habits, and various microgrid operational constraints. Simulation results show that the proposed approach can reduce the operation cost and emissions by 64.1% and 57.6%, respectively.