Hierarchical integrated energy system management considering energy market, demand response and uncertainties: A robust optimization approach

Abstract

In this research, optimal hierarchical energy management in an integrated energy system is introduced, considering the variabilities associated with renewable energy resources, uncertain loads like electric vehicles, energy market interaction uncertainties, and a demand response (DR) program that relies on a robust optimization (RO) technique. The energy hub (EH) is distributed in the microgrid (MG) framework, leading to the establishment of the MG/EH configuration. RO methods and the alternating direction method of multipliers are employed for formulating multi-objective functions and problem-solving. The local controller's role involves the optimal distribution of energy in EH by utilizing customer data, power generation units, storage devices, and energy market interactions. Conversely, the central controller within the MG platform is tasked with optimizing energy distribution by considering the DR execution based on loads and price elasticity, which is influenced by the energy carrier price uncertainty. Furthermore, the proposed model aims to minimize the greenhouse gas (GHG) emissions cost. This proposed method is evaluated under two scenarios, with/without DR programs, and is compared with other methodologies. The findings indicate that the suggested approach effectively manages optimal energy distribution, leading to a 15.4 % reduction in operational costs and GHG in the presence of DR programs.