Adaptive robust strategy for energy and regulation Service Management in Local Energy Communities

Abstract

Local energy communities (LECs) play a crucial role in enhancing energy self-sufficiency and sustainability by fostering local renewable generation and consumption. However, the inherent variability in renewable power generation and consumption presents challenges. This paper introduces a novel linear bi-level model designed to optimize resource scheduling within LECs, incorporating shared battery energy storage and hydrogen energy systems. Both systems are utilized for providing regulation services, including both generation and consumption modes. To account for uncertainties in demand and renewable power generation, we employ adaptive robust optimization (ARO), formulating the problem as a min-max-min framework. The outer and inner minimization sub-problems represent the optimal strategy of the LEC in the day-ahead energy and reserve markets, and real-time regulation markets, respectively. The worst-case realization of uncertain consumption and photovoltaics generation (PV) is addressed through middle maximization. The proposed model identifies the optimal energy trading strategy for LECs to ensure the provision of regulation services under these uncertainties, based on a defined budget of uncertainty. The application of bi-level optimization and decomposition techniques facilitates solving the min-max-min problem. Simulation results reveal that integrating hydrogen energy systems significantly enhances community flexibility and reduces overall energy supply costs. • Using ARO-based model to optimize LEC resources in energy and regulation • Integration shared battery and hydrogen systems to improve flexibility of LECs • Proposing bi-level optimization to solve min-max-min problem

Topics & Concepts