Integration of Small Pumped-Hydro Storage Units in the Day-ahead Scheduling of Renewable Energy Communities

Abstract

This paper presents a stochastic day-ahead scheduling model for energy communities (ECs) with small-scale pumped hydro storage (PHS) units. The communities do not manage the distribution network, which is owned by an independent operator with a third-party connection obligation. The model allows direct energy transactions among the members of the same community and includes different storage capabilities. The model uses a two-stage stochastic mixed-integer linear programming (MILP) methodology that represents the uncertainty in load and photovoltaic (PV) production profiles through copula-based scenario generation. The PHS unit model uses a piecewise linear representation based on special ordered sets of type 2 (SOS2) to limit the number of binary variables and ensure computational performance. The case study involves multiple ECs in a real medium-voltage distribution network. Load and production data are available for each user on three different days with 15-minute resolution. Internal transactions and PHS use improve flexibility and reduce electricity procurement costs: in the operating conditions considered, removing EC transactions increases costs by up to approximately 120%, while removing PHS increases costs by up to approximately 7%.