A risk-based optimal self-scheduling of smart energy hub in the day-ahead and regulation markets

Abstract Utilizing multi-carrier energies such as wind energy, electric vehicle (EV) and battery banks are a significant step toward cleaner production. Hence, This paper proposes stochastic-based decision-making framework for the efficient short-term management of smart energy hub (EH) in restructured power systems with high penetration renewable energy. The electrical natural gas carriers input EH, while electricity heat demands considered outputs. including storage system (BSS) EV fleet, is managed regulation market day-ahead (DA) horizons. operator makes optimal decisions regarding network supply thermal customers. An self-scheduling model developed to take into account day ahead markets (RM) generations devices well aggregator decisions. primary goal proposed minimizing cost procuring DA markets, upward/downward regulations via stochastic mixed-integer linear programming (MILP) approach. In order get uncertainty around exact outcomes RM prices, generation, patterns, also takes conditional value at risk (CVaR) term. formulation examined by applying EH. Results show effectiveness usefulness managing EHs efficiently.