Network-aware MILP model for scheduling multi-energy systems considering carbon emissions and customers’ satisfaction: A DRCC approach

Abstract

The rapid growth of multi-energy systems (MESs) with electricity, natural gas, and heat generations and conversions necessitates efficient management frameworks to harness the offered flexibility by the system components. This paper proposes a mixed-integer linear programming (MILP) model to optimally manage a MES equipped with electric vehicle (EV) charging stations, combined heat and power (CHP) units, renewable energy sources (RESs), energy storage systems, and electric heat pumps. The objective function of the problem is to minimize the operation cost of MES, which includes the cost of purchased energy from the external electricity and gas networks, the cost of carbon emissions, and the penalty to compensate for the dissatisfaction of households and EV owners. The constraints of the electricity grid and natural gas network are incorporated into the proposed energy management scheme using the linearized AC power flow and gas flow equations. Moreover, the uncertainties associated with RESs and demand are modeled using the distributionally robust chance-constrained (DRCC) approach to not only guarantee the robustness of the optimal scheduling plan against uncertainties, but also incorporate the probabilistic nature of these uncertain parameters. Finally, the IEEE 33-bus electricity grid and 14-node gas network are employed to validate the effectiveness and applicability of the presented methodology from the viewpoints of the system operator and customers.