Scenario-based sensitivity-driven energy storage optimization for economic and carbon efficiency in multi-vector energy communities

The pressure to decarbonize power systems is increasing. Nonetheless, there are serious challenges in performing economic analyses of zero-carbon power systems. The sustainable operation of integrated heat and power systems is vital to ensure economic efficiency in the heat generation sector and reduce carbon emissions in power system dispatch. A bi-objective mixed-integer linear programming approach with scenario-based sensitivity evaluation is proposed in this study to break the silo between heat and power dispatch in local integrated smart energy communities (LISEC). The approach uses a dual-objective optimization algorithm that minimizes carbon production while minimizing cost and balancing economic and emission objectives. Dispatch analysis has also considered the impact of heat pumps and electric-thermal storage devices. The proposed method is applied to a LISEC system that consists of a 33-bus distribution network and a 58-node heating network with 26 heating demands to validate the proposed method. It comprises the distribution network interacting with the main grid for power exchange, wind turbines, battery energy storage, and combined heat and power (CHP) units. On the other hand, the district heating network consists of heat pumps, CHP, a gas boiler, and thermal storage tanks. A wide-ranging scenario-based sensitivity evaluation assesses the stability of optimization outcomes, principal components that determine economic feasibility and carbon output, and the effects of changes in energy storage capability and demand variability. The results underline that energy storage systems have proved to be a key component in strengthening the adequacy and performance of the electricity grid in extreme weather conditions.