Low carbon regional energy management system with conditional value at risk

Regional energy systems are widely used to collect and uniformly manage the energy consumption within individual districts. This paper proposes a novel framework for a regional energy system, featuring five districts within one region, aimed at reducing both regional energy cost and carbon emissions, while taking into account the Conditional Value at Risk (CVaR). The energy consumption models are developed for residential and commercial areas, incorporating variables like electric vehicle (EV) usage, photovoltaic (PV) output, and energy storage. A Regional Energy Management System (REMS) is designed to set dynamic transaction prices and optimize revenue and risk indicators. Using Stackelberg game theory and Karush–Kuhn–Tucker conditions, the regional energy objective is unified into a mixed-integer linear programming problem with CVaR, considering EV and PV risks. The Carbon Emission Intensity (CEI) is updated analytically, and REMS determines dynamic transaction prices and manages consumption across districts. Numerical results demonstrate that the proposed REMS framework with CVaR risk considerations significantly reduces both regional energy costs and carbon emissions. Additionally, the updating of CEI is found to notably impact the regional carbon emissions. This paper provides valuable guidance on quantifying districts energy interaction risk, reducing regional building energy consumption and carbon emissions under EVs extensive using for regions.