Low Carbon Economic Dispatch for Distribution Networks Considering the Uncertainty of Charging Stations with Photovoltaic and Energy Storage

This paper presents a comprehensive study on the low carbon economic dis-patch for distribution networks, incorporating the uncertainty of photovoltaic (PV) and electric vehicle (EV) charging stations. With the proliferation of electric vehicles and the need for sustainable charging infrastructure, this pa-per emphasizes the significance of PV and energy storage (ES) systems in reducing carbon emissions and enhancing grid stability. The study introduces a carbon trading mechanism to account for the environmental impact of power generation, aligning with China’s carbon emission trading market. The core of the research is an optimization model that minimizes the overall op-erating cost of the distribution network, factoring in the costs of grid power purchase, network losses, storage operations, and carbon trading. The model employs second-order cone relaxation (SOCR) to handle non-convex and nonlinear power flow constraints, ensuring a more precise representation of the power system dynamics. To address the unpredictability of PV output and EV load, a distributionally robust chance constraint (DRCC) method is utilized, balancing the need for feasible scheduling with a reasonable level of conservatism. The effectiveness of the proposed method is validated through simulation on an improved IEEE-33 node system, demonstrating its ability to achieve optimal scheduling results while significantly reducing operating costs compared to traditional methods. This paper concludes that the integra-tion of carbon trading and robust optimization strategies can lead to more sustainable and cost-effective power system operations in the context of re-newable energy and EV integration.