Coordinated operation of active distribution network considering demand response

To tackle the issues arising from fluctuations in power market prices, uncertainties in renewable energy production, and the uncoordinated charging of electric vehicles (EVs), this study introduces a novel optimization scheduling approach for active distribution networks (ADNs) that incorporates carbon trading and EV charging-swapping-storage integrated stations. Firstly, the paper outlines the calculation methods for carbon emissions and carbon quotas related to ADN, clarifying the ownership of equipment assets and responsibilities for carbon emissions. It then proposes an optimized scheduling strategy for ADN participating in the carbon trading market, which combines day-ahead (CTM) and real-time market transactions (RTM) to enhance ADN operational profits. Additionally, to address the tendency of the optimization scheduling model to become trapped in local optima, the Particle Swarm Optimization-Second-Order Oscillation and Particle Replacement Algorithm (PSO-SOO-PR) is proposed. Finally, a simulation within an industrial park demonstrates that the overall operating income for a single day increased by 63.8%, with income from electricity transactions rising by 24.1%. The case studies signify that the proposed optimization scheduling method for ADNs can effectively enhance their economic benefits while concurrently reducing carbon emissions.