Quantifying the Emission Reduction Potential and Optimization Path of Virtual Power Plants in Urban Energy Management

In view of the core problems of insufficient absorption of distributed renewable energy and high carbon emission intensity faced by urban energy systems, this study proposes a collaborative optimization framework based on virtual power plant (VPP) to quantify their emission reduction potential and reveal optimization paths. The method is divided into three steps: first, a VPP multi-source aggregation model is constructed (integrating distributed photostatic, wind power, energy storage and adjustable loads); second, a dynamic scheduling model with dual objectives of minimizing economic costs and minimizing carbon emissions is established; finally, an improved multi-objective genetic algorithm (based on the NSGA-II framework fused with differential evolution operations) is used to solve the Pareto frontier, and the existing algorithms are compared through typical day scenarios. In the typical summer/winter day scenario, the VPP mode reduces carbon emissions by 23.8% (summer) and 18.7% (winter), achieving a total system cost of 1.439 million yuan in summer, and the wind and solar power consumption rate is increased to 97.3%. VPP can release the emission reduction potential of urban distributed resources through multi-time scale coordination and carbon cost internalization mechanisms, and provide a quantifiable path for the low-carbon transformation of urban energy systems.