Abstract
Orderly charging of electric vehicles (EVs) functions as a flexible tool for peak shaving to help integrate renewable energy sources effectively. Coordinating investments in charging resources and generation resources can enhance social benefits while ensuring the charging needs of EVs are met. This study aims to optimize the long-term investment strategies for these resources using a heuristic algorithm-informed system dynamic (SD) approach. Firstly, a SD model is developed to simulate the long-term interactions among generation resources, EVs, and power demand, capturing their dynamic relationships. Then, the fixed feedback functions of investment parameters in the SD model are replaced with an optimization model, enabling parameter optimization for environmental and economic objectives. Given that the process must quantify the aforementioned long-term interrelationships with potential high-order, nonlinear feedback loops, the solution is implemented using Particle Swarm Optimization (PSO). Data from a specific province in China was used for the case study. The results show that the optimized investment portfolio reduced carbon costs and total investment by 2.4% and 22%, respectively, while total social costs decreased by 17% compared to the pre-set investment strategy. Lastly, we also conducted sensitivity analysis and algorithm comparison.
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