Economic Analysis and Optimization of Energy Storage Configuration for Park Power Systems Based on Random Forest and Genetic Algorithm
This work addresses economic efficiency and sustainability in park power systems, though it is incremental as it applies existing methods (random forest and genetic algorithm) to a specific domain.
The study tackled optimizing energy storage configuration in park power systems to reduce operational costs and improve economic performance, achieving cost reductions and decreased wind and solar power curtailment after optimization.
This study aims to analyze the economic performance of various parks under different conditions, particularly focusing on the operational costs and power load balancing before and after the deployment of energy storage systems. Firstly, the economic performance of the parks without energy storage was analyzed using a random forest model. Taking Park A as an example, it was found that the cost had the greatest correlation with electricity purchase, followed by photovoltaic output, indicating that solar and wind power output are key factors affecting economic performance. Subsequently, the operation of the parks after the configuration of a 50kW/100kWh energy storage system was simulated, and the total cost and operation strategy of the energy storage system were calculated. The results showed that after the deployment of energy storage, the amount of wind and solar power curtailment in each park decreased, and the operational costs were reduced. Finally, a genetic algorithm was used to optimize the energy storage configuration of each park. The energy storage operation strategy was optimized through fitness functions, crossover operations, and mutation operations. After optimization, the economic indicators of Parks A, B, and C all improved. The research results indicate that by optimizing energy storage configuration, each park can reduce costs, enhance economic benefits, and achieve sustainable development of the power system.