In view of the serious economic losses caused by the unreasonable investment decision-making scheme of the current medium and low-voltage distribution network project, an optimization method for the investment decision-making of the medium and low-voltage distribution network project based on genetic algorithm is proposed. Combined with the optimization of the multi energy topology of the distribution network based on Genetic algorithm, the investment income model for the multi energy storage, renewable energy power generation and line reconstruction of the distribution network is established. According to the model, the required investment is planned by stages, an investment evaluation system considering efficiency and benefit is constructed, the evaluation algorithm is optimized, and an investment decision-making model is constructed based on the calculation results. Finally, based on the actual operation data of a regional multi-energy power grid, a multi-energy investment decision-making simulation model of distribution network is established. The simulation results verify that the optimization method of investment decision-making of medium and low-voltage distribution network projects based on genetic algorithm can improve the efficiency and benefit of multienergy investment.
In order to optimize the investment incremental distribution strategy of power grid companies, a distribution network investment path allocation model based on investment demand and investment capacity is proposed. Starting from the multiple dimensions of investment demand, investment capacity, planning risk, market risk, management risk and economic risk, a distribution network investment path allocation management model is constructed through the multi-level grey correlation area analysis method, which provides support for the optimization of investment paths for power grid companies. Finally, the experiment proves that the distribution network investment path allocation model based on investment demand and investment capacity has high practicability and fully meets the research requirements.
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