Multi-timescale power self-balancing optimization and regulation of remote rural microgrids based on stochastic monte carlo method

Abstract Conventional microgrid power self-balancing optimization and regulation methods are mainly based on real-time regulation mechanisms, but there is the problem of local optimum, which affects the regulation effect. Therefore, a stochastic Monte Carlo method is designed to optimize the power self-balancing regulation of remote rural microgrids in multiple time scales. The multi-time scale power imbalance characteristics of remote rural microgrids are extracted, and the load differentiation characteristics of remote rural areas are judged according to the regional economic development and resource endowment. Based on stochastic Monte Carlo, a microgrid multi-timescale power self-balancing optimization and regulation model is constructed to quantify the self-balancing capability of the microgrid, taking into account the temporal difference between wind power and remote rural load demand. Taking into account the inter-seasonal characteristics of remote rural loads and the dynamic balancing demand of sources and loads, the multi-timescale adjustable power of remote rural microgrids is optimally allocated to further balance the complexity of the regulation model and the solution time. Comparative experiments are conducted to verify that the self-balancing optimization effect is better and can be applied in real life.