Dynamic evaluation on microgrids operation model and benefit realization through optimized RBFNN

While renewable energy has become the common sense to deal with climate change and ensure energy security, microgrids have been widely deploying as a green and clean energy, which can achieve flexible and efficient application of distributed power sources solving the problem of large-scale and diverse forms of power grid connection. However, the deployment of microgrids does not have regular development forms and experience to follow in the electricity market, for they face various internal and external uncertainties. Thus, this paper suggests using new RBF neural networks mechanisms from many aspects to adapt to multi-objective optimization methods by comprehensively considering multiple objectives, construct a multi-dimensional benefit dynamic evaluation model to cope with such uncertain factors from both supply and demand sides of microgrids. The final practical instances indicate the framework can dynamically and reasonably measure the multi-object benefits in such volatile environment, and promote the highquality construction of renewable power station to realize the sustainable development of microgrids systems.