This paper presents an integrated operation model of renewable energy, distributed generators (DGs), energy storage, and demand response to overcome the challenges of renewable energy market participation. Additionally, an accurate prediction model for wind, photovoltaic (PV) power, and market price is introduced based on ensemble empirical model decomposition. In this model, radial basis function neural network (RBFNN) is used for the forecasting of subsignals from the analysis model. The training model of RBFNN is shaped based on the B-water cycle algorithm (WCA) optimization method. Additionally, by considering the adjusted market, demand response, and uncertainties analysis, this method reduced the economic damages of generators and loads. A case study test has consisted of a wind turbine, photovoltaic energy, fuel cell energy, demand response, and energy storage.