As the global reliance on renewable energy sources grows, wind and photovoltaic power, as pivotal components, pose significant challenges to power system dispatch due to their volatility and uncertainty. To effectively address this challenge, this paper proposes a renewable energy optimization dispatch strategy based on a prediction model. First, this paper constructs a prediction model combining functional data analysis and recurrent neural networks (RNNs) to achieve an accurate prediction of renewable energy output. On this basis, considering the economic and environmental benefits of system operation, an optimal multi-objective dispatch model for renewable energy is established, and the multi-objective optimization problem is transformed into a single-objective optimization problem using weighting methods to reduce the complexity of the solution. Finally, a typical microgrid test system is used to verify the effectiveness and feasibility of the proposed method. The results of the numerical example show that the proposed model can achieve an accurate prediction of renewable energy sources, reduce the conservatism of traditional dispatch decisions, and balance economic and environmental benefits.

With the growing global emphasis on renewable energy, the issue of renewable energy consumption has emerged as a hot topic of current research. In response to the volatility and uncertainty in the process of renewable energy consumption, this study proposes a renewable energy consumption strategy based on scenario reduction and flexible resource utilization. This strategy aims to achieve the efficient utilization of renewable energy sources through optimized resource allocation while ensuring the stable operation of the power system. Firstly, this study employs scenario analysis methods to model the volatility and uncertainty of renewable energy generation. By applying scenario reduction techniques, typical scenarios are selected to reduce the complexity of the problem, providing a foundation for the construction of the optimization model. At the same time, by fully considering the widely available small-capacity energy storage units within the system, a flexible cloud energy storage scheduling model is constructed to assist in renewable energy consumption. Finally, the validity and feasibility of the proposed method are demonstrated through case studies. Through analysis, the proposed scenario generation method achieved a maximum value of 26.28 for the indicator $I_{DBI}$ and a minimum value of 1.59 for the indicator $I_{CHI}$. Based on this foundation, the cloud energy storage model can fully absorb renewable energy, reducing the net load peak-to-trough difference to 1759 kW, a decrease of 809 kW compared with the traditional model.