Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

Abstract

The energy problem in today’s society is becoming increasingly prominent, and the smart grid has become one of the important ways to solve the energy problem. Smart grid energy storage capacity planning and scheduling optimization is an important issue in the smart grid, which can make the grid more efficient, reliable, and sustainable to meet energy demand better and protect the environment. The core of smart grid energy storage capacity planning and scheduling optimization is maximizing the use of energy storage devices to balance the difference between power supply and demand to ensure the grid operation’s stability. Traditional planning methods are usually based on experience and rules, have low precision, and cannot adapt to the dynamic changes in the long-term development of the power grid. Therefore, this paper proposes a method that combines PSO-GRU (particle swarm Optimization (PSO)-gated recurrent unit (GRU)) and Multihead-Attention to realize smart grid energy storage capacity planning. And scheduling optimization. First, PSO-GRU models and predicts power grid data by searching for the optimal GRU model parameters; second, Multihead-Attention improves the model’s performance through the self-attention mechanism. Finally, we use the method to determine the optimal energy storage capacity and dispatching scheme for the efficient operation of smart grids. Our experiments use real power grid datasets and compare them with other common methods. Experimental results show that our proposed method has higher accuracy and stability than other methods and can better adapt to the dynamic changes of the power grid. This indicates that our method has good feasibility and applicability in practical applications and is significant for realizing the efficient operation of smart grids and energy saving and emission reduction.