A Multivariate Load Trading Optimization Method for Energy Internet Based on LSTM and Gaming Theory

Energy Internet is a complex nonlinear system. There are many stakeholders in the load trading market, which usually regarded as multi-player gaming. Although gaming theory has been introduced to solve Multivariate Load problems, different conditions should be considered accurately optimize multivariate problem. For example, selling side needs reduce reserve capacity and improve profits, but consumer costs minimize impact on its own electricity consumption. These contradictory require multiple Nash equilibrium achieve obviously. To address this issue, unified architecture of power system cloud constructed paper, combined with classification In addition, according market operation mechanism, price-guided game strategy designed. More importantly, optimization method based LSTM (Long Short-Term Memory) proposed work. for real time prediction, can searching. The global stability optimal solution prove feasibility neural network, finally effectiveness verified by using numerical simulation.