Research on peak load shifting for hybrid energy system with wind power and energy storage based on situation awareness

The uncertainty of wind power and load fluctuations can elevate the peaking pressure on the power grid and influence the optimization strategy for peak load shifting. Additionally, there is a need to explore the trade-off and dynamic adjustment between economic considerations and the effectiveness of peak load shifting strategies. In this paper, based on the situation awareness theory, an optimization model on peak load shifting is proposed for a hybrid energy system with wind power and energy storage unit. First, in the situation perception stage, simulations that incorporate stochastic volatility are executed for renewable energy outputs and electric loads, leading to the establishment of uncertainty models for both wind power and electric load. During the situation comprehension stage, an optimization model with multi-objective is proposed to simulate the hybrid system situation, in which the mutual constraints and games are considered between peak load shifting effects and system economy. Then, in the stage of situation presentation and situation orientation, by setting quantitative indicators to analyze and judge the system situation, the problem of changing uncertainty from the weights of various attributes in multi-objective decision-making is transformed into the prediction problem of the adjustable factor, and a hybrid network model is proposed to realize the interval prediction of the adjustable factor, further balancing the goals of load leveling and system economy. Example simulations demonstrate that the proposed optimization model for peak load shifting can effectively reduce the peak-valley difference ratio of the net load by over 39.08 %, thereby smoothing the overall net load fluctuation. In the face of uncertainty, the model shows minimal impact on the peak-valley difference ratio of the net load, experiencing only 3.91 % and 7.13 % changes in response to wind power and load fluctuations, respectively. These findings underscore the model's sensitivity to different levels of uncertainty in both wind power and load scenarios. The MAML-LSTM-FC model can clarify the selection range of the adjustable factor according to the requirements of system quantitative indexes, thereby achieving dynamic coordination between the effects of peak load shifting and economics.