Accurate photovoltaic (PV) power prediction plays an increasingly crucial role to maintain the safety and reliability of power grid operation. However, the fluctuation and nonstationarity of PV power make it a challenging task to optimize the accurate results. This paper presents a novel prediction model which is the combination of Hodrick-Prescott (HP) filter, optimized variational mode decomposition (OVMD), and enhanced emotional neural network (EENN). It overcomes the adverse effects of random changes under highly volatile weather conditions. First, the trend component and fluctuation component of PV power are screened through HP filter as the pre-step to alleviate the non-linearity impact of PV power data. Then, OVMD is used to decompose the residual PV power time series into a series of relatively stationary intrinsic modes. Finally, the EENN model optimized by the grey wolf optimization (GWO) is established to predict each subseries, and the prediction results of each subseries are reconstructed to obtain the final predicted results. The numerical results based on actual PV power data show that the prediction accuracy of the proposed model is significantly improved compared with the contrast models, and the proposed model achieves the best accuracy against the OVMD-GWO-EENN, VMD-GWO-EENN, and GWO-EENN models.

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The output power of photovoltaic (PV) power station has strong fluctuation and randomness. Large-scale photovoltaic grid connection will affect the safe operation of power grid. In this paper, the smoothing strategy of PV output fluctuation is designed based on the adaptive moving average algorithm, which combined with the PV power prediction technology. The energy storage system compensates the difference between the grid-connected reference power and the actual generation power in real time, smoothing the grid-connected power of PV power station. Firstly, the relationship between the length of fixed sliding window and smoothness, as well as volatility in the moving average algorithm is explored to provide theoretical basis for subsequent parameter selection. Then, in order to enhance the adaptive performance of the algorithm, an adaptive moving average algorithm is proposed to dynamically adjust the length of the sliding window according to the actual power volatility. The PV power prediction curve is smoothed based on the algorithm so that the grid-connected reference power curve can be obtained. Finally, three typical weather conditions of sunny day, cloudy day and overcast day are taken as examples to simulate. The results show both feasibility and effectiveness of the strategy designed to smooth output fluctuation of PV power station.

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In order to realize the economic operation of PV-integrated EV charging station and reduce the additional construction and transformation brought by the charging station to the power grid, an optimal operation strategy of energy storage system in PV-integrated EV charging station based on the improved NSGA-II is proposed. Firstly, with the power of the energy storage system and the capacity of the transformer as constraints, the optimization operation model of energy storage is built with the minimum variance of side loads of the power grid and the minimum purchase cost from the power grid as objective functions. Then, aiming at the low efficiency of the traditional NSGA-II gene recombination operator, the improved NSGA-II based on the adaptive recombination operator is proposed to solve the model, and the optimal operation strategy is obtained from the final Pareto solution set by using fuzzy clustering method. Finally, the effectiveness of the proposed algorithm is verified by example simulation, indicating that the improved NSGA-II can further improve the operation economy of charging stations and the load level of the power grid.