Improving the accuracy of power system load forecasting is important for economic dispatch. However, a load sequence is highly nonstationary and hence makes accurate forecasting difficult. In this paper, a method based on wavelet decomposition (WD) and a second-order gray neural network combined with an augmented Dickey-Fuller (ADF) test is proposed to improve the accuracy of load forecasting. First, the load sequence is decomposed by WD to reduce the nonstationary load sequence. Then, the ADF test is adopted as the test method for the stationary load sequence of each decomposed component after WD in which the test results determine the best WD level. Finally, because forecasting the wavelet details characterized by high frequencies is difficult owing to its fluctuation, a second-order gray forecasting model is used to forecast each component after WD. Furthermore, to obtain the optimum parameters of the second-order gray forecasting model, the neural network mapping approach is used to build the second-order gray neural network forecasting model. The simulation result of a real load sequence verifies that the method proposed in this paper can effectively improve the load-forecasting accuracy.

Selection of the kernel function by the support vector regression (SVR), for the purposes of load forecasting, is affected by the power load characteristics. The non-ideal SVR with a kernel function has low forecasting accuracy and poor generalization ability. A novel load forecasting method combining SVR and stacking is proposed in this paper. Base models are constructed based on SVRs with different kernel functions, then multiple base models are merged to obtain a base model layer via stacking algorithm. Finally, an SVR is connected as the meta-model layer. The stacking fusion model is composed of base model layer and meta-model layer. This model is trained with k-fold cross validation to enhance its generalization ability. An improved artificial fish swarm algorithm is employed to optimize the parameters to improve the forecasting accuracy of the stacking fusion model; speed variables are introduced to replace step lengths and improve the convergence speed and search ability. The forecasting accuracy and generalization ability of the proposed method are verified by comparative analysis.

Integrating renewable energy into power grids is seen in increase in recent years since these energy sources are sustainable and clean. However, the integration brings about considerable technical challenges associated with fluctuations and uncertainties of the energy availability whilst maintaining the stability of smart grids. The prediction of renewable energy generation is key to achieve optimal power dispatch in renewable-intensive smart grids. However, uncertain interruption and prediction errors will make an optimal decision more challenging. Model predictive control (MPC) is an effective way to overcome the discrepancies between the prediction and the real-world system through a closed-loop correction over iteration process. This study develops an improved MPC scheme used with a hybrid energy storage system for optimal power dispatch in a smart grid. This hybrid renewable energy system consists of a wind farm, a hydrogen/oxygen storage system and several fuel cells (FCs). In this study, particle swarm optimization (PSO) with a back propagation (BP) artificial neural network is developed to predict the wind energy availability by using measured data. Then, a genetic algorithm (GA) is combined with a state space model (SSM) to achieve the MPC control. A dataset of 24-hour ahead predictive generation is calibrated from the measured data and is defined for optimal power flow between the grid, the wind farm and the storage subsystem so as to balance the supply and load. The optimization target is to achieve a minimal energy exchange between the power grid and the hybrid renewable energy storage system. Based on actual measured data, the test results have shown that the proposed methodology can maximize the local usage of wind power whilst minimizing the power exchange with the grid. An optimal power dispatch strategy is proved to be effective to meet the demand and efficiency with dynamic control of the FCs. The usage of the intermittent wind power is increased from 45% to 90% in the four test studies. Therefore, this work can minimize the impact of fluctuating renewable energy on the power grid and enhance uptakes of FC-based energy systems. This is particularly economic and relevant to the remote and under-developed regions where their power networks are weak and vulnerable.

A new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follows a two-step process. In the first step, PSO is applied to optimize the VMD decomposition method. The original electricity load sequence is decomposed, and the fitness function uses sample entropy to describe the complexity of the time series. The decomposed sub-sequences are combined with relevant features, such as meteorological data, to form the input sequence of the prediction model. In the second step, TCN is selected as the prediction model, and it is embedded with an attention mechanism to improve prediction accuracy. The above input sequence is fed to the model to obtain the PSO-VMD-TCN-Attention prediction framework. Load datasets and various prediction models validate the PSO-optimized VMD decomposition method and the TCN-Attention prediction model. Simulation results demonstrate that the PSO-optimized VMD decomposition method enhances the model’s prediction accuracy, and the TCN-Attention prediction model outperforms other prediction models in terms of prediction accuracy and ability.