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.

Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models.

Microgrid has flexible composition, a complex operation mechanism, and a large amount of data while operating. However, optimization methods of microgrid scheduling do not effectively accumulate and utilize the scheduling knowledge at present. This paper puts forward a microgrid optimal scheduling method based on Deep Deterministic Policy Gradient (DDPG) and Transfer Learning (TL). This method uses Reinforcement Learning (RL) to learn the scheduling strategy and accumulates the corresponding scheduling knowledge. Meanwhile, the DDPG model is introduced to extend the microgrid scheduling strategy action from the discrete action space to the continuous action space. On this basis, this paper holds that a microgrid optimal scheduling TL algorithm on the strength of the actual supply and demand similarity is proposed with a purpose of making use of the existing scheduling knowledge effectively. The simulation results indicate that this paper can provide optimal scheduling strategy for microgrid with complex operation mechanism flexibly and efficiently through the effective accumulation of scheduling knowledge and the utilization of scheduling knowledge through TL.

The capacity configuration of the energy storage system plays a crucial role in enhancing the reliability of the power supply, power quality, and renewable energy utilization in microgrids. Based on variational mode decomposition (VMD), a capacity optimization configuration model for a hybrid energy storage system (HESS) consisting of batteries and supercapacitors is established to achieve the optimal configuration of energy storage capacity in wind–solar complementary islanded microgrids. Firstly, based on the energy mapping relationship between the frequency domain and time domain, the decomposition mode number $K$ of VMD is determined based on the principle of minimum total mode aliasing energy. Then, considering the smoothing fluctuation characteristics of different energy storage components, the dividing point N of high frequency and low frequency in the unbalanced power between the source and load in the microgrid is selected to allocate charging and discharging power instructions for the battery and supercapacitor. Finally, taking the annual comprehensive cost of the HESS as the objective function, a hybrid energy storage capacity optimization configuration model is established, and the dividing point N is used as the optimization variable to solve the model in order to obtain the optimal configuration results. The case study results show that the proposed method is more economical and feasible than the existing energy storage configuration methods.

With the development and application of large-scale renewable energy sources, the electric power grid is becoming huge and complicated; one of the most concerning problems is how to ensure coordination between a large number of varied controllers. Differential games theory is used to solve the problem of collaborative control. However, it is difficult to solve the differential game problem with constraints by using conventional algorithm. Furthermore, simulation models established by existing research are almost linear, which is not conducive to practical engineering application. To solve the above problem, we propose a co-evolutionary algorithm based on the improved weighted fruit fly optimization algorithm (IWFOA) to solve a multi-area frequency collaborative control model with non-linear constraints. Simulation results show that the control strategy can achieve system control targets, and fully utilize the various characteristics of each generator to balance the interests of different areas. Compared with a co-evolutionary genetic algorithm and a collaborative multi-objective particle swarm optimization algorithm, the co-evolutionary algorithm based on the IWFOA has a better suppression effect on the frequency deviation and tie-line power deviation caused by the disturbance and has a shorter adjustment time.