In this article, by using distributed consensus algorithm, the power compensation problem of network losses in a microgrid is considered with battery energy storage systems (BESSs). At first, the battery storage unit (BSU) is assembled at each node to maintain the power supply demand balance of the microgrid, then the power constraint is considered, which demands each BSU must run between its upper and lower bounds, otherwise, the upper or lower bounds will be used to replace the real power of the BSU. In order to keep the power balance of microgrid when network losses exist, a novel distributed consensus algorithm is proposed to compensate the missing power through the discharging process of BSUs, and the state of charge (SOC) of BESS is also considered. Besides, the case of battery faults is also considered, where the minimum layout point constraint is presented to ensure the completely compensation of the whole transmission losses. Finally, the test results of IEEE 14-bus and IEEE 57-bus microgrid systems are provided to verify the effectiveness and validity of the proposed method, respectively.

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

With the large-scale wind integration into power grid, the intermittent and stochastic nature of wind power possess a threat to the safety of power grid, and wind power prediction has become one of the most important solutions to the current grid connection problem. However, the prediction error in point prediction of wind power cannot be ignored, so it is necessary to make probability prediction of wind power and improve reliable information to the power grid dispatch department. In this paper, by combing with the wavelet decomposition technology and the long short-term memory (LSTM) network, an ultra short-term probability model is proposed. Firstly, the original time series are smoothed by wavelet decomposition technology, then the LSTM network prediction model of each sub-series sample is developed. The Gaussian distribution function of the prediction error is obtained by using the maximum likelihood estimation method, and finally the probability interval prediction of the future wind power in four hours could be realized. Finally, by using the data collected from a wind farm in northeast china, a numerical example is presented to illustrate the usefulness of the proposed method, which show that combining wavelet decomposition with deep learning method can improve the accuracy of prediction, improve the interval reliability of probability prediction, and enhance the generalization ability of the model.

The consensus algorithm is widely used in the microgrid, but it is affected by the communication of distributed generators. If the communication delay is serious, it can affect the system’s performance and cause the system’s instability. To ensure the stability of the microgrid under communication constraints, aiming at the extensive penetration of the flexible loads in the microgrid, the transmission delay of the communication network is considered, this paper proposes a microgrid distributed economic dispatch method based on the consensus algorithm under communication constraints. By coordinating the information exchanges between the generator and the total load demand, the flexible load and output of the generator are optimized in the system. Compared with the traditional consensus control strategy, this strategy can ensure that the system has higher stability and convergence speed under the same delay. Finally, the IEEE-14 node system is used to verify the effectiveness and correctness of the proposed algorithm.