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.

The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value.

Regional photovoltaic (PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals (PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering (GC) and direct optimization programming (DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction (NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.