Recent advances in battery energy storage technologies enable increasing number of photovoltaic-battery energy storage systems (PV-BESS) to be deployed and connected with current power grids. The reliable and efficient utilization of BESS imposes an obvious technical challenge which needs to be urgently addressed. In this paper, the optimal operation of PV-BESS based power plant is investigated. The operational scenarios are firstly partitioned using a self-organizing map (SOM) clustering based approach. The revenue optimization model is adopted for the PV-BESS power plants to determine the optimal operational modes under typical conditions for a set of considerations, e.g. power generation revenue, assessing rewards/penalties as well as peak shaving/valley filling revenue. The solution is evaluated through a set of case studies, and the numerical result demonstrates the effectiveness of the suggested solution can optimally operate the BESS with the maximal revenue.

With the increasing permeability of photovoltaic (PV) power production, the uncertainties and randomness of PV power have played a critical role in the operation and dispatch of the power grid and amplified the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and fluctuation characteristics of the PV power forecast error, a short-term PV output forecast method that considers the error calibration is proposed. Firstly, typical climate categories are defined to classify the historical PV power data. On the one hand, due to the non-negligible diversity of error amplitudes in different categories, the probability density distributions of relative error (RE) are generated for each category. Distribution fitting is performed to simulate probability density function (PDF) curves, and the RE samples are drawn from the fitted curves to obtain the sampling values of the RE. On the other hand, based on the fluctuation characteristic of RE, the recent RE data are utilized to analyze the error fluctuation conditions of the forecast points so as to obtain the compensation values of the RE. The compensation values are adopted to sequence the sampling values by choosing the sampling values closest to the compensation ones to be the fitted values of the RE. On this basis, the fitted values of the RE are employed to correct the forecast values of PV power and improve the forecast accuracy.

The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on “meteorological similarity day (MSD)” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.

To solve the problem of solar abandoning, which is accompanied by the rapid development of photovoltaic (PV) power generation, a demonstration of a photovoltaic-battery energy storage system (PV-BESS) power plant has been constructed in Qinghai province in China. However, it is difficult for the PV-BESS power plant to survive and develop with the current electricity price mechanism and subsidy policy. In this paper, a three-part electricity price mechanism is proposed based on a deep analysis of the construction and operation costs and economic income. The on-grid electricity price is divided into three parts: the capacity price, graded electricity price, and ancillary service price. First, to ensure that the investment of the PV-BESS power plant would achieve the industry benchmark income, the capacity price and benchmark electricity price are calculated using the discounted cash flow method. Then, the graded electricity price is calculated according to the grade of the quality of grid-connected power. Finally, the ancillary service price is calculated based on the graded electricity price and ancillary service compensation. The case studies verify the validity of the three-part electricity price mechanism. The verification shows that the three-part electricity price mechanism can help PV-BESS power plants to obtain good economic returns, which can promote the development of PV-BESS power plants.