Abstract
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.
Highlights
The pursuit of low-carbon economy has significantly promoted the development of renewable energy across the world, in China
There is a deviation between the predicted output and the actual output of PV power plant, which leads to the increase of the system rotation reserve capacity
3.1 Objective functions In this paper, the revenue of photovoltaic-battery energy storage systems (PV-Battery energy storage systems (BESS)) power plants in typical scenario is divided into three parts: power generation revenue, assessing rewards or penalties and peak shaving and valley filling revenue of the BESS
Summary
The pursuit of low-carbon economy has significantly promoted the development of renewable energy across the world, in China. Some references about the typical scenarios analysis of wind farm and Microgrid, and the literatures about the economic operation control strategy and the optimal allocation of energy storage capacity can be referred to in this paper. A typical scenarios segmentation method based on SOM clustering algorithm is proposed based on the historical power generation data of a PV power plant. The PV output vector Ppv,=[Ppv(1), Ppv(2), ..., PPV(n)] for in total 96 time slots is used as the input vector of SOM network, as illustrated in Fig. 1: Taking the spring as an example, the main steps of the generation of forecasted PV output in each typical scenario based on the SOM algorithm are as follows:.
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