Although photovoltaic (PV) power is a green energy source, the high output variability of PV power generation leads to lags in network availability. To increase PV power plant reliability, an energy storage system can be incorporated. However, improper selection of storage size increases system cost or decreases network availability due to over- or under-sizing of the storage capacity, respectively. For this reason, we develop a generalized Markov chain-based battery charging–discharging procedure for determining proper storage size. In this work, multi-objective clustering and fuzzy decision-making (FDM) techniques for selecting the most optimal storage size with optimal availability are also proposed to avoid issues associated with conventional selection procedures. The reliability of our approach is analyzed based on forecasted data. As better prediction yields more dispatchable storage sizing, thereby improving system reliability, a first-order fuzzy time-series, Markov chain-based prediction method is used owing to its high prediction efficiency. To explore PV power generation performance in both cool and hot, sunny conditions, the temperature effect is considered in the storage sizing–availability analysis.

中文翻译：

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基于模糊马尔可夫链模型的光伏电池系统数据预测与存储规模

尽管光伏（PV）电源是一种绿色能源，但是PV发电的高输出可变性导致网络可用性的滞后。为了提高光伏电站的可靠性，可以合并一个储能系统。但是，由于存储容量的过大或过小，对存储大小的不正确选择会分别增加系统成本或降低网络可用性。因此，我们开发了一种基于马尔可夫链的通用电池充放电程序来确定合适的存储容量。在这项工作中，还提出了用于选择具有最佳可用性的最佳存储大小的多目标聚类和模糊决策（FDM）技术，以避免与常规选择过程相关的问题。我们的方法的可靠性是根据预测数据进行分析的。由于更好的预测会产生更多可调度的存储大小，从而提高系统可靠性，因此，由于其高预测效率，因此使用了基于马尔可夫链的一阶模糊时间序列预测方法。为了探索在凉爽，炎热，晴天的情况下的光伏发电性能，在存储大小-可用性分析中考虑了温度影响。