This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission.

中文翻译：

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用于优化独立可再生电池系统规模的需求侧管理方法

本文开发了一种新颖的需求侧管理 (DSM) 方法，以将太阳能光伏 (PV)、风力涡轮机 (WT) 和电池存储 (BS) 的最佳尺寸纳入独立家庭。DSM 策略基于电池的充电状态以及日照和风速的日前预测。DSM 的核心是一种模糊逻辑方法，它决定有效的负荷转移和/或负荷削减。通过人工神经网络技术获得的日前预测误差不仅在 DSM 策略中被考虑，而且在维持操作储备中也被考虑在内。使用Rainflow 计数算法计算电池容量退化，以获得真实的电池模型并估计其寿命。南澳大利亚 (SA) 的一个典型家庭被视为案例研究。使用所提出的方法优化了供电系统的三种不同配置（PV-BS、WT-BS 和 PV-WT-BS）。发现 PV-WT-BS 系统是最佳配置，无论是否应用所提出的 DSM 策略，它都能提供最低的电力成本。将最佳系统配置与 SA 中的实际系统以及最近发表的两篇文章的结果进行比较，表明所提出的方法在降低零排放的电力成本方面非常有效。