Assessment of forecasting methods on performance of photovoltaic-battery systems
Assessment of forecasting methods on performance of photovoltaic-battery systems
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DOI：
10.1016/j.apenergy.2018.03.154
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发表时间：
2018-07
期刊：
影响因子：
11.2
通讯作者：
G.B.M.A. Litjens;E. Worrell;W. Sark
中科院分区：
文献类型：
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作者：
G.B.M.A. Litjens;E. Worrell;W. Sark
光伏（PV）系统越来越多地部署在城市地区的建筑物上，导致低压电网出现额外的功率流和频率波动。光伏电池储能系统（BESS）的控制策略有助于减少流向电网的功率并提高光伏发电的自消耗。因此，这些控制策略需要准确预测光伏发电量和用电量。我们使用5分钟分辨率的数据开发和评估了相对简单的预测方法，以预测光伏发电量并预测一年的电力消耗。我们将这些预测与预测控制策略结合使用，以增加光伏自耗、减少限电损失并提高 BESS 收入。使用了荷兰 48 栋住宅和 42 栋商业建筑的电力需求模式。使用预测天气数据的光伏发电量预测方法显示出最低的预测误差。用于预测住宅建筑能耗的最佳预测方法需要前 7 天的历史能耗数据。商业系统需要前一个工作日的历史能耗。使用预测控制策略可以显着减少弃风损失，特别是在使用晴空辐射数据来预测光伏发电量时结合使用。预测控制的自我消耗率与实时控制相似。这表明，在维持光伏自消纳水平的同时，可以结合减少弃风损失。电池存储的收入通过预测方法增加，并且高度依赖于光伏电池系统的边界条件，例如上网限制（FIL）和上网电价。因此，我们建议根据这些系统边界条件定制电池控制策略，以提高储能潜力。
Photovoltaic (PV) systems are increasingly deployed on buildings in urban areas, causing additional power flows and frequency fluctuation on the low voltage electricity grid. Control strategies for PV-battery energy storage systems (BESS) assist in reducing power flows to the grid and improve the self-consumption of PV generated electricity. Therefore, these control strategies require accurate forecasts of PV electricity production and electricity consumption. We developed and assessed relatively simple forecasting methods using 5 min resolution data, to predict the PV yield and to forecast electricity consumption for one year. We used these forecasts with a predictive control strategy to increase PV self-consumption, decrease curtailment losses and improve BESS revenues. Electricity demand patterns of 48 residential and 42 commercial Dutch buildings were used. PV yield forecast methods that uses predicted weather data shows the lowest forecast error. The best performing forecast method for predicting energy consumption of residential buildings requires historical energy consumption data of the previous seven days. Commercial systems require historical energy consumption of the previous weekday. Significant reduction in curtailment losses is achieved using predictive control strategies, especially in combination when clear-sky radiation data is used to forecast PV yield. Similar self-consumption rates were found for predictive control as for real-time control. This indicates that reduction of curtailment loss can be combined while maintaining the level of PV self-consumption. Revenues from battery storage are increased by forecast methods and are highly dependable on boundary condition of a PV-battery system, such as the feed-in limit (FIL) and the feed-in tariff. Therefore, we recommend customizing battery control strategies based on these system boundaries conditions to improve energy storage potential.