Improved PV Forecasts for Capacity Firming

Abstract

Some balancing authorities give owners of medium to large photovoltaic (PV) generation plants a choice between firming the production of their plants using battery energy storage or paying the balancing authority for the cost that these imbalances impose on the system. If the owner of a PV plant decides to do capacity firming, the net production of the PV plant and the battery must match a forecast value. A more accurate forecast of the PV production reduces the energy throughput of the battery and hence its degradation. This article compares capacity firming using persistence forecasts with predictions based on long short-term memory recurrent neural networks (LSTM-RNN), encoder-decoder LSTM-RNN and multi-layer perceptrons. This article also proposes to use the type-of-day, such as sunny, cloudy etc, which can be generated by clustering historical PV generation data according to the total daily PV generation, as a feature of the PV forecasting model. Results based on the Snohomish County Public Utility District's Arlington Microgrid show that the machine learning techniques perform significantly better than the persistence method in forecasting PV generation. In particular, encoder-decoder LSTM-RNN would reduce the yearly battery energy throughput by 29% and the number of battery cycles with a greater than 10% depth-of-discharge (DoD) by 51%. Including the day-type as a feature in PV forecasting reduces the battery energy throughput by 5.3% and the number of cycles with a DoD larger than 10% by 5.9%.

Keywords

• Capacity firming
• solar smoothing
• PV forecasting
• encoder-decoder long short-term memory networks
• recurrent neural networks
• multi-layer perceptrons