Load forecasting techniques are proposed to optimize performance of a BESS on a distribution grid with high PV penetration.
The parallel side of techniques is based on searching for similar days of historical data with similar weekly indexes.
For series forecasting side of techniques, previous real data of each day are used in a moving window.
The proposed techniques were tested with real data acquired from a distribution feeder on Maui Island, HI.
A battery energy storage system (BESS) is an available solution for utilities to deal with intermittency issues resulting from renewable energy resources. A BESS needs to have a control algorithm to provide a very good estimation of the load on the grid at each time step. A short-term load forecast (STLF) is necessary for efficient and optimized control of BESSs that are connected to the grid. In this work, two parallel-series techniques for load forecasting are proposed to optimize the performance of a grid-scale BESS (1 MW, 1.1 kWh) in 15-min steps within a moving 24-h window. In both techniques, a complex-valued neural network (CVNN) is used for parallel forecasting. The parallel component is based on the search for similar days of historical data that have a weekly index comparable to the forecast day. For series forecasting, historical data of each day is used within a moving forecast window by CVNN along with the spline method. For both techniques, parallel forecasting is mixed with series forecasting by an adjustment coefficient. Both techniques are tested on a set of real data for a grid with high PV penetration, and the obtained results are compared.
- Battery energy storage system;
- Load forecasting;
- High PV penetration;
© 2017 Elsevier Ltd. All rights reserved.