Adaptive Scheduling of the Battery for Peak shaving using Model Predictive Control

Abstract

The objective of this project is to develop a load forecasting technique and demand management algorithm using the Model Predictive Control (MPC). This paper will demonstrate the present state of the art with respect to demand control strategies within the market place, the taxonomy of such control algorithms and their respective strengths and weaknesses. The algorithm uses a two-part forecaster where a deterministic part using exponentially weighted moving average (EWMA) model accounts for longer term trends and a second order regression model (AR2) accounts for the short term variations. The algorithm is to be evaluated on a 50 kW, 165 kWh energy storage system installed behind the meter at a Clemson University academic building. The next phase of this work involves augmenting this algorithm for the energy storage system with the building management system to realize greater opportunities.