Electricity providers are using variable pricing to encourage customers to shift energy consumption from “peak” times. This presents a great opportunity for the customers to reduce their bills using this variable pricing strategy coupled with a battery energy storage system (BESS). The schedule of charging and discharging the BESS depends on the forecasted customer demand data. Forecasts are bound to have a certain degree of error associated with them, and hence it becomes important to understand the effect of these forecasting errors on the cost and the emissions associated with scheduling of the BESS. In this work, a method has been developed to gauge the impact of the demand forecast errors on the economics and the emissions associated with charging and discharging a BESS using a multi-objective scheduling optimization model. Statistical tools of Linear Regression Analysis (LRA) and Analysis of Variance (ANOVA) have been used to measure the correlation if it exists between the Mean Absolute Percent Error (MAPE) of a demand forecast and the total cost (Energy usage cost + cost of CO2 emissions) of charging a BESS for the state of California and the city of Phoenix, Arizona. It was found that the geographics and the size of the sample area were critical to the accuracy of the model and that the effect of the errors vary in magnitude from one area to another. The results revealed that improving the demand forecast accuracy by as small as 0.05 for a customer, for the two areas under study, could lead to energy usage savings worth $88 - $132 annually, while the improvement can reduce carbon dioxide emission from 22.8 – 112.5kg annually per customer. The cost savings would increase, and the emissions would decrease further as the difference by which the MAPE is reduced, increases.
Industrial and Systems Engineering (MS)
Department, Program, or Center
Industrial and Systems Engineering (KGCOE)
Purohit, Mudit Bhadresh, "Quantifying the Effects of Customer Predictability on Battery Energy Storage System Cost and Emissions" (2022). Thesis. Rochester Institute of Technology. Accessed from
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