Accounting for forecast uncertainty in the optimized operation of energy storage

This paper presents and empirically evaluates two approaches to accounting for forecast uncertainty when attempting to optimize the operation of a residential battery energy storage system. Data-driven methods are used for forecasting, and dynamic programming, within a receding horizon controller, is used for operational optimization. The first method applies a discount factor to costs incurred at later intervals in a deterministic dynamic programming control horizon, provided with point forecasts. In the second approach probabilistic (scenario) forecasts are generated using Lloyd-Max quantization of the distribution of forecast errors, to allow the use of a stochastic dynamic programming formulation. These methods are applied to maximizing the cost-savings delivered from a residentially owned and operated battery, using a case-study of residential consumers with roof-top PV systems in New South Wales, Australia. It is found that scenario forecasts can offer an 8% increase in annual cost-savings, on average, when using a univariate multiple linear regression forecast.