Dispatch Scheduling for a Wind Farm With Hybrid Energy Storage Based on Wind and LMP Forecasting

In a deregulated power market, the real-time wholesale market price of electricity varies dramatically within a single day due to the availability of the resources. Moreover, the price of electricity can be different from one location to the other at the same time period due to the location of the available resources and transmission constraints. This is the so-called locational marginal price (LMP). Since wind power is noncontrollable and partially unpredictable, it is difficult to schedule its output to exploit LMP variations. While energy storage system (ESS) may accommodate wind farm output, it requires significant initial financial commitment. Accurately forecasted wind power and LMP information can reduce the required capacity and make it financially feasible for the ESS to perform desired functions. In this paper, artificial neural network (ANN) technique is employed to forecast the day-ahead wind power and LMP, and a hybrid ESS consisting of two storage facilities is developed. The primary ESS is utilized for the optimizing wind-storage system production schedule with day-ahead forecasting data, while the secondary ESS is applied to address the forecasting errors during real-time operation. With this hybrid ESS design, financial benefits are achieved for the wind farm.