PROJECT TITLE :
Dispatch Scheduling for a Wind Farm With Hybrid Energy Storage Based on Wind and LMP Forecasting
In an exceedingly deregulated power market, the $64000-time wholesale market value of electricity varies dramatically inside a single day due to the supply of the resources. Moreover, the value of electricity will be totally different from one location to the opposite at the identical time amount thanks to the placement of the available resources and transmission constraints. This can be the thus-called locational marginal worth (LMP). Since wind power is noncontrollable and partially unpredictable, it is difficult to schedule its output to take advantage of LMP variations. Whereas energy storage system (ESS) could accommodate wind farm output, it requires vital initial money commitment. Accurately forecasted wind power and LMP info can cut back the specified capacity and create it financially feasible for the ESS to perform desired functions. In this paper, artificial neural network (ANN) technique is used 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 information, whereas the secondary ESS is applied to address the forecasting errors during real-time operation. With this hybrid ESS design, monetary advantages are achieved for the wind farm.
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