Load-Reduction Capability Estimation for an Aggregator in Demand Bidding Program
In the recent decades, as a result of the increase in demand for electricity, it has been getting increasingly more frequent that the spinning reserve rate of the generators in Taiwan reaches lower level which reflects the emergency of power supply. The paper employs neural network (NN) to forecast the clearing price of the bidding through spinning reserve ratio and temperature data. Subsequently, the load-reduction of customers is forecasted through NN and fuzzy logic system. Fuzzy system is adopted for forecasting of low voltage LV customer to simulate the uncertainties of load reduction considering different situations during demand response (DR). In order to improve the forecasting accuracy when realistic data of DR is available, another procedure of correcting the customers’ model for forecasting is proposed. Afterwards, the feasible contract capacity of load-reduction signed with Taiwan Power Company (TPC) is determined through an optimization algorithm. To actually assess the benefit, the real load data from Taiwan and Texas are used in the simulation.
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