Abstract
In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.
Highlights
IntroductionThe power grid operations are provided more and more pressure when electricity consumption increases sharply
Over the last decades, the power grid operations are provided more and more pressure when electricity consumption increases sharply
In r2, the numerator part represents the sum of the square difference between real value and predicted value, and the denominator part tells the sum of the square difference between real value and mean value. e value range of r2 is [0, 1]
Summary
The power grid operations are provided more and more pressure when electricity consumption increases sharply. E behind-the-meter (BTM) energy storage system is able to unify communicating, automatic control, and sensor technologies to reshape the electricity consumption activity efficiently and has been widely applied for both the gridconnected and islanded operation of microgrids [1]. In the attractive electricity market, the price of electricity always fluctuates with changes in the supply and demand of the market. At this time, the BTM system can be employed to control the peak price for large customers [4], who expect to purchase electricity at a relatively lower price and deliver it to end users at a higher price. The BTM system can be employed to control the peak price for large customers [4], who expect to purchase electricity at a relatively lower price and deliver it to end users at a higher price. erefore, in this paper, we give most attention to a multistep electricity price forecasting method that benefits optimization of scheduling in BESS for economic objectives
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