Abstract
We synthesize scenarios of hourly electricity price, which is known as the system marginal price (SMP), for thirty-years based on the oil price. Hourly SMP scenarios are very important when planning new generators because the revenue and cost of new capacity margins are determined based on the SMP. Because the SMP contains both short-term and long-term periodic patterns, designing a single model based on these patterns to predict the SMP is difficult. Although oil price affects SMP, they can not be directly used in the forecasting model because the resolution of SMP is at hourly intervals, but that of oil price is at yearly intervals. To overcome these problems, we decompose the SMP into annual, monthly, and daily components, and the components are predicted based on different models. The model for the annual component (AC) is designed to predict the long-term trend based on fuel price scenarios. The model for the monthly component (MC) is designed to predict the seasonal trends based on the long short term memory (LSTM) model. The model for the daily component (DC) is designed to predict the daily SMP fluctuation. Finally, we synthesize SMP scenarios by aggregating three components. We make three types of SMP scenarios (high, reference, and low), and the performance of the scenarios is tested using previous data for two years on the basis of mean absolute error (MAE). Due to the global COVID-19 pandemic, the low type of SMP scenario is most accurate. We also verify that the reliability of long-term scenarios can be secured by using oil price while maintaining monthly and daily patterns.
Highlights
We synthesize scenarios of hourly electricity price, which is known as the system marginal price (SMP), for thirty-years based on the oil price
We forecast three components based on fuel price scenarios while maintaining their periodic patterns
We analyze the effect of fuel prices on the SMP, and we verified that the Brent Crude oil price has the greatest effect on the SMP fluctuation
Summary
The AC accounts for the long-term trends of SMP, the MC accounts for the seasonal trends of SMP, and the DC accounts for the daily patterns of consumer behaviour. We extract the AC from SMP, and the residual is defined as the first residual (FR). We predict the MC using various algorithms to account for seasonal patterns, and select the optimal one. We use the long-short term memory (LSTM), gated recurrent unit (GRU), autoregressive model (AR), and support vector regression (SVR) to predict MC. LSTM and GRU are designed to have a double layer and to use the output as the input to generate long-term seasonal trends. We predict the mean and STD based on AC magnitude and generate 24 Gaussian distributions to utilize the mean and STD values. The SMP scenarios are synthesized by aggregating the three components
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