Search

In this paper, we present a study on forecasting hourly electricity spot prices for India. Forecasting electricity prices is challenging for its special characteristics. Based on hourly data covering several years, ARMAX models are estimated for forecasting for individual regions. Along with ARMA, intra-day, inter-day and hourly variations and seasonalities on weekdays, holidays and festive days are incorporated. Combinations of static and dynamic forecasts are also examined. The results indicate that prices for Sundays and holidays differ significantly from weekdays; also, a clear region-specific difference between prices in winter and summer months is observed. Price behaviour in the Southern region is different from other regions. The fitted models perform well for forecasting horizons of hourly prices of up to five days. The fitted ARMAX models have an easily implementable simple structure. Such forecasts provide valuable insights for policymakers and market participants, as they incorporate several features of the Indian electricity market, including various dimensions of seasonality of demand. JEL Classifications : C53, Q47, C51 [ABSTRACT FROM AUTHOR]

Energy storage, both short- and long-term, will play a vital role in the energy system of the future. One storage technology that provides high power and capacity and that can be operated without carbon emissions is compressed air energy storage (CAES). However, it is widely assumed that CAES plants are not economically feasible. In this context, a mixed-integer linear programming (MILP) model of the Huntorf CAES plant was developed for revenue maximization when participating in the day-ahead market and the minute-reserve market in Germany. The plant model included various plant variations (increased power and storage capacity, recuperation) and a water electrolyzer to produce hydrogen to be used in the combustion chamber of the CAES plant. The MILP model was applied to four use cases that represent a market-orientated operation of the plant. The objective was the maximization of revenue with regard to price spreads and operating costs. To simulate forecast uncertainties of the market prices, a rolling horizon approach was implemented. The resulting revenues ranged between EUR 0.5 Mio and EUR 7 Mio per year and suggested that an economically sound operation of the storage plant is possible.

Day-ahead electricity prices are generally used as reference prices for decisions done in energy trading, e.g. purchase and sale strategies are typically based on the day-ahead spot prices. Therefore, well-performing forecast methods for day-ahead electricity prices are essential for energy traders and supply companies. In this paper, a methodology based on artificial neuronal networks (ANN) is presented to forecast electricity prices. As the performance of an ANN forecast model depends on appropriate input parameter sets, the focus is set on the selection and preparation of fundamental data that has a noticeable impact on electricity prices. This is done with the help of different cluster algorithms, but also by comparing the results of the pre-selected model configurations in combination with different input parameter settings. After the determination of the optimal input parameters, affecting day-ahead electricity prices, and well-performing ANN configuration, the developed ANN model is applied for in-sample and out-of-sample analyses. The results show that the overall methodology leads to well-fitting electricity price forecasts, whereas forecast errors are as low as or even lower than other forecast models for electricity prices known from the literature. [ABSTRACT FROM AUTHOR]

Energy storage, both short- and long-term, will play a vital role in the energy system of the future. One storage technology that provides high power and capacity and that can be operated without carbon emissions is compressed air energy storage (CAES). However, it is widely assumed that CAES plants are not economically feasible. In this context, a mixed-integer linear programming (MILP) model of the Huntorf CAES plant was developed for revenue maximization when participating in the day-ahead market and the minute-reserve market in Germany. The plant model included various plant variations (increased power and storage capacity, recuperation) and a water electrolyzer to produce hydrogen to be used in the combustion chamber of the CAES plant. The MILP model was applied to four use cases that represent a market-orientated operation of the plant. The objective was the maximization of revenue with regard to price spreads and operating costs. To simulate forecast uncertainties of the market prices, a rolling horizon approach was implemented. The resulting revenues ranged between EUR 0.5 Mio and EUR 7 Mio per year and suggested that an economically sound operation of the storage plant is possible. [ABSTRACT FROM AUTHOR]