Forecasting Spot electricity market prices using time series models - Mazengia, Dawit Hailu (My University, 2008)
The worldwide electricity industry is in an era where an overwhelming transition towards deregulation is taking place. Since its start in the early 1980s, the industry has been in a continuous change to a different atmosphere; the ultimate benefit being providing the end-use customer with a reliable but yet cheaper electricity supply. In the old monopolistic system, utilities were the only authoritative body to set the tariff based on their aggregate cost. In the contrary, as a newly emerging structure, deregulation has come-up with a new way of functioning; leading generation, transmission and distribution to be independent activities. This market is generally a customer driven market and since the early days of deregulation, price forecasting has become an important task to all the market players engaged. Unless each body is fairly aware of the market, it might result in losses to a generating company or inadequate supply to the system; resulting in a huge crisis. Hence, understanding the market behavior is of vital importance for the well-functioning of the industry and the benefit of each party. This thesis addresses the importance of electricity price forecasting in the deregulated market. After a comprehensive review of previous works in the subject, it is concluded that forecast accuracy varies depending on the forecast method used and the electricity market under study. In this thesis multiple linear regression approach are proposed to predict next day’s electricity prices. The developed models are tested in the Nord Pool and the Ontario electricity markets and satisfactory results are achieved. Comparing the forecast results from the two markets, results in the Nord Pool market are significantly more accurate than the Ontario market. This arises from the fact that Ontario market is very volatile and its market prices are hardly predictable. The forecast results in this thesis work show that the proposed models perform very well with the absence of volatility. For instance, the developed models for the first week of December 2007 generated results with weekly Mean Absolute Percentage Error (MAPE) of about 5.04%. In early spring even better results are achieved where the models generated results with a daily MAPE of up to as low as 1.83% and weekly MAPE of about 2.96%. On the other hand, during summer where prices vary considerably even between hours of the same day, the models generated a relatively poor forecast. A day with a price of as low as 2.96 EURO/MWh to as high as 23.52 EURO/MWh is observed and this price fluctuation contributed for poor forecast. For instance, the models generated forecasts with a weekly MAPE of about 13% for the period from August 29 to Sept. 4, 2007. For the Ontario market, these models generated forecast results with a weekly MAPE of about 17%. The issue of strategic bidding is also discussed in this thesis. Once the next day electricity price is forecasted, generating companies schedule their production based on the forecasted electricity price and bid strategically in order to maximizing their profit. An analysis on the correlation between reservoir level and market clearing price is also carried out. Taking the Nord Pool market as a case study, it is found out that during winter there is a strong positive correlation between the two; showing system price increasing with reservoir level and vice versa. However, for other periods of the year a logical negative correlation is observed; price goes down with an increase in reservoir level and vice versa.
Keywords: Elkraftteknik; Electric power engineering