Session 06: Electricity Pricing
Mean-based Method of Estimating Financial Cost of Load Forecasting
1Faculty of Electrical Engineering and Computing, Croatia; 2HEP Trgovina d.o.o.
In liberalized electricity markets participants are obliged to schedule their electricity plans in advance because in a power system, equilibrium of production and consumption must be kept at all times. In order for power system to function, market participants forecast their plans trying to minimize their load forecasting error. Deviations from their realized energy through sale and purchase of balancing energy present a financial cost. Since it is a competitive market, in order to remain profitable, participants try to minimize their financial cost and risk exposure to balancing energy mainly by lowering their load forecasting error. In this paper we investigate behaviour of balancing energy and its dependency on forecasting strategy, load forecasting error, size of a market participant and energy prices. As a result we propose an equation for estimation of financial cost of load forecasting.
The Short Term Electricity Prices Forecasting Using Markov Chains
1University of Tuzla, Bosnia and Herzegovina; 2University of Ljubljana, Slovenia
This paper presents a method for short-term electricity price forecasting based on combination of the Monte Carlo simulation and Markov chains. The method provides an estimation of the probabilities of various electricity price ranges, average prices, and probabilities of the highest price range, for each hour of the next 24 hours. The external variables have been implicitly accounted for through the Monte Carlo simulation. Using the market data of the European Power Exchange (EPEX) as a test case, the effectiveness of the proposed method has been verified by comparison with the best regression methods.
Forecasting Prices of Electricity on HUPX
1Energy institute Hrvoje Pozar, Croatia; 2Faculty of Electrical Engineering, University of Zagreb
The development of new simulation techniques by using Artificial Intelligence (AI) has become an improved tool to better forecast energy prices. This paper demonstrates building and validating a short term model for Hungarian day ahead power market - HUPX electricity price forecasting. This models takes into account multiple sources of information; the data set used is a table of historical hourly loads, electricity prices and other regional information’s. Paper is discussing the application of intelligent systems to short term electricity prices forecasting and outlining proposed research direction.
Forecasting Next-Day Electricity Prices by a Neural Network Approach
University of Calabria, Italy
Forecasting short-term electricity market prices has been the focus of several studies in recent years. Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize averages values as the basis for making short-term scheduling decisions. With the aim of enhancing the accuracy of the next-day electricity price forecasting, this paper proposes an approach to forecast the dayahead electricity prices by means of n Artificial Neural Networks (ANNs), based on the estimation of the mean prices of n blocks of hours, with n identified according to the values of correlation factors computed on the basis of field records of the Italian electricity market. Simulation results show that forecasting nextday prices on an hourly basis induces to an error which results worse than the one made when average prices are forecasted according to groups of hours.
Short-term Consumer Benefits of Dynamic Pricing
1Katholieke Universiteit Leuven, Belgium; 2VITO NV, Belgium
Consumer benefits of dynamic pricing depend on a variety of factors. Consumer characteristics and climatic circumstances widely differ, which forces a regional comparison. This paper presents a general overview of demand response programs and focuses on the short-term benefits of dynamic pricing for an average Flemish residential consumer. It reaches a methodology to develop a cost reflective dynamic pricing program and to estimate short-term bill savings. Participating in a dynamic pricing program saves an average consumer 2.32 percent of his initial bill. While this result seems insufficient to justify implementation, it contains only a small proportion of a series of dynamic pricing benefits.
Electricity Price Forecasting - ARIMA Model Approach
1HEP d.d.; 2HEP Trade d.o.o.
Electricity price forecasting is becoming more important in everyday business of power utilities. Good forecasting models can increase effectiveness of producers and buyers playing roles in electricity market. Price is also a very important element in investment planning process. This paper presents a forecasting technique to model day-ahead spot price using well known ARIMA model to analyze and forecast time series. The model is applied to time series consisting of day-ahead electricity prices from EPEX power exchange.
The Impact of Wind Power in the Netherlands on Day-ahead Electricity Prices
Energy research Centre of the Netherlands, Netherlands, The
A detailed analysis was conducted to assess to what extent availability of wind energy has influenced day-ahead electricity prices in the Netherlands over the past four years. With a meteorological model, time series of day-ahead wind forecasts were generated for the period 2006-2009, and these were compared with APX-ENDEX day-ahead market prices. Wind energy contributes to only 4% of electricity generation in the Netherlands, but was found to depress average day-ahead market prices by about 5%.
With the help of the bid curves on the APX-ENDEX day-ahead market for 2009, a model was made to assess the impact of increasing levels of wind generation on power prices in the Netherlands. One of the main findings is that the future impact on prices will be less than in the past. With an increase of installed wind capacity from 2200 MW to 6000 MW, average day-ahead prices are expected to be depressed by an additional 6% in case no additional conventional generation is assumed. Taking into account existing government policy on wind and ongoing work on new conventional power plants, prices in 2016 will be only 3% lower due to wind.