The electricity market experiences pronounced short term volatility due to its peculiarities, first of all continuos load variations associated to the impossibility to profitably store electric energy. Electricity price forecast is extremely important for all market players and in particular for generating companies, that must manage their units, and the associated economic risk, in the short, medium and long term. In general, price volatility depends on a large number of parameters such as: fuel prices (often related to currency exchange rates), availability of generating units, hydro generation production, demand elasticity and variations, network congestion and management rules of any specific electricity market. This study provides an analysis of day-ahead spot market price volatility in Spain, California, UK and PJM markets. Electricity prices for the years 1999 and 2000 have been used for the study. The analysis has been carried out on the basis of an original definition of price volatility.
Abstract. Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23 % of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.
Flat tariff has always been the default condition for residential customers in Italy and only starting from July, 1st 2010, the Italian Authority for Electricity and Gas (AEEG) approved the entry into force of a mandatory time-of-use (ToU) tariff at two-part rate periods. In order to assess the impact of the tariff in the short and medium term among Italian customers, R.S.E. has started a research project on residential loads and demand, in collaboration and under the patronage of AEEG. The results of such analysis show that, even if there has been a limited shift of consumptions from peak hours to off-peak hours, the change in the behavior of the users is not negligible. Particular attention has been paid to the possible causes that have contributed to limit the amount of energy shifted from peak hours to off-peak hours and to analyse some possible solutions in order to make the tariff more effective.
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