Antonio Guillamón

A New Approach to Measure Volatility in Energy Markets

Several measures of volatility have been developed in order to quantify the degree of uncertainty of an energy price series, which include historical volatility and price velocities, among others. This paper suggests using the permutation entropy, topological entropy and the modified permutation entropy as alternatives to measure volatility in energy markets. Simulated data show that these measures are more appropriate to quantify the uncertainty associated to a time series than those based on the standard deviation or other measures of dispersion. Finally, the proposed method is applied to some typical electricity markets: Nord Pool, Ontario, Omel and four Australian markets.

Development of a methodology for improving the effectiveness of customer response policies through electricity-price patterns

The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario can not be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology (self-organizing maps and statistical wards linkage) to classify high electricity market prices is analyzed. Besides, with the help of non-parametric estimation, some price-patterns were found in the above mentioned clusters. The knowledge contained within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.

KERNEL ESTIMATION OF RESIDUAL ENTROPY

Recently, [1] proposed a dynamic measure based on differential entropy applied to the residual lifetime. This measure has been used for the classification and ordering of survival functions. More recently, [2] has considered the problem of testing the monotonicity of this measure. We propose and study several kernel type estimators of the entropy of residual life through the estimation of f(x) log f(x). These estimators can be applied to the classification and comparison of lifetime distribution.

Development of a methodology for forecasting electricity-price series to improve Demand Response Initiatives

The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario can not be achieved without the full participation of the electricity demand. The aim of this paper is to propose a procedure, through the detection of electricity-price patterns based on what happened in the energy markets the previous day, which could help customers and aggregators to take decisions in Electricity Markets. In this way, the capacity of a methodology (Statistical Wards Linkage) to classify and forecast high electricity market prices is analyzed. Besides, some price-patterns were found in the abovementioned clusters. The knowledge contained within these patterns supplies customers with market-based information on which to focus its energy use decision to improve the usefulness of Demand Response Initiatives.