Modeling prices in electricity Spanish markets under uncertaintyG-TeC research group, Complutense University, MadrideKergy Technologies, SL Madrid, SpainISKE 2013, ShenZhen – China

G-TeC members:Guadalupe Miñana, Raquel Caro, Beatriz. González, Victoria LopezeKergy Technologies members:Hugo Marrao, Jesús Gil 

Index1. Introduction to the electricity market2. Motivation & objectives3. Modeling price 4. Mibel's Day-ahead Market5. Variables6. The model7. Results8. Conclusions9. Future work

The electricity market

• Electricity is the main energy source from today society.

• Electricity can't be store

• Electrical market depends on the distribution grid and it requires that generation equals consumption at every instant.

• In general the full electrical system is divided in 4 activities that required a higher coordination: o Generation, Transport, Distribution and consumption.

• Traditionally, the electrical market prices were regulated by the government. With the evolution and growing of such good in Europe, the electrical market deregulation led countries to create electrical markets in order to fulfill their needs.

• In Spain, Mibel 2009, is the case of Spain and Portugal as response to the deregulation.

• Other countries in Europe have created similar markets, for example Nord Pool or EEX

Motivation & Objectives

• Web platform for statistical modeling and profiling energy consumption for home users.

• Mathematical tool for modeling, statistical profiling and consumption simulation scenarios.

• Aimed at promoting green energy cooperatives and assistance in forecasting consumption, purchasing power and customer management

Hour price setting evaluation in the Mibel's Day-ahead Market

Modeling price

• The object of this work has been to analyze and identify the variables that most influence on the price.

• To achieve this goal we applied Multiple Linear Regression (MLR) using SPSS tool.

• A Linear Dependency Analysis among Electric Market Prices and the amount of energy produced by each technology, the electric demand and the wind power generated is due.

• We aim to discover if is correct and necessary analyze the dataset according to the season, working or non-working days, and the time slot.

• Several techniques are studied to offer different models depending on these variables.

Variables

The model

• First phase of this work, we apply Multiple Linear Regression (MLR) to analyse the linear correlations between two or more independent variables to select the optimal set of able to explain the behaviour of electric price. A model of multiple linear regressions

 

• Dependent variable: Yi=EMPi

• Independent variables:

• Xi,j{REi , WGi , NVi ,SRVi , HVi , ICVi, CCVi, FGVi}

Results• A new dataset is generated with the inputs and outputs of

day-ahead market of the years 2012 (information obtained from the market operator).

• There is one day with 23 hours and other day with 26 hours (due to the time change that takes place twice a year)

Results

• In order to study the calendar effect on price market, we have split the dataset generating 18 samples. These samples depend of the variables:o S (season), o Dl (non-working day or working day) o TS (time slot) and they have been represented by this way:

.

The S and Dl variables have been defined previously.

The TS variable represents the different time slots in which the day can split. These time slots are based in the hours of high, normal and low electric demand taking into account also the seasonality. This is the recommended partition by the electrical grid operator and it tries to reflect the Iberian Peninsula reality.

Results• This table shows summary of descriptive statistics for some of the

samples. We can note the variations of the mean price and the standard deviation by our calendar criterion. For instance, the mean price in the whole data set is 48,02 units and the standard deviation is 12,22 units. However, for the sample f(0,0,2) the mean price is 65,38 units and the standard deviation is 7,21 units. These results confirm the importance of the calendar effect on the market price.

• Table 1 Summary of descriptive statistics for some of the samples

Results

• This table shows the Pearson correlation coefficients for some of the samples.

• Variables WG (wind power generation) and SRV (traded special regimen volume) reduce marginal price and the other variables increase it.

• For nuclear energy, the values show that there are samples in which this variable reduces the price and others in which this variable increases the price. This is because this method does not reflect the character constant of the traded volume of this technology.

Results

• Another observation we can make is that, in most of the samples, the variables that most influence on the price are the traded volume of imported coal, combined cycle and conventional hydraulics. Also, there are some samples in which the demand, the wind generated and the traded special regimen volume become important. All of these variables have different weights depending on the sample. These results also confirm the importance of the calendar effect on the marginal price.

Results

The following equations show the models obtained for some samples. 

In the statistics summary of the each model, we can see that the figures of R squared corrected are greater than 0.06, and the figures of P-value are less than 0.04, for all samples. This mean the proposed models are suitable.

Conclusions and Future Work• A Linear Dependency Analysis among Electric Market Prices and the amount

of energy produced by each technology, the electric demand and the wind power generated is presented in this work.

• This method has confirmed that is correct and necessary analyze the data set in function of the season, if the day is working day or non-working day, and the time slot. Therefore, this technique offers different models depending on these variables.

• This method reveals deficiencies in interpreting the marginal character of the price of electricity. Therefore, other modelling and forecasting techniques must take into account due to the special characteristics of nuclear energy and the energies of special regime, the marginal character of the electricity market and the uncertainty introduced by demand and wind power.

• In conclusion, we can say that this analysis shows us that it is good to have a range of predictions models and a decision-making algorithms to choose the best model for each situation sMeCoop could be a useful tool for electricity market managers.

Conclusions and Future Work

• In order to find the best method to model and predict the electric energy price for each sample, the next step is to apply different prediction techniques (Exponential Smoothingand, Moving Average, the near-neighbors, neuronal networks) and make a comparison among them.

• After the modeling and forecasting, we are going to develop a decision Making Model using fuzzy logics. This will allow us to choose the best prediction model for each situation. The codification that we have presented in this work will be used in order to obtain the logical fuzzy system that uses the defined Models.

• In the future work we will work with R language instead of using SPSS. With R we want to achieve better results and modeling of the problem.

Modeling prices in electricity Spanish markets under uncertaintyG-TeC research group, Complutense University, MadridIndizen Technologies, SL Madrid, SpainISKE 2013, ShenZhen – China

G-TeC members:Guadalupe Miñana, Raquel Caro, Beatriz. González, Victoria LopezeKergy Technologies members:Hugo Marrao, Jesús Gil