Eugenio F. Sánchez-Úbeda

Short-term Forecasting in Power Systems: A Guided Tour

In this paper, the three main forecasting topics that are currently getting the most attention in electric power systems are addressed: load, wind power and electricity prices. Each of these time series exhibits its own stylized features and is therefore forecasted in a very different manner. The complete set of forecasting models and techniques included in this revision constitute a guided tour in power systems forecasting.

Representative Operating and Contingency Scenarios for the Design of UFLS Schemes

This paper studies an approach to identify representative operating and contingency (OC) scenarios for the design of underfrequency load-shedding (UFLS) schemes. In small isolated power systems, contingency scenarios are outages of generating units. Usually, only N-1 outages are considered. In this paper, simultaneous outages of several units are also taken into account. Data mining techniques such as K-Means and Fuzzy C-Means algorithms are used to group scenarios in terms of system frequency and to identify representative OC scenarios. The approach has been applied to the design of UFLS schemes of two of the Spanish isolated power systems. The results have also been compared to the common practice of scenario selection. Clustering techniques yielded to satisfactory results, i.e., representative OC scenarios can be identified. Furthermore, these representative OC scenarios cover a wider range of possible system responses than the scenarios selected following the common practice.

SGO: management information system for strategic bidding in electrical markets

This paper describes SGO, a management information system for bidding in deregulated electricity markets, developed for the Spanish case. SGO has a client-server architecture and consists of a set of integrated cooperative and flexible software tools for assisting the users during the whole bidding process: resources identification, bids generation, market performance characterisation, bidding strategy analysis and optimisation, generation of markets results reports and automatic performance monitoring for suggesting on-line corrective actions.

A Goal Programming Model for Rescheduling of Generation Power in Deregulated Markets

In deregulated electrical systems, production schedule for power plants is the result of an auction process. In the Spanish case, this schedule includes two main concepts: energy production (to be actually produced) and secondary reserve (to maintain available). The generation company faces the problem of converting energy schedule into a power schedule, respecting the reserve schedule as well as technical constraints, and trying to accomplish different goals: to minimise the production costs, to obtain smooth shapes for the power schedules and to optimise eventual compensation in schedules. A weighted goal mixed integer programming model with a real-size application to deal with this problem is presented.

Residual Demand Curves for Modeling the Effect of Complex Offering Conditions on Day-Ahead Electricity Markets

Residual demand curves (RDCs) can be used to represent the strategic interaction of participants in electricity markets. RDCs relate the energy that an agent can buy or sell in one hour with the clearing market price that would be obtained in such hour, assuming the market is organized as simple bid independent auctions. Despite the fact that they have been widely used in the literature, the existence of time and/or spatial constraints in the market clearing algorithm makes the RDCs not directly applicable. This paper tries to overcome these difficulties by extending the concept of RDCs to zonal pricing markets where complex offering conditions and transmission constraints are taken into account. Therefore, the RDCs are redefined in order to capture such effects, which are usually neglected or oversimplified. A new method for computing the redefined RDCs is established and its application to the Iberian electricity market is presented. The results show that modeling complex conditions and transmission constraints in RDCs can have a significant effect when compared to the standard approach found in the literature. Therefore, the method presented in this paper modeling the effect of firms decisions on market prices in a more accurate way.

Modeling bidding curves: the linear hinges model versus the sigmo model

In this paper, we present and compare two approaches to model supply and demand curves of a sealed-bid auction market. Both the linear hinges model and the sigmo model are able to extract the relevant information from these bidding curves, without losing significant market information. We discuss their main similarities and important differences using a unified framework, to highlight their main strengths and weakness. A practical comparative study based on real supply functions from the Californian electricity market has been included to derive practical conclusions.

Automatic specification of piecewise linear additive models: application to forecasting natural gas demand

When facing any forecasting problem not only is accuracy on the predictions sought. Also, useful information about the underlying physics of the process and about the relevance of the forecasting variables is very much appreciated. In this paper, it is presented an automatic specification procedure for models that are based on additivity assumptions and piecewise linear regression. This procedure allows the analyst to gain insight about the problem by examining the automatically selected model, thus easily checking the validity of the forecast. Monte Carlo simulations have been run to ensure that the model selection procedure behaves correctly under weakly dependent data. Moreover, comparison over other well-known methodologies has been done to evaluate its accuracy performance, both in simulated data and in the context of short-term natural gas demand forecasting. Empirical results show that the accuracy of the proposed model is competitive against more complex methods such as neural networks.

Representative contingency identification using data mining

This paper studies an approach to identify representative operating and contingency scenarios for the design of underfrequency load-shedding (UFLS) schemes. In small isolated power systems, contingency scenarios are outages of generating units. Not only N-1 outages are considered, but simultaneous outages of several units must be considered. Cluster analysis and classification trees are used to group scenarios in terms of system frequency and power-system parameters and to identify representative scenarios. The approach has been applied to the design of the UFLS scheme of one of the Spanish isolated power systems. Clustering techniques yielded to satisfactory results, i.e. representative operating and contingency scenarios can be identified. In addition, classification trees are able to classify new operating and contingency scenarios according to the clusters obtained.

Short-Term Evolution of Forward Curves and Volatility in Illiquid Power Markets

We propose in this paper a model for the description of electricity spot prices, which we use to describe the dynamics of forward curves. The spot price model is based on a long-term/short-term decomposition, where the price is thought of as made up of two factors: A long-term equilibrium level and short-term movements around the equilibrium. We use a non-parametric approach to model the equilibrium level of power prices, and a mean-reverting process with GARCH volatility to describe the dynamics of the short-term component. Then, the model is used to derive the expression of the short-term dynamics of the forward curve implicit in spot prices. The rationale for the approach is that information concerning forward prices is not available in most of power markets, and the direct modeling of the forward curve is a difficult task. Moreover, power derivatives are typically written on forward contracts, and usually based on average prices of forward contracts. Then, it is difficult to obtain analytical expressions for the forward curves. The model of forward prices allows for the valuation of power derivatives, as well as the calculation of the volatilities and correlations required in risk management activities. Finally, the methodology is proven in the context of the Spanish wholesale market

Risk Analysis in Electricity Markets by using Decision Trees

This paper describes a procedure for medium-and long-term risk analysis by using decision trees. A market equilibrium model is presented in order to assess the impact of the different sources of uncertainty. Decision trees are defined and applied in a study case, showing the advantages of these techniques for medium-term operation and planning. The paper analyzes different scenarios of five main risk factors: hydro-inflows, fuel (coal and gas) costs, system demand, and CO 2 emission price