Álvaro Baíllo

A medium-term integrated risk management model for a hydrothermal generation company

This paper presents a methodology to manage the market risk faced by a hydrothermal generation company in the medium-term (one year). This risk is due to uncertainty in fuel prices, power demand, water inflows, and electricity prices. The proposed methodology includes three steps: the generation of scenarios for these random parameters, the approximation of these scenarios by a multivariate scenario tree, and the optimization of the companys operational and financial hedging decisions under a stochastic programming framework. The optimization model permits the representation of a diversified generation portfolio and measures risk exposure by means of conditional value-at-risk. A realistic numerical example is solved to illustrate the possibilities of our approach.

Stochastic Power Generation Unit Commitment in Electricity Markets: A Novel Formulation and a Comparison of Solution Methods

We propose a stochastic unit commitment model for a power generation company that takes part in an electricity spot market. The relevant feature of this model is its detailed representation of the spot market during a whole week, including seven day-ahead market sessions and the corresponding adjustment market sessions. The adjustment market sessions can be seen as an hour-ahead market mechanism. This representation takes into account the influence that the companys decisions exert on the market-clearing price by means of a residual demand curve for each market session. We introduce uncertainty in the form of several possible spot market outcomes for each day, which leads to a weekly scenario tree. The model also represents in detail the operation of the companys generation units. The model leads to large-scale mixed linear-integer problems that are hard to solve with commercial optimizers. This suggests the use of alternative solution methods. We test four solution approaches with a realistic numerical example in the context of the Spanish electricity spot market. The first is a direct solution with a commercial optimizer, which illustrates the mentioned limitations. The second is a standard Lagrangean relaxation algorithm. The third and fourth methods are two original variants of Benders decomposition for multistage stochastic integer programs. The first Benders decomposition algorithm builds approximations for the recourse function relaxing the integrality constraints of the subproblems. The second variant strengthens these cuts by performing one iteration of the Lagrangean of each subproblem. We analyze the advantages of these four methods and compare the results.

Modeling Risk Management in Oligopolistic Electricity Markets: A Benders Decomposition Approach

This paper presents a model for addressing the market risk management problem faced by a hydrothermal generation company trading in an oligopolistic market. The risk is due to uncertainty in fuel prices, power demand, water inflows, and electricity prices. The model permits the representation of a diversified generation portfolio and measures risk exposure by means of conditional value at risk. The model is formulated and solved as a stochastic linear complementarity problem. In order to deal with realistically sized problems, Benders decomposition technique is adapted to solve equilibrium models. A numerical example illustrates the possibilities of the algorithm we propose.

Strategic bidding in electricity spot markets under uncertainty: a roadmap

The liberalization of the power industry and the creation of electricity spot markets throughout the world has triggered a significant research effort both by academic and practitioners. A part of this effort has been oriented to the development of methodologies and tools devoted to the optimization of bidding strategies for electricity spot markets. Uncertainty with respect to the behavior of the rest of participants is a major ingredient of this problem. After several years a vast literature has been produced on this particular topic. The purpose of this paper is to contribute to the definition of a general body of knowledge by characterizing the most relevant features of some of the lines of research that have been followed

Strategic bidding in a competitive electricity market: a decomposition approach

Daily bidding is an activity of paramount importance for generation companies operating in day-ahead electricity markets. The authors have developed a strategic bidding procedure based on stochastic programming to obtain optimal bids. In this paper, the problem is decomposed under the Benders framework to permit the solution of large-size problems. A numerical example illustrates the advantages of the proposed approach.

Strategic Unit Commitment for Generation in Deregulated Electricity Markets

In this chapter we address some of the new short-term problems that are faced by a generation company in a deregulated electricity market, and we propose a decision procedure to address them. Additionally, we propose a strategic unit commitment model, which deals with the weekly operation of the firm’s generating facilities. In it we combine traditional cost-evaluation techniques and technical constraints that grant a feasible schedule with new market-modeling equations. We suggest strategic constraints that allow the accomplishment of the firm’s medium-term objectives. We have formulated the model as a mixed-integer-programming problem and solved it by means of a commercial algorithm, instead of using the traditional Lagrangian relaxation approach. Results of the application of the method to a numerical example are presented. The procedure is a simplified version of one of several tools currently being used by a leading Spanish generation company, Iberdrola, for the weekly operation of its generation assets in the Spanish wholesale electricity market.

Strategies to fulfill medium-term objectives through short-term operation in competitive power markets

This work presents two different approaches in order to guarantee that short-term decisions made by a generation company taking part in a wholesale electricity market are consistent with its operation objectives formulated from a medium-term perspective. In particular, it shows how certain economic signals must be used so as to orient short-term decisions toward medium-term objectives. The paper focuses in the case of a generation company that establishes a minimum-market-share objective for the medium term, and shows the implications of the different guidelines that can be used in the short-term operation in order to achieve that objective.

Stochasticity in Electric Energy Systems Planning

Electric energy systems have always been a continuous source of applications of planning under uncertainty. Stochastic parameters that may strongly affect the electric system are demand, natural hydro inflows and fuel prices, among others. A review of some estimation methods used to approximate those parameters is presented. Reliability and stochastic optimisation are widespread techniques used to incorporate random parameters in the decision-making process in electric companies. A unit commitment, a market-based unit commitment, a hydrothermal coordination and a risk management model are typical models that can incorporate uncertainty in the decision framework.

Valuation of forward contracts in electricity markets. Application to the ecuadorian market

One of the main characteristics in the electricity markets is the volatileness of the spot price. The uncertainty associated to the price is a source of risk as much for the selling agents (generation companies) like for the buying agents. For this reason, is necessary to develop tools and methodologies of analysis, valuation and risk management associated to the generation business. This paper proposes, formulates and develops a procedure for the valuation of forward contracts with the objective to obtain a suitable balance between risk and rentability in the context of the generation companies that operate in the Ecuadorian electricity market.