This work presents a binary expansion (BE) solution approach to the problem of strategic bidding under uncertainty in short-term electricity markets. The BE scheme is used to transform the products of variables in the nonlinear bidding problem in to amixed integer linear programming formulation, which can be solved by commercially available computational systems. The BE scheme is applicable to pure price, pure quantity, or joint price/quantity bidding models. It is also possible to represent transmission networks, uncertainties (scenarios for price, quantity, plant availability, and load), financial instruments, capacity reinforcement decisions, and unit commitment. The application of the methodology is illustrated in case studies, with configurations derived from the 80-GW Brazilian system.

South America is facing important challenges in electricity supply to allow for future economic development. Current electricity market designs are being reviewed to avoid supply difficulties and couple the existing outlook of primary energy resources and the investment interest by the private sector. Examples of these developments are the giant Brazil,the economically troubled Argentina,and the pioneer of electricity reform,Chile. While Brazil and Chile progress into a second stage of reforms with public power purchase agreement (PPA) auctions in a private environment,Argentina makes a backward movement to significant state intervention,as in the times previous to reform. With these diverging approaches,their primary challenge is to ensure sufficient capacity and investment to reliably serve their growing economies.

The objective of this work is to present a decision support system to determine the optimal dispatch strategy of thermal power plants taking into account the particular specifications of fuel supply agreements, such as take-or-pay and make-up clauses. Opportunities for energy purchase and selling at the spot market as well as a detailed modeling of the power plant (maintenance cycles, influence of temperature, etc) are also considered in the optimization. In an integrated way, the model also determines the plants’ optimal schedule for maintenance. Since decisions in one stage impact the future stages, the problem is time-coupled in a multi-stage framework. Moreover, the main driver for the decision-making is the energy spot price, which is unknown in the future and is modeled here through scenarios. Therefore, the optimal dispatch strategy is a decision under uncertainty problem, where at each stage the objective is to determine the optimal operation strategy that maximizes the total revenues taking into account constraints and characteristics of the fuel supply contract. The methodology applied is the Stochastic Dual Dynamic Programming (SDDP). Examples and case studies will be shown with the Brazilian system.

The objective of this paper is to present an optimization model for hydro scheduling and risk management in deregulated electricity markets where hydropower companies are likely to face stochastic inflows, spot prices and forward prices. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem for a given risk profile. The model seeks to maximize company’s revenues by simultaneously determining generation, selling and purchasing decisions on both the spot and the forward markets. The company’s risk aversion is apprehended by penalizing release and contracting policies that lead to unacceptable financial performances. A hybrid stochastic dynamic programming (SDP)/stochastic dual dynamic programming (SDDP) formulation is adopted to solve this large-scale optimization problem. The state variables of the SDP/SDDP model are the volume in storage at the beginning of each time period, the previous inflows, the accumulated balance of forward contracts, the accumulated revenues and the accumulated profit for each profit period. Spot price and hydrologic uncertainties are captured through scenarios. The model also used a Markov chain description for forward prices. Market liquidity must be defined for each financial product. A hydropower company in the French market is used to illustrate this integrated physical and financial planning model.

The objective of this work is to present a decision support system to determine the optimal dispatch strategy of thermal power plants taking into account the particular specifications of fuel supply agreements, such as take-or-pay and make-up clauses. Opportunities for energy purchase and selling at the spot market as well as a detailed modeling of the power plant (maintenance cycles, influence of temperature, etc) are also considered in the optimization. In an integrated way, the model also determines the plants’ optimal schedule for maintenance. Since decisions in one stage impact the future stages, the problem is time-coupled in a multi-stage framework. Moreover, the main driver for the decision-making is the energy spot price, which is unknown in the future and is modeled here through scenarios. Therefore, the optimal dispatch strategy is a decision under uncertainty problem, where at each stage the objective is to determine the optimal operation strategy that maximizes the total revenues taking into account constraints and characteristics of the fuel supply contract. The methodology applied is the Stochastic Dual Dynamic Programming (SDDP). Examples and case studies will be shown with the Brazilian system.

The objective of this paper is to provide a methodology for pricing, under a generation company (Genco) point of view, long-term energy contracts signed across different price zones in a zonal pricing hydro-based power system where classical Financial Transmission Rights (FTRs) are not available. The main result is the establishment of the overprice that a Genco must include in the contract signed in a neighbor zone (where the Genco faces the congestion risk) when compared to the same contract offered in its own zone (with no congestion risk). All relevant risks (hydrological risk, congestion risk, etc) are captured for the long-term through the use of scenarios. Based on these scenarios and on the risk profile of the agent modeled by Utility Functions (UFs), the pricing of crosszones contracts are determined. The approach will be illustrated with practical examples deriving from the Brazilian electricity market, which is hydro-based, has a zonal-pricing scheme and does not offer instruments to hedge against congestion risks, such as FTRs.

Latin America has emerged in the recent years as one of the most dynamic regions for natural gas and electricity developments. The continent boasts abundant natural gas reserves and high-growth energy markets. The need to diversify away from heavy investments on hydropower and expensive oil is driving many countries to promote natural gas use, especially for power generation. On the other hand, several challenges are being observed, such as the competition between hydro and thermal generation, the breaking of cross-countries natural gas agreements, competition between natural gas for power generation and electric transmission, among others. This paper addresses natural gas-electricity resource adequacy planning for four countries in Latin America (Brazil, Chile, Mexico, Colombia) as well as the perspectives for creation of an integrated market in the Southern cone of Latin America.

This paper describes the main findings of the work carried out on behalf of Cigré Task Force C5-2-1 "Classification of Electricity Markets" within Study Committee C5 of Cigré. The work is based on a questionnaire that was answered by 23 countries covering all continents. This paper provides an overview of various international operating electricity markets. It describes and classifies the organization and functioning of electricity markets independent of industry structures, management of congestion, ancillary services management and regulatory aspects.

O objetivo deste trabalho é apresentar uma ferramenta computacional que determine a estratégia ótima de despacho de usinas térmicas considerando as especificações do contrato de combustível e suas cláusulas de take-or-pay, as oportunidades de compra e venda de energia no mercado spot, as características detalhadas da usina, tais como ciclos de manutenção, potência e taxa de conversão como função da temperatura ambiente etc. De forma integrada, o modelo determina ainda o cronograma ótimo de manutenção das unidades geradoras. Como as decisões de uma etapa têm impacto nas etapas seguintes, há um acoplamento temporal entre as decisões tomadas e o problema tem um caráter de decisão multi-estágio. Além disso, o principal driver para a tomada de decisão é o preço de curto prazo, que é desconhecido no futuro e modelado através de cenários. Desta forma, a estratégia ótima de despacho torna-se um problema de decisão sob incerteza, onde a cada etapa o objetivo é determinar a operação que maximize a rentabilidade total (ao longo de vários períodos) da central térmica. A metodologia empregada é a Programação Dinâmica Dual Estocástica (PDDE). Exemplos serão ilustrados com o sistema brasileiro.

O objetivo deste trabalho é apresentar uma ferramenta computacional para servir como base na elaboração de estratégias de atuação de distribuidores nos leilões de energia instituídos pelo novo Modelo Institucional do Setor Elétrico Brasileiro. O problema consiste em determinar a declaração anual de compra de energia das distribuidoras em cada leilão, dado um conjunto de instrumentos de gerência de risco. A metodologia de solução é a otimização estocástica multi-estágio, levando em consideração, principalmente, a incerteza no crescimento da demanda, os diversos horizontes de contratação e preços da energia. Exemplo e estudo de caso serão apresentados com dados do Sistema Elétrico Brasileiro.

O objetivo deste trabalho é desenvolver uma metodologia e apresentar uma ferramenta para estratégia de oferta de agentes geradores em leilões de contratos de energia existente. Assim, para um dado gerador, deve-se determinar a quantidade ótima de energia a ser ofertada em cada contrato, para cada nível de preço corrente do leilão, de maneira a maximizar a utilidade esperada do agente. A metodologia apresentada, leva em consideração o perfil de risco de cada agente frente aos riscos relevantes associados à contratação, como por exemplo: risco de preço e quantidade (devido à correlação negativa entre geração e PLD) e o risco de redução de montante contratado (devido à opção que as distribuidoras têm de reduzir os montantes contratados em até 4% ao ano – o que é equivalente a precificar uma put). Com a estratégia de atuação individual de um agente gerador já desenvolvida, é então apresentada e exemplificada uma metodologia de simulação de leilões, que modela a atuação de todas as empresas participantes em um esquema competitivo, por um processo iterativo.

Este artigo apresenta um método para seleção de cenários multivariados de afluência para ser aplicado ao problema de planejamento da operação de curto prazo (horizonte de até seis meses e estágios semanais e mensais). Os cenários propostos constituem uma árvore reduzida porém representativa das alternativas de afluências aos diversos reservatórios do sistema gerador de energia elétrica. Um estudo de caso com o sistema interligado brasileiro ilustra a aplicação da técnica proposta, examinando resultados tanto do ponto de vista hidrológico quanto da sua aplicação ao modelo DECOMP de otimização da operação hidrotérmica de curto prazo.