Regulatory reforms of the power sectors in South America have always been driven by the need of attracting enough investment to guarantee an expansion rate capable of covering the fast-paced demand growth in the continent. In order to achieve this objective, in the last decade several countries in the region have reshaped their regulatory frameworks in the direction of long-term auctioning. This paper first provides a detailed description of the regulatory evolution that resulted in the implementation of auction schemes and then it presents an updated review of the mechanisms implemented in five South American countries, comparing their main design elements.

The Brazilian Electricity Regulatory Agency (ANEEL) presented a proposal to revise the tariff structure of distribution companies in Brazil. One of the main approved suggestions was to establish a mechanism called Tariff Flags, which aims to foster a demand response program in Brazil via an increase in the energy tariff. In this work, the proposed mechanism is reviewed in detail and the expected results of its application are simulated and analyzed under different perspectives. This paper shows that the system operation directly impacts the demand response program, since the spot prices will define which tariff flag should be triggered. In order to encompass and assess the main consequences of its application, this paper presents the expected effects on energy spot prices, system operating costs, probability of triggering each flag, investment recovery for utilities and finally, the impact for the final consumers. The case studies presented in this paper were developed using real information about the Brazilian electrical system for each economic sector and the price-demand elasticity is discussed using the literature for this application. Finally, some conclusions and guidelines are provided to improve the application of the mechanism.

This paper presents an integrated assessment of sugarcane mills considering sales of ethanol, sugar and bioelectricity to evaluate the impact of the gasoline price freeze on the sugar cane industry. Brazil is the main producer of sugarcane in the world, the number one exporter of sugar and second of ethanol. In addition to these two products, sugarcane mills have added bioelectricity as a third business opportunity to their portfolio. This study uses OptValue, a software developed by PSR, to evaluate investments in sugarcane mills and cogeneration. Its main conclusion was that the gasoline price freeze policy and the fuels tax exemption of 2012 (enacted by Presidential Decree No. 7764) diminished the average internal rate of return (IRR) of a sugarcane mill by ten pp. This reduction in the IRR is particularly sensitive to the mill’s portfolio. In addition, our findings showed that the consideration of bioelectricity makes project IRR less volatile, decreasing in this way sugarcane mills investors perceived risks.

Nos Leilões de Energia Nova ocorridos nos últimos 3 anos no Brasil, apenas 6% da energia contratada provém de termelétricas a gás natural. Por mais que uma expansão baseada em fontes renováveis seja louvável, térmicas a gás possuem atributos importantes para a segurança do suprimento energético que não estão presentes nas fontes renováveis predominantes no sistema brasileiro e se fazem necessários diante da perspectiva da situação de suprimento para os próximos anos. Um entrave para a inserção de usinas a gás natural no mercado brasileiro é o conflito de interesses econômicos entre geradores térmicos e fornecedores de combustível. Para o setor elétrico, o ótimo econômico é a compra de gás natural apenas nos momentos de despacho solicitado pelo ONS, o que permite o atendimento do contrato de venda de energia elétrica a um custo menor do que o do gás natural nos momentos de boa hidrologia. Para o setor de gás, como a infraestrutura de investimento na exploração, produção e transporte do gás está baseada principalmente em custos fixos, são impostos nos contratos de suprimento de combustível cláusulas de Take or Pay (ToP), que resultam em uma geração mínima da usina, e de pagamento por capacidade. Estas cláusulas transformam custos variáveis, precificados pelos seus valores esperados na parcela do COP do ICB dos leilões, em custos fixos, precificados diretamente na Receita Fixa do ICB e podem penalizar a competitividade de empreendimentos termelétricos nos leilões. Neste sentido, dentro das condicionantes estabelecidas para participação nos certames, existem diversas combinações de preço da commodity, nível de ToP e pagamento por capacidade ou Ship or Pay (SoP) que resultam na remuneração necessária para o supridor de gás natural. No entanto, estas combinações possuem impactos distintos nas parcelas que compõem o ICB, resultando no final do dia em um trade-off da alocação de custos na componente fixa ou variável deste índice. O objetivo deste trabalho é apresentar um modelo matemático que maximiza a competitividade de um gerador térmico em um leilão de energia nova a partir da otimização dos parâmetros de um contrato de suprimento de gás (GSA). O modelo otimiza o preço da commodity, nível de ToP e valor do pagamento por capacidade, visando minimizar o valor do ICB, garantindo a remuneração necessária do supridor de gás. As contribuições principais deste trabalho consistem em: (i) buscar alternativas para a incongruência existente entre o setor elétrico e o setor de gás; (ii) discutir através de análises quantitativas a alocação de risco entre os diversos agentes envolvidos, ou seja, o supridor de gás natural, o gerador e o consumidor de energia elétrica; e (iii) propor mecanismo de contratação de termelétricas com o objetivo de reduzir o prêmio de risco referente à flexibilidade no suprimento de combustível.

A capacidade eólica instalada no Brasil vem crescendo significativamente nos últimos anos. Esta fonte é considerada intermitente, característica que acaba gerando algumas barreiras na implementação e aceitação da mesma. Quanto maior a sua inserção, mais difícil torna-se prever a quantidade de energia gerada por ela, o que pode dificultar o planejamento a longo prazo e aumentar os riscos de déficit no sistema. A operação do Sistema Elétrico Brasileiro (SEB) conta com o auxílio de modelos matemáticos/computacionais para o despacho hidrotérmico, a fim de se obter a melhor aproximação do despacho ótimo que minimiza os custos operativos totais, e garante o atendimento à demanda. Este trabalho tem como objetivo analisar os impactos no âmbito energético da variabilidade na produção de energia eólica no SEB através dos Custos Marginais de Operação (CMO) considerando diferentes níveis de penetração de eólicas. Também foi analisado o benefício de considerar a estocasticidade eólica no cálculo da política operativa do sistema. Para isto, foi utilizado um estudo de caso real com dados do SEB de 2016 até 2030.

This work presents a methodology to incorporate reliability constraints in the optimal power systems expansion planning problem. Besides LOLP and EPNS, traditionally used in power systems, this work proposes the utilization of the risk measures VaR (Value-at-Risk) and CVaR (Conditional Value-at-Risk), widely used in financial markets. The explicit consideration of reliability constraints in the planning problem can be an extremely hard task and, in order to minimize computational effort, this work applies the Benders' decomposition technique splitting the expansion planning problem into an investment problem and two sub-problems to evaluate the system's operation cost and the reliability index. The operation sub-problem is solved by Stochastic Dual Dynamic Programming (SDDP) and the reliability sub-problem by Monte Carlo Simulation. The proposed methodology is applied to the real problem of optimal expansion planning of the Bolivian power system.

The modelling of modern power markets requires the representation of the following main features: (i) a stochastic dynamic decision process, with uncertainties related to renewable production and fuel costs, among others; and (ii) a game-theoretic framework that represents the strategic behaviour of multiple agents, for example in daily price bids. These features can be in theory represented as a stochastic dynamic programming recursion, where we have a Nash equilibrium for multiple agents. However, the resulting problem is very challenging to solve. This work presents an iterative process to solve the above problem for realistic power systems. The proposed algorithm is consist of a fixed point algorithm, in which, each step is solved via stochastic dual dynamic programming method.

The application of the proposed algorithm are illustrated in case studies with the Panama systems.

The optimal scheduling of hydrothermal systems requires the representation of uncertainties in future streamflows to devise a cost-effective operations policy. Stochastic optimization has been widely used as a powerful tool to solve this problem but results will necessarily depend on the stochastic model used to generate future scenarios for streamflows. Periodic autoregressive (PAR) models have been widely used in this task. However, its parameters are typically unknown and must be estimated from historical data, incorporating a natural estimation error. Furthermore, the model is just a linear approximation of the real stochastic process. The consequence is that the operator will be uncertain about the correct linear model that should be used at each period.

The objective of this work is to assess the impacts of incorporating the uncertainty of the parameters of the PAR models into a stochastic hydrothermal scheduling model. It will be shown that when the uncertainty of the parameters is ignored, the policies given by the stochastic optimization tend to be too optimistic.

Many capacity planning models used today are based on a Benders decomposition scheme [1, 2] composed of: (i) a MIP-based “investment module” which determines a trial expansion plan; (ii) a SDDP-based “operation module” which calculates the expected operation costs for the trial plan; and (iii) Benders cuts from the operation to the investment module, whose coefficients are calculated from the expected marginal costs of the capacity constraints in the operation module at the optimal solution.

Although this “traditional” planning model has been successfully applied in many countries, it has an inherent limitation, which has become more significant with the penetration of renewables with short construction times, such as solar: the optimal expansion plan is “static”, i.e. investment decisions do not change as the system state evolves (e.g. load growth is lower than expected, a very rainy season occurs etc.). As a consequence, there is a growing interest in the calculation of an integrated stochastic investment & operations strategy.

The application of the proposed algorithm will be illustrated in realistic capacity planning studies of the Central America system.

No Brasil, o processo de tomada de decisões operativas das usinas geradoras despachadas centralizadamente é baseado em simulações de modelos computacionais de otimização. Nesses modelos, o objetivo é minimizar o valor esperado do custo operativo ao longo de um período de estudo considerando incertezas futuras associadas principalmente às vazões naturais. Atualmente, no modelo de médio prazo, como período de análise, utiliza-se cinco anos e mais cinco anos de pós-estudo, para evitar que a água, como recurso mais barato, seja utilizada demasiadamente no final do horizonte de estudo. A redução tanto do período de estudo quanto do pós-estudo, se não comprometer a tomada de decisões do curto prazo, pode trazer ganhos tais como a redução da dimensão dos problemas matemáticos a serem resolvidos e, consequentemente, a redução do tempo computacional das simulações. Também haveria a diminuição de incertezas associadas aos dados de entrada, como projeção de carga, cronograma da expansão das usinas e linhas de transmissão. O objetivo desse estudo é analisar a possibilidade de redução desses horizontes nas simulações realizadas com o sistema Brasileiro.

This work proposes a dynamic model to devise the optimal risk-averse investment policy in a portfolio of complementary renewable sources for a generation company in the Brazilian power system. The proposed method merges a static energy-contracting model, based on a hybrid robustand- stochastic optimization approach, with a mean reverting binomial lattice model for real-option valuation. The proposed merge extends previous works by providing support to riskaverse investment decisions in complementary renewable sources dynamically distributed over time. The most important results of the model are: how much capacity to invest or build from each renewable source, how much to sell from the energy portfolio in bilateral contracts, and the optimal timing to invest. Unlike previous reported works, our model takes into account three classes of uncertainties simultaneously: renewable production of candidate sources and prices in the spot and contract markets. A case study with realistic data from the Brazilian power system is presented to illustrate the value of our model.

The optimal scheduling of hydrothermal systems requires the representation of uncertainties in future streamflows to devise a cost-effective operations policy. Stochastic optimization has been widely used as a powerful tool to solve this problem but results will necessarily depend on the stochastic model used to generate future scenarios for streamflows. Periodic autoregressive (PAR) models have been widely used in this task. However, its parameters are typically unknown and must be estimated from historical data, incorporating a natural estimation error. Furthermore, the model is just a linear approximation of the real stochastic process. The consequence is that the operator will be uncertain about the correct linear model that should be used at each period. The objective of this work is to assess the impacts of incorporating the uncertainty of the parameters of the PAR models into a stochastic hydrothermal scheduling model. The proposed methodology is tested with case studies based on data from the Brazilian hydroelectric system. It is shown that when the uncertainty of the parameters is ignored, the policies given by the stochastic optimization tend to be too optimistic.