Long-term assessment of power capacity incentives by modeling generation investment dynamics under irreversibility and uncertainty

In actual energy-only markets, the high volatility of power prices affects the expected returns of generators. When dealing with irreversibility under uncertainty, deferring decisions to commit in new power plants, waiting for better information, is therefore a rational approach. Theoretical and empirical evidence suggests that such investment pattern determines the occurrence of construction cycles, which strongly compromise supply security. In order to supplement generators´ revenues, several remuneration mechanisms have been devised over past years. Along this line, this work addresses the long-run dynamics of capacity adequacy and market efficiency with both a price-based and a quantity-based capacity remuneration policy. For that purpose, a recently-developed, stochastic simulation model is used as a benchmark. Hence, the optimal postponement of generation investment decisions is integrated into a long-run power market model by formulating the decision-making problem in the framework of Real Options Analysis. Results suggest that policymakers may exchange supply security (effectiveness) for energy prices to be paid by consumers (efficiency) when designing and implementing capacity remuneration mechanisms. By doing so, this article contributes to the ongoing debate regarding the design of incentive policies and efficient power markets by considering the microeconomics of investors? decision-making under irreversibility and uncertainty.Fil: Rios Festner, Daniel. Universidad Nacional de Asunción; ParaguayFil: Blanco, Gerardo. Universidad Nacional de Asunción; ParaguayFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Real options approach-based demand forecasting method for a range of products with highly volatile and correlated demand

To achieve a competitive edge needed for marketing highly competitive products, modern enterprises have actively sought to provide the marketplace with an expansive range of products with high random volatility of demand and correlations between demands of product. Consequently, traditional forecasting methods for separately forecasting demand for these products are likely to yield significant deviations. Therefore, this study develops a real options approach-based forecasting model to accurately predict future demand for a given range of products with highly volatile and correlated demand. Additionally, this study also proposes using Monte Carlo simulation to solve the demand forecasting model. The real options approach associated with Monte Carlo simulation not only deals effectively with random variation involving a particular demand stochastic diffusion process, but can handle the correlations in product demand.Demand forecasting Demand correlation Real options approach Monte Carlo simulation

Strategic Decision-Making of Flexible Investments under Uncertainties in Long-Term Electricity Markets

In liberalized electricity markets, the investment postponement option is deemed to be decisive for understanding the addition of new generating capacity. Basically, it refers to the investors’ chance to postpone projects for a period while waiting for the arrival of new and better information about the market evolution. When such development involves major uncertainties, the generation business becomes riskier, and the investors’ “wait-and-see” behavior might limit the timely addition of new generation capacity. The literature provides solid empirical evidence about the occurrence of construction cycles in the deregulated electricity industry. However, the strategic flexibility inherent to defer investments in power plants has not been yet rigorously incorporated as an explicit input for investment signals in the revised long-term market models. Therefore, this paper proposes a new methodology to assess the long-term development of liberalized power markets based on a more realistic approach for valuing generation investments. The proposal is based on a stochastic dynamic market model, built upon a System Dynamics simulation approach. The model considers that the addition of new generation capacity is driven by the economic value of the strategic flexibility associated to defer investments under uncertainties. The value of the postponement option is quantified in monetary terms by means of Real Options analysis. Simulations explicitly confirm the cyclical behavior of the energy-only market in the long-run, as suggested by the empirical evidence found in the literature. Furthermore, the proposed method is used to test three regulatory schemes, implemented in order to dampen the arising construction cycles. Results show that, for ensuring the supply security in markets under huge uncertainties, investors would need complementary capacity incentives in order to deploy power generation investments in timely manner.CONACYT - Consejo Nacional de Ciencia y TecnologíaPROCIENCI

Modelo de expansão de capacidade com equilíbrio espacial de mercados

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2013.O objetivo deste trabalho é investigar, propor e elaborar um modelo que determina a política ótima de expansão de capacidade de empresas inseridas em diferentes estruturas de mercados espacialmente distribuídos, considerando situações de competição perfeita, monopólio e oligopólio. Assume-se que o produto em questão é uma commodity e a demanda cresce de forma determinística ao longo do tempo. Os investimentos das empresas são realizados em sequência segundo um processo estocástico que governa este sequenciamento, de forma a garantir a unicidade da solução. O modelo proposto aborda o problema em dois estágios: em um primeiro estágio, chamado de jogo de longo prazo, as empresas a cada período fazem suas escolhas de investimento de forma a adequar capacidade de produção e maximizar os seus lucros. O conceito de Equilibro Perfeito de Markov foi aplicado para a obtenção da política ótima de expansão. A solução ótima de longo prazo é obtida através da Programação Dinâmica. Em um segundo estágio, chamado de jogo de curto prazo, as capacidades são fixadas e a competição volta-se para a participação de mercado. É quando as empresas produtoras se comportam segundo o modelo de Cournot-Nash, pelo qual cada empresa considera as decisões das demais empresas como fixas, quando decide o seu nível ótimo de produção e fluxos do produto para os mercados consumidores. O conceito de equilíbrio espacial de mercados foi aplicado e a solução de curto prazo, que é obtida através da formulação de um problema de inequações variacionais. Utilizou-se a técnica de simulação de Monte Carlo para analisar as distribuições de probabilidade das variáveis de decisão do modelo. O modelo foi aplicado a um exemplo numérico teórico de duopólio, através do qual foi possível analisar comportamentos qualitativos de competições entre empresas envolvendo um mercado distribuído espacialmente. O modelo permite considerar diferentes estruturas competitivas de mercados, tais como, oligopólios, monopólios e concorrência perfeita. Entre os principais resultados, destaca-se a verificação de que os custos logísticos impactam significativamente a política ótima de expansão de capacidade. Abstract : This work aims at investigating, proposing and implementing a model that determines the optimal expansion capacity strategy for companies that operate in spatially distributed markets, regarding perfect competition, monopoly and oligopoly market scenarios. It is assumed that the product in question is a commodity and deterministic demand grows over time. Companies? investments are undertaken in sequence according to a stochastic process that governs this sequencing in order to ensure the uniqueness of the solution. The proposed model addresses the problem in two stages: in the first stage, called long-term game, companies make their investment choices in each period in order to adjust production capacity and maximize their profits. The concept of Perfect Markov Equilibrium was applied to obtain the optimal expansion strategy, using dynamic programming. In the second stage, called short-term game, once investments in capacity expansion have already been performed in a given period of time, capacities are fixed and then competition turns to market share, when companies are focused on determining its optimal level of production and product flows to the consumer markets. The concept of spatial equilibrium market was applied for companies competing according to a Cournot-Nash model in a spatially distributed market. The short-term equilibrium was obtained through the formulation of a variational inequality problem. Monte Carlo simulation has been used to analyze the probability distributions of the companies' decision variables. The model was applied to a theoretical numerical example of a duopoly, where it was possible to analyze various qualitative behaviors of competition involving a spatially distributed market. Among the main results is the finding that logistic costs have a significant impact in the optimal capacity expansion policy