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This report assesses the potential of electricity contract auctions as a procurement option for the World Bank's client countries. It focuses on the role of auctions of electricity contracts designed to expand and retain existing generation capacity. It is not meant to be a 'how-to' manual. Rather, it highlights some major issues and options that need to be taken into account when a country considers moving towards competitive electricity procurement through the introduction of electricity auctions. Auctions have played an important role in the effort to match supply and demand. Ever since the 1990s, the use of long-term contract auctions to procure new generation capacity, notably from private sector suppliers, has garnered increased affection from investors, governments, and multilateral agencies in general, as a means to achieve a competitive and transparent procurement process while providing certainty of supply for the medium to long term. However, the liberalization of electricity markets and the move from single-buyer procurement models increased the nature of the challenge facing system planners in their efforts to ensure an adequate and secure supply of electricity in the future at the best price. While auctions as general propositions are a means to match supply with demand in a cost-effective manner, they can also be and have been used to meet a variety of goals.

This paper uses a panel data of 72 U.S. electricity distribution companies during the period 1972–2009 to estimate structural demand and reduced-form price models. I find the own-price and income elasticity of demand for residential, commercial, and industrial customers that are generally consistent with the published economics literature. While static models work well for residential demand, dynamic models are more appropriate for the larger customer classes who require more time to adjust. Conditioning on the regressors, I find that residential and commercial electricity demand has been increasing slowly while industrial electricity demand and deflated electricity prices have been decreasing. In all price models I find that total factor productivity is consistently the most significant explanatory factor with a 1% increase in total factor productivity resulting in a reduction in deflated electricity prices ranging between 0.05% and 0.30%, depending on the model. Lastly, I find that retail electricity competition is associated with lower deflated electricity prices with the mean total impact being −4.3%, −8.2% and −11.1% for residential, commercial and industrial customers, respectively and with the impact diminishing over the sample period for residential customers, remaining relatively constant for commercial customers and increasing for industrial customers.

Residential electricity competition is under investigation in a number of U.S. states due to alleged market imperfections including consumer behavior that is supposedly inconsistent with rational, economic decision-making. In this paper, I examine these issues and use a panel data of distribution utilities in Illinois during the period 2011–2017 to estimate demand models for regulated and competitive electricity services. I find that residential electricity consumers in Illinois are acting in a manner consistent with standard consumer behavior theory, with price elasticity of demand estimates that are generally in line with those in the literature, ranging between − 0.40 and − 0.60. Importantly, I find evidence that customers served by competitive suppliers are sensitive to the regulated default service price. Specifically, I find that a 1% decrease in the regulated default service price will lead to approximately 0.5% of customers served by competitive suppliers switching to the regulated default service. These findings call into question some of the underpinnings of policymakers’ critique of residential electricity competition.

This chapter presents a comprehensive decision support system for addressing the generation planning and operation. It is hierarchically divided into three planning horizons: long, medium, and short term. This functional hierarchy requires that decisions taken by the upper level model will be internalized by the lower level model. With this approach, the position of the company is globally optimized. This set of models presented is specially suited for hydrothermal systems. The models described correspond to long-term stochastic market planning, medium-term stochastic hydrothermal coordination, medium-term stochastic hydro simulation, and short-term unit commitment and bidding. In the chapter it is provided a condensed description of each model formulation and their main characteristics regarding modeling detail of each subsystem. The mathematical methods used by these models are mixed complementarity problem, multistage stochastic linear programming, Monte Carlo simulation, and multistage stochastic mixed integer programming. The algorithms used to solve them are Benders decomposition for mixed complementarity problems, stochastic dual dynamic programming, and Benders decomposition for SMIP problems.