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This paper discusses alternative approaches that have been adopted around the world for guaranteeing the appropriate level of investment in electric generation capacity. We argue that long term reserves should be viewed as price insurance and be treated as a private good. However, political realities and asymmetries and distortions in risk management incentives may necessitate imposition of mandatory levels of such insurance on load serving entities. Furthermore, centralized markets may be needed as a supplement to bilateral contracting in order to facilitate efficient procurement of such insurance and to bridge the gap between the needs of generators and load serving entities with regard to duration of hedging instruments. We discuss the origins and shortcomings of capacity payments and capacity obligations and explain how long term supply contracts in the form of call options with premiums that depend on the contracts' strike prices can meet the need for ensuring supply adequacy and the financial health of the generator sector. We also outline a scheme where regulatory intervention in generation adequacy assurance takes the form of a hedging requirement imposed at the state level on load serving entities.

This book brings together researchers and practitioners from academia, the utility industry, private and public organizations and regulatory agencies to address various challenges and opportunities related to product differentiation in the electric power industry. The book's objectives are to: review the state of the art in implementing reliability differentiated electric services; address the entire process for developing and implementing reliability differentiated product menus including research, design, marketing, implementation and evaluation; consider technical, economics, and regulatory barriers to implementing reliability differentiated product design; and bring together a variety of theoretical and practical experience in electric power product differentiation

In competitive wholesale electricity markets, regulated load-serving entities (LSEs) and marketers with default service contracts have obligations to serve fluctuating load at predetermined fixed prices while meeting their obligation through combinations of long-term contracts, wholesale purchases, and self-generation that are subject to volatile prices or opportunity cost. Hence, their net profits are exposed to joint price and quantity risk, both of which are correlated with weather variations. In this paper, we develop a static hedging strategy for the LSE (or marketer) whose objective is to minimize a mean-variance utility function over net profit, subject to a self-financing constraint. Because quantity risk is nontraded, the hedge consists of a portfolio of price-based financial energy instruments, including a bond, forward contract, and a spectrum of European call and put options with various strike prices. The optimal hedging strategy is jointly optimized with respect to contracting time and the portfolio mix, which varies with contract timing, under specific price and quantity dynamics and the assumption that the hedging portfolio, which matures at the time of physical energy delivery, is purchased at a single point in time. Explicit analytical results are derived for the special case where price and quantity follow correlated Ito processes.

AbstractA new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior‐point algorithm known as the affine‐scaling primal algorithm. Using this single‐objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.

AbstractThis paper considers the problem of modeling the reliability of a repairable system or device that is experiencing reliability improvement. Such a situation arises when system failure modes are gradually being corrected by a test‐fix‐test‐fix procedure, which may include design changes. A dynamic reliability model for this process is discussed and statistical techniques are derived for estimating the model parameters and for testing the goodness‐of‐fit to observed data. The reliability model analyzed was first proposed as a graphical technique known as Duane plots, but can also be viewed as a nonhomogeneous Poisson process with a particular mean value function.