Incentive contracts using simulation benchmarks for natural gas local distribution companies regulation

Tarik Aouam, Purdue University


Cost-of-service is the typical regulatory scheme used for natural gas local distribution companies (LDCs). The profit of a regulated LDC is a return on investment, based on the capital she owns. The cost paid by consumers is, in general, equal to the cost incurred by the LDC for gas procurement including risk management, plus the return on investment. Major problems with such a scheme arise when natural gas prices are very volatile: (i) The LDC has little incentive to efficiently reduce her procurement cost, (ii) the cost of procurement by the LDC can vary substantially depending on her risk management strategy, (iii) and, the regulator does not know what the adequate cost should be and can rarely argue that the LDC is run inefficiently. ^ This work proposes the use of simulation benchmarks in incentive contracts for LDC regulation. Simulation benchmarks are costs associated with feasible LDC policies that incorporate natural gas price dynamics, using public information, and are based on particular properties of an LDC. These benchmarks can serve as a basis of comparison for a regulator who aims to reduce a trade-off between expectation and variance of the cost paid by consumers. ^ We propose a mean-variance framework to study the behavior of linear incentive contracts using simulation benchmarks. The analysis suggests that the regulator should select a benchmark on the efficient frontier of benchmarks that reflects his risk preference. Two families of simulation benchmarks are designed based on two types of policies, namely: optimization policies and fixed fraction hedging policies. An experimental implementation illustrates how an efficient frontier of benchmarks can be constructed and compares the performance of various simulation benchmarks. ^




Major Professors: Kumar Muthuraman, Purdue University, Ronald L. Rardin, Purdue University.

Subject Area

Engineering, Industrial|Operations Research

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