The electric power industry in the United States is undergoing substantial changes in power gene ration business as well as in environmental regulation. Under these changes, it is highly desirable for the electric power industry to objectively and quantitatively examine generation planning, which often involves a multiple number of different experts with multi-criteria for decision making. In this paper, we consider these two key aspects in generation planning (multi-experts/multi-criteria), and integrate an analytic hierarchy process for multi-criteria decision making and a Bayesian approach for combining experts' opinions. Our efforts lead to a comprehensive numerical example that illustrates multi-experts/multi-criteria generation planning for the electric power industry. Managerial insights and economic implications are provided throughout this paper.
In the United States, there have been fundamental changes in how electric power businesses are conducted and regulated (see e.g., Wang and Min (2000)). These fundamental changes are often directly related to the growing importance of market-based economics/finance as well as environmental concerns in the electric power industry.
From the economics/finance perspective, the increasing emphasis on market competition often implies that the fair rate of return on investment may no longer be guaranteed (see e.g., Subramaniam and Min (2000)). In such circumstances, for electric power generation planning, it is highly desirable to carefully consider important economic/financial criteria such as net present value (NPV), internal rate of return (IRR), and variance in cash flow (VCF) prior to making any significant commitment (e.g., construction of a new generation unit).
From the environmental concern perspective, the current trend toward stringent environmental protection standards and sophisticated implementation mechanisms is expected to continue (see e.g., Jorgenson and Wilcoxen (1994)). This implies that the emission of critical pollutants such as sulfur dioxide (SO^sub 2^), nitrogen oxides (NO^sub x^), and carbon dioxide (CO2) must also be carefully considered in electric power generation planning prior to any significant financial commitment.
We note that there are diverse attributes to be considered within the economics/finance concerns such as NPV, IRR, and VCF and within the environmental concerns such as SO^sub 2^, NO^sub x^, and CO2. In addition, we note that these attributes should be simultaneously considered across the two main concerns of economics/finance and environments. Under these circumstances, a multi-criteria analysis for generation planning will be both logical and intuitive for decision support purposes.
In a recent paper by Son and Min (1998), an analytic hierarchy process (AHP) approach that simultaneously considers both economic/financial and environmental concerns is presented. In particular, this paper shows how the priority weights for the two main criteria (economics/finance and environments) as well as the priority weights for the subcriteria such as NPV, IRR, VCF, SO^sub 2^, NO^sub x^, and CO2 can be computed. These priority weights can then be used for various decision support purposes such as capital budgeting.
Even though Son and Min (1998) provides a basic framework for multi-criteria decision making in generation planning, the model in the paper implicitly assumes that there is a single expert whose estimation is far superior to other experts' estimation. On the other hand, if there is no single authoritative expert, but a group of experts, then the model can not be directly utilized for generation planning. This observation is the motivation for the model and analysis in this paper.
In this paper, we propose a major extension of Son and Min (1998) by introducing steps to combine opinions of experts in computing priority weights of the competing criteria. In this way, we hope to provide the necessary concrete steps to apply the basic framework in Son and Min (1998) in generation planning when there is a group of experts (instead of a single authoritative expert). …