We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions independently minimizing the expected cost and the Conditional Value-at-Risk; i.e., the risk of significantly larger-than-expected operational costs. We introduce stochastic process models to capture demand and fuel price uncertainty, which are in turn used to generate trees that accurately represent the uncertainty space. Using a realistic problem instance based on theMidwest US, we explore two fundamental, unexplored issues that arise when solving any stochastic generation expansion model. First, we introduce and discuss the use of an algorithm for computing confidence intervals on obtained solution costs, to account for the fact that a finite sample of scenarios was used to obtain a particular solution. Second, we analyze the nature of solutions obtained under different parameterizations of this method, to assess whether the recommended solutions themselves are invariant to changes in costs. The issues are critical for decision makers who seek truly robust recommendations for generation expansion planning. Abstract We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions minimizing the expected cost or the Conditional Value-at-Risk, i.e., the risk of significantly larger-than-expected operational costs. We introduce stochastic process models to capture demand and fuel price uncertainty, which are in turn used to generate trees that accurately represent the uncertainty space. Using a realistic problem instance based on the Midwest US, we explore two fundamental, unexplored issues that arise when solving any stochastic generation expansion model. First, we introduce and discuss the use of an algorithm for computing confidence intervals on obtained solution costs; i.e., to account for the fact that a finite sample of scenarios was used to obtain a particular solution. Second, we analyze the nature of solutions obtained under different parameterizations of this method, to assess whether the recommended solutions themselves are invariant to changes in costs. These issues provide critical information to decision-makers, and are required to ensure truly robust recommendations relating to generation expansion planning.
We develop a tri-level model of transmission and generation expansion planning in a deregulated power market environment. Due to long planning/construction lead times and concerns for network reliability, transmission expansion is considered in the top level as a centralized decision. In the second level, multiple decentralized GENCOs make their own capacity expansion decisions while anticipating a wholesale electricity market equilibrium in the third level. The collection of bi-level games in the lower two levels forms an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem (CP) reformulation. We propose a hybrid iterative solution algorithm that combines a CP reformulation of the tri-level problem and DM solutions of the EPEC sub-problem. KeywordsComplementarity problem, equilibrium problem with equilibrium constraints, generation expansion planning, mathematical program with equilibrium constraints, Nash equilibrium, transmission expansion planning Abstract-We develop a tri-level model of transmission andgeneration expansion planning in a deregulated power market environment. Due to long planning/construction lead times and concerns for network reliability, transmission expansion is considered in the top level as a centralized decision. In the second level, multiple decentralized GENCOs make their own capacity expansion decisions while anticipating a wholesale electricity market equilibrium in the third level. The collection of bi-level games in the lower two levels forms an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem (CP) reformulation. We propose a hybrid iterative solution algorithm that combines a CP reformulation of the tri-level problem and DM solutions of the EPEC sub-problem.
Constraints in fuel supply, electricity generation, and transmission interact to affect the welfare of strategic generators and price-sensitive consumers. We consider a mixed integer bilevel programming model in which the leader makes capacity expansion decisions in the fuel transportation, generation, and transmission infrastructure of the electricity supply network to maximize social welfare less investment cost. Based on the leader's expansion decisions, the multiple followers including the fuel suppliers, ISO, and generation companies simultaneously optimize their respective objectives of cost, social welfare, and profit. The bilevel program is formulated as a mathematical program with complementarity constraints. The computational challenge posed by the discrete character of transmission expansions has been managed by multiple model reformulations. A lower bound provided by a nonlinear programming reformulation increases the efficiency of solving a binary variable reformulation to global optimality. A single-level optimization relaxation serves as a competitive benchmark to assess the effect of generator strategic operational behavior on the optimal capacity configuration.
We study a tri-level integrated transmission and generation expansion planning problem in a deregulated power market environment. The collection of bi-level sub-problems in the lower two levels is an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem (CP) reformulation. This paper is a continuation of its Part I, in which a hybrid iterative algorithm is proposed to solve the tri-level problem by iteratively applying the CP reformulation of the tri-level problem to propose solutions and evaluating them in the EPEC sub-problem by DM. It focuses on the numerical results obtained by the hybrid algorithm for a 6-bus system, a modified IEEE 30-bus system, and an IEEE 118-bus system. In the numerical instances, the (approximate) Nash equilibrium point for the sub-problem can be verified by examining local concavity. This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/imse_pubs/7 1 Abstract-We study a tri-level integrated transmission and generation expansion planning problem in a deregulated power market environment. The collection of bi-level sub-problems in the lower two levels is an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem (CP) reformulation. This paper is a continuation of its Part I, in which a hybrid iterative algorithm is proposed to solve the trilevel problem by iteratively applying the CP reformulation of the tri-level problem to propose solutions and evaluating them in the EPEC sub-problem by DM. It focuses on the numerical results obtained by the hybrid algorithm for a 6 bus system, a modified IEEE 30 bus system and an IEEE 118 bus system. In the numerical instances, the (approximate) Nash equilibrium point for the sub-problem can be verified by examining local concavity.
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