Planning for New Electric Generation Technologies A Stochastic Dynamic Programming Approach

DapkLus,; Bowe,

doi:10.1109/mper.1984.5526120

Cited by 8 publications

(4 citation statements)

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“…Alternatively, the risk neutral solution that minimizes the expected cost may result in poor solutions in certain unlikely but undesirable conditions. There are many studies in the literature where the uncertainties associated with the generation expansion planning problems such as demand growth, fuel cost, construction delay and component availabilities are considered [1][2][3][4][5][6][7][8][9][10][11][12]. There are also studies where the risk also incorporated into the problem [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

Tekiner‐Moğulkoç

Coit

Felder

2015

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

Tekiner‐Moğulkoç

Coit

Felder

2015

International Journal of Electrical Power & Energy Systems

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“…In Murphy et al [14], the uncertainty in demand is incorporated into stochastic programming with recourse. Dapkus and Bowe [6] developed a stochastic dynamic programming method for expansion planning. Gorenstin et al [10] examined an approach to expansion planning based on stochastic programming and decision analysis.…”

Section: Introductionmentioning

confidence: 99%

Multistage stochastic programming model for electric power capacity expansion problem

Shiina

Birge

2003

Japan J. Indust. Appl. Math.

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This paper is concerned with power system expansion planning under uncertainty. In our approach, integer programming and stochastic programming provide a basic framework. We develop a multistage stochastic programming model in which some of the variables are restricted to integer values. By utilizing the special property of the problem, called block separable recourse, the problem is transformed into a two-stage stochastic program with recourse. The electric power capacity expansion problem is reformulated as the problem with first stage integer variables and continuous second stage variables. We propose an L-shaped algorithm to solve the problem.

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“…One of the first applications of stochastic programming to electric capacity expansion modeling is Dapkus and Bowe [9]. This work employs stochastic dynamic programming methodology (e.g., Winston [37]) by considering a number of stages (time periods), and states (the result of combining technologies, their availability, and the peak level of demand).…”

Section: Introductionmentioning

confidence: 99%

A risk function for the stochastic modeling of electric capacity expansion

Marı́n

Salmerón

2001

Naval Research Logistics

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Abstract:We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate-size problems are presented along with comparison to a general-purpose nonlinear optimization package.

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Planning for New Electric Generation Technologies A Stochastic Dynamic Programming Approach

Cited by 8 publications

References 0 publications

Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

Multistage stochastic programming model for electric power capacity expansion problem

A risk function for the stochastic modeling of electric capacity expansion

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