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Power plant investment planning by stochastic programming

Abstract

Although the problem of rational power generation has been extensively studied, traditional approaches for power optimization do not offer good solutions to this purpose, especially in a competitive electricity market environment where many factors are uncertain. In this paper, within the framework of two‐stage linear stochastic programming, the method for power planning has been developed, with uncertain factors taken into account, through a continuously distributed set of scenarios. The objective is to find the structure of the power plants capacity in the region which minimizes the sum of the investment and the expected operating costs over the long‐term planning horizon, taking into account the environmental impact. The structure of the considered task corresponds to a power investment planning problem that often arises in the developing regions. The method is developed for solving the stochastic optimization problem by the sequence of Monte‐Carlo sampling estimators. The procedures developed make it possible to solve stochastic problems with an admissible accuracy by means of an acceptable amount of computations. As follows from numerical experiments the approach presented enables us to decrease the total expected costs of power planning versus deterministic planning solution.


Article in English.


Stochastinio programavimo naudojimas planuojant elektros energijos infrastruktūrą ir gamybą


Santrauka. Nors elektros energijos infrastruktūros ir gamybos uždavinys sprendžiamas intensyviai, tradiciniai optimizavimo metodai nepateikia tinkamų sprendinių, ypač kai elektros energijos rinkoje daugelis veiksnių yra neapibrėžti. Šiame straipsnyje pateikiamas elektros energijos gamybos planavimo metodas, sukurtas remiantis dviejų etapų stochastiniu programavimu, kai neapibrežtumas aprašomas tolydžiaisiais pasiskirstymo scenarijais. Uždavinio tikslas – rasti tinkama regiono elektros jėgainiu struktūra, kuri minimizuotų investavimo ir ilgalaikės energijos gamybos sąnaudas. Sprendžiant uždavinį atsižvelgiama į gamtosaugos problemas. Taikant optimizavimo metodą naudojami baigtinių Monte Karlo sekų įverčiai. Siūloma procedūra leidžia išspręsti stochastinius uždavinius gana tiksliai, naudojant priimtinus skaičiavimo išteklius. Skaitiniai eksperimentai rodo, kad siūlomas metodas padeda sumažinti bendrasias elektros energijos gamybos sąnaudas, palyginti su deterministiniu uždavinio sprendiniu.


Reikšminiai žodžiai: stochastinis programavimasMonte Karlo metodaselektros energijos gamybastochastinis gradientasstatistiniai kriterijaiϵ leistinoji kryptis.


First published online: 10 Feb 2011

Keyword : stochastic programming, Monte Carlo method, power planning, stochastic gradient, statistical criteria, ϵ‐feasible direction

How to Cite
Sakalauskas, L., & Žilinskas, K. (2010). Power plant investment planning by stochastic programming. Technological and Economic Development of Economy, 16(4), 753-764. https://doi.org/10.3846/tede.2010.46
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Dec 31, 2010
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