Pareto front of multi-objective optimal reactive power dispatch

Zhang, Cong; Chen, Haoyong; Xu, Xuanhao; Cai, Runqing

doi:10.1109/appeec.2014.7066057

Cited by 6 publications

(7 citation statements)

References 10 publications

(13 reference statements)

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“…There are many ways to obtain a Pareto front [20,[25][26][27], and they can be divided into classical optimisation methods and evolutionary algorithms (EAs). However, the classical optimisation methods (including the multi-criterion decision-making methods) are known to be time-consuming because they involve converting the multi-objective optimisation problem into a single-objective optimisation problem by emphasising one particular Pareto-optimal solution at a time.…”

Section: Obtaining the Pareto Front Of The Multi-objective Rpoiu Usinmentioning

confidence: 99%

Solution of reactive power optimisation including interval uncertainty using genetic algorithm

Zhang

Chen

Ngan

et al. 2017

IET Generation Trans & Dist

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The reactive power optimisation (RPO) is generally used to design an optimal profile of the voltage and reactive power of power systems in the steady state for deterministic sets of demand loads and generation values, and it is a significant procedure in voltage control. However, the input data of a power system is actually uncertain in practise, which makes RPO an uncertain non‐linear programme. To address this problem, the input data were considered as intervals, and the RPO incorporating interval uncertainties model was proposed. To solve this model, the genetic algorithm was employed as the solution algorithm, where the reliable power flow calculation was used to judge the constraints of the model. The Pareto front was established as the solution of the proposed model since it has two objective functions, i.e. the midpoint and radius of real power losses. The application of this technique to the uncertain RPO was explained in detail. Two numerical results were analysed to demonstrate the effectiveness of the proposed method, especially in comparison with the previously proposed chance‐constrained programming method.

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Section: Obtaining the Pareto Front Of The Multi-objective Rpoiu Usinmentioning

confidence: 99%

Solution of reactive power optimisation including interval uncertainty using genetic algorithm

Zhang

Chen

Ngan

et al. 2017

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

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“…Como se comentó en secciones anteriores, se analizaron múltiples fuentes bibliográficas de optimización multiobjetivo en sistemas eléctricos de potencia enfocadas sobre todo, aunque no exclusivamente, a la eficiencia económica y medioambiental; de este último tipo son, por ejemplo [120], [121], [34], [122], [36], etc. En otras ocasiones son OPFs económicos combinadas con otros objetivos característicos de flujos, tales como el despacho de potencia reactiva [105] o las pérdidas del sistema [80].…”

Section: Programación De La Optimización Multiobjetivounclassified

“…Los métodos tradicionales de MOO se encuentran en múltiples artículos y publicaciones más antiguas o más recientes. Por ejemplo, siguiendo el método ε constraint con objetivos económicos y medioambientales [37] y [39], o económicos y minimización de pérdidas [80], el método de goal programming con objetivos económicos y medioambientales en [121], o el método de la combinación lineal de pesos, con objetivos económico y medioambientales en [40] y [36], o económico y de despacho de reactiva [105].…”

Section: Programación De La Optimización Multiobjetivounclassified

“…Tanto en [80] como en [105], se describen con cierto detalle los dos métodos básicos (pesos y ε constraint) y seleccionan uno de los dos. En el presente trabajo se decidió utilizar el método de las ε constraint frente al de la combinación de pesos, por varios motivos que se describirán a continuación, entre los cuales se encuentran los que cita el artículo de Berizzi et al [80]:…”

Section: Programación De La Optimización Multiobjetivounclassified

“…[37] [105], o [106] que compara varios algoritmos multiobjetivo de tipo evolutivos; es menos habitual no integrarlo como en [41].…”

Section: Introductionunclassified

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Influencia de las restricciones medioambientales en el sector eléctrico

Lázaro¹

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The aim of this thesis is the characterization of electrical generation based on combustion processes representative of the actual power plants, for the latter modelling and simulation of an electrical grid and the development of economic-environmental multiobjective optimization studies.In this line, the first step taken is the analysis of the current energetic and electrical framework, focused on the peninsular one, where the fuel power plants have been shut down, and the only ones remaining are coal units of different types and combined cycle. Then it is carried out an analysis of the main environmental impacts of the thermal power plants, represented basically by the emissions of CO 2 , SO 2 y NO x , their control and reduction measures and the applicable regulations.Next, based on the combustibles properties and the information about the units heat rates, the different power plants are characterized in relation to the outstanding functions that define their energy, economic and environmental behaviour, in terms of hourly output functions depending on their load. Optional denitrification and desulfurization is considered.Given that there are multiple objectives, and that they go in conflictive directions, it has been decided the use of multiobjective techniques, that have the ability of identifying the optimal points set, which is called the Pareto front, where taken a solution there will be no other point that can beat the former in an objective without worsening it in another objective. Several multiobjective optimization methods were analysed and pondered, selecting the 𝜀 constraint technique, which is able to find no convex fronts and it is opened to be tested to prove the strict Pareto optimality of the obtained solutions.A balanced representation of the thermal power plants, formed by anthracite, lignite, bituminous national and imported coals and combined cycle, was integrated in the IEEE-57 network case. This system was selected because it deals with a total power that will admit seven units without distorting significantly the actual size of the power plants. Next, an AC optimal power flow with the multiobjective method implemented in the routines was programmed.The Pareto fronts of the combination of operative costs with each of the three emissions functions were found, and also the front of all of them together. The optimal production costs of the system for all the emissions range were obtained. It is also evaluated the cost of reducing an additional emission ton of any of the emissions when the optimal production mix is displaced towards cleaner points. The obtained solutions assure that under a determined level of emissions they cannot be improved economically or, in the other way, at a determined cost it cannot be found points of lesser emissions. The Pareto fronts are also applied for the search of optimal strategic paths to follow the hourly load changes. apenas se consume carbón en el sector industrial, comercial o transporte, prácticamente la totalidad, el 93%, se destina a la generación el...

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