Multi-objective reactive power compensation

Reactive power compensation in electric systems is usually studied as a constrained single-objective optimization problem where an objective function is a linear combination of several factors, such as, investment and transmission losses. At the same time, constrains limit other parameters as reliability and voltage profile. This paper presents a new approach using multi-objective optimization evolutionary algorithms. It proposes a variant of the strength Pareto evolutionary algorithm (SPEA) that independently optimizes several parameters, turning most traditional constraints into new objective functions. That way, a wide set of optimal solutions, known as Pareto set, is found before deciding which solution best combines different features. Several sets of solutions calculated by different methods are compared to a Pareto set found with the proposed approach using appropriate test suite metrics. Comparison results emphasize outstanding advantages of the proposed computational approach, such as: ease of calculation, better defined Pareto front and a larger number of Pareto solutions.

[1]  G. Darling,et al.  Capacitor placement, replacement and control in large-scale distribution systems by a GA-based two-stage algorithm , 1997 .

[2]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[3]  P. Kundur,et al.  Power system stability and control , 1994 .

[4]  J.C. Carlisle,et al.  A review of capacitor placement techniques on distribution feeders , 1997, Proceedings The Twenty-Ninth Southeastern Symposium on System Theory.

[5]  Mulukutla S. Sarma,et al.  Power System Analysis and Design , 1993 .

[6]  Bala Venkatesh,et al.  A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy LP technique , 2000 .

[7]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[8]  P. Marannino,et al.  Optimal capacitor placement using deterministic and genetic algorithms , 1999 .

[9]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[10]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Youyi Wang,et al.  A new approach to power system VAr planning aimed at voltage stability enhancement with feedback control , 1999, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376).