Halilčević, S., Softić, I. (2018). Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation. Journal of Operation and Automation in Power Engineering, 6(1), 69-79. doi: 10.22098/joape.2018.3438.1273

S. Halilčević; I. Softić. "Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation". Journal of Operation and Automation in Power Engineering, 6, 1, 2018, 69-79. doi: 10.22098/joape.2018.3438.1273

Halilčević, S., Softić, I. (2018). 'Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation', Journal of Operation and Automation in Power Engineering, 6(1), pp. 69-79. doi: 10.22098/joape.2018.3438.1273

Halilčević, S., Softić, I. Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation. Journal of Operation and Automation in Power Engineering, 2018; 6(1): 69-79. doi: 10.22098/joape.2018.3438.1273

Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation

^{2}University of Tuzla, Faculty of Electrical Engineering, Department for Power and Energy Engineering

Receive Date: 02 March 2017,
Revise Date: 22 June 2017,
Accept Date: 19 October 2017

Abstract

This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects of generation. In the past, the optimal scheduling of electricity generation resources has been based on the optimal activation levels of power plants over time to meet demand for the lowest cost over several time periods. At the same time, the result of that type of optimization is single-dimensional and constrained by numerous limitations. To avoid an apparently optimal solution, a new concept of optimality is presented in this paper. This concept and the associated algorithm enable one to calculate the measure of a system’s state with respect to its optimal state. The optimal system state here means that the fuzzy membership functions of the considered attributes (the characteristics of the system) have the value of one. That particular measure is called the “degree of optimality” (DO^{system}). The DO^{system} can be based on any of the system's attributes (economy, security, environment, etc.) that take into consideration the current and/or future state of the system. The calculation platform for the chosen electric power test system is based on one of the unit commitment solvers (in this paper, it is the genetic algorithm) and fuzzy logic as a cohesion tool of the outcomes obtained by means of the unit commitment solver. The DO-based algorithm offers the best solutions in which the attributes should not to distort each other, as is the case in a strictly deterministic nature of the Pareto optimal solution.

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