A Strength Pareto Evolutionary Algorithm 2 (SPEA 2) approach for solving the multi-objective Environmental / Economic Power Dispatch (EEPD) problem is presented in this paper. In the past fuel cost consumption minimization was the aim (a single objective function) of economic power dispatch problem. Since the clean air act amendments have been applied to reduce SO2 and NOX emissions from power plants, the utilities change their strategies in order to reduce pollution and atmospheric emission as well, adding emission minimization as other objective function made economic power dispatch (EPD) a multi-objective problem having conflicting objectives. SPEA2 is the improved version of SPEA with better fitness assignment, density estimation, and modified archive truncation. In addition fuzzy set theory is employed to extract the best compromise solution. Several optimization run of the proposed method are carried out on 3-units system and 6-units standard IEEE 30-bus test system. The results demonstrate the capabilities of the proposed method to generate well-distributed Pareto-optimal non-dominated feasible solutions in single run. The comparison with other multi-objective methods demonstrates the superiority of the proposed method.
Fuzzy logic is used to solve the load flow and contingency analysis problems, so decreasing computing time and its the best selection instead of the traditional methods. The proposed method is very accurate with outstanding computation time, which made the fuzzy load flow (FLF) suitable for real time application for small- as well as large-scale power systems. In addition that, the FLF efficiently able to solve load flow problem of ill-conditioned power systems and contingency analysis. The FLF method using Gaussian membership function requires less number of iterations and less computing time than that required in the FLF method using triangular membership function. Using sparsity technique for the input Ybus sparse matrix data gives reduction in overall computation time and storage requirements. The performance of the used methods had been tested on two typical test systems being the IEEE 14-bus and 30-bus systems in addition to the 362-bus Iraqi National Grid. All the obtained results under normal operating conditions show that the computation time of the fuzzy Load Flow (FLF) is less than the fast decoupled load flow (FDLF).
This research presents a fast, reliable, and new method for solving the load (power) flow problem of electrical power systems. The proposed method is a second order load flow technique based on the "Taylor series expansion" of a multivariable function. This approach takes the first three terms of the Taylor series. The method has advantages over Newton's method in terms of computation time for solution (no. of iterations), and reliability of convergence. By inserting a minimization technique in this proposed method, the algorithm exhibits a control of the convergence. By means of this control, the method converges for cases when conventional Newton's method and some other popular methods diverge. Also this paper presents a comparison between the proposed method and Newton-Raphson method according to the major criteria, namely reliability of convergence and speed of solution. Two test systems (five busbars typical test system and forty busbars practical system based on Iraqi National Grid) are used to examine the performance of each method.
The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms of the calculated voltages of the generator buses, after a derivation of equations for calculating the voltages of the load busbars. The proposed method was demonstrated on 14-bus IEEE test systems and the practical system 362-busbar IRAQI NATIONAL GRID (ING). The proposed method has reliable convergence, a highly accurate solution and less computing time for on-line applications. The method can conveniently be applied for on-line analysis and planning studies of large power systems.
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