Optimal Power Flow with Reactive Power Compensation for Cost and Loss Minimization on Nigerian Power Grid System

Abstract

One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile.

Sustainable Environmental Economic Dispatch Optimization with Hybrid Metaheuristic Modification

“…Furthermore, assuming industrial tariff in Malaysia is at average cost about 44. 10 In order to define the best performance of single objective functions in solving EELD problems, Table 2 was used to show the finest result of all identified single objective functions among those three techniques along with Base technique for both standard IEEE 26 bus system and 57 bus system. Thoroughly, the NMEP approach was compared between two other common optimization techniques known as AIS and Meta-EP methods and also with the base values as recommended by Hadi Saadat.…”

“…The research approach was to design a new optimization technique taking some inspiration from the Meta-EP mutation strategy. The development also included identifying suitable objective functions which were significant to EELD problem along with some constraints as discussed in the following section [10]. In order to achieve the research objective, the development of the new single objective technique was to be accomplished.…”

“…The convergence criterion was specified by the difference between the maximum and minimum fitness to be less than 0.0001. If the convergence condition was not satisfied, the mutation, tournament and selection process would be repeated until convergence criterion was met using equation (10). maximum fitness -minimum fitness ≤ 0.0001 (10) , ( (0,1))…”