Open Access Open Access  Restricted Access Subscription Access

Comparative Analysis of Multi Objective Optimal Power Flow in Power Systems

T. Nireekshana, Poonam Upadhyay, J. Bhavani, N. Krishna Kumari

Abstract


This paper consists of the comparative analysis of different methods applied to loss minimization in power system. The objective is to minimize the total power loss and keep the power outputs of generators; bus voltages, shunt capacitors/reactors and transformer tap setting in their specified limits. By maintaining the whole system power loss as minimum there by minimum cost allocation can be achieved. This project explains a comparative analysis between Gradient methods, Search methods and Genetic Algorithm Approach (GA). The Gradient and Search methods are the iterative local optimization methods applied to the power system till the loss minimization is realized. The GAs is the part of evolutionary algorithms family, which is computational models, inspired in the nature. GAs is powerful stochastic search algorithms based on the mechanism of natural selection in natural genetics. GAs works with a population of binary string searching many peaks in parallel. By employing genetic operators, they exchange information between peaks, hence reducing the possibility of ending at a local optimum. IEEE 30 Bus system has been studied to show the effectiveness of algorithm. Three methods are implemented to IEEE 30 bus system for minimization of active power loss and comparative analysis is made regarding computer memory and total time for computation.

 

Keywords: Genetic algorithm, optimal power flow, gradient method, power loss minimization

Full Text:

PDF

References


Bouktir, T., Slimani, L., & Belkacemi, M. (2004). A genetic algorithm for solving the optimal power flow problem. Leonardo Journal of Sciences, 4, 44-58.

Fonseca, W. D. S., Barros, F. G. N., Bezerra, U. H., Oliveira, R. C. L., & Nunes, M. V. A. (2009, June). Genetic algorithms and treatment of multiple objectives in the allocation of capacitor banks in an electric power distribution system. In 2009 IEEE Bucharest PowerTech (pp. 1-8). IEEE.

Singh, D., Singh, D., & Verma, K. S. (2008). GA based energy loss minimization approach for optimal sizing & placement of distributed generation. International Journal of Knowledge-based and Intelligent Engineering Systems, 12(2), 147-156.

Ellithy, K., Al-Hinai, A., & Moosa, A. (2008). Optimal shunt capacitors allocation in distribution networks using genetic algorithm practical case study. International journal of innovations in energy systems and power, 3(1), 18-45.

Shukla, T., Singh, S., & Naik, K. (2010). Allocation of optimal distributed generation using GA for minimum system losses in radial distribution networks. International Journal of Engineering, Science and Technology, 2(3), 94-106.

Sedighizadeh, M., & Rezazadeh, A. (2008). Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile. World Academy of Science, Engineering and Technology, 37(200), 8.

Kamal, M. M., Rahman, T. K. A., & Musirin, I. (2004, August). Improved Genetic Algorithms (IGA) for Optimal Reactive Power Planning in Loss Minimisation Scheme. In Proceedings of the WSEAS International Conference on Applications of Electrical Engineering, (p. 1493).

Nireekshana, T., & Rao, G. K. (2012) Enhancement of ATC in Deregulation based on Continuation Power Flow with FACTS Devices using Real-code Genetic Algorithm. International Journal of Electrical Power and Energy Systems (IJEPES), Elsevier, 43(1), 1276-1284.


Refbacks

  • There are currently no refbacks.