Congestion Management in Deregulated Power System Using Different Control Technique

Abstract

In today’s scenario there is a challenge for power companies to meet the expected demand load due to continuous increases in load demand causes unpredictable failure in the components of power system including transmission line, generator, transformer and various other equipment this leads to over loading in the power system and the line become congested if this failure not removed on time the system reaches to emergency state, therefore we are using various techniques to control or manage these situations these methods includes Generator Rescheduling(GR),Load Shedding, Particle Swarm Optimizer(PSO), Grey Wolf Optimization (GWO),Harmony search algorithm etc. Optimal load shedding is effective control action for congestion management. The various algorithm applied on IEEE 30 bus system.

Introduction

I. INTRODUCTION

A power system comprises of three kinds of units - (1) generation (2) transmission (3) distribution. With these three unit of operation and working the power system becomes complicated and complex and depends on condition of existing technology as well as on other  factors economy, social advancement and various environmental impacts.

A deregulated power system  market allow the market competitors to sell and purchase power by investing in power plant and transmission line .This is a three step process, the GENCO ( generation companies) offer electricity to retailers the retailers further set it for customers according to costumer cost[1]. It become beneficial for customer providing them to look at the rates and separate third party organization .The main goal of deregulated power system is to separate power production and sale of power to all network .The deregulated power system is beneficial for customers because it provide the minimum cost due  to competition among the different GENCO companies

Due to less  effective functioning of newly joined companies, it goes to deregulation by isolating its  operation into three separate service station i.e .generation companies, transmission companies and distribution companies. In this scenario the generation companies became more accountable for transferring the power to loads. The ISO is central authority to buyers and sellers it maintain safe and dependable procedure of power industry.

Congestion management is biggest concern in modern power system these can be various reason for congestion of load that is the less supply of power system than demand so the power system gets congested,it occurs due to transformer failure, sudden breakout, equipment failure, transmission line faults, voltage instability ,deficiency of reactive power etc[2].These situations leads to exceed the constraints and operational limits causes the system become more congested over burden the limit and reaches the emergency state,so this can be control by various methods in this paper we mainly focuses on two methods (1) Optimal load shedding  (2)  Generation rescheduling.

[3]In optimal load shedding to control congestion the load shedding is done to reduce load at minimum level by least affecting the  power system  for this we use different techniques (1) Grey wolf optimization (2)  Harmony search algorithm

[2]The Grey wolf optimization based on meta -heuristics algorithm ,taken from Grey wolves hunting approach  and social hierarchy ,it made up of four parts-

1. Encircling the prey (ii) hunting (iii) attacking (iv) searching

In harmony search algorithm ,the transmission line load can be physically removed and electricity cost can be made equal [4]-[5].

Load remove should be optimal and the effect of removal determined by using sensitivity factor based on dc power flow model .The improved harmony can improve power system security to avoid voltage downfall.

[7]-[8]The second method to control over congested load is Generator Rescheduling ,for this we find out the generator rescheduling factor (GSF)  which help in deciding the decreasing order of generator using the PDF (power distributing factor), this method can reduce the greater burden on slack bus in which any change in active power transmission line automatically changes all the other regions.

In G.R. there are several algorithm methods used PDF firefly algorithm generator, power generator rescheduling based on cuckoo search algorithm, power generator rescheduling based on firefly algorithm[5]-[6].

The main aim of congestion management is to keep the system stable and within its limit ,for this assessment should be done to know the limits when lines gets congested.

VI. RESULTS

The proposed assessment of congestion problem and control in this report are verified on IEEE-30 bus test network. The system includes six generators, 24 load buses mentioned in the Appendix. The different operating conditions are created by outage of transmission line and outage of transmission line with increasing load. Security status of the system is carried out for base loading and different congested states. For the insecure cases, generation rescheduling is also proposed..

A. No Contingency

The loads are set at base load on the buses. When using DC optimal power flow then the value of LMP is fixed for all the buses and the value LMP for all the buses is 3.789 ($/MVA-hr). At DC optimal power flow total generation capacity and on-line capacity both are 335(MW). Actual generation and actual load both are 189.2(MW). Total inter-tie flow for DC optimal power flow is 52.3(MW), losses are zero.

The loads are set at base load on the buses. When using AC optimal power flow then the value of LMP is different for all the buses. At AC optimal power flow total generation capacity and on-line capacity both are 335(MW) and -95 to 405.9(MVAr). Actual generation is 192.1(MW) and 105.1(MVAr). Actual load is 189.2(MW) and 107.2(MVAr). Total inter-tie flow is 51(MW) and 58.1(MVAr). Losses are 2.86(MW) and 13.33(MVAr)

B. Consider Outage  of  Line 1-2

The load we take same as base load .when using the AC optimal power the value on both the lines are same .Actual generation is 192.3(MW) and 109 (MVAr) .the total inter line flow is 51.1(MW0 and 56.3(MVAr).losses are 3.09(MW) and 14.46(MVAr).during the outage of transmission line the 1-2 there are transmission lines having more than 0.9 overloading factor,the three transmission lines are 6-8,21-22and 25-27.in this paper the transmission lines declared.

Table 6.5 presents that congestion cost from GSF based method is 296.79($/h) and congestion cost from zonal pricing (ZP) based method is 289.98($/h). The congestion cost is less in market based ZP method as compared to GSF based method. Total amount of rescheduled real power in GSF based method is 55.87(MW) and for ZP based method is 56.03(MW). The rescheduled amount of real power is less in GSF based method. Also, the congestion is relieved from all the congested transmission lines and system is in secure condition.

Conclusion

These results are obtained for the standard test system for assessment of congestion problem and the control of congestion. The results are verified on the IEEE-30 bus system. Values of overloading factor and LMP for different case (different outages of line with different load conditions). The congested transmission lines identified. For these congested transmission lines generation rescheduling is applied using two different approaches, based on generator sensitivity factor (GSF) and zonal pricing (ZP). The results obtained are found to be satisfactory. This work has proposed an optimum algorithm to obtain the optimal amount of generation rescheduling. The proposed procedure can recover the secure condition for electric system. From result, it is found that zonal pricing (ZP) method for congestion management is market-based method. Therefore, this method has less congestion cost as compared to GSF based method and the amount of real power rescheduled can be higher or lower for any method.

References

[1] H. Poor, “An Introduction to Signal Detection and Estimation”, New York: Springer-Verlag, Chap. 4, 1985. [2] Fatma Sayed, Salah Kamel, Marcos Tostado and Francisco Jurado,“Congestion Management in Power System Based on Optimal Load Shedding Using Grey Wolf Optimizer”, 2018 Twentieth International Middle East Power Systems Conference (MEPCON), Cairo University, Egypt. [3] Lekshmi R. R., S. Swathy, B. Lakshmi, N. Vamsi Sai, V. Suraj Vijaykumar, “ Market Clearing Mechanism Considering Congestion under Deregulated Power System”, 8th International Conference on Advances in Computing and Communication (ICACC-2018). [4] T. Niimura, and Y. Niu, “Transmission Congestion Relief by Economic Load Management” 0-7803-7519-X/02, IEEE, 2002. [5] Y. Niu, Y. L. Cong, and T. Niimura, “Transmission Congestion Relief Solutions by Load Management” ,Proceedings of the 2002 IEEE Canahan Conference on Electrical L Computer Engineering 0-7803-7514-9/02, 2002. [6] Fatma Sayed, Salah Kamel, “Optimal Load Shedding for Voltage Collapse Prevention Using Improved Harmony Search Algorithm”, Nineteenth International Middle East Power Systems Conference ( MEPCON), Menoufia University, Egypt, 19-21 December 2017. [7] D.Devaraj and J.P Roselyn ,’’Genetic algorithm based reactive power dispatch for voltage stability improvement,’’International journal of electrical power and energy systems,vol 32,pp.1151- 1156,2010 [8] Bozorg-Haddad,’’Advanced Optimization by Nature-Inspired Algorithm,’’2018. [9] Z. Alaywan, “Tacilitating the Congestion Management Market in Califomia”, Proc. 1555 North American Power Symposium, pp. 19. [10] Anusha Pillay , S. Prabhakar Karthikeyan , D.P. Kothari, “Congestion management in power systems – A review” Electrical Power and Energy Systems 70, 83–90, 2013

Copyright

Copyright © 2022 Harshita Rajora, Mr. Sunil kumar Mahapatro, Dr. Parmod Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.