International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: Volume 13 Issue 1 MARCH 2015.

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1 Optimal Active Power Rescheduling of Generators for Congestion Management using New Definition of Sensitivity G. Kumaravelan 1, N. Chidambararaj 2, Dr.K.Chitra 3 PG Scholar 1, M.E (Power Systems Engg), St Joseph s College of Engineering,Chennai, Tamilnadu,India Associate professor 2, Department of EEE,St Joseph s College of Engineering, Chennai, Tamilnadu,India Professor 3, School of Electronics& Communication Engg,VIT-Chennai campus, Chennai, Tamilnadu,India kurupsn19@gmail.com 1,mailtochidha@gmail.com 2,chitra_kris@gmail.com 3 ABSTRACT: The restructuring of the electricity industry in the word has made the problem of transmission congestion increasingly significant. Congestion is the overloading in transmission lines. It could be caused by unexpected outages of generation, sudden increase of load, tripping of transmission lines, and failure of other equipment. It aggravates the smooth functioning of competitive markets and typically high costs are associated with it, which have to be eventually borne by the consumers. Therefore, investigation of techniques for congestionfreewheeling of power is of paramount interest. This paper presents a Congestion Management (CM) algorithm by optimal rescheduling of active powers of generators which minimize the re-dispatch cost of participating generators satisfying power balance, generator operating limit and line flow limits constraints while managing congestion effectively. New definition of generator sensitivities is introduced based on the cost factor. Contributions made in this paper are twofold. Firstly a technique for optimum selection of participating generators using new definition of generator sensitivities to the power flow on congested lines. Secondly it proposes an algorithm based on Particle Swarm Optimization (PSO) which minimizes the deviations of rescheduled cost values of generator power outputs from scheduled levels. The effectiveness of the methodology has been analyzed on modified IEEE 30-bus system. Index Terms New generator sensitivities, optimal rescheduling, particle swarm optimization, transmission congestion management. I.INTRODUCTION The restructuring of the electricity industry has brought huge shifts in the planning, operations and management of power systems. The introduction of competitive markets did not only bring Benefits, but also made the industry face unprecedented problems. Unlike other markets, the Electricity market has salient characteristics, which make the operation of competitive markets a Major challenge. The lack of major storage capability, the just-in-time-manufacturing nature of Electricity and the central role that is played by the transmission and distribution networks, are some of the principal complexities in electricity. With the increasing number of market participants in terms of generation, transmission and Distribution owners, the number of desired transactions between the various players is growing. Congestion occurs whenever one or more constraints are violated under which the system operates in the normal state or in any of the contingency cases in a list of specified contingencies. These constraints can either be physical limits like thermal or voltage limits or specified limits to ensure system security and reliability [5].There are two broad paradigms that may be employed for congestion management. These are the cost-free means and the non-cost-free means. The former include actions like outages of congested lines or operation of transformer taps, phase shifters, or FACTS devices. These means are termed as cost-free only because the marginal costs involved in their usage are nominal. The Non- Cost-Free means include: i) Rescheduling Generation This leads to generation operation at an equilibrium point away from the one determined by equal incremental costs. Mathematical models of pricing tools may be incorporated in the dispatch framework and the corresponding cost signals obtained. These cost signals may be used for congestion pricing and as indicators to the market participants to rearrange their power injections/extractions such that congestion is avoided. ii) Prioritization and Curtailment of Loads/Transactions A parameter termed as willingness-to-pay-to-avoidcurtailment was introduced. This can be ineffective instrument in setting the transaction curtailment strategies which may then be incorporated in the optimal power flow framework. Several methods have been reported that address the congestion management problem in deregulated electricity markets. II. PROBLEM FORMULATION The optimal congestion management minimizing re-dispatch cost can be expressed as [3]: Minimize: (1) Subject to: (2) Operating limit constraints Line Flow Constraints And (3) (4) 111

2 The basic power flow equation on congested line in Newton Raphson method can be written as Where and are the voltage magnitude and phase angle respectively at the ith bus; and represent, respectively, the conductance and susceptance of the line connected between buses i and j; neglecting P-V coupling, can be expressed as (5) (6) To find the value of and in (3), [M] needs to be found out. However, [H] is a singular matrix of rank one deficiency. So it is not directly invertible. The slack bus in the present work has been considered as the reference node and assigned as bus number 1. The elements of first row and first column of [H] can be eliminated to obtain a matrix [ ] which can be inverted to obtain a matrix [M-1]. Using these relations the following equation can be obtained: The actual vector [ ] can be found by simply adding the elements [ 1] to the above equation as shown I the relation: (16) The first terms of the two products in (3) are obtained by differentiating (4) as follows: = (9) The active power injected at a bus-s can be represented as = (10) Where, is the active load at bus s and Ps can be expressed as (7) (8) (11) Where n is the number of buses in the system. Differentiating (10) w.r.t. s andt, the following relations can be obtained: (12) (13) Neglecting P-V coupling, the relation between incremental change in active power at system buses and the phase angles of voltages can be written in matrix form as (14) [Δ] = [Δ] = [M] [ ] Where [M] = (15) The modified [M] represents the values of and to calculate GS values. Large GS generators will be selected for re-dispatch since they are more influential on the congested line. The system operator selects the generators having non uniform and large magnitudes of sensitivity values as the ones most sensitive to the power flow on the congested line and to participate in congestion management by rescheduling their power outputs. III.GENERATOR SENSITIVITY FACTOR (GS) The Generator sensitivity (GS) technique indicates the change of active power flow due to change in active power generation. The generators in the system under consideration have different sensitivities to the power flow on the congested line. A change in real power flow in a transmission line k connected between bus i and bus j due to change in power generation by generator g can be termed as generator sensitivity to congested line (GS). Mathematically, GS For line k can be written as [3]. Where is the real power flow on congested line - k. is the real power generated by the i th generator. IV. NEW DEFINITION OF GENERATOR SENSITIVITY FACTOR ( ) This definition of sensitivity, cost has not been considered and GS is just based on power flow of each generator on congested line. As to cost issue, this is completely manifest that minimizing the cost using optimization algorithm based on this definition of sensitivity of generators will not give the best answer. So we introduce the new definition of generator sensitivity [13]. A new definition of generator sensitivity, which considers the cost, is expressed as : (17) 112

3 Where is the old definition of generator sensitivity, is new definition of generator sensitivity, considering the cost and is the cost of increasing the power of transmission line k ( ) by increasing the power of generator g ( ). Can be classified into two types. They are positive and negative s. Positive means that increasing the power of generator g causes increasing the power of congested line k and the Negative means that increasing the generator g causes decreasing the power of congested line k. To remove the congestion, the generators with negative must increase their generation and the generators with positive must decrease their generation until the power flow of congested line reaches to its permissible limits. The new definition of sensitivity ( ) with cost valuable divides the generators into two groups with positive or negative. The maximum values of in each group, without considering the signal of, are desired to select by operator. This method causes that the generators which have low GS but have low cost generation to increase their generation or have high cost to decrease their generation and were not used by operator, participate in CM process. The generators which are participating in CM must be selected based on their cost and sensitivity to the power flow of congested line. To have minimum cost, increasing power generation must be done by the generators with minimum cost generation and decreasing power generation must be done by generators with maximum cost of generation. V.PARTICLE SWARM OPTIMIZATION PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms [2]. PSO applies the concept of social interaction to problem solving. It was developed in 1995 by James Kennedy (social-psychologist) and Russell Eberhart (electrical engineer). It uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each particle is treated as a point in an N-dimensional space which adjusts its flying according to its own flying experience as well as the flying experience of other particles. Each particle keeps track of its coordinates in the solution space which are associated with the best solution (fitness) that has achieved so far by that particle. This value is called personal best, pbest. Another best value that is tracked by the PSO is the best value obtained so far by any particle in the neighborhood of that particle. This value is called gbest. Each particle tries to modify its position using the following information: the current positions, the current velocities, the distance between the current position and pbest, the distance between the current position and the gbest. The modification of the particle s position and velocity can be mathematically modeled according the following equation: Where w is the inertia weight; r1 and r2 are random values between 0 and 1; C1 and C2 are two positive constants, called acceleration constants; generally C1=C2=2. j represents iteration number. i represents population. VI.RESULTS AND DISCUSSION TEST CASE 1: MODIFIED IEEE 30 BUS SYSTEM In this case, line such as 1-2 get overloaded as consequence of outage of line 1-2. The actual power flow in those lines is MW (flow limit is 130 MW in each case). Net power violation is found to be 33.79MW as given in table 1. For secure system, the power flow in the transmission line should not exceed their permissible limit. TABLE I: CONGESTED LINES DETAILS OF MODIFIED IEEE 30-BUS SYSTEM Type of Contingency Outage line 1-2 of Congested Lines Line Power Line limit % overload The generation sensitivities are computed corresponding to overloading of line 1-2. The values computed are given in table II. The generation sensitivities for all the generators are almost in the closely enclosed in the small range. Hence this indicates that all the generators are chosen to be participating for rescheduling to tackle congestion. The generator cost curves have been assumed to be quadratic such that cost of rescheduling is proportional to the square of the change in active power output. (3) GENERATOR PRICE BIDS FOR IEEE 30-BUS SYSTEM ($/MW -DAY) Gen.no

4 Cg Table II: GS & New Definition of GS for Modified IEEE30-Bus System (Congested Line 1-2) Gen.no Gs Gs` The minimum change in active power generation is computed using particle swarm optimization algorithm subjected to the bidding cost of increase or decrease in power generated so that the total cost of generation is minimized. Although slack bus generator is considered to take into account the losses. In this work the change in power is computed including slack generator. The results obtained for change in power generation of participating generators are given in table III. TABLE III: ADJUSTMENT OF ACTIVE POWER GENERATION OF PARTICIPATING GENERATOR PG PG PG PG4 5 PG PG Total rescheduling Cost($/MWh) Approximate cost of CM ($/day) Power flow on 129 (2-1) Table III provides the best solution to relieve the congested lines completely without causing overloading of any other line. The rescheduling of active power generation requires the decrease in active power generation from generator 1 and generator 6 and increase the power generation from generator 2,3,4,5. The cost incurred for relieving congestion is $/MWh based on the bidding cost of generators for change in power generation. Table IV gives the actual active power flow generation and the rescheduling of active power flow generation. TABLE IV: POWER FLOW TO THE GENERATION GEN.NO ACTUAL ACTIVE POWER FLOW GENERATION RESCHEDULED ACTIVE POWER FLOW GENERATION Table V gives total system losses before congestion management are found to be MW, while the system losses after congestion management are decreased to 13.92S MW. 0 GS'G VS RESCHEDULE ACTIVE POWER GS'G RESCHEDULE ACTIVE POWER TABLE V: SYSTEM LOSSES Before Congestion After Congestion Management Management VII.CONCLUSION: In this work, the optimal congestion management approach based on PSO is efficiently minimizing re-dispatch cost and active power reschedule to the generators. It can be consists of cost factor Re-dispatched generators are selected based on the large magnitude of New definition of generator sensitivity. Unexpected line outage & sudden load variation & reschedule the active power to the generators &reducing the losses is considered in this work. The method has been tested on modified IEEE 30-bus systems successfully. This work can be further extended considering rescheduling of reactive power generation and its compensation using FACTS/STATCOMs at 114

5 appropriate locations in the system or hybrid of load shedding and rescheduling generators. REFERENCES [1] M. Ghayeni, R. Ghazi., Transmission Cost Allocation in Restructured Power Systems Based on Nodal Pricing Approach by Controlling the Marginal Prices, Iranian Journal of Electrical & Electronic Engineering, Vol. 6, No. 2, June [2] A.Kennedy and R. Eberhart., Particle Swarm Optimization in Proc. IEEE Int. Conf. Neural Networks, Nov. 29 Dec , vol. IV, pp [3] Dutta, S.P. Singh., Optimal Rescheduling of Generator for Congestion Management based on Particle Swarm Optimization, IEEE transactions on power system, vol. 23, no. 4, pp , [4] H.Glatvitsch and F.Alvarado., Management of Multiple Congested Conditions in an Electricity Market, IEEE Transactions on Power System, vol. 13, no. 3,pp , August [5] Sadat H., Power System analysis, Tata McGraw Hill Ltd, [7] Ettore Bompard, Pedro Correia, George Gross, Mikael Amelin., Congestion- Management Schemes: A Comparative Analysis under a Unified Framework, IEEE transactions on power systems, vol. 18, no. 1, February [8] Ashish Saini and A.K. Saxena., Optimal Power Flow based Congestion Management Methods for Competitive Electricity Markets, International Journal of Computer and Electrical Engineering, Vol. 2, No. 1, February, [9] Hossein Emami, Jalal Addin Sadri., Congestion management of transmission lines in the market environment, International Research Journal of Applied and Basic Sciences, ISSN X / Vol, 3 (S): [10] Qinghai Bai. Analysis of Particle Swarm Optimization Algorithm Computer and Information Science, Vol. 3, No.1. [11] Jagabondhu Hazra, and Avinash K. Sinha.., Congestion Management Using Multiobjective Particle Swarm Optimization, IEEE transactions on power systems, vol. 22, no. 4, November [12] R.S. Fang A.K. David., Transmission Congestion Management in an Electricity Market, IEEE Transactions on Power Systems, Vol. 14, No. 3, August [13] FarzadVazinram, MajidGandomkar, Mehdi BayatMokhtari., Optimal Active Power Rescheduling of Generators for Congestion Management Based On Big Bang-Big Crunch Optimization Using New Definition of Sensitivity, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: , Volume-3, Issue-2, December [14] Mohammad Shahidehpour, Hatim Yamin, Zui Li., Market Operation In Electric Power Systems, The Institute of Electrical and Electronics Engineers, Inc., New York, A JOHN WILEY & SONS, INC.,PUBLICATION, ISBN [15] S. B. Warkad, Dr. M. K. Khedkar, Dr. G. M. Dhole., Optimal Electricity Transmission Pricing in a Restructured Electricity Market, International Journal of Computer and Electrical Engineering, Vol. 1, No. 4, October, 2009, interests in the power systems Analysis, Congestion Management, Demand response and FACTS role in Congestion Management. N. Chidambararaj was born in the year He completed his Diploma in Electrical and Electronics Engineering in the year He received his B.E degree in Electrical and Electronics engineering in the year 2003 and proceeded with pursuing Masters in Power Systems Engineering and graduated in the year He has been working as an Associate professor at St. Joseph s College of Engineering in the department of Electrical and Electronics engineering since 2005 and he has almost 8 years of experience in the respective field. He is currently pursuing Ph.D. in Satyabhama University. His subject of interest includes Power systems, Engineering Electromagnetics, Digital signal processing and Machine design and his core research is on deregulated power system. Dr. K. Chitra is working as a Professor, in the department of Electronics and communication Engineering at VIT- Chennai Campus. Dr. K. Chitra received her B.E. Degree in Electronics and communication Engineering from Bharathiar University, Coimbatore in 1990, M.E. Degree in Applied Electronics from Bharathiar University in 1992 and Ph.D. degree in Optical Communication from Anna University Chennai in She has spent her 23 years of experience in teaching and guiding projects for undergraduate and postgraduate students. She has added 15 international publications to her credit. She has few funded projects from Government of India. Dr.K.Chitra s areas of interests include optical communication, optical networks, wireless sensor and computer networks, Biomedical Engineering and microwave Engineering. Bibliography of Authors KUMARAVELAN G received his B.E degree in Electrical and Electronics engineering in the year 2013 in Sri Balaji Chokalingam Engineering College, Arni, Affiliated by Anna University, Chennai. Now currently pursuing Masters in Power Systems Engineering in the Department of Electrical Engineering in St. Joseph s college of Engineering, Chennai. He has 115