JCHPS Special Issue 1: February Page 275

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1 Journal of Chemical and Pharmaceutical Sciences ISS: Computation of Short Run Marginal Cost in Open Access Transmission System PL. Somasundaram, V. Jayakumar Department of EEE, M. Kumarasamy College of Engineering, Karur, Tamil adu, India. *Corresponding author: ABSTRACT In many of the developing country restructured power system plays a vital role in electricity market. This paper presents a concept of short run marginal cost in restructured power systems. is one of the wheeling charge computation methods. How it minimizes the cost of power in a system and how short run marginal cost is efficient in restructured power market are discussed here. This paper also explains about the optimal power flow methods and transmission pricing methodologies. This could be analysed by transaction of power between the many buses. is determined by using bus incremental cost obtained by interior point algorithm (OPF) in Mat lab software. This can be examined with IEEE 14 bus reliability test system. KEY WORDS: Optimal Power Flow (OPF), Open Access Transmission System (OAT), Bus incremental cost (BIC), Wheeling Charges. 1. ITRODUCTIO Restructured power system invites many private sector to an electricity power market. In a restructured power system the transmission, generation and distribution system are independent to each other (Tabors, 1994). If there is losses in any of the transmission, distribution and generation system it does not affect other system. The main objective of restructured power system is to provide continuous, safe, reliability and quality of supply to customers (Wood, 1996). In this electric power market both generations company and distribution Company achieved the profit. The restructured power market is otherwise known as open access transmission system. In an open access transmission system the customer can buy a power from any of the generation system through wheeling process. In this system all the parties involved in a system should pay an equal amount (Wood, 1996). The transaction should be taken through the wheeling process. The power can be diverted to many of the transmission lines to reach the desired destination is called as wheeling process (Wood, 1996). In wheeling the agreement is done between the buyer and seller in a system. Because of the limited transmission line capacity the agreed power cannot be transmitted through a transmission line connected between the buyer and seller. In such a way some power is transacted directly between buyer and seller and remaining power is transmitted to many of parties in a system which is not involved in agreement (El-Keib, 2005). For that the buyer has to pay the charges. These charges are named as wheeling charges. This paper explains about the short run marginal cost (Gnanadass, 2005) as a type of wheeling charges. And also concentrates only in the transmission system. Overview: The optimal power flow has to be calculated for the given system. From the result of OPF the bus incremental cost is determined and by using that BIC the short run marginal cost is to be calculated (Gnanadass, 2005). Transmission Pricing Methodologies: In a deregulated system it is difficult to build an independent transmission system at each bus. So various methods are developed for the transmission system to share by utilities and generators. The main aim of transmission pricing (Murali, 2011) in deregulated environment is to recover the operating cost, capital cost and wheeling charges of a system (Karthikeyan, 2011). The transmission pricing in a system is to charge all customer in an equal manner basis for transmission services (Gowtham, 2011). The different methods of transmission pricing are listed below. a. Incremental transmission pricing methods: Short run incremental cost pricing, Long run incremental cost pricing, Short run marginal cost, Long run marginal cost. b. Embedded transmission pricing methods c. Composite embedded/incremental transmission pricing methods. Optimal Power Flow Methods: The minimization of overall generation cost by allocating the given load among the generating units in a system is objective of OPF (Amarnath, 2011). It is an optimization technique. The difference between optimal power flow and normal power flow are as stated. In normal power the reactive power limits only consider (Perez-Arriaga, 1997). If the reactive constraints exceeds while calculating either lower limit or upper limit are taken as reactive power according to expedition (Yog Raj Sood, 2002). It is same as for real power. But in optimal power flow if there is any violation in constraints then the calculation has to be repeated until it s satisfy its constraints (Rana Mukerji, 1992). The parameters used for OPF calculations are cost coefficients, transmission line data, etc. The optimal power flow of different methods is follows; OPF Method, Gradient method, ewton Method, Linear Programming Method, Quadratic Programming Method, Interior Point Method. JCHPS Special Issue 1: February Page 275

2 Journal of Chemical and Pharmaceutical Sciences ISS: Interior Point Method: In an interior point algorithm an interior point is chosen before an optimal point had to be found (Sundararaju, 2011). After choosing an interior point the problem has to be solved by moving towards the boundary in a better direction. Thus an optimal solution is achieved after a number of iterations. If the better moving direction is achieved then then computations involves less number of iteration or elsewise versa. An interior point method is used to solve both equality and inequality constraints. IP is one of the most efficient methods for solving a problem in a very fast convergence. The average speed to report a large problem is 12:1 as compared to the simplex method. 2. PROPOSED SYSTEM In a proposed system the short run marginal cost is calculated by using the bus incremental cost. Here an interior point algorithm is used for an optimal power flow. Short Run Marginal Cost: Short run marginal cost is used for wheeling services for the additional megawatt producing at each bus. It can be calculated by difference in costs between buyer and seller. It is expressed in dollars per hour. It depends on four parameters. Those are (a) marginal generating cost, (b) losses, (c) quality of supply for generation, and (d) quality of supply for the network. These costs are used to recover excessive revenue in compensation for losses and generation rescheduling. Bus Incremental Cost: A useful set of data that are obtainable when the transmission network is incorporated in the scheduling process is called as incremental cost of power at various points in the network. ƛ is indicated as bus incremental cost (Wood, 1996). Bus incremental cost without considering losses, df dp = λ By considering the losses in the system the penalty factor is included as; P fi = 1/ (1- ( P loss / P i)) The relation between the incremental cost at two buses is given by, i = (P fj / P fi ) * j The incremental cost to deliver the power at bus is called as bus incremental cost. This can be stated as follows. a) For a single generator the cost of deliver an additional small increment of power at the generator bus is equal to the incremental cost of power for that generator. b) For multiple generators at different buses the accurate penalty factor can be calculated and used in economic dispatch then the cost of delivery of an additional small increment of power at any individual generator bus will be that generators own increment cost. Objective Function: The objective function of is defined as; = ( I - j)*a T i = incremental cost of i th bus in $/MWhr, where power is injected due to feasible transaction j = incremental cost of j th bus in $/MWhr, where power is taken out due to feasible transaction A T = magnitude of feasible transaction in MW OPF constraints: For solving the OPF problem the following constraint are considered. They are, Load flow equations: g g Q Q Q 0 Gi Di loss i1 i1 Where, P V V Y cos( ) P P P 0 Gi Di loss i1 i1 loss i j ij ij j i i1 Q V V Y sin( ) loss i j ij ij j i i1 Where, : umber of buses of the System, P loss,q loss: Active & Reactive power loss, Active and reactive power generation limits. P P P G min Gi G max Q Q Q G min Gi G max P Q S i=1 g 2 2 Gi Gi Gi,max 3. SIMULATIO AD RESULT In this paper, implementation of generator cost function in IEEE 14 bus system has been accomplished. The result of systems is simulated with real power in the objective function. JCHPS Special Issue 1: February Page 276

3 Journal of Chemical and Pharmaceutical Sciences ISS: Fig.1. Single line diagram of 14 bus system Case studies: Bilateral transactions 10MW, Multilateral transactions 20MW, Simultaneous transactions 20MW, Feasible transactions 25MW, Various real power demand (load curve). Table.1. Bilateral Transaction Power removal Power injected Magnitude of Power Table.2. Multilateral Transaction Power removal Power injected Magnitude of Power 3 10, 14, Table.3. Simultaneous Transaction Power Removal Power Injected Magnitude of power 3, 4 10, 14, 12, Table.4. values for above 3 cases CASES, Bilateral 6.28 Multilateral 16.6 Combined Fig.2. Comparison of bus incremental cost for above 3 cases Feasible Transaction: The bus no 3 is considering as reference bus and transacted power to various buses., 14, Fig.3. Feasible transaction graph JCHPS Special Issue 1: February Page 277

4 Journal of Chemical and Pharmaceutical Sciences ISS: Table.5. Priority list SI O $/HR SI O $/HR SI O $/HR Real Power Demand Analysis of a Day: The load curve has been analysed in this section and the resulted graph are listed below. Fig.4. Demand with respect to load demand versus Fig.5. value with respect to time time Some of the wheeling charge value will be negative and some of them are positive. If the losses value is lower than the base case loss value then the value become negative if it is greater than the base case loss value the value become positive. The negative value will favor the system. These values are used to assign a priority list (Murali, 2011). 4. COCLUSIO In this paper the wheeling charge is calculated by using Short Run Marginal Cost. The wheeling charge is obtained from the result of an optimal power flow algorithm in IEEE 14 bus RTS at various conditions such as bilateral, multilateral, simultaneous transactions. When bus number 3 is considered as seller and transactions are done between remaining buses (buyers) and the result are analysed. The load curve (for 24 hours) is also incorporated with the same RTS. REFERECES Amarnath R.V, State of Art in Optimal Power Flow Solution Methodologies, Journal of Theoretical and Applied Information Technology, 30 (2), El-Keib A.A, Calculating Short-Run Marginal Costs of Active and Reactive Power Production, IEEE Transactions on Power Systems, 12 (2), Gnanadass R, Determination of for Practical Power Systems with on-smooth Fuel Cost Functions, IE journal, 2005, 86. Gowtham J, Bharathwajanprabu R, Srikanth A, Automated Urban Drinking Water supply control and Water Theft Identification System, Student s Technical Symposium (Tech Sym), IEEE, 2011, Karthikeyan R, Chenthur Pandian S, A ovel 3-D Space Vector Modulation Algorithm for Cascaded Multilevel Inverter, International Review of Electrical Engineering, 6 (7), Karthikeyan R, Chenthurpandiyan S, Generalized Space Vector PWM Algorithm for Minimizing THD in Hybrid Multilevel Inverters, International Review of Electrical Engineering, 6 (5), 2011, Karthikeyan R, Pandian SC, An Efficient Multilevel Inverter System for Reducing THD with space Vector Modulation, International Journal of computer applications, 23(2), Murali M, A Comparison of Fixed Cost Based Transmission Pricing Methods, Scientific & Academic Publishing, Electrical and Electronic Engineering, 1 (1), 2011, Perez-Arriaga I.J, Wholesale Marginal Prices in Competitive Generation Markets, IEEE Transactions on Power Systems, 12 (2), Rana Mukerji, Evaluation of Wheeling And on-utility Generation (UG) Options Using Optimal Power Flows, IEEE Transactions on Power Systems, 7 (1), Sundararaju K, irmal Kumar A, Cascaded and Feed forwarded Control of Multilevel Converter Based STATCOM for Power System Compensation, International Review on Modelling and Simulations, 5 (2), 2012, JCHPS Special Issue 1: February Page 278

5 Journal of Chemical and Pharmaceutical Sciences ISS: Sundararaju K, irmal Kumar A, Control Analysis of STATCOM with Enhanced Methods for Compensation of Load Variation, European Journal of Scientific Research, 53 (4), 2011, Sundararaju K, irmal Kumar A, Jeeva S, andhakumar A, Performance Analysis And Location Identification of STATCOM On IEEE-14 Bus System Using Power Flow Analysis, Journal of Theoretical & Applied Information Technology, 60 (2), 2014, Sundararaju K, irmal Kumar A, andha Kumar A, Jeeva S, Performance analysis of STATCOM in real time power system, International Conference on Advances in Electrical Engineering (ICAEE), 2014, 1-4. Tabors D, Transmission System Management and Pricing: ew Paradigms and International Comparisons, IEEE Transactions on Power Systems, 9 (1), Wood A.J and Wollenberg F, Power Generation Operation, and Control, John Wiley & Sons, Inc., Yog Raj Sood, Wheeling of Power Under Deregulated Environment Of Power System-A Bibliographical Survey, IEEE Transactions on Power Systems, 17 (3), JCHPS Special Issue 1: February Page 279