Evaluation of TCSC and DR Abilities for Congestion Management

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1 Australian Journal of Basic and Applied Sciences, 5(6): , 2011 ISSN Evaluation of TCSC and DR Abilities for Congestion Management 1 Y. Hoseynpoor, 2 T. PirzadehAshraf, 1 Sh. Sajedi, 1 T. Karimi 1,2 Parand Branch, Islamic Azad University, Parand, Iran Abstract: Competitive market is established for market participants who will encounter many new problems in market operation and regulation. Transmission congestion management is an essential part in operating an electric power system. FACTS devices, which enable many objectives to be achieved in an electric power system, can be used to reduce the flow on the overloaded lines and to increase the use of alternative paths to excess network capacity. Also Demand Response (DR) programs. DR can be defined as the changes in electric usage by end-use customers from their normal consumption patterns and so are able to congestion management. For this aim, in this paper GA has been used for the determination of optimal location and size of FACTS devices and DR program for congestion management. The system cost has been determined by implementation of DR and FACTS devices for a pool model separately in deferent scenarios. Finally, an economic analysis is performed for economic comparison. The proposed method is evaluated by numerical studies based on IEEE 30-bus test system. Key words: FACTS, TCSC, Congestion Management, Demand Response, Genetic Algorithm. INTRODUCTION Deregulation and restructuring of the electric power industry has been occurred in many countries throughout the world. Competitive market is established for market participants who will encounter many new problems in market operation and regulation. Transmission congestion management is an essential part in operating an electric power system. Resources are re-dispatched based on security requirements due to thermal overload, dynamic instability, and voltage problems. An efficient alternative to solve this problem is implementation of FACTS devices in power system (Kazemi and Sharifi, 2006), (Ippolito and Siano, 2004), (Lie and Hailong, 2004). FACTS devices, which enable many objectives to be achieved in an electric power system (Ippolito and Siano, 2004), can be used to reduce the flow on the overloaded lines and to increase the use of alternative paths to excess network capacity. This allows for increased transfer capability in existing transmission and distribution systems under normal conditions, allowing the possibility to load lines closer to their thermal limits. Another method that can be used for congestion management is Demand Response (DR) programs. DR can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over the time. Furthermore, DR can be also defined as the incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. DR includes all intentional modifications to consumption patterns of electricity of end use customers that are intended to alter the timing, level of instantaneous demand, or the total electricity consumption (Albadi and El-Saadany, 2007). In this paper, FACTS devices and DR program, as two methods for congestion management, are compared from an economic viewpoint. For this aim, GA has been used for the determination of optimal location and size of FACTS devices and DR program. Optimal power flow (OPF) algorithm is used as a market clearing procedure under which both generation costs and transmission constraints are optimally accounted. Here, the customer response is modeled base on demand elasticity, for which a new economic load model is used (Lie and Hailong, 2004). Numerical studies are conducted in two DR scenarios incentives and three scenarios for simulation interval by using IEEE 30-bus test system. The rest of the paper is organized as follows. Section III describes OPF-based market clearing procedure. Section IV discusses the congestion management using FACTS devices. Section V explains the implementation of DR program for congestion management. Section VI presents the numerical results which are tested on IEEE 30-bus system and finally, section VII is dedicated to conclusions. Corresponding Author: Y. Hoseynpoor, Parand Branch, Islamic Azad University, Parand, Iran. 1011

2 OPF Based Auction: Besides submitting offers to sell and bids to buy energy, the system operator clears such a market by scheduling all the energy offers and bids so as to imize the system social welfare while satisfying all operational constraints. A modified formulation of joint energy/reserve market with DB program can be formulated as: NG NGj ND NDi Min : ( Ej, l. PEj, l ) ( Di, k. PDi, k ) j1 i1 i1 k1 (1) Subject to: 0 P P i 1,..., N, k 1,..., N Di, k Di, k D Di (2) 0 P P j 1,..., N, l 1,..., N Ej, l Ejl, G Gj (3) N Gj uj j,min PEj, l uj j, l 1 P P jg A A P P B ( ) 0 n1,..., N Gn Dn n m mn P B ( ) P n1,..., N, m n m n u j {0,1} jg (4) (5) (6) (7) The objective function (1) represents the producers surplus of energy market minus the consumers payment (i.e., negative of the net social welfare). The first term of (1) is the sum of accepted generation bids of energy market times their corresponding bid prices. The second term is the sum of accepted demand bids times their corresponding bid prices. It should be noted that producer bids are considered convex and monotonically increasing; and consumer bids, are concave and monotonically decreasing. The block of constraints (2) specifies the sizes of the demand bids. Constraint (3) limits the sizes of the generation bids in energy, while (4) ensures that every generator if running, runs between its minimum and its imum power output. Constraint (5) states that the production in each bus should be equal to the demand balance. The set of constraints (6) enforces line capacity limits. And constraint (7) is the binary variable declaration. Congestion Management by FACTS Devices: TCSC Model: FACTS devices are usually categorized into three types, the first category is characterized as injection of current in shunt, and the second one is as injection of voltage in series with the line and the third as a combination of current injection in shunt and voltage injection in series. In this paper, TCSC which is a shunt controller for injection of reactive voltage is used for congestion management The existing steady state models of FACTS devices can be classified in to two categories: decoupled and coupled. The decoupled model replaces the FACTS device by a fictitious bus. The coupled model can be further sub classified as Voltage Source Model (VSM) and Power Injection Model (PIM). In VSM the FACTS device is incorporated as a series/shunt inserted voltage source according to device s operating principle. However, it destroys the symmetric characteristics of admittance matrix. On the other hand, the PIM converts the inserted voltage source into power injection at related buses. This allows keeping the symmetry of admittance matrix; therefore this model is widely used in FACTS studies (Chanana and Kumar, 2006). The model of a transmission line with a TCSC connected between bus-n and bus-m is shown in Fig

3 Fig. 1: Equivalent circuit diagram of TCSC. During the steady state, TCSC can be considered as a static reactance ( jx C ). The equations for static modeling of TCSC in OPF formulation are given as below P V G VV ( G cos B sin ) C 2 n n m C Q V 2 ( B B ) VV ( G sin B cos ) n sh n m P V G VV ( G cos B sin ) C 2 mn m n m C Q V 2 ( B B ) VV ( G sin B cos ) mn m sh n m (8) (9) (10) (11) where G B r r x x 2 2 ( C ) ( x x ) r x x C 2 2 ( C ) (12) (13) 0 xcn xcn (14) Cost of TCSC: It becomes evident to include the investment cost of FACTS devices in the objective function. The investment cost of FACTS includes the capital cost and the installation cost. The installation cost per MVA is fixed but the capital cost of TCSC may vary due to economies of scale. Quadratic cost functions used to estimate the overall costs are given as: C( S ) as bs c 2 FACTS FACTS FACTS (15) where a, b and c is constant positive values. The TCSC cost during the study horizon is calculated by (Chanana and Kumar, 2006): C S S kvar TCSC ($ / ) (16) 1013

4 where S is the capacity of TCSC in MVar. For the economic analysis conducted here a horizon of 10 year, 1 year and 1 month is considered. This horizon is according to the studying period. Problem Formulation for Identification Optimal Number and Location of TCSC by Genetic Algorithm: In this work, the GA are used for selecting the optimal number and the location of the TCSC devices to be settled on a given electric system in order to minimize a given objective function, while the control parameters of the devices are calculated in a procedure, nested in the GA, which solves the OPF. In the implemented GA, having assumed the system in normal conditions, the objective function to be minimized is NG NGj ND NDi Min : ( Ej, l. PEj, l ) ( Di, k. PDi, k ) CTCSC j1 l 1 i1 k 1 (17) The term C TCSC in the objective function (17) is introduced to consider the TCSC devices cost and represents the total cost of installation and maintenance of the TCSC devices. Implementation of DR for Congestion Management: Load Economic Model: Elasticity is defined as the demand sensitivity with respect to the price (Kirschen, 2004): q 0 dq E. q dp 0 (18) If the electric energy prices vary for different periods, then the demand reacts one of followings: 1. Some of loads are not able to move from one period to another (e.g. illuminating loads) and they could be only "on" or "off". So, such loads have a sensitivity just in a single period and it is called "self elasticity" (Lie and Hailong, 2004), and it always has a negative value. 2. Some consumption could be transferred from the peak period to the off-peak or low periods. Such behavior is called multi period sensitivity and it is evaluated by "cross elasticity". This value is always positive. According to equation (18), self elasticity (E aa ) and cross elasticity (E ab ) can be written as: E E aa ab Da 0 a Da 0 b (19) The detailed process of modeling and formulating how the DR program affects on the electricity demand and how the imum benefit of customers is achieved, are discussed in several researches. Accordingly the final responsive economic model is presented by (20): 24 d0() i di ( ) d0( i) E0( ij, )..[ ( j) 0( j) Aj ( ). j 1 0( j) Ei ()[ () i () i Ai () 0() i 0 1 i 1,2,...,24 (20) The above equation shows how much should be the customer's demand in order to achieve imum benefit in a 24 hours interval. 1014

5 Problem Formulation for Identification Optimal Location and Quantity of DR by Genetic Algorithm: In this work, the GA are used for selecting the optimal location and quantity of DR on a given electric system in order to minimize a given objective function, In the implemented GA, having assumed the system in normal conditions, the objective function to be minimized is N N N D N Di Min : (. ) ( Di, k. PDi, k ) G Gj Ej, l PEj, l j1 l 1 i1 k 1 N N red Di ( redi, k. PreDi, k ) i1 k 1 (21) The objective function (21) represents the producers surplus of energy market minus the consumers payment plus the cost of responsive demands (i.e., negative of the net social welfare). Numerical Studies: The proposed algorithm has been tested on IEEE-30 bus test system which has 6 generator buses, 21 load buses and 41 lines as represented in Fig. 2. It is considered that every generator bids at its marginal costs. This simple bidding criterion is used for simplicity, because it does not affect the comparison process. For each load bus, the load curve is normalized to the standard value. A typical load curve of a real world network is selected to test and analyze the effect of DR program as shown in Fig. 3. The load curve is divided into three intervals: low load period (00.00 to 9:00), off-peak period (10:00 to 19:00) and peak period (20:00 to 24:00). The studied system is congested just in peak load, so the simulation study is performed in peak load hours. In Table 1, the generator s economic characteristic coefficients are studied (Ippolito and Siano, 2004). The numerical study is performed in two different scenarios for DR coefficients and three scenarios for the duration of the study. Table 2 shows the customer characteristics in two different scenarios. Fig. 2: IEEE 30-bus system. 1015

6 Fig. 3: The selected load curve for testing the effect of DR program. Table I: Generator characteristics. Bus number a i2 a i1 a i0 P i,min P i, Table II: Customer characteristics in two different scenarios: Initial demand Scenario 1 Scenario Bus number b i2 b i1 b i0 b i2 b i1 b i Also, the durations of the simulation is considered to be 10 years, 1 year and 1 month. The results of congestion management by FACTS devices and DR programs are compared for the mentioned scenarios and are presented later in subsection C. For tuning the GA, a population size of 20 and a number of 200 generations are set. The simulation experience shows that these values, for the power system under study, guarantee the convergence of the algorithm to a satisfactory solution. 1016

7 DR Program Implementation: The amount of the price of electrical energy in DR program formulation is assumed to be equal to 50 $/MWh. The elasticity of the load is shown in Table 3. In this study for simplicity, the elasticity of all load buses is assumed to be equal. For different load elasticities the results are nearly similar, but calculations will be more complicated. The load curves before and after implementation of DR program is represented in Fig. 4. Table III: Self and cross elasticity. Peak Off-Peak Low Peak Off-Peak Low Fig. 4: The load curve before and after DR implementation, by incentive equal to 50 $/Mwh. Table IV: Demand reduction by DR in two DR incentive values scenarios (MW). Bus number Initial demand Scenario 1 Scenario Total scenario cast ($/hour) The results obtained from the GA for amount of demand reduction by DR in two DR coefficients scenarios are shown in Table

8 TCSC Implementation: As it was mentioned before, the durations of simulation is considered in three different scenarios. So, TCSC investment cost is divided into the respected duration study. The results obtained from the GA, which demonstrate the optimal numbers of TCSC to be installed in the network in three different study durations, are shown in Table 5. Table V: Optimal location and costs of TCSC in three scenarios. scenarios TCSC location TCSC cost ($/hour) Total cost ($/hour) 10 year 10, 11, 12, 14, 15, 18, 27, 29, 30, 32, 35, 36, 40, year 29, 35, month In Table 5, TCSC cost is the cost of installation, maintenance and operation of TCSC. Also, total cost is the sum of TCSC cost and operation cost of the power system in one hour at peak load. Discussion: As it can be seen in Table 4, the increment in DR incentive value result in reduction of costumer's participation in DR and to increment of system operation cost. Also, Table 5 shows that by increment of the duration TCSC implementation, its cost and the total cost of the system operation will be decreased and the implementation of TCSC is more rational in long term. By comparison the results of Tables 4 and 5, it becomes clear that DR implementation for congestion management is rational if the system congestion period is less than 1 month. As shown in Fig. 5, for congestion management periods more than 1 month the implementation of FACTS devices can be more economic than DR program. It is noticeable that the DR incentive values have the most important role in justification of DR implementation for congestion management. As it is shown in Figs. 5 and 6 just in scenario 2 that a DR incentive value is low, the implementation of DR is economic for 1 month period. It is clear that for DR incentive less than the incentive of scenario 1, DR program implementation is more rational. Fig. 5: Total costs of DR and TCSC implementation in scenario 2 (with low DR incentive value). Fig. 6: Total costs of DR and TCSC implementation in scenario 1 (with high DR incentive value). 1018

9 Conclusions: In this paper, FACTS devices and DR program, as two methods for congestion management, were compared from economic viewpoint. For this aim, GA has been used for determination of optimal location and size of FACTS devices and DR program. Numerical studies have been conducted in two scenarios for DR incentive values and three scenarios for simulation interval by using IEEE 30-bus test system. The results confirmed that implementation of DR program needs higher cost, but regarding its fast response, it can be used in emergency conditions. Also, by implementation of DR program, the system faces with less technical problems. REFERENCES Albadi, M.H. and E.F. El-Saadany, Demand Response in Electricity Markets: An Overview, IEEE Power Engineering Society General Meeting, 1-5. Chanana, S., A. Kumar, Effect of Optimally Located FACTS Devices on Active and Reactive Power Price in Deregulated Electricity Markets, IEEE Power India Conference, Ippolito, L., P. Siano, Selection of optimal number and location of thyristor-controlled phase shifters using genetic based algorithms, IEE Proceedings Generation, Transmission and Distribution, 151(5): Kazemi, A., R. Sharifi, Optimal Location of Thyristor Controlled Phase Shifter in Restructured Power Systems by Congestion Management, IEEE International Conference on Industrial Technology, ICIT: Kirschen, D.S., G. Strbac, Fundamentals of Power System Economics, Wiley. Lie, T.T., Hui Hailong, Optimal Dispatch in Pool Market with FACTS Devices, IEEE Power Engineering Society General Meeting, 1: