CHAPTER I INTRODUCTION

Transcription

1 CHAPTER I INTRODUCTION 1.1 GENERAL Power capacitors for use on electrical systems provide a static source of leading reactive current. Power capacitors normally consist of aluminum foil, paper, or film-insulated cells immersed in a biodegradable insulating fluid and sealed in a metallic container. Depending on size and rating, they are available as either single- or three-phase units. Power capacitors are rated for a fundamental frequency, voltage, and kilovar (kilovoltamperes-reactive) capacity and are generally available in various voltage kilovar ratings. Individual units may be connected in series and multiples to provide banks of various capacities and voltage ratings. The terms series capacitor and shunt capacitor are used to identify the type of connection and do not indicate a difference in the power capacitor construction. Shunt capacitors installed in transmission and distribution networks will increase transmission capability, reduce losses and improve the power factor. High voltage banks for any voltage and power rating can be designed by series and parallel connection of single-phase capacitor units. 1

2 ٢ Shunt capacitors are primarily used to improve the power factor (cos ө) in the network. Inductive loads consume reactive power, e.g. magnetization power for transformers, motors and reactors. The reactive power needed is generated by capacitors. On the other hand, Series capacitors in transmission systems increase power transfer capability and reduce losses. Series capacitors are also installed in distribution systems, mainly to improve the voltage stability. Series capacitors are installed in transmission systems mainly in order to increase the power transfer capability and to reduce losses by optimizing load distribution between parallel transmission lines. Series capacitors are also installed in distribution systems. Here, the main reason is to improve the voltage stability of the network. Series compensation of a network positively affects the voltage and the reactive power balance. When the load current passes through the capacitor, the voltage drop over the capacitor varies in proportion to the current. The voltage drop is capacitive, i.e. it compensates the inductive voltage drop, which also varies with the load current. The result is an automatic stabilizing effect on the voltage in a network. Simultaneously, series capacitors generate reactive power, the power factor in the network is improved, whereby the line current and the line losses are reduced, as the impedance of the line is reduced, and the load capacity is increased. The power transferred through the transmission line is given by: VS VR P sin ( X X ) (1.1) SC line The generated reactive power varies proportionally to the square of the load current. Thus, the reactive power is automatically regulated. The general capacitor placement problem (CPP) is defined as the problem of how to determine the location, type, number and size of capacitors to be installed in the system. The objective is to minimize the energy losses while considering the capacitor

3 ٣ installation costs. In other words, the revenue savings resulting from the energy loss reductions are weighed against the installation cost of capacitors. The objective is to achieve the optimal or maximum value while satisfying the system constraints. 1.2 LITERATURE REVIEW The problem of optimal reactive compensation has been addressed in many papers and reports. Linear and non-linear programming methods have been proposed to solve the placement problem. In the following the previous work is reviewed in brief. Kaplan presented a computerized trail and error heuristic method for optimizing the present worth of revenue savings [1]. The savings are associated with released system capacity and energy loss reductions. The non-uniform load distribution and conductor size were taken into consideration. Both fixed and switchable capacitor banks and their installation cost were also considered. The availability of the capacitor banks in accordance with the standards and released capacity cost were included as well. The effects of the main and lateral branches were studied. Kuppurajulu et al [2] developed a non-linear model for minimizing the total cost of capacitor installation and energy loss satisfying the bus voltage constraints. The solution of this minimization problem gives an optimal switching schedule for the static capacitors in the network. Maliszewski et al [3] formulated a linear programming model for determining the optimal capacitor allocation for maintaining the voltage under normal and emergency conditions. A dynamic programming model was used by Duran [4] to determine the in-service capacitor units in distribution systems.

4 ٤ Yount [5] used a method using discrete dynamic optimization for solving the problem of static capacitor allocation in power systems. Chenoweth and Mamandur [6] developed a method for minimizing the system losses and for improving the voltage profile by changing generator voltages, transformer tap settings and adjustable VAR sources. In this model the power flow sensitivities with respect to each of the control variables are determined using the load flow sensitivity matrix. The optimization problem is solved by Dual Linear Programming technique and involves load flow calculations at the end of each iteration. A stopping criterion is incorporated in the algorithm as the solution zigzagged about the optimal point. In [7] S. Rama Iyer, K. Ramachanran and S. Hariharan, treated the VAR planning problem as a mixed integer programming problem using zero-one variables to establish whether new capacitor banks should be installed. They presented a fast converging method for finding the optimal capacitor allocation in a power system for minimizing power losses and for improving the quality of the supply system which is achived by optimal allocation of all reactive power sources in the system in a coordinated way. The optimization problem is solved by M.I.L.P based on Bender s decomposition algorithm in which the mixed problem is decomposed into two smaller sub-problems. The solutions of these two sub-problems are combined to get the solution of the original problem. The solution algorithm were tested to sample system. More recently, the use of various nondeterministic methods to solve the VAR planning problem have been described. These new proposals includes Genetic Algorithms (GA ) Simulated Annealing (SA), Tabu Search (TS), Expert Systems (ES s) and Artificial Neural Networks (ANN s). Karen Nan Min et al. proposed to solve the general capacitor placement problem by Genetic Algorithm followed by a sensitivity-based heuristic method [8]. Genetic

5 ٥ Algorithm is employed to find a high quality solution that is used as an initial guess for the sensitivity based heuristic. The objective function includes the cost of real power loss. The problem constraints are the power flow constraints, operational constraints on the bus voltages & the line flow ratings. Simulation results revealed that the proposed hybrid algorithm outperformed the genetic algorithm alone and the sensitivity heuristic alone in terms of both speed and quality. Yann-Chang Huang et al. introduced a Tabu Search-based method to solve the capacitor placement problem [9]. He started with heuristic and engineering judgments to select the potential locations where capacitors can be installed. Then, a sensitivity analysis is used to determine the candidate locations. In comparison with simulated annealing, the authors concluded that Tabu Search method gives the same results with shorter computing time. Salama et al. [10],[11] developed an Expert System (ES) containing technical literature experties (TLE) and human experties (HE) for reactive power control of a distribution system. The TLE included the capacitor allocation for maximum savings from the reduction of peak power and energy losses. The HE component of the knowledge base contained information to guide the user to perform reactive power control for the planning, operation, and expansion stages of distribution systems. N. I. Santoso and O. T. Tan [12] used ANN s for the optimal control of switched capacitors. In their work, two neural networks are used. One network is used to predict the load profile from a set of previous load values obtained from direct measurement at various buses and a second neural network is used to select the optimal capacitor tap positions based on the load profile as predicted by the first network. The first network is trained with a set of prerecorded load profiles and the second network is trained to maximize the energy loss reduction for a given load condition.

6 ٦ Once both networks are trained, iterative calculations are no longer required and a fast solution for a given set of inputs can be provided. Wanhong Deng Tjing T Lie [13], presented a development of a mathematical model representation of variable series capacitors which also known as Flexible AC Transmission systems (FACTS) in power system economic dispatch. The objective of their research is to find the optimal locations of FACTS devices for improved economic dispatch. The proposed approach is based on the decomposition-coordination method and the network compensation technique. Taking the advantages of accumulated experience in power system optimization and the existence of the Optimal Power Flow (OPF) software, the software development cost for implementing the proposed algorithm is reduced. In this paper, digital simulation studies on small power systems with and without variable series capacitors were conducted respectively. The purpose of these simulation studies is to assess the effectiveness of the proposed algorithm in minimizing the operating cost and enhancing the system performance. In [14] S. Gerbex, Rachid Cherkaoui, and Alain J. Germond, used a genetic algorithm to seek the optimal location of multi-type FACTS devices in a power system. The optimizations are performed on three parameters: the location of the devices, their types and their values. The system loadability is applied as measure of power system performance. Four different kinds of FACTS controllers are used and modeled for steady-state studies: TCSC, TCPST, TCVR and SVC. Simulations are done on a 118-bus power system for several numbers of devices.

7 ٧ Results show the difference of efficiency of the devices used in this context. They also show that the simultaneous use of several kinds of controllers is the most efficient solution to increase the loadability of the system. In all the cases (single- and multi-type FACTS devices), we observe a maximum number of devices beyond which this loadability cannot be improved. Edimar J. de Oliveira, et al [15]. In their work, the influence of a contingency in the Series compensation (SC) device allocation and size is evaluated. The SC placement and size are determined using an optimal power flow (OPF) program via the Benders cut technique to include the contingency influence. This problem is solved in two steps: 1. The SC size and placement are determined as an investment OPF sub-problem. The SC cost and energy production cost are included in the objective function. 2. The operation OPF sub-problem is solved considering the specified line and generation outages. The Benders cut are generated according to the outage occurrence probability. In this case the objective function is the minimum load shedding and energy production cost. The OPF sub-problems are solved using linear programming technique. The IEEE-14 bus and IEEE-118 bus test systems are used to show the effectiveness of the proposed methodology. L.J. Cai, I. Erlich and G.Stamtsis [16], deal with the optimal choice and allocation of FACTS devices in multi-machine power systems using genetic algorithm. The objective is to achieve the power system economic generation allocation and dispatch in deregulated electricity market. Using the proposed method, the locations of the FACTS devices, their types and ratings are optimized simultaneously. Different kinds of FACTS devices are simulated in this study: UPFC, TCSC, TCPST, and SVC. Furthermore, their investment costs are also considered. Simulation results

8 ٨ validate the capability of this new approach in minimizing the overall system cost function, which includes the investment costs of the FACTS devices and the bid offers of the market participants. The proposed algorithm is an effective and practical method for the choice and allocation of suitable FACTS devices in deregulated electricity market environment. 1.3 OBJECTIVES OF THE PRESENT INVESTIGATION The main scope of this study is to investigate the effect of MVar variation in an electrical power system under the effect of capacitor compensation. Three types of capacitor compensation are considered which are shunt compensation, series compensation and mixed shunt and series compensation. The study will be conducted on part of the Saudi Electricity Company (SEC) power network (western-region). The SEC system in the western region is divided into five subsystems which are : the 380 KV network, Jeddah network, Makkah network, Taif network and Madinah network. The purpose is to find the optimal number, location(s) of shunt and series capacitors to be installed, in each city, which results in minimum reactive power generation while satisfying the system constraints. Therefore, the main objective of this thesis can be summarized as follows: 1. Load flow studies are to be performed on different SEC system levels, i.e.380 kv and 110 kv. The load flow results will be given for each city. These load flow results should be compared with the actual load flow as recorded in the SEC load dispatch centers. 2. Develop an optimization program for shunt capacitor compensation placement, and link it to the main load flow program.

9 ٩ 3. find the optimum number, location(s) of shunt capacitors which minimize the objective function while satisfying the system constraints. The results should recommend the optimal number and size, at both peak and light load, for each city as specified above. 4. Develop an optimization program for series capacitor compensation placement and link it to the main load flow program. 5. find the optimum number, location(s) of series capacitors which minimize the objective function while satisfying the system constraints. The results should recommend the optimal number and size, at both peak and light load, for each city as specified above. 6. Develop an optimization program for mixed type compensation capacitor placement, and link it to the main load flow program. 7. find the optimum number, location(s) of shunt and series capacitors which minimize the objective function while satisfying the system constraints. The results should recommend the optimal number and size, at both peak and light load, for each city as specified above. 8. Recommendation which size of capacitors might be needed. 9. Investigating the economic benefits of each case of compensation for each city by comparing the cost of capacitors with the saving in the cost of the saved energy.