Allocation Of Static Var Compensators Research Articles

Smart and coordinated allocation of static VAR compensators, shunt capacitors and distributed generators in power systems toward power loss minimization

ABSTRACT This paper introduces a smart coordinated allocation of distributed generation (DG) units, shunt-connected capacitors (SC), and static VAR compensators (SVC) for power loss minimization. The problem is formulated as a mixed-integer non-linear programming and is solved using a BONMIN solver embedded in General Algebraic Modeling System (GAMS). The proposed method’s evaluation was performed on the IEEE 33-bus test system, where three cases were discussed. GAMS/BONMIN was used to find optimal locations and power outputs of DGs in the first case. Simulation results, in this case, showed that losses were reduced by 48.42% with one DG unit, 58.58% with two DG units, and 65.44% with three optimally allocated DG units. In the second case, the proposed method was used to coordinate DGs and SCs, while in the third case, coordinated allocation of DG and SVC was investigated. The results showed that SC/SVC coordination further reduces active power losses. With three SCs allocated, the reduction in the active power losses was 93.5% compared to the base case, and 82.18% compared to losses in the case where only 3 DGs are connected, while in the case of three SVC allocated, it was 93.85% compared to the base case, and 82.21% compared to the case where 3 DGs are connected. The second and third cases also showed that the coordination of SC/SVC leads to an improved voltage profile, where the bus voltages are close to the acceptable range, and voltage fluctuation is significantly reduced. The comparison between different non-linear programming solvers embedded in GAMS for solving optimal power flow was also presented.

Optimal SVC allocation in power systems using lightning attachment procedure optimization

Flexible AC transmission systems (FACTS) technology is widely adopted and utilized to maintain the performance of power systems. However, the improvements of power system performance achieved by FACTS devices depend on the right sizing and allocation of such devices. For technical and economic considerations, a FACTS device's location and size should be selected very carefully in order to maximize its benefits to the power system. In this paper, the sizing and location of a static VAR compensator (SVC) are optimally determined using a new optimization technique called lightning attachment procedure optimization (LAPO). The optimal allocation of the SVC is determined regarding the improvement of voltage deviation index and the reduction of total active power losses. The system is optimized in two cases: once without SVC installation in order to find out the optimal settings of the system that achieve the objective functions, and another time with SVC installation to determine its optimum sizes and locations by which the required objective functions are achieved. Then the system performance is analyzed after optimization with and without SVC devices to show the impact of the optimum sizing and location of the SVC on the system. The study is validated using the standard IEEE 30-bus system, while the developed LAPO is performed by MATLAB M-Files and the system performance analysis in different cases is performed by NEPLAN software.

Optimal Allocation of Static Var Compensators in Electric Power Systems

In the current age, power systems contain many modern elements, one example being Flexible AC Transmission System (FACTS) devices, which play an important role in enhancing the static and dynamic performance of the systems. However, due to the high costs of FACTS devices, the location, type, and value of the reactive power of these devices must be optimized to maximize their resulting benefits. In this paper, the problem of optimal power flow for the minimization of power losses is considered for a power system with or without a FACTS controller, such as a Static Var Compensator (SVC) device The impact of location and SVC reactive power values on power system losses are considered in power systems with and without the presence of wind power. Furthermore, constant and variable load are considered. The mentioned investigation is realized on both IEEE 9 and IEEE 30 test bus systems. Optimal SVC allocation are performed in program GAMS using CONOPT solver. For constant load data, the obtained results of an optimal SVC allocation and the minimal value of power losses are compared with known solutions from the literature. It is shown that the CONOPT solver is useful for finding the optimal location of SVC devices in a power system with or without the presence of wind energy. The comparison of results obtained using CONOPT solver and four metaheuristic method for minimization of power system losses are also investigated and presented.

Allocation of Static Var Compensator in Electric Power Systems Considering Different Load Levels

This paper proposes an approach to determine the optimal location of static var compensators (SVCs) in electric power systems in order to improve voltage profile and minimize active power losses. A multi-scenario framework that includes different load levels with different time periods is considered in this approach. The problem is formulated as a mixed-integer nonlinear programming problem using an optimal power flow (OPF). The SVC value and location are modeled as a variable susceptance inside the bus admittance matrix and as a binary decision variable, respectively. The problem is solved using the branch and bound algorithm associated with the OPF. Studies and simulations were conducted on the IEEE 118-bus test system considering variations in both the objective function and the amount of SVCs to be allocated. Analysis of results demonstrate that the performance of the power system can be effectively enhanced due to the optimal allocation of SVC equipment if considering different load levels with different time periods for the allocation of SVCs, rather than allocate the SVCs separately.

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems. Pareto Envelope-based Selection Algorithm II (PESA-II) with fuzzy logic decision maker is developed to determine the optimal location and size of SVC based on the minimum total power losses and Voltage Deviation (VD). Combined cumulants and gram-chalier expansion are used for solving the probabilistic load flow problem. The proposed algorithm is tested on 33- bus and 69-bus distribution systems. The developed algorithm gives an acceptable solution with low number of iterations and less computation cost compared with the Monte Carlo method.

Review of Impacts of Static Var Compensator Allocation on Radial Distribution Networks

ABSTRACT This paper primarily focuses on an investigative study on allocating Flexible AC transmission system (FACTS) devices on radial distribution systems. The FACTS device that is used in this paper is static var compensator (SVC). In this paper, SVC is placed on each bus except substation bus and study the results to find the best location for allocation. A design of SVC was done by fixed reactance method. The total power loss of network and voltage of nodes have been analyzed for each allocation and the sequences of each implementation have been compared to find the best location of SVC. The proposed algorithm has been executed on IEEE 33-bus and 69-bus standard test systems.

Optimal allocation of static VAR compensator by a hybrid algorithm

Flexible AC transmission system (FACTS) devices are gaining popularity in electrical transmission networks for the last few years due to their ability to improve a system’s performance without restructuring or expanding the existing network. But, placing the FACTS device at any arbitrary location in the network may not be optimally beneficial. Also, judging the optimally beneficial location of these devices becomes necessary prior to investment owing to their high expense of installation and maintenance. In this paper, a novel hybrid algorithm combining Cuckoo Search Algorithm (CSA) and Chemical Reaction Optimization (CRO) is proposed to solve the problem of optimally allocating a static VAR compensator (SVC) for the standard IEEE 14-bus, 30-bus and 57-bus transmission systems under heavily loaded condition. The optimal allocation of SVC is done by considering technical aspects like voltage stability, power generation minimization and line loss reduction and also by considering commercial facets like savings on annual cost of power generation and return-on-investment (ROI) time period for the expenditure incurred on the SVC. The ROI calculation is a novel addition to the problem formulation which adds a practical outlook to the feasibility of investment. Thus, a holistic approach is followed for determining the optimal location of the SVC in the power network and the results validate the superior performance of the proposed algorithm.

Allocation of Static VAr Compensators Using Optimal Reactive Power Flow and Branch & Bound Algorithm

This paper presents a model based on a Optimal Reactive Power Flow (ORPF) to allocation of Static VAr Compensator (SVC) in the power transmission network. The SVC was modeled as a variable susceptance and inserted into Ybus matrix. For discretization of the devices to be allocated in the system were used binary variables associated to SVC susceptance. A multiobjective function formed by two parts, active power losses and voltage deviation, was used in the ORPF formulation. In each one of parts was associated a parameter that allows to give more or less weight to each part. Due to the problem characteristics, Mixed Integer Nonlinear Programming (MINLP), was used a Branch & Bound (B&B) algorithm associated with the ORPF. The model was tested in the IEEE 118 bus system. The results show that the modeling and the technique of solution used are appropriate for SVCs allocation and powersystem analysis

An approach to optimal allocation of SVC in power systems connected to DFIG wind farms based on maximization of voltage stability and system loadability

This paper proposed an approach to determine optimal location of SVC (Static Var Compensator) to improve the voltage profile and maximize system loadability in power networks connected to a doubly-fed induction generator (DFIG)-based wind farm. A variable reactance model for SVC is presented in steady state studies and implemented in the load flow program with embedded Flexible AC Transmission Systems (FACTS) devices. The continuation power flow method is used to determine optimal location of SVC and steady state stability margin, based on closing to point of voltage collapse. As an important result in this paper, we obtained an optimal location of SVC devices on wind farm by calculating the new indices such as maximum loading point (MLP) and mega watt margin (MWM). A case study and simulation are done on modified IEEE14 bus test system.

Optimal Allocation of SVC for Minimization of Power Loss and Voltage Deviation using NSGA - II

Modern this paper proposes non dominated sorting genetic algorithm (NSGA-II) which has feature of adaptive crowding distance for finding optimal location and sizing of Static Var Compensators (SVC) in order to minimize real power losses and voltage deviation and also to improve voltage profile of a power system at the same time. While finding the optimal location and size of SVC, single line outages are considered as contingencies and voltage limits for the buses are taken as security constraints. To demonstrate the effectiveness of the proposed approach, NSGA-II has been applied for finding optimal location and sizing of SVC on IEEE 30-bus test system. The obtained results are highly encouraging and reveal the capability of the NSGA-II to generate well-distributed non-dominated Pareto front.

Sensitivity Analysis based Optimal Location and Tuning of Static VAR Compensator using Firefly Algorithm

This paper presents a new Meta heuristic optimization algorithm called firefly algorithm (FA) used to solve the multi objective optimal power flow to identify the optimal setting of Static VAR Compensator (SVC) and optimal location is identified by using sensitivity analysis when the system is operating under normal and overloaded conditions. Sensitivity analysis based Voltage collapse proximity Index is proposed for placing the SVC at appropriate location under normal and over loaded conditions. Once the location to install SVC is identified, the optimal allocation of SVC is determined through firefly algorithm based A multi-criterion objective function comprising of four objectives minimize total power loss, minimize total voltage magnitude deviations, minimize the fuel cost of total generation and minimize the branch loading to obtain the optimal power flow. Simulations have been implemented in MATLAB and the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems have been used as a case study. The results we have obtained indicate that installing SVC can significantly enhance the voltage stability of power system. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Genetic Algorithm (GA). The results we have obtained indicate that FA is an easy to use, robust, and powerful optimization technique compared with GA.

Sensitivity Analysis Based Optimal Location and Tuning of Static Var Compensator Using Firefly Algorithm

This paper presents a new Meta heuristic optimization algorithm called firefly algorithm (FA) used to solve the multi objective optimal power flow to identify the optimal setting of Static VAR Compensator (SVC) and optimal location is identified by using sensitivity analysis when the system is operating under normal and overloaded conditions. Sensitivity analysis based Voltage collapse proximity Index is proposed for placing the SVC at appropriate location under normal and over loaded conditions. Once the location to install SVC is identified, the optimal allocation of SVC is determined through firefly algorithm based A multi-criterion objective function comprising of four objectives minimize total power loss, minimize total voltage magnitude deviations, minimize the fuel cost of total generation and minimize the branch loading to obtain the optimal power flow. Simulations have been implemented in MATLAB and the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems have been used as a case study. The results we have obtained indicate that installing SVC can significantly enhance the voltage stability of power system. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Genetic Algorithm (GA). The results we have obtained indicate that FA is an easy to use, robust, and powerful optimization technique compared with GA.

Optimal Locations and Sizes of Static Var Compensators Using NSGA II

This paper presents an approach to find optimal locations and sizes of static var compensators (SVCs) in transmission systems. The approach comprises two main parts. In the first part, a mixed continuous-discrete multi-objective optimisation problem is formulated in which SVCs locations and sizes are the variables. The second part develops an optimisation method based on NSGA II (non-dominated sorting genetic algorithm) to solve the problem. The outcome will be a set of possible solutions from which the most desirable, based on any nominated criteria, can be selected in a systematic manner. The proposed approach has been successfully tested on IEEE 14-bus system tofind the optimal locations and sizes of SVC devices. The obtained results show that with the allocation of SVC devices determined by the proposed method, system security expressed in terms of power flows and voltages in steady-state operation is maintained with an increased margin in both normal state and critical contingencies.

Optimal locations and sizes of static var compensators using NSGA II

This paper presents an approach to find optimal locations and sizes of static var compensators (SVCs) in transmission systems. The approach comprises two main parts. In the first part, a mixed continuous-discrete multi-objective optimisation problem is formulated in which SVCs locations and sizes are the variables. The second part develops an optimisation method based on NSGA II (non-dominated sorting genetic algorithm) to solve the problem. The outcome will be a set of possible solutions from which the most desirable, based on any nominated criteria, can be selected in a systematic manner. The proposed approach has been successfully tested on IEEE 14-bus system to find the optimal locations and sizes of SVC devices. The obtained results show that with the allocation of SVC devices determined by the proposed method, system security expressed in terms of power flows and voltages in steady-state operation is maintained with an increased margin in both normal state and critical contingencies.

A Novel SVC Allocation Method for Power System Voltage Stability Enhancement by Normal Forms of Diffeomorphism

Location of the static VAR compensator (SVC) and other types of shunt compensation devices is important for the enhancement of the voltage stability for practical power systems. With the theory of the normal forms of diffeomorphism, this paper proposes a new method to solve this problem. The proposed method makes use of the nonlinear participation factors, in which the nonlinearity of power systems can be taken into consideration. As a result, the most suitable location where the SVC should be used in power system can be determined, even for the cases in which the system is characterized with strong nonlinearity. In order to show the effectiveness of the proposed method, the New England 39-bus power system with SVC is used as an example. Calculation results show that with the SVC located at the place where the proposed method determined, the voltage stability is considerably enhanced. The steady-state voltage stability index and the time domain simulation results verify the effectiveness of the proposed method.