Genetic algorithm based optimization for allocation of static VAr compensators

The placement of FACTS devices in a power network is of great importance to ensure the utilization of the full potential of the transmission network. In this study firstly, a static VAr compensators (SVCs) are placed optimally in a power network by taking into consideration the generation costs of both active and reactive power and the cost of the SVCs. The optimal power flow (OPF) and the low discrepancy sequences (LDS) based optimization procedure are employed to solve the problem of optimal allocation of SVCs. Secondly, a small disturbance stability study is performed in order to investigate how installing the SVCs with additional control loops at such locations could contribute to system small disturbance stability. The study demonstrated that the optimal allocation of SVCs is a cost effective measure since the installed SVCs reduce overall generation costs and additionally contribute to the enhancement of the security of the transmission network by improving the damping of electromechanical oscillations

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.

In this paper, the structure, coordinate control method, and parameter design of a hybrid system are proposed for active power injection and nonactive power (reactive, harmonic, and unbalance power) compensation. The proposed hybrid system consists of a static var compensator (SVC) in parallel with a capacitive-coupling grid-connected inverter (CGCI) (SVC//CGCI). In SVC//CGCI, the SVC part is used to dynamic compensate the reactive power and unbalance power, while the low rating CGCI part is used to inject active power, provide harmonic power and a fixed amount of capacitive reactive power. Compared with conventional inductive-coupling grid-connected inverter (IGCI), the CGCI can provide active, reactive and harmonic power with low rating of active inverter part. The cost of the SVC part is much lower than that of active inverter part, thus the reduction of inverter rating can lead to a decrease in the total cost of the SVC//CGCI. Therefore, the SVC//CGCI can be a cost effective solution for active power injection and nonactive power compensation. Finally, simulation and experimental results are provided to show the advantages and validity of the proposed SVC//CGCI in compared with the conventional IGCI and SVC//IGCI.

In this paper, a stochastic search technique, namely Particle Swarm Optimization (PSO) is used to determine the optimal locations of two Static Var Compensators (SVCs) in an IEEE 14-bus system. Static Var compensators (SVC) are the most widely used shunt FACTS devices within power networks because of their smaller costs and significant system enhancements. Appeared about two decades ago, the SVC is mainly installed for voltage support and furthermore, when installed in a proper location, it can reduce the power loss. Here the problem is framed as an optimization task and the optimal locations of SVC are identified using the novel technique. The efficacy of the new algorithm is tested with extensive computer simulations and further compared with Genetic Algorithm (GA) based approach. The performance analysis is done through extensive simulations and shows that the proposed dispensation is on a par with existing techniques.

Coordinated reactive power management in power networks with wind turbines and FACTS devices

the multifunctional capabilities of Flexible AC Transmission System (FACTS) devices paved new paths for controlling and enhancing the power transfer and usable capacity of transmission lines. The ability to enhance power transfers lead to their applications in a multi machine power system for the purpose of an overall reduction in power generation cost, among the others. The study presented in this paper deals with evaluation of economic viability of application of FACTS devices for generation cost reduction. It starts with optimal placement of four types of FACTS devices, namely Static Var compensator (SVC), Thyristor-Controlled Series Capacitor (TCSC), Thyristor-Controlled Voltage Regulator (TCVR), and Thyristor-Controlled Phase Shifting Transformer (TCPST) in a multi machine power system. The allocation methodology accounts for the cost of generated active and reactive powers, load growth, and cost of selected FACTS devices over a wide range of operating conditions. The allocation task is handled by the Optimal Power Flow (OPF) and Genetic Algorithm (GA) based optimization procedures. The economic viability of the solution is assessed using Net Present Value (NPV) calculation. Finally, Tornado diagram is used to assess sensitivity of the solution to various parameters involved in the optimization procedure.

This work aims to analyze the effects of a reactive power market on the operation of a power pool. For this, it proposes a reactive power generation cost function which is based on two facts: (i) the profit of a generation company depends on the amount of active power it sells to the market, and (ii) a generator's active power output is limited by its reactive power output. Thus, first of all, this work represents the active power generating capacity of a synchronous machine in terms of its reactive power output. Next, it associates the cost of lost opportunity to the cost of reactive power generation. This reactive cost function is introduced in a reactive optimal power flow model which is parameterized in its active and reactive loads. The solution to this problem is tracked, via a continuation method, until the maximum loadability limit. In this way, it is possible to analyze the influence of the reactive power costs on the maximum loadability limit and the market power of any generator at different load levels. Results are presented for different test systems.

This paper presents solution of optimal power flow (OPF) problem of a power system via Differential Evolution (DE) algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC) parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC). To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.

This paper presents solution of optimal power flow (OPF) problem of a power system via differential evolution (DE) algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, static VAR compensator (SVC) parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static VAR compensators (SVC). To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.

This paper presents an application of a flexible AC transmission system (FACTS) device as additional control in reactive power (VAR) optimization problem and analyzes the impact on system loss minimization. The adopted FACTS device is a new static VAR compensator (SVC). The general SVC consists of internal devices, and local devices, and the operation mode of the general SVC model is voltage control mode only. The new SVC model controls local, remote, internal and external devices simultaneously. It has two modes of operation: (1) Voltage control mode; and (2) Q control mode. This paper analyzes the functions of the new SVC, and implements them in the practical power systems. The impact of VAR optimization with new SVC model on loss minimization is also discussed by comparing it with VAR optimization with general SVC model as well as VAR optimization without SVC model.

The static var compensator (SVC) has fast dynamic characteristics that can support effective system voltage following disturbances. Keeping reactive power reserve in an SVC during steady-state operation is always needed to provide reactive power requirements during system dynamics. This paper presents a new SVC control strategy with two stages of regulation slopes and two voltage regulation controls, which are fixed-voltage reference control and variable (floating) voltage reference control. The aim of this control is to limit the reactive power output from the SVC to the desired value during the steady-state voltage range and compensate the reactive power requirement from the upstream networks via the coordination with the under-load tap changer (ULTC). However, when the voltage deviates the steady-state voltage range, the SVC will react to support the system voltage using the SVC reactive power reserve. When a disturbance results in a new operating point, with a steady-state reactive-power output, the variable voltage reference control effectively changes the SVC output slowly and returns it within the steady-state margin.

Shunt FACTS devices, such as, a Static Var Compensator (SVC), are capable of providing local reactive power compensation. They are widely used in the network to reduce the real power loss and improve the voltage profile. This paper proposes a planning model based on mixed integer conic programming (MICP) to optimally allocate SVCs in the transmission network considering load uncertainty. The load uncertainties are represented by a number of scenarios. Reformulation and linearization techniques are utilized to transform the original non-convex model into a convex second order cone programming (SOCP) model. Numerical case studies based on the IEEE 30-bus system demonstrate the effectiveness of the proposed planning model.

The inherent time-varying nature of the wind farm power causes undesired voltage flicker in the power network. In order to mitigate the flicker to enhance the performance of the wind power system with very fast dynamics, the static VAr compensator (SVC) is utilized. However, the SVC operates with some delays which negatively affects its performance. This persuades us to predict the reactive power of the wind farm to compensate for the real-world delay. The predicted reactive power is then utilized in the SVC. Therefore, this paper develops a novel fuzzy one-step-ahead prediction approach for the wind farm reactive power. The proposed fuzzy prediction uses a Takagi-Sugeno (TS) fuzzy representation whose unknown parameters are tuned online based on an extended Kalman filter (EKF). The wind farm is modeled as a time-varying current source which its amplitude and phase change every 0.01 s. A large set of the actual data of a wind farm in Manjil, Iran is gathered and directly utilized in the simulation process. Several flicker indices are calculated to evaluate the proposed prediction method. The obtained results show the performance enhancement and flicker mitigation of the suggested power scheme.

The control of reactive power in any transmission network is important to ensure voltage stability of the entire power grid. Flexible AC Transmission Systems (FACTS) have proven to be efficient in providing controllability to reactive power for voltage stability management. However, the performance of the FACTs devices largely depends on the installation location. A fuzzy logic technique for finding the optimal location for the Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensator (SVC) FACTS devices with specific security indices is proposed in this paper. The proposed indices for SVC placement are the Loss Sensitivity Factor (LSF) and critical voltage, while the considered indices for TCSC placement are the loss sensitivity factor (LSF) and line stability index (LSI). The indices are fuzzified to obtain a suitability index which determines the suitable line(s) and node(s) accordingly. The proposed approach is implemented on the IEEE 30 bus network. The SVC reduced the overall transmission loss by 1.6%, while the TCSC reduced the transmission loss by 4.94 %. The loading margin also improved by 24.87% and 2.92% using SVC and TCSC, respectively. The voltage profile also remained close to 1 p.u under varying load conditions. The results indicate the effectiveness of the proposed technique at reducing transmission loss, improving voltage stability and improving voltage profile simultaneously.

Electricity has now become an inherent feature of today's electricity generation; the integrated control grid has been. When there are different modes of generation, transmission and delivery, it has become more and more difficult. There is also a tremendous task for adequate power flow and device reliability to be ensured. The introduction of high-performance systems is welcomed, considering the diverse uses of classical controls (mechanical switching devices). FACTS (Flexible Alternating Current Transmission) are the current device that may adjust the power flow parameters such as voltage, power angle, damping oscillation, etc., in order to increase the performance of the power flow. SVC (Static Var Compensator) is one of the most common, reliable and economical controllers used in modern power systems among the numerous FACTS instruments. For its better functioning in the power network, it is obtained to have very good design and efficiency analysis of the SVC system. The SVC model's functionality is checked by the 9-bus device that controls the movement of reactive current through it. This is useful for the control of the voltage of the line. Firstly, the voltage management analysis was conducted with an uncompensated device and then the compensated system for SVC by adjusting the SVC implementation position from bus 8. MATLAB/SIMULINK programmed was used to do all the performance reviews.