Evolutionary Computing Techniques for Optimal Reactive Power Dispatch: An Overview and Key Issues

In this paper one of the reliable and effective optimization algorithms called “artificial bee colony” algorithm (ABC) for solving the optimal reactive power dispatch (ORPD) problem with the discrete and continuous control variables in an electric power system is presented. In this work ABC algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the number of capacitors banks to be switched, for optimal reactive power dispatch. The original ABC algorithm designed for the continuous nature of optimization problems, cannot be used for discrete cases; but the real ORPD problem has two different nature types of control variables (discrete and continuous), for this reasons a simple rounding operator is included in the main steps of original ABC algorithm to ensure the discretization process. Then, the feasibility and performance of the proposed algorithm are verified by the serial simulations on the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus power systems. The numerical results are compared to those yielded by the other recently published evolutionary optimization algorithms in the literature. This comparison shows that the ABC algorithm is superior to the other mentioned algorithms and can be efficiently used for solving the discrete ORPD problem.

Reactive power optimization is a mixed integer nonlinear programming problem where metaheuristics techniques have proven suitable for providing optimal solutions. Optimal reactive power dispatch (ORPD) is a key instrument to achieve secure and economic operation of power systems. Due to complex characteristics of ORPD, heuristic optimization has become an effective solver. In this paper, swarm and evolutionary algorithm have been applied for reactive power optimization. A two-phase hybrid particle swarm optimization (PSO) approach is used to solve optimal reactive power dispatch (ORPD) problem has been presented in this paper. In this hybrid approach, PSO is used to explore the optimal region and direct search is used as local optimization technique for finer convergence. This paper also presents a particle swarm optimization for reactive power and voltage control considering voltage stability. The proposed method determines a control strategy with continuous and discrete control variables such as AVR operating values, OLTC tap positions, and the amount of reactive power compensation equipment. A novel heuristic optimization algorithm namely the Mean-Variance Mapping Optimization (MVMO) is proposed to handle the ORPD problem. The objective of the proposed PSO is to minimize the total support cost from generators and reactive compensators. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation.

ABSTRACTIn the present work, symbiotic organism search (SOS) algorithm is applied for the solution of optimal reactive power dispatch (ORPD) problem of power system considering flexible AC transmission system (FACTS) devices. The proposed method is tested and examined on modified IEEE 30 bus test system with two types of FACTS devices namely thyristor-controlled series capacitor (TCSC) and thyristor-controlled phase shifter (TCPS), placed at some strategic locations. In this study, ORPD problem has been formulated as single objective, constrained optimization problem that aims to reduce active power loss or total absolute voltage deviation individually. The formulation for conventional OPRD problem is evaluated by considering the settings of control parameters and maintaining the constraints of the FACTS devices as an additional entity. The effectiveness of the proposed method is analysed and compared to the results offered by some other optimization methods. The outcomes of the present work prove the potential of the SOS algorithm in solving ORPD problems of the power system incorporating FACTS devices.

Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms

The Flexible alternating current transmission systems (FACTS) is considered one of the most developed technologies applied for enhancing the performance of system due to their ability of adjusting different parameters in the transmission systems such as the buses voltage, the transmission line impedance, the active and reactive powers flow in transmission lines. The static synchronous series compensator (SSSC) is an effective member of the FACTS which is connected in series with the transmission lines and it consists of a solid-state voltage source inverter coupled with a transformer which aims to control and secure the operation of the power system. The main function of SSSC is inserting a controllable voltage in series with the transmission line to control the active and reactive powers flow in transmission lines. Solving the optimal reactive power dispatch (ORPD) problem is nonlinear, non-convex and it becomes a complex problem with integration of the SSSC. The contributions of article include, 1) an efficient and reliable optimization algorithm is developed to solve the ORPD problem and identify the optimal location and ratings of the SSSC, 2) The proposed algorithm is based on modifying the salp swarm algorithm (SSSA) using Levy Flight Distribution and spiral movement of particles to enhance the searching capabilities of the SSA, 3) an efficient model of SSSC based on power injection approach is used for representation the SSSC in ORPD. The ORPD is solved with and without the SSSC controller to minimize power losses and voltage deviations as well as improve the voltage stability. The proposed algorithm for ORPD is tested on the standards IEEE 30-bus and 57-bus systems. The simulation results demonstrate that MSSA is more effective and superior for solving the ORPD compared with some other reported meta-heuristic techniques. Moreover, the system performance is enhanced considerably with optimal inclusion the SSSC.

This paper presents a new solution method for optimal reactive power dispatch (ORPD) problem based on principal component analysis (PCA). In heavily loaded power system, security, reliability and economy are some unavoidable concerns. Therefore, ORPD becomes an essential tool to achieve these goals. It is a highly non-linear constrained optimisation problem. A PCA-based real coded genetic algorithm (PCA-RCGA) is proposed in this paper to deal with such type of problem. In PCA-RCGA, PCA theory is applied in mutation operator to enhance convergence of conventional genetic algorithm by guiding the direction of stochastic search to reach near the global optimal solution effectively. The proposed PCA-RCGA is tested on standard IEEE-30 bus and IEEE-118 bus system for ORPD problem. The simulation results for both cases are compared with various optimisation techniques applied to ORPD available in literature.

This study addresses a shuffled frog leaping algorithm for solving the multi-objective reactive power dispatch problem in a power system. Optimal Reactive Power Dispatch (ORPD) is formulated as a nonlinear, multi-modal and mixed-variable problem. The intended technique is based on the minimization of the real power loss, minimization of voltage deviation and maximization of the voltage stability margin. Generator voltages, capacitor banks and tap positions of tap changing transformers are used as optimization variables of this problem. A memetic meta-heuristic named as shuffled frog-leaping algorithm is intended to solve multi-objective optimal reactive power dispatch problems considering voltage stability margin and voltage deviation. The Shuffled Frog-Leaping Algorithm (SFLA) is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The most important benefit of this algorithm is higher speed of convergence to a better solution. The intended method is applied to ORPD problem on IEEE 57 bus power systems and compared with two versions of differential evolutionary algorithm. The simulation results show the effectiveness of the intended method.

A decentralized approach for optimal reactive power dispatch using a Lagrangian decomposition method

Solving the Optimal Reactive Power Dispatch (ORPD) problem in power system is a vital task to capture the most secure and stable operation of reactive power resources. In this paper, the ORPD problem is solved using a new modified sine cosine algorithm (MSCA). Sine cosine algorithm (SCA) is a well-known population-based optimization technique. Despite the SCA is an effective algorithm, it may prone to stagnation and stuck in local minima for some cases. The modified sine cosine algorithm (MSCA) is based on Levy flight distribution with adaptive operators. This algorithm is developed to avoid the shortage of the conventional SCA and enhance its searching abilities. The considered objective functions are minimizing the power losses, improving the voltage profile and enhancing the stability of system. The proposed algorithm is applied to IEEE 30-bus system and the yielded results are compared with other well-known optimization algorithms. The simulation results demonstrate the superiority and efficiency of the MSCA for solving the ORPD compared to the other listed techniques in literature.

The shift from regulated to restructured power system results in an increased competition among the electricity market. In restructured power system, the separation of transmission services from generation and distribution makes it necessary to find the contribution of power from individual generator to individual load. The power flow tracing method is used to obtain the generator power output to a particular load. The reactive power has to be maintained in order to sustain the voltage level throughout the system for reliable and secure operation. Hence the reactive power cost allocation has become imperative in the power system. In this paper, the tracing method is integrated with the optimal reactive power dispatch problem to trace the generator minimal reactive power for sustaining the real power transaction and enhancing the system security by meeting the demand. The Differential Evolution is used for optimal reactive power dispatch. The cost allocation to the generators for the reactive power service based on the opportunity cost method is obtained for 62 Bus Indian Utility Systems.

An optimal reactive power dispatch is a real power transmission losses minimization problem, while maintaining real and reactive power balances at all system buses and satisfying different system inequality constraints. It is a highly nonlinear complex optimization problem which requires a robust and effective solution technique for achieving global optimal solution efficiently. This paper proposes a principle component analysis based mutation operator for improving the performance of real coded genetic Algorithm to deal such type of problems. The proposed algorithm is tested for IEEE-57 bus system for optimal reactive power dispatch problem. The simulation results obtained are compared with various optimization methods available in literature.

Secure and economical operation of distribution networks needs the management of reactive power resources. Optimal Reactive Power Dispatch (ORPD) optimally manages the reactive power scheduling of generators and distribution generations as well as the Reactive Power Compensation (RPC) devices. This paper investigates the effect of load models on the multi-objective ORPD problem in active distribution networks. Moreover, a modified Grey Wolf Optimizer (GWO), which is called in this paper as Civilized GWO (CGWO), is introduced to solve the ORPD problem. The proposed strategy including multi-objective function, various load models, DG’s reactive power, RPCs and the introduced CGWO, is tested on standard IEEE 33- and 69-bus distribution systems. The obtained results indicate the load models have significant impact on the cost function amount. Moreover, the performance of the proposed algorithm is evaluated using ten standard benchmark functions. The optimization results demonstrate the robustness of the introduced optimization algorithm and its ability in finding the better solutions compared to the Particle Swarm Optimization (PSO), Exchange Market Algorithm (EMA), and original GWO.

This paper introduces a self adaptive differential evolution (SADE) and self-tuning Hybrid differential Evolution (HDE) methods for dealing with optimal reactive power dispatch aiming at power loss reduction. The optimum reactive power dispatch of power systems is to allocate reactive power control variables so that the objective function composed of power losses is minimized and the prescribed voltage limits are satisfied. The proposed method determines the optimum settings of reactive power control variables such as, generator excitation, tap changing transformers, and shunt compensation that reduces the power loss, while maintaining the voltage stability. IEEE 14-bus test system from the literature is used to exemplify the performance of the proposed methods. Numerical results show that the proposed methods are better methods than those reported in the literature.

This paper presents an algorithm to solve the multi-objective Optimal Reactive Power Dispatch (ORPD) problem in an unbalanced bus system. Unlike the traditional ORPD problem, loadability maximization, voltage profile enhancement, loss minimization, and ATC enhancement are achieved under unbalanced conditions. Here, ORPD is effectuated through determining the optimal location and size of the UPFC using a hybrid version of GA and FF. Experimental results on the benchmark IEEE test bus systems demonstrate the ORPD performance of the proposed algorithm over the traditional algorithms, under unbalanced conditions. Moreover, the proposed algorithm is proved for its performance in maintaining a better trade-off among multiple objectives, even under overloading conditions. The analysis also includes an investigation on the sensitivity of the algorithm against the overloading conditions of the bus system. Finally, the experimental results confirm that the proposed algorithm is superior to the conventional algorithms like GA, PSO, a hybrid version of GA-PSO, and the traditional FF algorithm.Abbreviations: ATC: Available Transfer Capability; CMAES: Covariance Matrix-Adapted Evolutionary Strategy; CPF: Continuation Power Flow; DE: Differential Evolution; FACTS: Flexible AC Transmission System; FF: Firefly Algorithm; GA: Genetic Algorithm; GSA: Gravitational Search Algorithm; KHA: Krill Herd Algorithm; LSI: Loss Sensitivity Index; NSGA-II: Non-Dominated Sorting Genetic Algorithm-II; OKHA: Oppositional Krill Herd Algorithms; ORPD: Optimal Reactive Power Dispatch; PSO: Particle Swarm Optimization; UPFC: Unified Power Flow Controller; VDI: Voltage Deviation Index

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. This paper introduces, an evolutionary algorithm based on the hybrid genetic algorithm (GA) and particle swarm optimization (PSO), denoted by HGAPSO is used to solve reactive power dispatch problem.