In this paper, a novel two-archive Multi-Objective Grey Wolf Optimizer (2ArchMGWO) is proposed for solving Multi-Objective Optimal Reactive Power Dispatch (MORPD) problems. The optimizer has been improved from its original Multi-Objective Grey Wolf Optimizer (MGWO) by modifying the reproduction operator and adding the 2-archive concept to the algorithm. It is then implemented on solving MORPD with objective functions being active power loss minimization and voltage profile improvement (voltage deviation minimization). The generator bus voltages, tap setting transformers and shunt reactive power sources or flexible alternating current transmission systems are set as design variables. The proposed algorithm along with other existing multiobjective optimizers are applied to solve three test problems with the standard IEEE 30-bus, IEEE 57-bus, and the IEEE 118-bus power systems. The optimum results obtained from the various optimizers performance are compared based on the hypervolume indicator and they reveal that 2ArchMGWO is clearly superior to the others.

Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer

This work proposes an Integer-coded, multiobjective Genetic Algorithm (IGA) applied to the full Reactive-power Compensation Planning (RCP) problem considering both intact and contingent operating states. The IGA is used to simultaneously solve both the siting problem-optimization of the installation of new devices-and the operational problem-optimization of preventive transformer taps and the controller characteristics of dynamic compensation devices. The aim is to produce an optimal siting plan that does not violate any system or operational constraint and is optimal in terms of the voltage deviation from the ideal and the cost incurred through the installation and use of reactive power compensation devices. This multiobjective problem is solved through the use of Pareto optimality. The developed algorithm is tested on the IEEE 30-bus system and on a reduced practical system that was developed with the cooperation of the National Grid. The algorithm is validated via the comparison with the SCORPION software package, which is a Linear Programming-based (LP) planning tool developed and used by the National Grid for the England and Wales transmission system. This work demonstrates that the IGA is superior to the LP-based method, both in terms of system conditions and installation and utilization cost when fixed and dynamic compensation devices are being sited; the system performance is optimized via the adjustment of tap settings and controller characteristic across multiple operating states.

Genetic algorithms for optimal reactive power compensation on the national grid system

This paper proposes a modified integer genetic algorithm (IGA) applied to the full reactive-power compensation planning (RCP) problem. The algorithm solves both the siting and the operational problem over intact/base case and typical contingent cases. The algorithm is tested on a practical sized system that was developed with the cooperation of National Grid. It is compared with the results generated by the package SCORPION: a RCP program currently being used by National Grid. This paper demonstrates that GAs can be successfully applied to practical sized systems when multiple operating states are considered; the system performance is optimised via adjustment of tap settings and controller characteristics; and both fixed and dynamic compensation devices are being sited.

Genetic algorithms for optimal reactive power compensation on the National Grid system

This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature.

https://www.mdpi.com/1996-1073/11/8/2134/pdf

Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm

This paper presents a new approach to solve the optimal reactive power dispatch (ORPD) problem based on hybridizing Particle Swarm Optimization (PSO) and Tabu-Search (TS) meta-heuristics (PSO-TS). The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss is to be minimized. The proposed approach is used to find the settings of the control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices, to optimize power transmission loss. The study was implemented on IEEE 30-bus systems, and the results were compared with non-hybridized PSO and TS and other evolutionary algorithms reported in the literature.

Hybrid PSO-tabu search for the optimal reactive power dispatch problem

Scheduling of reactive power in an optimum manner reduces circulating VAr promoting flatter voltage profile which lead to appreciable MW saving on account of reduced power system losses. Hence, the optimal reactive power dispatch assumes extremely important functioning both in the planning stage as well as in day to day operation of the power system. This paper presents an alternative approach to optimal reactive power dispatch based on genetic algorithm techniques (GAs). The technique is used to search for transformer tap settings and the value of shunt capacitors of a given power system such that the total system power loss is minimised. The proposed method is tested on IEEE 14 and 30 bus test systems and their results are compared with those obtained using load flow calculation.

Genetic algorithm for optimal reactive power dispatch

The aim of this paper is to demonstrate the superiority of using multiple UPFCs to regulate the desired power flows through any two areas in a power network and to provide the best voltage profile in the system as well as to minimise the system transmission losses by applying the proposed ANN based direct control algorithm (ABDCA). A complete UPFC mathematical model using the power injection concept is derived and a simple way of the adaptation of the derived model in a general Newton Raphson power flow program is also presented. The basic operating principles and the control characteristics of a UPFC in steady state power flow control are fully investigated. In addition, the proposed power flow control scheme using multiple UPPCs and the ABDCA are tested in a real 6-bus system and an IEEE 14-bus test system.

A novel power flow control concept using ANN based multiple UPFCs scheme

This paper describes for the first time the chaotic motion and diffusion for an example multi-machine power system using Hamiltonian formation. The existence of chaotic motion under some conditions is confirmed by the calculation of maximum Lyapunov exponents. It is revealed numerically that the random-like motion of Arnold diffusion can carry the system state arbitrarily close to any region of the phase space consistent with energy conservation while the ordinary chaos is only inhabited in a specific region of the whole phase space. A brief analysis of the reason for diffusion is also included.

K.L. Lo

Papers

Genetic algorithm for optimal reactive power dispatch

A novel power flow control concept using ANN based multiple UPFCs scheme

Chaotic motion and diffusion in a power system