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In the present work, reactive power planning problem along with voltage stability margin is addressed by effective co-ordination of reactive power sources. Modal analysis and L-index methods are used to detect weak nodes of the system accordingly. Differential Evolution (DE) and Genetic Algorithm (GA)-based optimization techniques are applied for the proper co-ordination of Var sources under base and increased loading conditions maintaining voltage stability of the connected power network. The problem is multi-objective and IEEE 30 bus system is taken as the standard system. It is observed that modal analysis based detection of weak nodes are more effective than the L-index-based detection. Moreover, the DE-based optimization algorithm gives better result compared to GA-based approach in maximizing reactive power reserves. [ABSTRACT FROM AUTHOR]

This study presents gravitational search algorithm (GSA)‐based optimisation technique for the optimum co‐ordination of multi‐type flexible AC transmission system (FACTS) devices with the existing reactive power sources present in a connected power network. Three types of FACTS devices: namely, unified power flow controller, static var compensator and thyristor controlled series capacitor are used in the present problem and IEEE‐30 and IEEE‐57 bus systems are taken as standard test systems. The existing reactive power sources are reactive generations of the generators and transformer tap setting arrangements. The main objective of the present study is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in transmission network. The result obtained with the proposed GSA‐based approach is compared with the results obtained by genetic algorithm, differential evolution and particle swarm optimisation techniques. It is observed that by optimal placement of FACTS devices along with the optimal setting of the transformer tap arrangements and control of reactive generations of the generators line loss, system operating cost and transmission congestion is reduced significantly by the GSA‐based method compared with other methods. [ABSTRACT FROM AUTHOR]

In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic Algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations. [ABSTRACT FROM AUTHOR]

Highlights: [•] Fuzzy based evolutionary algorithm. [•] FACTS devices for reactive power optimization. [•] FACTS devices for minimum operating cost of a power system. [Copyright &y& Elsevier]

This paper presents the application of Genetic Algorithm (GA) and Differential Evolution (DE) technique for the minimization of transmission loss and simultaneous reduction in the operating cost of the system using Flexible AC Transmission System (FACTS) devices. Three types of FACTS devices, Thyristor Controlled Series Compensator (TCSC), Static VAR Compensator (SVC) and Unified Power Flow Controller (UPFC) are considered for this purpose. Optimal placement of FACTS devices in the heavily loaded power system reduces transmission loss, control reactive power flow, improves voltage profile of all nodes and also reduces operating cost. The system is reactively loaded starting from base to 200% of base reactive load and the system performance is observed without and with FACTS devices. The proposed technique is applied on IEEE 30-bus system for the optimal setting of FACTS devices. Finally, how system performance is improved with the use of UPFC along with other series and shunt FACTS controller is illustrated. [ABSTRACT FROM AUTHOR]

Abstract—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods. [ABSTRACT FROM PUBLISHER]

Summary: Microgrids have lately proved to be the most efficient type of power system delivering uninterrupted power to a local area incurring negligible amount of transmission loss. Inclusion of the available renewable energy sources among the various distributed generation (DG) sources in a microgrid have decreased the emission of harmful toxic gases to a great extent. Dynamic economic dispatch indicates the economic dispatch of a microgrid allocating the timely optimal output of the distributed generation sources. However, the setup of a microgrid involves a huge amount of capital out of which installation cost, operation and maintenance cost, and depreciation cost instigated through the passing of time are a few. This paper uses three soft computing techniques viz. particle swarm optimization, differential evolution, and differential evolution with local global neighborhoods to perform a novel dynamic cost analysis of a microgrid and minimize its overall cost which includes fuel, emission, operation and maintenance cost, installation, and depreciation costs. Four different case studies are pulled off on a microgrid, and a comparative analysis is done to identify the best case. Numerical results and figures also point out the best optimization technique which was able to give the most efficient and minimal cost for the microgrid. [ABSTRACT FROM AUTHOR]