Heuristic methods for solution of FACTS optimization problem in power systems

This paper presents a comprehensive review on various heuristic methods for solution of flexible AC transmission systems (FACTS) optimization problem in power systems. First, it classifies FACTS optimization methods into four main groups, then subdivides heuristic methods into different subsets and discusses thoroughly about characteristics, advantages and disadvantages of each heuristic subset. Finally, some hints for future researches on this area will be offered

Optimal placement of multi-type FACTS devices in power systems using evolution strategies

In this paper, Evolution Strategies (ES) is used to find optimal placement of FACTS devices in power systems. The goal of optimization is to maximize the system loadability. Optimization is based on finding locations and settings of FACTS devices. Simulations are implemented on IEEE 30-bus test system. From different types of FACTS devices, SVC, TCSC and UPFC are used in this research. The results show that using FACTS devices, the loadability of power system increases significantly. It also shows that there exists a maximum number of devices beyond which, the loadability of power system can not be increased. The implementation results of the method are promising and encouraging, so it is a good method for implementation on the FACTS optimization problem

Parameter selection in particle swarm optimisation: a survey

Nowadays, particle swarm optimisation (PSO) is one of the most commonly used optimisation techniques. However, PSO parameters significantly affect its computational behaviour. That is, while it exposes desirable computational behaviour with some settings, it does not behave so by some other settings, so the way for setting them is of high importance. This paper explains and discusses thoroughly about various existent strategies for setting PSO parameters, provides some hints for its parameter setting and presents some proposals for future research on this area. There exists no other paper in literature that discusses the setting process for all PSO parameters. Using the guidelines of this paper can be strongly useful for researchers in optimisation-related fields

A comprehensive review on methods for solving FACTS optimization problem in power systems.

This paper presents a comprehensive review of various methods for solution of flexible AC transmission systems (FACTS) optimization problem in power systems. First, it explains the requirements of an ideal solution for FACTS optimization problem, then classifies the methods used by researchers in four main groups as classical methods, technical methods, heuristics and mixed methods, and discusses thoroughly about characteristics, advantages and disadvantages of each group of methods. Finally, according to the pros and cons of these methods, heuristic methods are determined as the most effective group of optimizers and also some hints are offered for future researches on this area

Exploring potential storage-based flexibility gains of electric vehicles in smart distribution grids

Flexibility is one of the most important solutions for facilitating the variability of renewable energy sources (RESs) in a distribution network. It is predicted that electric vehicles (EVs) can play an effective role in improving it in the distribution networks. So, this paper presents multiobjective scheduling of batteries of EVs in parking lots (EVPLs) to improve the storage-based flexibility of smart distribution networks (SDNs). The proposed formulation minimizes the energy cost and the voltage deviation function and maximizes the system flexibility (SF) as multiobjective functions that will be optimized subject to the AC load flow, RES and EV constraints, and the allowable limits of the flexibility and operation indices. The resulting model is in the form of a nonlinear programming (NLP) model. Therefore, an equivalent linear programming (LP) formulation is obtained for the original problem to achieve the global optimum result. The stochastic programming approach is used to model uncertainties of the load, active power generation of RESs, price of energy, and EV parameters. The flexible power management is formulated as one of the objective functions of the proposed multiobjective framework, which is solved by using the ε-constraint method, reaching the best possible compromise solution by a fuzzy decision-maker. The proposed framework is tested by using a 33-bus radial test distribution network in the GAMS software environment to evaluate the EVs capability in improving the flexibility indices. Based on the numerical results, it is observed that the proposed scheme with optimal energy management of EVs is able to obtain a high flexibility for SDN. It can also reduce energy losses in terms of network operation and provide a rather smooth voltage profile

An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Particle swarm optimisation applications in FACTS optimisation problem

FACTS optimisation is one of the most important and difficult problems in power systems. For solving this problem, so many different approaches have been proposed in the literature. Among them, particle swarm optimisation (PSO) has exposed so promising behavior. In this paper, applications of PSO in FACTS optimisation problem are explained and analysed from the viewpoint of the objectives, used basic PSO variant, PSO parameter selection, multi-objective handling, constraint handling and discrete variable handling. Eventually, some hints for future research is provided

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions