Environmental-economic dispatch using stochastic fractal search algorithm

Abstract

Summary Stochastic fractal search (SFS) is one of the latest metaheuristic global optimization algorithms, which has been introduced in 2015. It is a very promising algorithm and outperforms many of existing well‐known metaheuristic algorithms. This paper uses SFS algorithm to solve the highly nonlinear environmental‐economic dispatch problem in power systems operations considering generator physical constraints, gas emission level, and transmission line losses and limits. To verify the effectiveness of using this algorithm, it has been applied in this paper to different commonly used test systems. The results are compared with previous results obtained by 8 different methods such as genetic algorithm–based methods, particle swarm optimization–based methods, differential evolution–based methods, and gravitational search algorithm. Results will reveal that the optimal SFS‐based optimal solution can reduce the total system production cost and will also show that the optimal solutions obtained using the SFS algorithm perform better than many of the commonly used global optimization techniques, where the proposed algorithm has achieved a nearer global optimal solution compared to the other approaches with a short time.

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

“…This biobjective CEED optimization problem is converted to a single objective problem using penalty factors and then solved using various potent metaheuristic algorithms and their variants such as improved artificial bee colony algorithm (ABC) in [8], global particle swarm optimization (GPSO) in [9], the chaotic improved harmony search algorithm in [10], flower pollination algorithm in [11], biogeography based optimization in [12], the gravitational search algorithm in (GSA) [13], the stochastic fractal search algorithm in [14], the symbiotic organism search algorithm for multi area power system in [15], fluid mechanism inspired algorithm in [16], and the lightning flash algorithm in [17].…”

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

“…Consequently, the real performance of SSFS could not be demonstrated persuasively. In spite of the limitation, SSFS has been also applied for different optimization problems in electrical engineering fields such as status estimation of power system [51], DC motor control [52], environment and economic dispatch [53], PID voltage regulator [54], wind integrated multiobjective power dispatch [55], and position determination of Distributed Generators in distribution power network [56]. These studies have tried to prove the outstanding performance of SSFS over other ones via comparison of output parameters such as the fuel cost, emission, speed response, and obtained voltage; however, they have forgotten that convergence speed reflected via comparison of N spi and Max Iter is also a very important comparison criterion in addition to such objectives.…”

Dynamic Modelling of a Flexible Beam Structure Using Feedforward Neural Networks for Active Vibration Control

“…Although the SFS algorithm is relatively a new metaheuristic approach, there are several applications of SFS and its variant developments so far in the literature. The SFS algorithm has been applied to solve optimisation problems in control engineering area [30,31], parameter identification [32], training artificial neural networks (ANNs) [33,34], trajectory planning [35], optimal relay coordination problem [36], system reliability optimisation problem [37], wind integrated multi-objective optimal power dispatch problem [38][39][40], economic production quantity [41], environmental-economic dispatch problem [42], surface grinding process [43], protein structure prediction [44] and many more. The results show promising capabilities of SFS algorithm to outperform other established metaheuristic approaches.…”