Multi-objective thermal power load dispatch using chaotic differential evolutionary algorithm and Powell’s method

Singh, Nirbhow Jap; Dhillon, J.S.; Kothari, D.P.

doi:10.1007/s00500-016-2473-7

Cited by 10 publications

(6 citation statements)

References 46 publications

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“…Moreover, that is why chaotic maps have been combined with various optimization techniques to generate chaotic sequences instead of random number generators. These chaotic-based optimization techniques include but are not limited to chaotic local search-based DE [55], chaos TLBO-based algorithm [56], fireworks algorithm with chaos systems [57], chaotic evolutionary programming [58], improved tunicate swarm algorithm based on logistic map [59], chaotic search technique-based slap swarm algorithm [60], chaotic DE integrating Powell's method [61], and chaotic FPA [62]. In [63], another population-based method combining the advantages of Harris hawks optimization (HHO), chaos theory, and DE/pbad-to-pbest/1 strategy has been suggested for minimizing the operating cost of thermal units and maximizing the contribution of wind power in an interconnected power network.…”

Section: Multi Objectivementioning

confidence: 99%

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

2023

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The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

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Section: Multi Objectivementioning

confidence: 99%

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

2023

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“…On the contrary, an intelligent optimization algorithm has certain advantages in multiobjective, nonlinear, and high-dimensional optimization problems (Farag et al, 1995;Kennedy, 2003) and is widely used in environmental/economic scheduling problems (Sinha et al, 2003). Singh et al (2018) proposed a chaotic differential evolutionary and Powell's pattern search (CDEPS) algorithm to solve the multi-objective thermal power load dispatch (MTPLD) problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The high-dimensional multi-objective optimization algorithm based on the Pareto dominance relation can reduce the Pareto frontier area by combining preference information in the search process (Li et al, 2018c;Qi et al, 2018). The selection pressure of the algorithm can be enhanced through the relaxed Pareto dominance relation so that the advantages and disadvantages of some non-dominant individuals can be compared and the search ability of the algorithm can be enhanced, such as γ-domination (Singh et al, 2018), ε-domination (Hernandez-Diaz et al, 2007, α-domination (Yuan et al, 2016), fuzzy domination (He et al, 2014), and lattice domination (Yang et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal scheduling of power systems considering carbon markets: Based on blockchain theory and multi-objective particle swarm optimization algorithm

Wang

Lan²,

Shen³

et al. 2022

Front. Energy Res.

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In the context of double carbon, it is an inevitable requirement for the low-carbon power industry to take economic efficiency and low carbon into consideration. This article introduces the carbon emission constraint into the economic dispatching of the power system. Then, combined with the blockchain theories, the methods of particle swarm optimization and multi-objective particle swarm optimization (MOPSO) are employed to simulate the economic and environmental scheduling of a power generation system based on six thermal power units. Research shows that the constraint processing approach is practical and effective, and it can firmly adhere to equality requirements, which is superior to other algorithms’ constraint processing methods; the algorithm is stable, and the global optimal solution can be determined under different initial solutions. In the process of multi-objective optimization, the solutions of POF obtained by using the slope method are evenly distributed.

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“…To obtain the controller parameters these methods required the minimization of certain performance criteria such as integral square error (ISE) or integral of time multiplied by absolute error (ITAE). As per literature, the maintenance of balanced exploration and exploitation in a swarm-based meta heuristic algorithms is a well challenging issue [15,16]. Recently, the predator based concepts have been introduced as a metaheuristics to improve the performance of population based search algorithms [17].…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony with predator effect algorithm for PID controller tuning

Singh

Chopra

Pandey

et al. 2022

Preprint

Self Cite

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This paper proposed a novel artificial bee colony with predation effect (ABCPE) algorithm for tuning a proportional integral derivative (PID) controller. The mathematical model of ABCPE algorithm to introduce predator effect in the foraging behavior of artificial bees colony algorithm has been formulated. The proposed algorithm has been tested on tuning problems of different process models. The simulation results reveal that the closedloop responses are relatively fast and non-oscillatory as compared to the Ziegler-Nichols criteria. Further, the results obtained using ABCPE are also compared with other evolutionary algorithms. The exhaustive analysis shows that the ABCPE based solution approach leads to a set of tuning parameters having smaller overshoot, less setting time and rise time compared to other solution approaches. The stability analysis using Nichols plot reveals that the phase margin of proposed algorithm is higher as compared to other tuning methods.

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Multi-objective thermal power load dispatch using chaotic differential evolutionary algorithm and Powell’s method

Cited by 10 publications

References 46 publications

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

Optimal scheduling of power systems considering carbon markets: Based on blockchain theory and multi-objective particle swarm optimization algorithm

Artificial bee colony with predator effect algorithm for PID controller tuning

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