Quadratic approximation-based hybrid Artificial Cooperative Search algorithm for economic emission load dispatch problems

Abstract

Summary In this article, Artificial Cooperative Search (ACS) algorithm is incorporated with the quadratic approximation (QA) operator to solve the multi‐objective economic emission load dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence–based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, which is effective at global search; however, it does not perform so well at exploring promising regions. The QA operator, on the other hand, is a non‐derivative–based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D‐dimensional space. Solving the EELD problems with the hybridized ACS‐QA algorithm, as being proposed in the present article, leads to more accurate results with fewer function evaluations. Also, multi‐objectivity of the problem is handled by transforming it into a single‐objective problem by using the weighted sum method. The efficiency of the proposed ACS‐QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterward, the proposed ACS‐QA algorithm and the ACS algorithm are implemented on multi‐objective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS‐QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls.

Combined economic and emission dispatch considering conventional and wind power generating units

“…The emission allowance is an important constraint to satisfy the carbon cap in the electricity system. The emission allowances of the conventional generators are given by 0≤E≤EE limit (14) The generator ramp rates can have a noticeable impact on the power output and levels of emissions from a generator when the rat of change in the demand is sufficiently high in a dynamic system. In this research, ramp rate for the conventional generation units is not considered as this is a steady-state CEED model.…”

“…With the growing environmental problems, combined economic and emission dispatch (CEED) models have been developed for an electrical system consisting of fossil‐fired power plants in the 1990s . Initially, the CEED considered only conventional powered generators . Although the optimization algorithms are different, most of these studies used multi‐objective optimization to accomplish the balance between cost and emission minimizations.…”

“…[11][12][13] Initially, the CEED considered only conventional powered generators. [11][12][13][14][15][16] Although the optimization algorithms are different, most of these studies used multi-objective optimization to accomplish the balance between cost and emission minimizations. With the everincreasing use of renewable power, the power system network now is not only allocating system power from conventional generators but also from renewable power plants, such as wind farms, 17,18 solar PV plants, 19,20 and hydro power stations.…”

Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

“…A modified harmony search method (MHSA) 19 was proposed with a new improved method using wavelet mutation and new memory consideration scheme based on roulette wheel mechanism in order to increase the search capability. Mine blast algorithm (MBA) was discussed in Ali and Elazim 20 to solve MoPs, and other recent search techniques that were proposed to solve ELD, ED, and MoPs are floating search space, 21 enhanced moth-flame optimizer, 22 immune algorithm, 23 multiobjective biogeography-based optimization, 24 artificial bee colony algorithm, 25 ant colony optimization, 26 Franklin and Coulomb law-based algorithm, 27 population variant differential evolution, 28 stochastic fractal search algorithm, 29 quantum-inspired particle swarm optimization (QPSO), 30 quadratic approximation-based hybrid artificial cooperative search algorithm, 31 opposition-based harmony search algorithm (OHS), 32 spiral optimization algorithm, 33 chaotic firefly algorithm, 34 mixed integer optimization problem, 35 stochastic weight trade-off chaotic Non-dominated Sorting Particle Swarm Optimization (NSPSO), 36 and interior search algorithm (ISA). 37 Generally, there is no satisfactory performance registered from meta-heuristic algorithm in the multimodel fitness landscapes.…”

A new approach to economic-emission load dispatch using NSGA II. Case study

“…Thus, the proposed EELD optimization uses the nondominated sorting genetic algorithm II (NSGA II) to the minimization of the total fuel consumption and emission index. Some papers of the literature discuss the optimization process including consideration of consumption and emissions . To get a better idea of the environmental impact of each type of engine, the “specific emission index” (SEI) was also developed, taking into account not only the generators' efficiency but also the reduction of emissions in the atmosphere when planning the generation for power plants.…”