Summary This paper presents a chemical reaction optimisation algorithm based on the chemical molecular reaction to optimise total cost in wind–fossil‐fuel‐based power systems, while all operating constraints are satisfied. With the continuing search for alternatives to conventional energy sources and global warming, it is necessary to include wind energy conversion system generators in the economic load dispatch problem. The random nature of wind power is modelled using Weibull probability distribution function. Because of uncertainty in wind power, wind cost is generalised by including overestimation and underestimation cost of available wind power. The proposed approach is examined and tested on the standard Institute of Electrical and Electronics Engineers 30‐bus test system having three conventional thermal generators, two gas generators, one oil generator and two wind turbines and a 40‐unit test system having three wind turbines and 37 thermal generating units. The simulation results show that the proposed algorithm gives comparatively better operational cost compared with other optimisation techniques. Copyright © 2014 John Wiley & Sons, Ltd.

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A proficient algorithm, based on the moth‐flame optimization (MFO), is founded for solving economic and emission dispatch for hydro‐thermal‐wind (HTW) scheduling problem. The renewable wind power associated with hydropower‐integrated thermal power plant is a non‐linear, non‐convex optimization problem due to water discharge rate, hydraulic continuity constraint, reservoir storage limits, variable wind speed, scheduling time linkage, water transport delay, power balance constraints, as well as operation limits of renewable wind units. A renewable wind power‐oriented multi‐objective hydro‐thermal scheduling has significant value to trim down the power generation cost and emission. The usefulness of the projected method is verified on two case studies. In the most recent literature, the statistical outcomes of the applied MFO algorithm are compared with others evolutionary optimization. It is also observed that the proposed MFO method is skillful for developing hopeful outcomes and for significantly reducing the computation time.

Thisarticlepresentsanintegratedapproachtowardstheeconomicaloperationofahybridsystem which consists of conventional thermal generators and renewable energy sources like windmills usingagrasshopperoptimizationalgorithm(GOA).Thisisbasedonthesocialinteractionnatureof thegrasshopper,consideringacarbontaxontheemissionsfromthethermalunitanduncertaintyin windpoweravailability.TheWeibulldistributionisusedfornonlinearityofwindpoweravailability. Astandardsystem,containingsixthermalunitsandtwowindfarms,isusedfortestingthedispatch modelofthreedifferentloads.TheGOAresultsarecomparedwiththoseobtainedusingarecently developedquantum-inspiredparticleswarmoptimization(QPSO)optimizationtechniqueavailable intheliterature.ThesimulationresultsdemonstratetheefficacyandabilityofGOAovertheQPSO algorithmintermsofconvergencerateandminimumfitnessvalue.Performanceanalysisunderwind powerintegrationandemissionminimizationfurtherconfirmsthesupremacyoftheGOAalgorithm.

Fossil fuel power has limited its penetration into the power system network for the intermittency and unpredictability coordination. That's why, renewable wind energy incorporating load dispatch becomes a promising system. In this regard, this article proposes an economic load dispatch (ELD) in the existence of renewable wind technology for consuming less fossil fuel energy. For the stochastic scenery of wind speed, the Weibull probability density function (PDF) is used. To boost up the convergence swiftness and advance the simulation results, opposition-based learning (OBL) is integrated with the basic moth flame optimization (MFO) technique, which depends on the social dealings of the moth in nature. The performance of OMFO is evaluated through four cases and each case consists of three different load demands. The simulation results by these methods along with various other existing algorithms in the literature are presented to demonstrate the validity and usefulness of the proposed OMFO.