Power Generation from Coal-A review

Sarker, Mridul; Chowdhury, Debasish; Hossain, Iqbal

doi:10.3329/jce.v27i2.17802

Cited by 2 publications

(2 citation statements)

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“…The original expressions in (1), (2), and (3) were based on monthly time periods, but the resolution has been adjusted to daily time periods in this study. This is to allow for potential daily spikes in demand that are otherwise averaged out when a monthly resolution is used.…”

Section: The Proposed Coal Stockpile Simulatormentioning

confidence: 99%

“…Currently, more than 40 per cent of the world's electricity is generated at coal-fired power stations [1]. This figure does not seem that high, but many countries -such as South Africa (93%), Poland (92%), China (79%), India (69%), and the USA (49%) -rely primarily on coal for electricity generation [2]. The unpredictable demand for electricity, along with the uncertainties related to inventory replenishment and coal quality, places great emphasis on accurate and effective inventory planning at coal-fired power stations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multi-Objective Coal Inventory Management Model Using Monte Carlo Computer Simulation

Brits

Bekker

2016

SAJIE

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The ability of a coal-fired power station to meet its generation targets is influenced by periods of coal shortage. In this article, we propose a multi-objective inventory model to assist with the management of coal stockpiles. The model is applicable to power utilities with a network of two or more coal-fired power stations. The aim is to determine the near-optimal amount of coal inventory to stockpile at each station in the network. A Monte Carlo coal stockpile simulator is used to incorporate stochastic uncertainty into the stockpile levels, while a metaheuristic uses the simulator as an estimator of two objective functions. The metaheuristic finds good values for the coal stockpile level at each power station in the network. The algorithm for multi-objective optimisation using the cross-entropy method is proposed as a suitable metaheuristic. A hypothetical case study is used to validate the inventory model and to showcase the optimisation results. OPSOMMINGDie vermoë van 'n steenkool-aangedrewe kragsentrale om sy opwekkingteikens te bereik word beïnvloed deur periodes van 'n steenkooltekort. In hierdie artikel stel ons 'n meerdoelige voorraadmodel voor om te help met die bestuur van steenkoolvoorraad.Die model is van toepassing op elektrisiteitsverskaffers met 'n netwerk van twee of meer steenkoolaangedrewe kragsentrales. Die doel is om die nabyoptimale steenkoolvoorraadvlak te bepaal wat by elke kragsentrale in die netwerk gestoor moet word. 'n Monte Carlo steenkool voorraad-simulator word gebruik om stogastiese onsekerheid in die steenkoolverbruik te inkorporeer, terwyl 'n metaheuristiek die simulator gebruik as 'n beramer vir twee doelfunksies. Die metaheuristiek vind goeie waardes vir steenkoolvlakke by elke kragsentrale in die leweringsnetwerk. Die algoritme vir meerdoelige optimering met die kruis-entropie metode word voorgestel as 'n geskikte metaheuristiek. 'n Hipotetiese gevallestudie word gebruik om die voorraadmodel te valideer en optimeringsresultate te toon.

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Section: The Proposed Coal Stockpile Simulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%