1887

n South African Journal of Industrial Engineering - An intelligent RTP-based household electricity scheduling with a genetic algorithm in a smart grid

Volume 29 Number 2
  • ISSN : 1012-277X
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Abstract

Electricity scheduling for households based on real-time pricing (RTP) allows flexible and efficient consumption planning. However, this creates errors in predicted costs. Therefore this study used a genetic algorithm (GA) to reduce the error in predicted costs and suggested a model that offered better consumption planning. This model comprises a provider that supplies electricity and a subscriber that consumes electricity. Each subscriber has an energy management controller (EMC) that selects the optimal electricity scheduling. The provider and subscriber exchange real-time predicted costs and consumption plans to achieve an appropriate balance. During this process, the aforementioned prediction error — i.e., the difference between the predicted cost for each time slot and the final actual cost — occurs. This was addressed in this study using a GA. As a result, the presented model produced consumption plans with costs that were 22.60 per cent lower than the non-scheduled case, and 3.34 per cent lower than the model from a previous study. Furthermore, the fairness for each subscriber was improved by 15.96 per cent compared with the non-scheduled case, and by 0.62 per cent compared with the previous study model.

Die skedulering van elektrisiteit vir huishoudings op grond van reële-tyd pryse lei tot buigsame en doeltreffende beplanning. Dit lei egter tot foute in vooruitgeskatte kostes. Hierdie studie gebruik ʼn genetiese algoritme om die fout in die vooruitgeskatte kostes te verminder en stel ʼn model voor met verbeterde verbruiksbeplanning. Hierdie model bestaan uit ʼn elektrisiteitsverskaffer en ʼn eindverbruiker. Elke eindverbruiker het ʼn energiebestuurbeheerder wat die optimale elektrisiteitskedule kies. Die verskaffer en verbruiker ruil reële-tyd vooruitgeskatte kostes en verbruiksplanne om ʼn gepaste balans te vind. Gedurende hierdie proses is daar egter ʼn voorspellingsfout, dit is die verskil tussen die vooruitgeskatte koste vir elke tydgleuf en die werklike finale koste. Dit is deur die genetiese algoritme aangespreek. Die resultaat toon dat die kostes 22.6% laer is as die ongeskeduleerde geval en 3.34% laer as ʼn vorige studie. Verder is die regverdigheid vir elke verbruiker met 15.96% verhoog in vergelyking met die ongeskeduleerde geval en 0.62% in vergelyking met ʼn vorige studie.

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/content/journal/10520/EJC-10bf0919fc
2018-08-31
2019-05-26

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