AN INTELLIGENT RTP-BASED HOUSEHOLD ELECTRICITY SCHEDULING BY A GENETIC ALGORITHM IN SMART GRID

Byeong-Yeon Kim, Hyesung Seok

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.


Keywords


Demand management; Distributed decision making; Fairness; Stackelberg game; Power grid.

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DOI: https://doi.org/10.7166/29-2-1813

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Copyright (c) 2018 Byeong-Yeon Kim, Hyesung Seok


ISSN 2224-7890 (on-line) ; ISSN 1012-277X (print)


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