부산대학교 도서관

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.