Abstract
In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and day-ahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management.
Highlights
The energy demand has dramatically increased with continuous population and economic growth
We propose grey wolf modified enhanced differential evolution (GmEDE) algorithm, which is a hybrid of grey wolf and modified version of enhanced differential evolution algorithm
The home energy management controller (HEMC) based on day-ahead genetic algorithm (DA-genetic algorithm (GA)), DA-game-theoretic, and our proposed DA-GmEDE based strategies shift the load from high price hours to low price hours under day-ahead pricing signal, which leads to reduction in peak-to-average ratio (PAR)
Summary
The energy demand has dramatically increased with continuous population and economic growth. In [3] and [4], authors used mixed integer linear programming (MILP) to schedule the energy consumption of their homes under dynamic pricing scheme to minimize the electricity bill and smoothen the demand curve These objectives are achieved at the cost of increased system complexity. A game theoretic home energy management system (HEMS) is proposed for energy consumption scheduling of residential buildings under DR pricing schemes to reduce PAR and electricity bill in [10], [11]. These studies do not consider the tradeoffs between the electricity bill and user-discomfort. The acronyms and notations used in this paper are defined in NOMENCLATURE
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