Optimized energy consumption model for smart home using improved differential evolution algorithm

This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed...

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Vydané v:Energy (Oxford) Ročník 172; s. 354 - 365
Hlavní autori: Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.04.2019
Elsevier BV
Predmet:
Algorithms
Archives & records
Batteries
Classification
Computer simulation
Demand response
energy
Energy consumption
Energy management
Energy storage
Energy usage
Evolutionary algorithms
Evolutionary computation
Fitness
Genetic algorithms
HEMS
Multiple objective analysis
Optimization
Photovoltaics
Reproductive fitness
RES
Smart buildings
solar collectors
Sorting algorithms
South Africa
system optimization
South Africa
RES
HEMS
Demand response
Evolutionary algorithms
Optimization
ISSN:0360-5442, 1873-6785
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Shrnutí:This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms' ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio. •Evolutionary algorithms play significant role in demand response implementation.•Aggregator-consumer matching via evolutionary algorithms improves mutual benefits.•Two-archive strategy improves mutation and crossover speed in differential evolution.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2019.01.137