Abstract
There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.
Highlights
With the rapid growth in population and economic development, dependence on electrical energy is ever so increasing and, the energy consumption is on the hike
This study introduces time of use pricing scheme (ToUPS), day-ahead pricing scheme (DAPS), and real-time pricing scheme (RTPS) price-based demand response (DR) programs that are fed as an input to our proposed wind-driven bacterial foraging algorithm (WBFA)-based energy management controller (EMC) in the IoT-enabled environment
The proposed WBFA-based strategy is compared with benchmark strategies based on genetic algorithm (GA) [15,16], binary particle swarm optimization (BPSO) algorithm [17], genetic binary particle swarm optimization (GBPSO) algorithm [18], and genetic wind driven optimization (GWDO) algorithm [19] in simulations
Summary
With the rapid growth in population and economic development, dependence on electrical energy is ever so increasing and, the energy consumption is on the hike. The authors recorded that the electricity demand of the energy sector will increase to 40% and both the commercial and residential sectors will increase to 25% by 2025 [1]. The obsolete electric grid is not capable of coping with this rising electricity demand and contemporary challenges like hybrid generation, two-way communication, and two-way power flow. The modern power grid, namely smart grid (SG), developed as intelligent electric grid that accommodates internet-of-things (IoT), modern control technologies, information and communication technologies (ICTs), two-way power flow, and hybrid generation. In order to cope with this rising electricity demand, SGs may actively involve either of the two programs: installation of power generating plants or broadcast demand response (DR) programs for energy management [2].
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