Abstract
The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by incorporating the enhanced differential evolution (EDE) and harmony search algorithm (HSA). Moreover, to optimize the energy consumption, a hybridization based on HSA and EDE operators is performed. Further, multiple knapsacks are used to ensure that the load demand for electricity consumers does not exceed a threshold during peak hours. To achieve multiple objectives at the same time, hybridization proved to be effective in terms of electricity cost and peak to average ratio (PAR) reduction. The performance of the proposed technique; harmony EDE (HEDE) is evaluated via extensive simulations in MATLAB. The simulations are performed for a residential complex of multiple homes with a variety of smart appliances. The simulation results show that EDE performs better in terms of cost reduction as compared to HSA. Whereas, in terms of PAR, HSA is proved to be more efficient as compared to EDE. However, the proposed scheme outperforms the existing meta-heuristic techniques (HSA and EDE) in terms of cost and PAR.
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- \(\rho\) :
-
Power rating
- \(\varsigma _{a,t}\) :
-
Electricity price at time interval t
- \(E_{in}\) :
-
Power consumption of interruptible appliances
- \(\rho _{in}\) :
-
Power rating of interruptible appliances
- t :
-
Time slot
- IN :
-
Set of interruptible appliances
- \(sv_{in}\) :
-
ON/OFF status of interruptible appliances
- NI :
-
Set of non-interruptible appliances
- \(E_{ni}\) :
-
Power consumption of non-interruptible appliances
- \(\rho _{ni}\) :
-
Power rating of non-interruptible appliances
- \(sv_{ni}\) :
-
ON/OFF status of non-interruptible appliances
- B :
-
Set of base appliances
- \(E_{b}\) :
-
Power consumption of base appliances
- \(\rho _{b}\) :
-
Power rating of base appliances
- \(sv_{b}\) :
-
ON/OFF status of base appliances
- L(t):
-
Power consumption of all appliances at time interval t
- \(L_{total}^{sch}\) :
-
Per day total scheduled load
- \(L_{total}^{uns}\) :
-
Per day total unscheduled load
- \(C_{total}^{sch}\) :
-
Per day total scheduled cost
- \(C_{total}^{uns}\) :
-
Per day total unscheduled cost
- \(t_{\alpha }\) :
-
Start time of an appliance
- \(t_{\beta }\) :
-
End time of an appliance
- F:
-
Scaling factor
- NP:
-
Population size
- SG:
-
Smart grid
- SM:
-
Smart meter
- DSM:
-
Demand side management
- DR:
-
Demand response
- RES:
-
Renewable energy sources
- PAR:
-
Peak to average ratio
- TOU:
-
Time of use
- IBR:
-
Inclined block rate
- CPP:
-
Critical peak pricing
- DAP:
-
Day ahead pricing
- RTP:
-
Real time pricing
- HSA:
-
Harmony search algorithm
- DE:
-
Differential evolution
- EDE:
-
Enhanced differential evolution
- GA:
-
Genetic algorithm
- CR:
-
Cross over rate
- HEM:
-
Home energy management
- EMC:
-
Energy management controller
- HAN:
-
Home area network
- HMCR:
-
Harmony memory consideration rate
- bw:
-
Bandwidth
- PSO:
-
Particle swarm optimization
- MILP:
-
Mixed integer linear programming
- PA:
-
Pitch adjustment rate
- MKP:
-
Multiple knapsack problem
References
Agnetis A, de Pascale G, Detti P, Vicino A (2013) Load scheduling for household energy consumption optimization. IEEE Trans Smart Grid 4(4):2364–2373
Ahmad A, Javaid N, Alrajeh N, Khan ZA, Qasim U, Khan A (2015) A modified feature selection and artificial neural network-based day-ahead load forecasting model for a smart grid. Appl Sci 5(4):1756–1772
Arabali A, Ghofrani M, Etezadi-Amoli M, Fadali MS, Baghzouz Y (2013) Genetic-algorithm-based optimization approach for energy management. IEEE Trans Power Deliv 28(1):162–170
Arafa M, Sallam EA, Fahmy MM (2014) An enhanced differential evolution optimization algorithm. In: Digital Information and Communication Technology and it’s Applications (DICTAP), 2014 Fourth International Conference on IEEE, pp 216-225
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Geem ZW, Yoon Y (2017) Harmony search optimization of renewable energy charging with energy storage system. Int J Electr Power Energy Syst 86:120–126
Hashmi MHSMK, Hänninen S, Mäki K (2011) Survey of smart grid concepts, architectures, and technological demonstrations worldwide. In: Innovative Smart Grid Technologies (ISGT Latin America), 2011 IEEE PES Conference on, IEEE, pp 1–7
Jalili H, Sheikh-El-Eslami MK, Parsa Moghaddam M, Siano P (2018) Modeling of demand response programs based on market elasticity concept. J Ambient Intell Humaniz Comput 2018:1–12
Khan A, Javaid N, Ahmad A, Akbar M, Khan ZA, Ilahi M (2018) A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. J Ambient Intell Humaniz Comput 2008:1–24
Khan MA, Javaid N, Mahmood A, Khan ZA, Alrajeh N (2015) A generic demandside management model for smart grid. Int J Energy Res 39(7):954–964
Liu B, Kang J, Jiang N, Jing Y (2011) Cost control of the transmission congestion management in electricity systems based on ant colony algorithm. Energy Power Engi 3(01):17
Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252
Ma K, Yao T, Yang J, Guan X (2016) Residential power scheduling for demand response in smart grid. Int J Electr Power Energy Syst 78:320–325
Mary GA, Rajarajeswari R (2014) Smart grid cost optimization using genetic algorithm. Int J Res Eng Technol 3(07):282–287
Miao H, Huang X, Chen G (2012) A genetic evolutionary task scheduling method for energy efficiency in smart homes. Int Rev Electr Eng (IREE) 7(5):5897–5904
Mocnik J, Gornik M, Murovec B, Zemva A (2013) A concept to optimize power consumption in smart homes based on demand-side management and using smart switches/Koncept optimizacije porabe elektricne energije v pametni hisi z vodenjem porabe in pametnimi stikali. Elektrotehniski Vestn 80(5):217
Molderink A, Bakker V, Bosman MG, Hurink JL, Smit GJ (2009) Domestic energy management methodology for optimizing efficiency in smart grids. In: PowerTech, 2009 IEEE Bucharest, IEEE, pp 1-7
Moon S, Lee J (2018) Multi-residential demand response scheduling with multi-class appliances in smart grid. IEEE Trans Smart Grid 9(4):2518–2528. https://doi.org/10.1109/TSG.2016.2614546
Motevasel M, Seifi AR (2014) Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers Manag 83:58–72
Muralitharan K, Sakthivel R, Shi Y (2016) Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177:110–119
Ogwumike C, Short M, Denai M (2015) Near-optimal scheduling of residential smart home appliances using heuristic approach. In: Industrial Technology (ICIT), 2015 IEEE International Conference on, IEEE, pp 3128-3133
Ozturk Y, Senthilkumar D, Kumar S, Lee G (2013) An intelligent home energy management system to improve demand response. IEEE Trans Smart Grid 4(2):694–701
Rahim S, Javaid N, Ahmad A, Khan SA, Khan ZA, Alrajeh N, Qasim U (2016) Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build 129:452–470
Rahimi F, Ipakchi A (2010) Demand response as a market resource under the smart grid paradigm. IEEE Trans Smart Grid 1(1):82–88
Rasheed MB, Javaid N, Ahmad A, Awais M, Khan ZA, Qasim U, Alrajeh N (2016) Priority and delay constrained demand side management in real time price environment with renewable energy source. Int J Energy Res 40(14):2002–2021
Rasheed MB, Javaid N, Awais M, Khan ZA, Qasim U, Alrajeh N, Javaid Q (2016) Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies 9(7):542
Rastegar M, Fotuhi-Firuzabad M, Zareipour H (2016) Home energy management incorporating operational priority of appliances. Int J Electr Power Energy Syst 74:286–292
Reddy SS, Park JY, Jung CM (2016) Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm. Front Energy 10(3):355–362
Siano Pierluigi, Graditi Giorgio, Atrigna Mauro, Piccolo Antonio (2013) Designing and testing decision support and energy management systems for smart homes. J Ambient Intell Humaniz Comput 4(6):651–661
Soares J, Sousa T, Morais H, Vale Z, Faria P (2011) An optimal scheduling problem in distribution networks considering V2G. In: 2011 IEEE symposium on computational intelligence applications in smart grid (CIASG), IEEE, pp 1-8
Sousa T, Morais H, Vale Z, Faria P, Soares J (2012) Intelligent energy resource management considering vehicle-to-grid: a simulated annealing approach. IEEE Trans Smart Grid 3(1):535–542
Storn R, Price K (1995) Differential evolution a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, Berkeley
Tang L, Zhao Y, Liu J (2014) An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Trans Evol Comput 18(2):209–225
Tsui KM, Chan SC (2012) Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans Smart Grid 3(4):1812–1821
Yi P, Dong X, Iwayemi A, Zhou C, Li S (2013) Real-time opportunistic scheduling for residential demand response. IEEE Trans Smart Grid 4(1):227–234
Zhang J, Wu Y, Guo Y, Wang B, Wang H, Liu H (2016) A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl Energy 183:791–804
Zhao Z, Lee WC, Shin Y, Song KB (2013) An optimal power scheduling method for demand response in home energy management system. IEEE Trans Smart Grid 4(3):1391–1400
Zijian Wu, Kaili Yang, Jiangxin Yang, Yanlong Cao, Yi Gan (2018) Energy-efficiency-oriented scheduling in smart manufacturing. J Ambient Intell Humaniz Comput 2018:1–10
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This project was full financially supported by the King Saud University, through the Vice Deanship of Research Chairs.
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Khan, Z.A., Zafar, A., Javaid, S. et al. Hybrid meta-heuristic optimization based home energy management system in smart grid. J Ambient Intell Human Comput 10, 4837–4853 (2019). https://doi.org/10.1007/s12652-018-01169-y
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DOI: https://doi.org/10.1007/s12652-018-01169-y