In this manuscript, an Energy Management System (EMS) with Internet of Things (IoT) framework in distribution system (DS) based on hybrid technique is proposed. The proposed method is the combination of Dynamic Differential Annealed Optimization (DDAO) and Feedback Artificial Tree (FAT) algorithm; therefore called as DDFAT method. The main intention of proposed method is “to optimize the power control and DS resources by constantly tracking the data into communication framework based on IoT”. In this work, the DS is inter connected to the data acquisition method that is the Internet of things uses single IP address resulting in mesh wireless network devices. The internet of things based communication scheme used to facilitate the growth of demand response (DR) for energy management (EM) DS. The transmitting data is carried out by DDFAT method. In this way, the IoT distribution scheme increases the network flexibility and offers optimal utilization of the accessible resources. Moreover, the DDFAT method is reliable to meet global supply along energy demand. The DDFAT method is implemented in MATLAB Simulink platform under three test cases and its performance is analysed with the existing improved artificial bee colony (IABC), squirrel optimization with gravitational search–assisted neural network (SOGSNN), particle swarm optimization (PSO)–assisted artificial neural network (ANN), Fruit fly Optimization algorithm (FOA), and grasshopper optimization algorithm (GOAPSNN) methods.
MusaddiqAZikriaYBHahmO,et al.A survey on resource management in IoT operating systems. IEEE Access2018; 6: 8459–8482.
2.
ShroufFMiragliottaG. Energy management based on internet of things: practices and framework for adoption in production management. J Cleaner Prod2015; 100: 235–246.
3.
SilvaBNKhanMHanK. Integration of big data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management. Future Gener Comput Syst2020; 107: 975–987.
4.
Terroso-SaenzFGonzález-VidalARamallo-GonzálezAP,et al.An open IoT platform for the management and analysis of energy data. Future Gener Comput Syst2019; 92: 1066–1079.
5.
WeiMHongSHAlamM. An IoT-based energy-management platform for industrial facilities. Appl Energy2016; 164: 607–619.
6.
ChenZWuLFuY. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans Smart Grid2012; 3: 1822–1831.
7.
Martín-LopoMMBoalJSánchez-MirallesÁ. A literature review of IoT energy platforms aimed at end users. Comput Netw2020; 171: 107101.
8.
KhatuaPKRamachandaramurthyVKKasinathanP,et al.Application and assessment of internet of things toward the sustainability of energy systems: challenges and issues. Sustain Cities Soc2020; 53: 101957.
9.
RajeshPMuthubalajiSSrinivasanS,et al.Leveraging a dynamic differential annealed optimization and recalling enhanced recurrent neural network for Maximum power point tracking in wind energy conversion system. Technol Econ Smart Grids Sustain Energy2022; 7: 1–5.
10.
ShajinFHRajeshPThilahaS. Bald eagle search optimization algorithm for cluster head selection with prolong lifetime in wireless sensor network. J Soft Comput Eng Appl2020; 1: 7.
11.
RajeshPShajinFHVijayaAnandN. An efficient estimation model for induction motor using BMO-RBFNN technique. Process Integr Optim Sustain2021; 5: 777–792.
12.
ShajinFHRajeshP. FPGA Realization of a reversible data hiding scheme for 5G MIMO-OFDM system by chaotic key generation-based paillier cryptography along with LDPC and its Side channel estimation using machine learning technique. J Circuits Syst Comput2022; 31: 2250093.
13.
SaraerehOAAlsarairaAKhanI,et al.A hybrid energy harvesting design for on-body Internet-of-Things (IoT) networks. Sensors2020; 20: 407.
14.
TusharWYuenCLiK,et al.Design of cloud-connected IoT system for smart buildings on energy management. EAI Endorsed Trans Ind Netw Intell Syst2016; 3:150813.
15.
AinQUIqbalSKhanSA,et al.Iot operating system based fuzzy inference system for home energy management system in smart buildings. Sensors2018; 18: 2802.
16.
EjazWNaeemMShahidA,et al.Efficient energy management for the internet of things in smart cities. IEEE Commun Mag2017; 55: 84–91.
17.
HosseinMotlaghNMohammadrezaeiMHuntJ,et al.Internet of Things (IoT) and the energy sector. Energies2020; 13: 494.
18.
KellySDSuryadevaraNKMukhopadhyaySC. Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sensors J2013; 13: 3846–3853.
19.
LiuCHFanJBranchJW,et al.Toward QoI and energy-efficiency in Internet-of-Things sensory environments. IEEE Trans Emerg Topics Comput2014; 2: 473–487.
20.
MarinakisVDoukasH. An advanced IoT-based system for intelligent energy management in buildings. Sensors2018; 18: 610.
21.
Yami MASchaeferD. Fog computing as a complementary approach to cloud computing. In: 2019 International Conference on Computer and Information Sciences (ICCIS), 2019 pp.1–5: IEEE.
22.
HassanalieraghMPageASoyataT,et al.Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges. In: 2015 IEEE international conference on services computing, 2015 pp.285–292: IEEE.
23.
MahapatraCMoharanaAKLeungV. Energy management in smart cities based on internet of things: peak demand reduction and energy savings. Sensors2017; 17: 2812.
24.
DaveBKublerSFrämlingK,et al.Opportunities for enhanced lean construction management using internet of things standards. Autom Constr2016; 61: 86–97.
25.
RahimSJavaidNAhmadA,et al.Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build2016; 129: 452–470.
26.
PalSThakurSKumarR,et al.A strategical game theoretic based demand response model for residential consumers in a fair environment. Int J Electr Power Energy Syst2018; 97: 201–210.
27.
HuangYTianHWangL. Demand response for home energy management system. Int J Electr Power Energy Syst2015; 73: 448–455.
28.
CastroPMHarjunkoskiIGrossmannIE. New continuous-time scheduling formulation for continuous plants under variable electricity costInd Eng Chem Res2009; 48: 6701–6714.
29.
LunaACDiazNLGraellsM,et al.Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: modeling, design, and experimental verification. IEEE Trans Power Electron2016; 32: 2769–2783.
30.
BinghamRDAgelin-ChaabMRosenMA. Whole building optimization of a residential home with PV and battery storage in the Bahamas. Renew Energy2019; 132: 1088–1103.
31.
ShakeriMShayesteganMRezaSS,et al.Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source. Renew Energy2018; 125: 108–120.
32.
ThomasDDebleckerOIoakimidisCS. Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule. Appl Energy2018; 210: 1188–1206.
33.
LuYWangSSunY,et al.Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Appl Energy2015; 147: 49–58.
34.
NematiMBraunMTenbohlenS. Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Appl Energy2018; 210: 944–963.
35.
MeiqinMShujuanSChangL. Economic analysis of the microgrid with multi-energy and electric vehicles. In: 8th International Conference on Power Electronics-ECCE Asia, 2011 pp.2067–2072: IEEE.
36.
Rodrigues JuniorWLBorgesFARabeloRD,et al.A methodology for detection and classification of power quality disturbances using a real-time operating system in the context of home energy management systems. Int J Energy Res2021; 45: 203–219.
37.
KarthickTChandrasekaranK. Design of IoT based smart compact energy meter for monitoring and controlling the usage of energy and power quality issues with demand side management for a commercial building. Sustain Energy Grids Netw2021; 26: 100454.
38.
WangXMaoXKhodaeiH. A multi-objective home energy management system based on internet of things and optimization algorithms. J Build Eng2021; 33: 101603.
39.
SujaKR. Mitigation of power quality issues in smart grid using levy flight based moth flame optimization algorithm. J Ambient Intell Humaniz Comput2021; 12: 9209–9228.
40.
DumanACErdenHSGönülÖ,et al.A home energy management system with an integrated smart thermostat for demand response in smart grids. Sustain Cities Soc2021; 65: 102639.
41.
JavadiMSNezhadAENardelliPH,et al.Self-scheduling model for home energy management systems considering the end-users discomfort index within price-based demand response programs. Sustain Cities Soc2021; 68: 102792.
42.
RehmanAUWadudZElavarasanRM,et al.An optimal power usage scheduling in smart grid integrated with renewable energy sources for energy management. IEEE Access2021; 9: 84619–84638.
43.
BabarMTariqMUJanMA. Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid. Sustain Cities Soc2020; 62: 102370.
44.
SaidOTolbaA. Accurate performance prediction of IoT communication systems for smart cities: an efficient deep learning based solution. Sustain Cities Soc2021; 69: 102830.
45.
SinghSKJeongYSParkJH. A deep learning-based IoT-oriented infrastructure for secure smart city. Sustain Cities Soc2020; 60: 102252.
46.
AlhasnawiBNJasimBH. A new internet of things enabled trust distributed demand side management system. Sustain Energy Technol Assess2021; 46: 101272.
47.
AlhasnawiBNJasimBHSedhomBE,et al.A new decentralized control strategy of microgrids in the internet of energy paradigm. Energies2021; 14: 2183.
48.
AlhasnawiBNJasimBHEstebanMD,et al.A novel smart energy management as a service over a cloud computing platform for nanogrid appliances. Sustainability2020; 12: 9686.
49.
AlhasnawiBNJasimBHSianoP,et al.A novel real-time electricity scheduling for home energy management system using the internet of energy. Energies2021; 14: 3191.
50.
AlhasnawiBNJasimBHRahmanZA,et al.A novel robust smart energy management and demand reduction for smart homes based on internet of energy. Sensors2021; 21: 4756.
51.
FaheemMButtRARazaB,et al.Energy efficient and reliable data gathering using internet of software-defined mobile sinks for WSNs-based smart grid applications. Comput Stand Interfaces2019; 66: 103341.
52.
YassineASinghSHossainMS,et al.IoT big data analytics for smart homes with fog and cloud computing. Future Gener Comput Syst2019; 91: 563–573.
53.
NojavanSMajidiMZareK. Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program. Int J Hydrogen Energy2017; 42: 11857–11867.
54.
ZhongWXieKLiuY,et al.Auction mechanisms for energy trading in multi-energy systems. IEEE Trans Ind Inf2017; 14: 1511–1521.
55.
MoghimiMLiuJJamborsalamatiP,et al.Internet of things platform for energy management in multi-microgrid system to improve neutral current compensation. Energies2018; 11: 3102.
56.
LiWLogenthiranTPhanVT,et al.A novel Smart Energy Theft System (SETS) for IoT-based smart home. IEEE Internet Things J2019; 6: 5531–5539.
57.
GolpîraHBahramaraS. Internet-of-things-based optimal smart city energy management considering shiftable loads and energy storage. J Cleaner Prod2020; 264: 121620.
58.
TomRJSankaranarayananSRodriguesJJ. Smart energy management and demand reduction by consumers and utilities in an IoT-fog-based power distribution system. IEEE Internet Things J2019; 6: 7386–7394.
59.
AhujaKKhoslaA. Data analytics criteria of IoT enabled smart energy meters (SEMs) in smart cities. Int J Energy Sect Manage2018; 13: 402–423.
60.
ShenJJiangCLiuY,et al.A microgrid energy management system and risk management under an electricity market environment. IEEE Access2016; 4: 2349–2356.
61.
PascualJBarricarteJSanchisP,et al.Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy2015; 158: 12–25.
62.
GraditiGDi SilvestreMLGalleaR,et al.Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Trans Ind Inf2014; 11: 271–280.
63.
GhafilHNJármaiK. Dynamic differential annealed optimization: new metaheuristic optimization algorithm for engineering applications. Appl Soft Comput2020; 93: 106392.
64.
XiaoYChiHLiQ. An improved artificial tree algorithm with two populations (IATTP). Eng Appl Artif Intell2021; 104: 104324.
65.
LiQQHeZCLiE. The feedback artificial tree (FAT) algorithm. Soft Comput2020; 24: 13413–13440.
66.
DurairasanMRamprakashSBalasubramanianD. System modeling of micro-grid with hybrid energy sources for optimal energy management—A hybrid elephant herding optimization algorithm-adaptive neuro fuzzy inference system approach. Int J Numer Model Electron Networks Devices Fields2021; 34: e2915.
