A Conceptual and Systematics for Intelligent Power Management System-Based Cloud Computing: Prospects, and Challenges
Source abstract
Abstract
This review describes a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. The current study aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system. These methods and techniques monitor demand power (DC-device), load, and power mix between RE and other power sources. Cloud-based power optimization intelligent systems lead to an optimal power distribution solution that reduces power consumption or costs. Data has been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. The overall findings of these studies are visually explained in the proposed conceptual framework through the literature that are considered to be cloud computing based on storing and running the intelligent systems of power management and mixing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Grid Energy Management · Smart Grid Security and Resilience · Electricity Theft Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Computer Centre Department, University of Fallujah, Anbar 00964, Iraq
Department of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq
References (100+)
Show source-provided references
1. Technical change, inter-factor and inter-fuel substitution possibilities in Pakistan: A trans-log production function approach10.1016/j.jclepro.2016.03.065 ↗
2. Cyber security of a power grid: State-of-the-art10.1016/j.ijepes.2017.12.020 ↗
3. Success factors affecting the healthcare professionals to utilize cloud computing services
4. Microgrid Communication and Security: State-Of-The-Art and Future Directions10.51662/jiae.v1i1.11 ↗
5. Begovic, M.M. (2017). System protection. Power System Stability and Control, CRC Press. [3rd ed.].
6. Optimized controller for renewable energy sources integration into microgrid: Functions, constraints and suggestions10.1016/j.jclepro.2020.120419 ↗
7. Serverless computing for cloud-based power grid emergency generation dispatch10.1016/j.ijepes.2020.106366 ↗
8. Exploration of dispatch model integrating wind generators and electric vehicles10.1016/j.apenergy.2016.09.078 ↗
9. Energy consumption, carbon dioxide emissions and economic development: Evaluating alternative and plausible environmental hypothesis for sustainable growth10.1016/j.rser.2017.02.072 ↗
10. IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges10.1109/JIOT.2016.2619369 ↗
11. Radical transformation pathway towards sustainable electricity via evolutionary steps10.1038/s41467-019-08855-1 ↗
12. Evaluation of a proposal for reliable low-cost grid power with 100% wind, water, and solar10.1073/pnas.1610381114 ↗
13. Electricity market design for the prosumer era10.1038/nenergy.2016.32 ↗
14. A micro-market module design for university demand-side management using self-crossover genetic algorithms10.1016/j.apenergy.2019.113456 ↗
15. Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model10.1016/j.apenergy.2019.03.094 ↗
16. Optimization methods and advanced applications for smart energy systems considering grid-interactive demand response10.1016/j.apenergy.2019.113994 ↗
17. Matrix Exponential Learning Schemes with Low Informational Exchange10.1109/TSP.2019.2912875 ↗
18. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation10.1016/j.est.2020.101557 ↗
19. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends10.1016/j.rser.2020.110480 ↗
20. A fast-heat battery system using the heat released from detonated supercooled phase change materials10.1016/j.energy.2020.119496 ↗
21. Sharma, S., Kotturu, P.K., and Narooka, P.C. (2020). Implication of IoT Components and Energy Management Monitoring. Swarm Intell. Optim., 49–65.10.1002/9781119778868.ch4 ↗
22. Adaptive Resonant Beam Charging for Intelligent Wireless Power Transfer10.1109/JIOT.2018.2867457 ↗
23. Resilience oriented vehicle-to-home operation based on battery swapping mechanism10.1016/j.energy.2020.119528 ↗
24. Li, S., Li, J., and Wang, H. (2019, January 6–9). Big data driven Lithium-ion battery modeling method: A cyber-physical system approach. Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan.10.1109/ICPHYS.2019.8780152 ↗
25. A new approach to demand response in a microgrid based on coordination control between smart meter and distributed superconducting magnetic energy storage unit10.1016/j.est.2020.101748 ↗
26. Energy sustainable paradigms and methods for future mobile networks: A survey10.1016/j.comcom.2018.01.005 ↗
27. Maximizing Hosting Capacity of Uncertain Photovoltaics by Coordinated Management of OLTC, VAr Sources and Stochastic EVs10.1016/j.ijepes.2020.106627 ↗
28. Particle swarm optimised fuzzy controller for charging–discharging and scheduling of battery energy storage system in MG applications10.1016/j.egyr.2020.12.007 ↗
29. Performance evaluation of standalone double axis solar tracking system with maximum light detection MLD for telecommunication towers in Malaysia
30. Analysis of photovoltaic & battery energy storage system impacts on electric distribution system efficacy
31. Artificial intelligent based damping controller optimization for the multi-machine power system: A review10.1109/ACCESS.2018.2855681 ↗
32. A critical survey of live virtual machine migration techniques10.1186/s13677-017-0092-1 ↗
33. Optimal residential community demand response scheduling in smart grid10.1016/j.apenergy.2017.06.066 ↗
34. Long-Term Power Procurement Scheduling Method for Smart-Grid Powered Communication Systems10.1109/TWC.2018.2803181 ↗
35. A robust optimization approach for demand side scheduling considering uncertainty of manually operated appliances10.1109/TSG.2016.2564159 ↗
36. Integration of simulation technologies with physical system of reconfigurable material handling
37. Integrating Renewable Energy Resources into the Smart Grid: Recent Developments in Information and Communication Technologies10.1109/TII.2018.2819169 ↗
38. Impact of fossil fuels energy consumption, energy policies, and urban sprawl on carbon emissions in East Asia and the Pacific: A panel investigation10.1016/j.esr.2018.04.006 ↗
39. Kim, H., Kim, Y.J., Yang, K., and Thottan, M. (2011, January 17–20). Cloud-based demand response for smart grid: Architecture and distributed algorithms. Proceedings of the 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium.
40. Menshari, A., Salehi, G., and Ghiamy, M. (2018). A Novel Technique for Multiple Microgrids Planning by Considering Demand Response Programming and Social Welfare Enhancement in Power Market. Rev. Publicando, Available online: https://www.revistapublicando.org/revista/index.php/crv/article/view/1381.
41. Yang, C.T., Chen, W.S., Huang, K.L., Liu, J.C., Hsu, W.H., and Hsu, C.H. (2012, January 4–7). Implementation of smart power management and service system on cloud computing. Proceedings of the IEEE 9th International Conference on Ubiquitous Intelligence and Computing and IEEE 9th International Conference on Autonomic and Trusted Computing, Fukuoka, Japan.10.1109/UIC-ATC.2012.160 ↗
42. Autonomous Two-Tier Cloud-Based Demand Side Management Approach with Microgrid10.1109/TII.2016.2619070 ↗
43. Pacheco, L.A.B., Gondim, J.J.C., Barreto, P.A.S., and Alchieri, E. (November, January 31). Evaluation of distributed denial of service threat in the internet of things. Proceedings of the 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.10.1109/NCA.2016.7778599 ↗
44. Optimizing size of variable renewable energy sources by incorporating energy storage and demand response10.1109/ACCESS.2019.2929297 ↗
45. Big data framework for analytics in smart grids10.1016/j.epsr.2017.06.006 ↗
46. Cloud computing and big data: A comprehensive analysis
47. The Rise of Big Data and Cloud Computing10.11648/j.iotcc.20190702.12 ↗
48. Antunes, C.H., Soares, A., and Gomes, Á. (2016, January 21–24). An energy management system for residential demand response based on multiobjective optimization. Proceedings of the 2016 IEEE Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada.10.1109/SEGE.2016.7589506 ↗
49. Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective10.1109/TVT.2016.2582721 ↗
50. Optimal use of incentive and price based demand response to reduce costs and price volatility10.1016/j.epsr.2016.12.012 ↗
51. Optimisation of demand response in electric power systems, a review10.1016/j.rser.2018.12.054 ↗
52. Estimating the benefits of cooperation in a residential microgrid: A data-driven approach10.1016/j.apenergy.2016.07.105 ↗
53. Assessing the benefits of residential demand response in a real time distribution energy market10.1016/j.apenergy.2015.10.017 ↗
54. Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage10.1016/j.scs.2016.09.017 ↗
55. Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response10.1016/j.apenergy.2017.05.116 ↗
56. Occupancy-based demand response and thermal comfort opti-mization in microgrids with renewable energy sources and energy storage10.1016/j.apenergy.2015.10.140 ↗
57. A peak-load-reduction-based procedure to manage distribution network expansion by applying process-oriented costing of incoming components10.1016/j.energy.2019.115852 ↗
58. Huang, W., Zhang, N., Kang, C., Li, M., and Huo, M. (2019). From demand response to integrated demand response: Review and pro-spect of research and application. Prot. Control. Mod. Power Syst., 4.10.1186/s41601-019-0126-4 ↗
59. A critical review on the utilization of storage and demand response for the imple-mentation of renewable energy microgrids10.1016/j.scs.2018.04.008 ↗
60. U.S. Energy Information Administration (EIA) (2021, August 16). International Energy Outlook 2016, Available online: http://www.eia.gov/forecasts/ieo/.
61. Global energy perspectives to 2060—WEC’s World Energy Scenarios 201910.1016/j.esr.2020.100523 ↗
62. A critical review of comparative global historical energy consumption and future demand: The story told so far10.1016/j.egyr.2020.07.020 ↗
63. Issues of Power Quality in Electrical Systems10.11648/j.ijepe.20160504.12 ↗
64. An Enhancement Approach for Reducing the Energy Consumption in Wireless Sensor Networks
65. Mohammed, M.A., Mohammed, I.A., Hasan, R.A., Tapus, N., Ali, A.H., and Hammood, O.A. (2019, January 10–12). Green Energy Sources: Issues and Challenges. Proceedings of the 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet), Galati, Romania.10.1109/ROEDUNET.2019.8909595 ↗
66. An overview of energy efficiency techniques in cluster computing systems10.1007/s10586-011-0171-x ↗
67. Mondal, H.K., Gade, S.H., Kishore, R., Kaushik, S., and Deb, S. (2016, January 15–16). Power efficient router architecture for wireless Network-on-Chip. Proceedings of the 2016 17th International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA.10.1109/ISQED.2016.7479205 ↗
68. Power management in virtualized data centers: State of the art10.1186/s13677-016-0055-y ↗
69. Fountoulakis, E., Pappas, N., and Ephremides, A. (2021, August 16). Dynamic Power Control for Time-Critical Networking with Heterogeneous Traffic. Available online: http://arxiv.org/abs/2011.04448.
70. Do powerbanks deliver what they advertise? Measuring voltage, current, power, energy and charge of powerbanks with an Arduino10.1088/1361-6552/ab630c ↗
71. An automatically peak-shift control design for charging and discharging of the battery in an ultrabook10.1587/transinf.2015EDP7297 ↗
72. Chen, Z., Shu, X., Sun, M., Shen, J., and Xiao, R. (2017, January 7–10). Charging strategy design of lithium-ion batteries for energy loss minimization based on minimum principle. Proceedings of the 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), Harbin, China.10.1109/ITEC-AP.2017.8080833 ↗
73. Optimal allocation of electric vehicle charging stations with adopted smart charging/discharging schedule10.1109/ACCESS.2020.3033662 ↗
74. Fault-tolerant model based on fuzzy control for mobile devices
75. Battery thermal management system based on the forced-air convection: A review10.1016/j.etran.2020.100097 ↗
76. Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems10.1016/j.egyai.2020.100016 ↗
77. Gharehpetian, G.B., and Agah, S.M.M. (2017). Distributed Generation Systems: Design, Operation and Grid Integration, Butterworth-Heinemann.
78. Effects of state of charge on the degradation of LiFePO4/graphite batteries during accelerated storage test10.1016/j.jallcom.2015.03.169 ↗
79. The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems10.1155/2017/5678393 ↗
80. A computationally efficient optimization approach for battery systems in islanded microgrid10.1109/TSG.2017.2713947 ↗
81. Lipu, M.S.H., Hannan, M.A., Hussain, A., Ayob, A., Saad, M.H.M., and Muttaqi, K.M. (2020). State of charge estimation in lithium-ion batteries: A neural network optimization approach. Electronics, 9.10.3390/electronics9091546 ↗
82. Cuadras, A., Miró, P., Ovejas, V.J., and Estrany, F. (2020). Entropy generation model to estimate battery ageing. J. Energy Storage, 32.10.1016/j.est.2020.101740 ↗
83. Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries10.1016/j.energy.2019.06.128 ↗
84. Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction10.1016/j.est.2020.101271 ↗
85. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives10.1016/j.rser.2020.110048 ↗
86. Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression10.1109/TIE.2017.2764869 ↗
87. A novel integration of battery system in automotive vehicle10.1063/5.0024924 ↗
88. Boulmrharj, S., NaitMalek, Y., Elmouatamid, A., Bakhouya, M., Ouladsine, R., Zine-Dine, K., Khanidar, M., and Siniti, M. (2019). Battery characterization and dimensioning approaches for micro-grid systems. Energies, 12.10.3390/en12071305 ↗
89. Li-ion batteries for peak shaving, price arbitrage, and photovoltaic self-consumption in commercial buildings: A Monte Carlo Analysis10.1016/j.enconman.2021.113889 ↗
90. Power management of grid-integrated energy storage batteries with intermittent renewables10.1016/j.est.2020.101762 ↗
91. Bus voltage level choice for standalone residential DC nanogrid10.1016/j.scs.2019.101431 ↗
92. Estimation of the energy consumption of battery driven electric buses by integrating digital elevation and longitudinal dynamic models: Malaysia as a case study10.1016/j.apenergy.2020.115873 ↗
93. Masih, A., and Verma, H.K. (2021, August 16). Renewable Hybrid Battery Energy Management System Using ANN Controller. Available online: https://easychair.org/publications/preprint_download/sMG2.10.1109/INCET51464.2021.9456360 ↗
94. Igbinovia, F.O., Krupka, J., Hajek, P., Muller, Z., and Tlusty, J. (2020, January 19–21). Electricity storage in internet of renewable energy (IoRE) domain for sustainable smart cities. Proceedings of the 2020 21st International Scientific Conference on Electric Power Engineering (EPE), Prague, Czech Republic.10.1109/EPE51172.2020.9269241 ↗
95. Towards the next generation of intelligent building: An assessment study of current automation and future IoT based systems with a proposal for transitional design10.1016/j.scs.2016.08.019 ↗
96. Blockchain for Internet of Energy management: Review, solutions, and challenges10.1016/j.comcom.2020.01.014 ↗
97. Gunasekaran, M., Ismail, H.M., Chokkalingam, B., Mihet-Popa, L., and Padmanaban, S. (2018). Energy management strategy for rural communities’ DC micro grid power system structure with maximum penetration of renewable energy sources. Appl. Sci., 8.10.3390/app8040585 ↗
98. Multi-objective optimization and energy management in renewable based AC/DC microgrid10.1016/j.compeleceng.2018.01.023 ↗
99. DeSando, M. (2015). Universal Programmable Battery Charger with Optional Battery Management System, California Polytechnic State University.
100. Setore, Y.D. (2020). Modeling and Design of a Level-2 Onboard Lithium-ion Battery Charging System for ECADO Four-Wheel Electric Vehicle, Adama Science and Technology University.