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Computational awareness for smart grid: a review

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Abstract

Smart grid has been an active research area in recent years because almost all the technologies required to build smart grid are mature enough. It is expected that not only can smart grid reduce electricity consumption, but it can also provide a more reliable and versatile service than the traditional power grid can. Although the infrastructure of smart grid all over the world is far from complete yet, there is no doubt that our daily life will benefit a lot from smart grid. Hence, many researches are aimed to point out the challenges and needs of future smart grid. The question is, how do we use the massive data captured by smart meters to provide services that are as “smart” as possible instead of just automatically reading information from the meters. This paper begins with a discussion of the smart grid before we move on to the basic computational awareness for smart grid. A brief review of data mining and machine learning technologies for smart grid, which are often used for computational awareness, is then given to further explain their potentials. Finally, challenges, potentials, open issues, and future trends of smart grid are addressed.

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Notes

  1. The smart meter system usually includes smart meter, communication infrastructure, and control devices.

References

  1. Talebi M, Way T (2009) Methods, metrics and motivation for a green computer science program. In: Proceedings of the 40th ACM Technical Symposium on Computer Science Education, pp 362–366

  2. von Schirnding Y, Mulholland C (2002) Health in the context of sustainable development. In: World Health Organization Meeting: Planning the Health Agenda for the WSSD, Oslo, Norway. WHO, Geneva

  3. Wackernagel M, Schulz NB, Deumling D, Linares AC, Jenkins M, Kapos V, Monfreda C, Loh J, Myers N, Norgaard R, Randers J (2002) Tracking the ecological overshoot of the human economy. Proc Natl Acad Sci USA 99(14):9266–9271

    Article  Google Scholar 

  4. Postel S (1994) Carrying capacity: Earth’s bottom line. Challenge 37:4–12

    Google Scholar 

  5. Fanara A (2007) Report to congress on server and data center efficiency. Public Law 109-431, U.S. Environmental Protection Agency (EPA) ENERGY STAR Program

  6. Gustafsson A, Gyllenswärd M (2005) The power-aware cord: energy awareness through ambient information display. In: Proceedings of the CHI ’05 Extended Abstracts on Human Factors in Computing Systems, pp 1423–1426

  7. Aggarwal A, Kunta S, Verma P (2010) A proposed communications infrastructure for the smart grid. In: Proceedings of the Innovative Smart Grid Technologies, pp 1–5

  8. Massoud Amin S, Wollenberg B (2005) Toward a smart grid: power delivery for the 21st century. IEEE Power Energy Mag 3(5):34–41

    Article  Google Scholar 

  9. Cunjiang Y, Huaxun Z, Lei Z (2012) Architecture design for smart grid. Energy Procedia 17(Part B):1524–1528

    Article  Google Scholar 

  10. Depuru SSSR, Wang L, Devabhaktuni V (2011) Smart meters for power grid: challenges, issues, advantages and status. Renew Sustain Energy Rev 15(6):2736–2742

    Article  Google Scholar 

  11. Hassan R, Radman G (2010) Survey on smart grid. In: Proceedings of the IEEE SoutheastCon, pp 210–213

  12. Parikh P, Kanabar M, Sidhu T (2010) Opportunities and challenges of wireless communication technologies for smart grid applications. In: Proceedings of the IEEE Power and Energy Society General Meeting, pp 1–7

  13. Gao J, Xiao Y, Liu J, Liang W, Chen CP (2012) A survey of communication/networking in smart grids. Future Gener Comput Syst 28(2):391–404

    Article  Google Scholar 

  14. National Institute of Standards and Technology (2013) NIST framework and roadmap for smart grid interoperability standards, release 2.0. http://www.nist.gov/smartgrid/framework-022812.cfm. Accessed 8 Jan 2013

  15. European Standards Organisations (2013) Smart grid, ftp://ftp.cencenelec.eu/EN/News/PR/PR-2012-04.pdf. Accessed 8 Jan 2013

  16. Moslehi K, Kumar R (2010) A reliability perspective of the smart grid. Smart Transm Grid: Vis Framew 1(1):57–64

    Article  Google Scholar 

  17. Li F, Qiao W, Sun H, Wan H, Wang J, Xia Y, Xu Z, Zhang P (2010) Smart transmission grid: vision and framework. IEEE Trans Smart Grid 1(2):168–177

    Article  Google Scholar 

  18. Google Maps (2013) “Smart metering projects map. http://maps.google.com/maps/ms?ie=utf8&oe=utf8&msa=0&msid=115519311058367534348.0000011362ac6d7d21187. Accessed 8 Jan 2013

  19. Kok K, Karnouskos S, Nestle D, Dimeas A, Weidlich A, Warmer C, Strauss P, Buchholz B, Drenkard S, Hatziargyriou N, Lioliou V (2009) Smart houses for a smart grid. In: Proceedings of the International Conference and Exhibition on Electricity Distribution—Part 1, pp 1–4

  20. Galli S, Scaglione A, Wang Z (2011) For the grid and through the grid: the role of power line communications in the smart grid. Proc IEEE 99(6):998–1027

    Article  Google Scholar 

  21. Vyatkin V, Zhabelova G, Higgins N, Schwarz K, Nair N-KC (2010) Towards intelligent smart grid devices with IEC 61850 interoperability and IEC 61499 open control architecture. In: Proceedings of the IEEE PES Transmission and Distribution Conference and Exposition, pp 1–8

  22. Mohsenian-Rad A-H, Wong V, Jatskevich J, Schober R (2010) Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Proceedings of the Innovative Smart Grid Technologies, pp 1–6

  23. Rahimi F, Ipakchi A (2010) Demand response as a market resource under the smart grid paradigm. Smart Transm Grid Vis Framew 1(1):82–88

    Article  Google Scholar 

  24. Wang W, Xu Y, Khanna M (2011) A survey on the communication architectures in smart grid. Comput Netw 55:3604–3629

    Article  Google Scholar 

  25. Saputro N, Akkaya K, Uludag S (2012) A survey of routing protocols for smart grid communications. Comput Netw 56(11):2742–2771

    Article  Google Scholar 

  26. Cecati C, Mokryani G, Piccolo A, Siano P (2010) An overview on the smart grid concept. In: Proceedings of the Annual Conference on IEEE Industrial Electronics Society, pp 3322–3327

  27. Hasan M, Farahmand F, Jue J (2010) Energy-awareness in dynamic traffic grooming. In: Proceedings of the Conference on Optical Fiber Communication, Collocated National Fiber Optic Engineers Conference, pp 1–3

  28. Abowd GD, Dey AK, Brown PJ, Davies N, Smith M, Steggles P (1999) Towards a better understanding of context and context-awareness. In: Proceedings of the International Symposium on Handheld and Ubiquitous Computing, pp 304–307

  29. Song S, Moustafa H, Afifi H (2012) A survey on personalized TV and NGN services through context-awareness. ACM Comput Surv 44(14):1–418

    Article  Google Scholar 

  30. Boldrini C, Conti M, Passarella A (2008) Context and resource awareness in opportunistic network data dissemination. In: Proceedings of the International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp 1–6

  31. Gaber MM, Yu PS (2006) A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering. In: Proceedings of the ACM Symposium on Applied Computing

  32. Röhm U, Gaber MM, Tse Q (2007) Enabling resource-awareness for in-network data processing in wireless sensor networks. In: Proceedings of the Nineteenth Conference on Australasian Database, vol 75, pp 107–114

  33. Ostrand T, Weyuker E, Bell R (2005) Locating where faults will be (software testing). In: Proceedings of the Richard Tapia Celebration of Diversity in Computing Conference, pp 48–50

  34. Qureshi A, Ramzan M, Zaidi S (2010) Fault aware routing algorithm to enhance network resiliency & achieve load balancing in hybrid wireless optical broadband access network. In: Proceedings of the High-Capacity Optical Networks and Enabling Technologies, pp 252–257

  35. Devanur NR, Fortnow L (2009) A computational theory of awareness and decision making. In: Proceedings of the Conference on Theoretical Aspects of Rationality and Knowledge, pp 99–107

  36. Gullo F, Ponti G, Tagarelli A, liritano S, Ruffolo M, Labate D (2009) Low-voltage electricity customer profiling based on load data clustering. In: Proceedings of the International Database Engineering & Applications Symposium, pp 330–333

  37. Kim Y-I, Kang S-J, Ko J-M, Choi S-H (2011) A study for clustering method to generate typical load profiles for smart grid. In: Proceedings of the IEEE International Conference on Power Electronics and ECCE Asia, pp 1102–1109

  38. Flath C, Nicolay D, Conte T, Dinther C, Filipova-Neumann L (2012) Cluster analysis of smart metering data. Bus Inf Syst Eng 4:31–39

    Article  Google Scholar 

  39. Molina-Markham A, Shenoy P, Fu K, Cecchet E, Irwin D (2010) Private memoirs of a smart meter. In: Proceedings of the ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp 61–66

  40. Bergman D, Jin D, Juen J, Tanaka N, Gunter C, Wright A (2011) Distributed non-intrusive load monitoring. In: Proceedings of the Innovative Smart Grid Technologies, pp 1–8

  41. Kalogridis G, Efthymiou C, Denic S, Lewis T, Cepeda R (2010) Privacy for smart meters: towards undetectable appliance load signatures. In: Proceedings of the IEEE International Conference on Smart Grid Communications, pp 232–237

  42. Lines J, Bagnall A, Caiger-Smith P, Anderson S (2011) Classification of household devices by electricity usage profiles. In: Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, pp 403–412

  43. Erol-Kantarci M, Mouftah H (2010) Prediction-based charging of PHEVs from the smart grid with dynamic pricing. In: Proceedings of the IEEE Conference on Local Computer Networks, pp 1032–1039

  44. Chen F, Dai J, Wang B, Sahu S, Naphade M, Lu C-T (2011) Activity analysis based on low sample rate smart meters. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 240–248

  45. Ranganathan R, Qiu R, Hu Z, Hou S, Pazos-Revilla M, Zheng G, Chen Z, Guo N (2011) Cognitive radio for smart grid: theory, algorithms, and security. Bus Inf Syst Eng 2011:1–14

    Google Scholar 

  46. De Silva D, Yu X, Alahakoon D, Holmes G (2011) Incremental pattern characterization learning and forecasting for electricity consumption using smart meters. In: Proceedings of the IEEE International Symposium on Industrial Electronics, pp 807–812

  47. Chen C, Xu T, Piao Z, Yuan Y (2009) Power quality disturbances classification based on multi-class classification SVM. In: Proceedings of the International Conference on Power Electronics and Intelligent Transportation System, vol 1, pp 290–294

  48. Cepeda J, Colome D, Castrillon N (2011) Dynamic vulnerability assessment due to transient instability based on data mining analysis for smart grid applications. In: Proceedings of the IEEE PES Conference on Innovative Smart Grid Technologies

  49. Ranganathan P, Nygard K (2011) Smart grid data analytics for decision support. In: Proceedings of the IEEE Electrical Power and Energy Conference

  50. Hu G-S, Xie J, Zhu F-F (2005) Classification of power quality disturbances using wavelet and fuzzy support vector machines. In: Proceedings of International Conference on Machine Learning and Cybernetics, vol 7, pp 3981–3984

  51. Miller P (2010) The smart swarm: how understanding flocks schools and colonies can make us better at communicating decision making and getting things done. Avery Publishing Group Inc., Wayne

    Google Scholar 

  52. Huang Y, Brocco A, Kuonen P, Courant M, Hirsbrunner B (2008) Smartgrid: a fully decentralized grid scheduling framework supported by swarm intelligence. In: Proceedings of the International Conference on Grid and Cooperative Computing, pp 160–168

  53. Linda O, Manic M, Vollmer T (2012) Improving cyber-security of smart grid systems via anomaly detection and linguistic domain knowledge. In: Proceedings of the International Symposium on Resilient Control Systems, pp 48–54

  54. Ramos C, Liu C-C (2011) AI in power systems and energy markets. IEEE Intell Syst 26(2):5–8

    Article  Google Scholar 

  55. Awad A, German R (2012) Self-organizing smart grid services. In: Proceedings of the International Conference on Next Generation Mobile Applications, Services and Technologies, pp 205–210

  56. Fereidunian A, Zamani M, Boroomand F, Jamalabadi H, Lesani H, Lucas C, Shariat-Torbaghan S, Meydani M (2010) AAHES: a hybrid expert system realization of adaptive autonomy for smart grid. In: Proceedings of the IEEE Innovative Smart Grid Technologies Conference Europe, pp 1–7

  57. Wong J, Vargas A, Chadha K, Devdas A, Lin C, Kuyee J (2010) Integrated design and implementation of toronto’s smart distribution grid. In: Proceedings of the IEEE International Conference on Smart Grid Communications, pp 455–460

  58. Cho HS, Yamazaki T, Hahn M (2010) AERO: extraction of user’s activities from electric power consumption data. IEEE Trans Consum Electron 58(3):2011–2018

    Article  Google Scholar 

  59. Saini MK, Kapoor R (2012) Classification of power quality events a review. Int J Electr Power Energy Syst 43(1):11–19

    Article  MathSciNet  Google Scholar 

  60. Simonov M, Zich R, Mussetta M (2011) Information processing in smart grids and consumption dynamics. In: Soro A, Vargiu E, Armano G, Paddeu G (eds) Information retrieval and mining in distributed environments, vol 324. Springer, Berlin, pp 267–286

  61. Su W, Eichi H, Zeng W, Chow M-Y (2012) A survey on the electrification of transportation in a smart grid environment. IEEE Trans Ind Inf 8(1):1–10

    Article  Google Scholar 

  62. Belkacemi R, Feliachi A, Choudhry M, Saymansky J (2011) Multi-agent systems hardware development and deployment for smart grid control applications. In: Proceedings of the Power and Energy Society General Meeting, pp 1–8

  63. Saxena D, Singh S, Verma K (2010) Application of computational intelligence in emerging power systems. Int J Eng Sci Technol 2(3):1–7

    Google Scholar 

  64. Ma Y, Zhou L, Tse N, Osman A, Lai L (2009) An initial study on computational intelligence for smart grid. In: Proceedings of the International Conference on Machine Learning and Cybernetics vol 6, pp 3425–3429

  65. Singh A, Dagli C (2009) Multi-objective stochastic heuristic methodology for tradespace exploration of a network centric system of systems. In: Proceedings of the Systems Conference, pp 218–223

  66. Holland O, Attar A, Olaziregi N, Sattari N, Aghvami A (2006) A universal resource awareness channel for cognitive radio. In: Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

  67. Jakubowski M, Pastuszak G (2007) Multi-path adaptive computation-aware search strategy for block-based motion estimation. In: Proceedings of the International Conference on Computer as a Tool, pp 175–81

  68. Zhu J, Zhuang E, Ivanov C, Yao Z (2011) A data-driven approach to interactive visualization of power systems. IEEE Trans Power Syst 26(4):2539–2546

    Article  Google Scholar 

  69. Bank J, Omitaomu O, Fernandez S, Liu Y (2009) Visualization and classification of power system frequency data streams. In: Proceedings of the IEEE International Conference on Data Mining Workshops, pp 650–655

  70. Curry E (2012) System of Systems Information Interoperability using a Linked Dataspace. In: Proceedings of the IEEE 7th International Conference on System of Systems Engineering, pp 101–106

  71. Bou Ghosh S, Ranganathan P, Salem S, Tang J, Loegering D, Nygard K (2010) Agent-oriented designs for a self healing smart grid. In: Proceedings of the IEEE International Conference on Smart Grid Communications, pp 461–466

  72. Venayagamoorthy G (2009) Potentials and promises of computational intelligence for smart grids. In: Proceedings of the IEEE Power Energy Society General Meeting, pp 1–6

  73. He H (2010) Toward a smart grid: integration of computational intelligence into power grid. In: The 2010 International Joint Conference on Neural Networks, pp. 1–6

  74. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–54

    Google Scholar 

  75. Liu B (2007) Web data mining: exploring hyperlinks, contents, and usage data. Springer, Berlin

    Google Scholar 

  76. Han J (2005) Data mining: concepts and techniques. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  77. Tsai C-W, Liao M-Y, Yang C-S, Chiang M-C (2011) Classification algorithms for interactive multimedia services: a review. In: Multimedia Tools and Applications, pp 1–29

  78. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  79. Xu R, II DW (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  80. Liu B (2007) Web data mining: exploring hyperlinks, contents, and usage. Data data-centric systems and applications. Springer, Berlin

    Google Scholar 

  81. Steinbach M, Karypis G, Kumar V (2000) A comparison of document clustering techniques. In: Proceedings of the KDD Workshop on Text Mining, pp 1–2

  82. De Silva D, Yu X, Alahakoon D, Holmes G (2011) Semi-supervised classification of characterized patterns for demand forecasting using smart electricity meters. In: International Conference on Electrical Machines and Systems, pp 1–6

  83. Reeves, CR (eds) (1993) Modern heuristic techniques for combinatorial problems. Wiley, New York

    MATH  Google Scholar 

  84. Glover, F, Kochenberger, GA (eds) (1993) Handbook of metaheuristics. Kluwer Academic Publishers, Boston

    Google Scholar 

  85. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  86. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Berlin

    Book  MATH  Google Scholar 

  87. Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, New York

    Book  Google Scholar 

  88. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Sci Agric 220(4598):671–680

    Article  MATH  MathSciNet  Google Scholar 

  89. Černý V (1982) A thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. Technical Reports, Comenius University, Bratislava, Czechoslovakia

  90. Glover F (1989) Tabu search—part I.. ORSA J Comput 1(3):190–206

    Article  MATH  Google Scholar 

  91. Glover F (1990) Tabu search—part II.. ORSA J Comput 2(1):4–32

    Article  MATH  Google Scholar 

  92. Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, Boston

    Book  MATH  Google Scholar 

  93. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press

  94. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41

    Article  Google Scholar 

  95. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  96. Dorigo M, Stützle T (2004) Ant colony optimization (Bradford books). MIT Press, Cambridge

    Google Scholar 

  97. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp 1942–1948

  98. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: Proceedings of the International Conference on Evolutionary Programming VII, (London, UK). Springer, London, pp 591–600

  99. Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  100. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, New York

    Google Scholar 

  101. Harley R, Liang J (2011) Computational intelligence in smart grids. In: Proceedings of the IEEE Symposium Searies on Computational Intelligence

  102. Jiang Z (2009) Computational intelligence techniques for a smart electric grid of the future. In: Proceedings of the Advances in Neural Networks, vol 5551, pp 1191–1201

  103. Werbos P (2011) Computational intelligence for the smart grid-history, challenges, and opportunities. IEEE Comput Intell Mag 6(3):14–21

    Article  Google Scholar 

  104. Venayagamoorthy G (2011) Dynamic, stochastic, computational and scalable technologies for smart grids. IEEE Comput Intell Mag 6(3):22–35

    Article  Google Scholar 

  105. Gungor V, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke G (2012) Smart grid and smart homes: key players and pilot projects. IEEE Ind Electron Mag 6(4):18–34

    Article  Google Scholar 

  106. Welch R, Venayagamoorthy G (2006) Comparison of two optimal control strategies for a grid independent photovoltaic system. In: Proceedings of the Industry Applications Conference, vol 3, pp 1120–1127

  107. Welch R, Venayagamoorthy G (2007) A fuzzy-PSO based controller for a grid independent photovoltaic system. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp 227–233

  108. Vale Z, Morais H, Khodr H, Canizes B, Soares J (2010) Technical and economic resources management in smart grids using heuristic optimization methods. In: Proceedings of the IEEE Power and Energy Society General Meeting, pp 1–7

  109. Tanaka K, Uchida K, Ogimi K, Goya T, Yona A, Senjy T, Funabashi T, Kim C-H (2011) Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error. IEEE Trans Smart Grid 2(3):438–444

    Article  Google Scholar 

  110. Caputo D, Grimaccia F, Mussetta M, Zich R (2010) Photovoltaic plants predictive model by means of ann trained by a hybrid evolutionary algorithm. In: Proceedings of the International Joint Conference on Neural Networks, pp 1–6

  111. Lee J, Park G-L, Kwak H-Y, Jeon H (2011) Design of an energy consumption scheduler based on genetic algorithms in the smart grid. In: Proceedings of the Third International Conference on Computational Collective Intelligence: Technologies and Applications, vol 1, pp 438–447

  112. Pedrasa M, Spooner T, MacGill I (2010) Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans Smart Grid 1(2):134–143

    Article  Google Scholar 

  113. Wang Z, Yang R, Wang L (2010) Multi-agent control system with intelligent optimization for smart and energy-efficient buildings. In: Proceedings of the Annual Conference on IEEE Industrial Electronics Society, pp 1144–1149

  114. Pathak AK, Chatterji S, Narkhedec MS (2012) Artificial intelligence based optimization algorithm for demand response management of residential load in smart grid. Int J Eng Innov Technol 2(4):136–141

    Google Scholar 

  115. Jain A, Jain M (2013) Fuzzy modeling and similarity based short term load forecasting using swarm intelligence-a step towards smart grid. In: Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, vol 202, pp 15–27

  116. 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

    Article  Google Scholar 

  117. Saber AY, Venayagamoorthy GK (2010) Intelligent unit commitment with vehicle-to-grid—a cost-emission optimization. J Power Sources 195(3):898–911

    Article  Google Scholar 

  118. Su W, Chow M-Y (2011) Performance evaluation of a PHEV parking station using particle swarm optimization. In: Proceedings of the IEEE Power and Energy Society General Meeting, pp 1–6

  119. Su W, Chow M-Y (2012) Performance evaluation of an eda-based large-scale plug-in hybrid electric vehicle charging algorithm. IEEE Trans Smart Grid 3(1):308–315

    Article  Google Scholar 

  120. Church C, Morsi WG, El-Hawary ME, Diduch CP, Chang LC (2011) Voltage collapse detection using ant colony optimization for smart grid applications. Electr Power Syst Res 81(8):1723–1730

    Article  Google Scholar 

  121. Gonzalez FG (2013) An intelligent controller for the smart grid. Procedia Comput Sci 16:776–785

    Article  Google Scholar 

  122. Alipuria B, Asare-Bediako B, Groot RJWD, Sarker J, Slootweg J, Kling W (2012) Incorporating solar home systems for smart grid applications. In: Proceedings of the Universities Power Engineering Conference, pp 1–6

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Acknowledgments

The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the National Science Council of Taiwan, ROC, under Contracts NSC101-2221-E-041-012, NSC102-2219-E-006-001, and NSC99-2221-E-110-052 and in part by the Ministry of Education of Taiwan, ROC, under Contract D102-23015.

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  1. Department of Applied Informatics and Multimedia, Chia Nan University of Pharmacy and Science, Tainan, Taiwan, ROC

    Chun-Wei Tsai

  2. Department of Networks, Security and Multimedia, IMT/TELECOM Bretagne/IRISA, Cesson-Sévigné, France

    Alexander Pelov

  3. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC

    Ming-Chao Chiang

  4. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC

    Chu-Sing Yang

  5. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

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Tsai, CW., Pelov, A., Chiang, MC. et al. Computational awareness for smart grid: a review. Int. J. Mach. Learn. & Cyber. 5, 151–163 (2014). https://doi.org/10.1007/s13042-013-0185-1

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