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Risk-aware service level agreement modeling in smart grid

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Abstract

As advanced Smart Grid environments grow from a simple grid towards a complex provider ecosystem, there is an uncertain challenge on those grid environments that need to manage a risk paradigm. We present an automated risk-aware service level agreements modeling to the grid provider for speed automated pricing and getting better performance to program as an agent-oriented platform. The key idea of our novel approach is proposing a risk level agreements contract and a pricing model to decrease the complexity of previous methods from an off-line service level agreement to an on-line risk level agreement for managing the risk lifecycle of contracts used to record the rights and obligations of the services and their consumers. Based on a risk level agreements contract the model optimizes resource management according to the business objective level of the provider with an online risk-aware rendezvous to define the penalty level of the cost model. The corresponding quality of service criteria is defined based on multi-class risk-aware service level agreements between Smart Grid providers and their power consumers which include the tail distributions of the per-class costs in addition to the more standard quality of service metrics such as throughput and mean delays. Our empirical experiments show the benefits of the proposed approach.

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References

  1. Bakraouy Z et al (2018) Autonomous SLAs Negotiation Based on Agreement-Broker: Services Availability. In: 5th International Congress on Information Science and Technology (CiSt), pp 48–53

  2. Baldan P et al (2010) Petri nets for modelling metabolic pathways: a survey. Nat Comput 9(4):955–989

    Article  MathSciNet  MATH  Google Scholar 

  3. Bessa RJ et al (2017) Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energ J 10(9):1402

    Google Scholar 

  4. Birda S et al (2014) Distributed (green) data centers: a new concept for energy, computing, and telecommunications. Energy Sustain Dev 19:83–91

    Article  Google Scholar 

  5. Carpinelli G, Proto D, Russo A (2017) Optimal Planning of Active Power Filters in a Distribution System Using Trade-off/Risk Method. IEEE Trans Power Deliv 32(2):841–851

    Article  Google Scholar 

  6. Chrysoulas C, Fasli M (2017) Towards an adaptive SOA-based QoS & Demand-Response Provisioning Architecture for the Smart Grid. J Commun Softw Syst 13(2):77–86

    Article  Google Scholar 

  7. de Carvalho CAB et al (2017) State of the art and challenges of security SLA for cloud computing. Comput Electr Eng 59:141–152

    Article  Google Scholar 

  8. Djemame K et al (2011) Brokering of risk-aware service level agreements in grids. Concurr Comput Pract Exper 23(13):1558–1582. https://doi.org/10.1002/cpe.1721

    Article  Google Scholar 

  9. Faheem M et al (2018) Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Comput Sci Rev 30:1–30

    Article  Google Scholar 

  10. Gandhi R et al (2015) Duet: Cloud scale load balancing with hardware and software. ACM SIGCOMM Comput Commun Rev 44(4):27–38

    Article  Google Scholar 

  11. Garcia-Valls M, Marisol P, Gokhale A (2017) Reliable software technologies and communication middleware: A perspec- tive and evolution directions for cyber-physical system. Mob Cloud Comput:171–176

  12. Gouveia C et al (2013) Coordinating storage and demand response for microgrid emergency operation. IEEE Trans SmartGrid 4(4):1898–1908

    Google Scholar 

  13. Hassan HAH, Renga D, Meo M, Nuaymi L (2019) A Novel Energy Model for Renewable Energy-Enabled Cellular Networks Providing Ancillary Services to the Smart Grid. IEEE Transactions on Green Communications and Networking

  14. Hussain I et al (2019) Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers. Renew Energy 134:1017–1033

    Article  Google Scholar 

  15. ITIL (IT Infrastructure Library):ITIL Version 3-Service Improvement (2014). Available: http://www.itil-officialsite.com [Online]

  16. Jones KB, Mark J, Roxana-Andreea M (2017) Securing our energy future: three international perspectives on microgrids and distributed renewables as a path toward resilient communities. Environ Hazards 16(2):99–115

    Article  Google Scholar 

  17. Keskinen J (2018) Supercapacitors on flexible substrates for energy autonomous electronics. Tampere University of Technology. Publication, pp 1562

  18. Khatibzadeh A et al (2017) Multiagent-based controller for voltage enhancement in AC/DC hybrid microgrid using energy storages. Energ J (MDP) 10(2):169

    Google Scholar 

  19. Kopka R (2017) Estimation of supercapacitor energy storage based on fractional differential equations. Nanoscale Res Lett 12(1):636–646

    Article  Google Scholar 

  20. Kouchachvili L, Wahiba Y, Entchev E (2018) Hybrid battery/supercapacitor energy storage system for the electric vehicles. J Power Sourc 374:237–248

    Article  Google Scholar 

  21. Lou X, Yau DKY, Nguyen HH, Chen B (2013) Profit optimal and stability-aware load curtailment in smart grids. IEEE Trans Smart Grid 4(3):1411–1420

    Article  Google Scholar 

  22. Methenitis G, Kaisers M, La H (2018) PoutréRenewable electricity trading through SLAs. Energy Inf 1(1):57

    Article  Google Scholar 

  23. Murata T (2002) Petri nets: Properties, analysis and applications. Proc IEEE 77(4):541–580

    Article  Google Scholar 

  24. Nunna K, Doolla S (2013) Energy management in microgrids using demand response and distributed storage-a multiagent approach. IEEE Trans Power Deliv 28(2):939–947

    Article  Google Scholar 

  25. Pedram M et al (2010) Hybrid electrical energy storage systems. International symposium on Low power electronics and design ACM/IEEE, pp

  26. Ramey SM et al (2018) Technology scaling implications for BTI reliability. Microelectron Reliab 82: 42–50

    Article  Google Scholar 

  27. Rehman Z et al (2018) Prosumer based energy management and sharing in smart grid. Renew Sust Energ Rev 82:1675–1684

    Article  Google Scholar 

  28. Saleem A, Afzal MK, Ateeq M, Kim SW, Zikria YB (2020) Intelligent learning automata-based objective function in RPL for IoT. Sustain Cit Soc Jo 59:102234

  29. Sandeep K, Chanana S (2018) Smart operations of smart grids integrated with distributed generation: A review. Renew Sustain Energy Rev 81:524–535

    Article  Google Scholar 

  30. Seim LH (2012) Modeling, control and experimental testing of a supercapacitor/battery hybrid system: passive and semi-active topologies. Master’s thesis Norwegian University of Life Sciences

  31. SuperCapacitor User’s Manual Vol. 07. Electronic Components TOKIN (2017). Available: https://docs-emea.rs-online.com [Online]

  32. Tiemann PH et al (2020) Electrical energy storage for industrial grid fee reduction–A large scale analysis. Energy Convers Manag J 208:112539

  33. Walmir S, Kimura H, Sobreiro VA (2017) An analysis of the literature on systemic financial risk: a survey. J Financ Stab 28:91–114

    Article  Google Scholar 

  34. Wang YN (2018) Risk Control Cyber Physical System for Active Distributed Network, 2th IEEE Conference on Energy Internet and Energy System Integration, pp 1-6

  35. Yeo CS, Buyya R (2009) Integrated risk analysis for a commercial computing service in utility computing. J Grid Comput 7(1):1–24

    Article  Google Scholar 

  36. Ying-Chao H, Michailidis G (2018) Modeling and Optimization of Time-Of-Use Electricity Pricing Systems. IEEE Transactions on Smart Grid

  37. Zabbix Tool Market (2017). Available: https://www.zabbix.com [Online]

  38. Zikria YB, Afzal MK, Kim SW (2020) Internet of Multimedia Things (ioMT): opportunities, Challenges and Solutions. Sens J 20:2334. https://doi.org/10.3390/s20082334

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge the High Performance Computing Center (HPC) of Institute for Research in Fundamental Sciences (IPM-Iran) for providing high performance computing resources that have contributed to the research results reported within this paper.

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Authors and Affiliations

  1. Computer Engineering Department, Shahed University, Tehran, Iran

    Aminollah Mahabadi

  2. Acoustic Research Center, Shahed University, Tehran, Iran

    Aminollah Mahabadi

  3. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Aminollah Mahabadi

  4. Electrical Engineering Department, Shahed University, Tehran, Iran

    Mohammad Reza Besmi

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  1. Aminollah Mahabadi
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  2. Mohammad Reza Besmi
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Correspondence to Aminollah Mahabadi.

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Mahabadi, A., Besmi, M.R. Risk-aware service level agreement modeling in smart grid. Multimed Tools Appl 80, 1433–1456 (2021). https://doi.org/10.1007/s11042-020-09787-5

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  • DOI: https://doi.org/10.1007/s11042-020-09787-5

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