Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T10:44:12.242Z Has data issue: false hasContentIssue false
  • English
  • Français

OPTIMUM ENERGY FOR ENERGY PACKET NETWORKS

Published online by Cambridge University Press:  09 April 2017

Yonghua Yin
Yonghua Yin*
Affiliation:
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2BT, UK E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The concept of Energy Packet Network (EPN) proposed by Gelenbe, is a new framework for modeling power grids that takes distributed energy generation such as renewable energy sources into consideration, and which contributes to modeling the smart grid. Based on G-network theory, this paper presents a simplified model of EPN and formulates energy-distribution as an optimization problem. We analyze it theoretically, and detail its optimal solutions. In addition to using existing optimization algorithms, a heuristic algorithm is proposed to solve for EPN optimization. The optimal solutions and efficacy of the algorithm are illustrated with numerical experiments. Further, we present an EPN with disconnections and a similar optimization problem is investigated. Optimal solutions are presented, and numerical results using the analytic optimal solutions, random solutions, a cooperative particle swarm optimizer and a heuristic algorithm illustrate the power of different approaches for solving energy-distribution problems using the EPN formalism.

Keywords

queueing theorystochastic modeling
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 31 , Special Issue 4: G-Networks and their Applications , October 2017 , pp. 516 - 539
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Abdelrahman, O.H. & Gelenbe, E. (2016). A diffusion model for energy harvesting sensor nodes. in IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS’16). IEEEXplore, September 2016, pp. 154–158.CrossRefGoogle Scholar
2. Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R.E. (1995). Gambling in a rigged casino: The adversarial multi-armed bandit problem. in Foundations of Computer Science, 1995. Proceedings., 36th Annual Symposium on. IEEE, pp. 322–331.CrossRefGoogle Scholar
3. Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R.E. (2002). The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32(1): 48–77.CrossRefGoogle Scholar
4. Bamberger, Y., Baptista, J., Belmans, R., Buchholz, B.M., Chebbo, M., Doblado, J.L.D.V., Efthymiou, V., Gallo, L., Handschin, E., & Hatziargyriou, N. et al. (2006). Vision and strategy for Europe's electricity networks of the future.Google Scholar
5. Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., & Pentikousis, K. (2010). Energy-efficient cloud computing. The Computer Journal 53(7): 1045–1051.CrossRefGoogle Scholar
6. Brun, O., Wang, L., & Gelenbe, E. (2016). Big data for autonomic intercontinental overlays. IEEE Journal on Selected Areas in Communications 34(3): 575–583.CrossRefGoogle Scholar
7. Caldon, R., Patria, A.R., & Turri, R. (2004). Optimal control of a distribution system with a virtual power plant. Bulk power system dynamics and control, Cortina. d'Ampezzo, Italy.Google Scholar
8. Ceran, E.T. & Gelenbe, E. (2016). Energy packet model optimisation with approximate matrix inversion. In Proceedings of the 2nd International Workshop on Energy – Aware Simulation. ACM, p. 4.CrossRefGoogle Scholar
9. Den Bergh, V. & Engelbrecht, A.P. (2000). Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal 26: 84–90.Google Scholar
10. Fang, X., Misra, S., Xue, G., & Yang, D. (2012). Smart grid – the new and improved power grid: a survey. IEEE Communications Surveys & Tutorials, 14(4): 944–980.CrossRefGoogle Scholar
11. Fang, X., Yang, D., & Xue, G. (2011). Online strategizing distributed renewable energy resource access in Islanded microgrids. In 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), IEEE, pp. 1–6.CrossRefGoogle Scholar
12. Farhangi, H. (2010). The path of the smart grid. IEEE Power and Energy Magazine, 8(1): 18–28.CrossRefGoogle Scholar
13. Fourneau, J.-M. & Gelenbe, E. (2004). Flow equivalence and stochastic equivalence in G-networks. Computational Management Science 1(2): 179–192.CrossRefGoogle Scholar
14. Fourneau, J.-M., Gelenbe, E., & Suros, R. (1996). G-networks with multiple classes of negative and positive customers. Theoretical Computer Science 155(1): 141–156.CrossRefGoogle Scholar
15. Gelenbe, E. (1990). Stability of the random neural network model. Neural Computation 2(2): 239–247.CrossRefGoogle Scholar
16. Gelenbe, E. (1993). G-networks with signals and batch removal. Probability in the Engineering and Informational Sciences 7(3): 335–342.CrossRefGoogle Scholar
17. Gelenbe, E. (1993). Learning in the recurrent random neural network. Neural Computation 5(1) 154–164.CrossRefGoogle Scholar
18. Gelenbe, E. (1993). G-networks by triggered customer movement. Journal of Applied Probability 30(03): 742–748.CrossRefGoogle Scholar
19. Gelenbe, E. (1994). G-networks: a unifying model for neural and queueing networks. Annals of Operations Research 48(5): 433–461.CrossRefGoogle Scholar
20. Gelenbe, E. (2009). Steps toward self-aware networks. Communications of the ACM 52(7): 66–75.CrossRefGoogle Scholar
21. Gelenbe, E. (2012). Energy packet networks: smart electricity storage to meet surges in demand. In Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 1–7.CrossRefGoogle Scholar
22. Gelenbe, E. (2012). Energy packet networks: adaptive energy management for the cloud. In Proceedings of the 2nd International Workshop on Cloud Computing Platforms. ACM, p. 1.CrossRefGoogle Scholar
23. Gelenbe, E. (2014). Adaptive management of energy packets. In 2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW). IEEE, pp. 1–6.CrossRefGoogle Scholar
24. Gelenbe, E. (2014). A sensor node with energy harvesting. ACM SIGMETRICS Performance Evaluation Review 42(2): 37–39.CrossRefGoogle Scholar
25. Gelenbe, E. (2014). Error and energy when communicating with spins. In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, pp. 784–787.CrossRefGoogle Scholar
26. Gelenbe, E. (2015). Synchronising energy harvesting and data packets in a wireless sensor. Energies 8(1): 356–369. [Online]. Available: http://www.mdpi.com/1996-1073/8/1/356 CrossRefGoogle Scholar
27. Gelenbe, E. (2015). Errors and power when communicating with spins. IEEE Transactions on Emerging Topics in Computing 3(4): 483–488.CrossRefGoogle Scholar
28. Gelenbe, E. (2016). Agreement in spins and social networks. ACM SIGMETRICS Performance Evaluation Review 44(2): 15–17.CrossRefGoogle Scholar
29. Gelenbe, E. & Caseau, Y. (2015). The impact of information technology on energy consumption and carbon emissions. Ubiquity, vol. 2015, no. June, p. 1.CrossRefGoogle Scholar
30. Gelenbe, E. & Ceran, E.T. (2015). Central or distributed energy storage for processors with energy harvesting. ustainable Internet and ICT for Sustainability (SustainIT), 2015. IEEE, pp. 1–3.CrossRefGoogle Scholar
31. Gelenbe, E. & Ceran, E.T. (2016). Energy packet networks with energy harvesting. IEEE Access 4: 1321–1331.CrossRefGoogle Scholar
32. Gelenbe, E. & Fourneau, J.-M. (2002). G-networks with resets. Performance Evaluation 49(1): 179–191.CrossRefGoogle Scholar
33. Gelenbe, E., Gesbert, D., Gunduz, D., Kulah, H., & Uysal-Biyikoglu, E. (2013). Energy harvesting communication networks: Optimization and demonstration (the e-crops project). In 24th Tyrrhenian International Workshop on Digital Communications-Green ICT (TIWDC), 2013. IEEE, pp. 1–6.CrossRefGoogle Scholar
Gelenbe, E. & Gunduz, D. (2013). Optimum power level for communications with interference. Digital Communications-Green ICT (TIWDC), 2013 24th Tyrrhenian International Workshop on. IEEE, pp. 1–6.CrossRefGoogle Scholar
34. Gelenbe, E. & Hussain, K. (2002). Learning in the multiple class random neural network. IEEE Transactions on Neural Networks 13(6): 1257–1267.CrossRefGoogle ScholarPubMed
35. Gelenbe, E. & Labed, A. (1998). G-networks with multiple classes of signals and positive customers. European Journal of Operational Research 108(2): 293–305.CrossRefGoogle Scholar
36. Gelenbe, E. & Marin, A. (2015). Interconnected wireless sensors with energy harvesting. International Conference on Analytical and Stochastic Modeling Techniques and Applications. Springer International Publishing, May 2015, pp. 87–99.CrossRefGoogle Scholar
37. Gelenbe, E. & Oklander, B. (2014). Energy and QoS in a cognitive channel. European Wireless 2014; 20th European Wireless Conference. In Proceedings of VDE, pp. 1–6.Google Scholar
38. Gelenbe, E. & Timotheou, S. (2008). Random neural networks with synchronized interactions. Neural Computation 20(9): 2308–2324.CrossRefGoogle ScholarPubMed
39. Guan, X., Xu, Z., & Jia, Q.-S. (2010). Energy-efficient buildings facilitated by microgrid. IEEE Transactions on Smart Grid, 1(3): 243–252.CrossRefGoogle Scholar
40. Gelenbe, E. & Yin, Y. (2016). Deep learning with random neural networks. In International Joint Conference on Neural Networks (IJCNN’16). IEEEXpress, July 2016.CrossRefGoogle Scholar
41. Gelenbe, E. & Yin, Y. (2016). Deep learning with random neural networks. In SAI Intelligent Systems Conference, 2016. IEEEXpress, September 2016.CrossRefGoogle Scholar
42. Hledik, R. (2009). How green is the smart grid? The Electricity Journal 22(3): 29–41.CrossRefGoogle Scholar
43. Kadioglu, Y.M. & Gelenbe, E. (2016). Packet transmission with k energy packets in an energy harvesting sensor. In Proceedings of the 2nd International Workshop on Energy-Aware Simulation. ACM, p. 1.CrossRefGoogle Scholar
44. Lasseter, R.H. & Paigi, P. (2004). Microgrid: a conceptual solution. In 2004 IEEE 35th Annual Power Electronics Specialists Conference, 2004 (PESC 04), vol. 6. IEEE, pp. 4285–4290.CrossRefGoogle Scholar
45. Lombardi, P., Powalko, M., & Rudion, K. (2009). Optimal operation of a virtual power plant. In Power & Energy Society General Meeting, 2009 (PES’09). IEEE, pp. 1–6.CrossRefGoogle Scholar
46. McDaniel, P. & McLaughlin, S. (2009). Security and privacy challenges in the smart grid. IEEE Security & Privacy 7(3): 75–77.CrossRefGoogle Scholar
47. Molderink, A., Bakker, V., Bosman, M.G., Hurink, J.L., & Smit, G.J. (2010). Management and control of domestic smart grid technology. IEEE Transactions on Smart Grid, 1(2): 109–119.CrossRefGoogle Scholar
48. Palensky, P. & Kupzog, F. (2013). Smart grids. Annual Review of Environment and Resources 38: 201–226.CrossRefGoogle Scholar
49. Takuno, T., Koyama, M., & Hikihara, T. (2010). In-home power distribution systems by circuit switching and power packet dispatching. In 2010 First IEEE International Conference on Smart Grid Communications, 2010 (SmartGridComm, 2010), IEEE, pp. 427–430.CrossRefGoogle Scholar
50. Tashiro, K., Takahashi, R., & Hikihara, T. (2012). Feasibility of power packet dispatching at in-home dc distribution network. In 2012 IEEE Third International Conference on Smart Grid Communications, 2012 (SmartGridComm), IEEE, pp. 401–405.CrossRefGoogle Scholar
51. Timotheou, S. (2008). Nonnegative least squares learning for the random neural network. In Kůrková, V., Neruda, R. & Koutník, J. (eds), Artificial Neural Networks – ICANN 2008: 18th International Conference, Prague, Czech Republic, September 3–6, 2008, Proceedings, Part I. Springer, Berlin, Heidelberg, pp. 195–204.Google Scholar
52. Van den Bergh, F. & Engelbrecht, A.P. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3): 225–239.CrossRefGoogle Scholar
53. Wang, L. & Gelenbe, E. (2015). Adaptive dispatching of tasks in the cloud. IEEE Transactions on Cloud Computing PP(99): 1–1.CrossRefGoogle Scholar
54. Yin, Y. (2016). Line-search aided non-negative least-square learning for random neural network. In Abdelrahman, O.H., Gelenbe, E., Gorbil, G. & Lent, R. (eds), Information Sciences and Systems 2015: 30th International Symposium on Computer and Information Sciences (ISCIS 2015). Springer International Publishing, Cham, pp. 181–189.CrossRefGoogle Scholar