Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization

Brook W. Abegaz; Satish M. Mahajan; Ebisa O. Negeri

doi:10.1515/ijeeps-2015-0154

Article

Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization

Brook W. Abegaz , Satish M. Mahajan and Ebisa O. Negeri

Published/Copyright: April 21, 2016

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From the journal International Journal of Emerging Electric Power Systems Volume 17 Issue 3

Abstract

Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers’ level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.

Keywords: energy efficiency; energy management; energy welfare; prosumer; energy community; smart-grid

Appendix

To optimize the function LW,hit,λi, it was differentiated with the independent variables, x1=dfinh,a,t, x2=dgreenfinh,t, x3=sfint and x4=dpresh,t and the Lagrangian multipliers x5=λM and x6=λC as given in eqs (57) to (62).

∂LW,h1t,λM∂x1=∂Wh,a,t∂x1−∂λihit∂x1: x1=dfinh,a,t

(57)Σt∈T(Σh∈HΣa∈A[−α*dfin(h,a,t)−2*μ(a,t)*dfin(h,a,t)]−λC*Σh∈HΣa∈A[−2*μ(a,t)*dfin(h,a,t)])=0

∂LW,h2t,λM∂x2=∂Wh,a,t∂x2−∂λihit∂x2:x2=dgreenfinh,t

(58)Σt∈T(Σh∈HΣa∈A[−ωg+∏t∈T(μg,1)]−λM−λC*[−Σh∈HΣa∈A[∏t∈T(μg,1)])]=0

∂LW,h3t,λM∂x3=∂Wh,a,t∂x3−∂λihit∂x3: x3=sfinh,t

(59)Σt∈T{Σh∈H[−ωs+∏t∈T(θs,1)]}−λM−λC*[∏t∈T(θs,1)]]=0

∂LW,h4t,λM∂x4=∂Wh,a,t∂x4−∂λihit∂x4: x4=dpresh,t

(60)Σt∈T{Σh∈H[−ωp−∏t∈T(μp,1)]}+λM−λC*[−∏t∈T(μp,1)]]=0

∂LW,h5t,λM∂x5=∂Wh,a,t∂x5−∂λihitt∂x5: x5=λM

(61)dgreenfint−Pgt+Pextt+sfint−dinitt−dshiftt=0

∂LW,h6t,λM∂x6=∂Wh,a,t∂x6−∂λihit∂x6: x6=λC

(62)Mct−Ict−ih=0

After solving for λM and λC values, the optimal energy management values were computed in eqs (63) to (66).

(63)dfin∗a,t=α∗∑i=1Tdinita,tλC−1∗−384∗μa,t−13824∗C+442368

(64)dshift∗t=dinitt+dfin∗a,t

(65)sfin∗t=Pextt+dshift∗t+S.R.

(66)dgreenfin∗t=Pgt−Pextt−sfin∗t+dinitt+dshiftt

References

1. Madrigal M, Bhatia M, Elizondo G, Sarkar A, Kojima M. Twin peaks: Surmounting the global challenges of energy for all and greener, more efficient electricity services. IEEE Power Energy Mag May-Jun 2012;10:20–9.10.1109/MPE.2012.2188666Search in Google Scholar

2. Rudnick H. Evolution of energy: Global developments and challenges. IEEE Power Energy Mag 2012;10(3):12–19.10.1109/MPE.2012.2187228Search in Google Scholar

3. Rathnayaka AJD, Potdar VM, Dillon T, Hussain O, Kuruppu S. Goal-oriented prosumer community groups for the smart grid. IEEE Technol Soc Mag Mar 2014;33:41–8.10.1109/MTS.2014.2301859Search in Google Scholar

4. Logenthiran T, Srinivasan D, Shun TZ. Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid Sep 2012;3(3):1244–52.10.1109/TSG.2012.2195686Search in Google Scholar

5. Albadi MH, El-Saadany EF. Demand response in electricity markets: an overview. IEEE Power Eng. Society General Meeting. Tampa, FL, Jun 2007:1–5.10.1109/PES.2007.385728Search in Google Scholar

6. Strbac G. Demand side management: benefits and challenges. Energy Policy Dec 2008;35(1):4419–26.10.1016/j.enpol.2008.09.030Search in Google Scholar

7. Olivares DE, Cañizares CA, Kazerani M. A centralized energy management system for isolated microgrids. IEEE Trans Smart Grid Jul 2014;5(4):1864–75.10.1109/TSG.2013.2294187Search in Google Scholar

8. Wu Y, Lau VK, Tsang DH, Qian LP, Meng L. Optimal energy scheduling for residential smart grid with centralized renewable energy source. IEEE Syst J Jun 2014;8(2):562–76.10.1109/JSYST.2013.2261001Search in Google Scholar

9. Orwig KD, Ahlstrom ML, Banunarayanan V, Sharp J. Recent trends in variable generation forecasting and its value to the power system. IEEE Trans Sustainable Energy Jul 2015;6:924–33.10.1109/TSTE.2014.2366118Search in Google Scholar

10. Tan L, Zhu Z, Ge F, Xiong N. Utility maximization resource allocation in wireless networks: Methods and algorithms. IEEE Trans Syst Man Cybern Syst Jul 2015;45:1018–34.10.1109/TSMC.2015.2392719Search in Google Scholar

11. Khemka B, Friese R, Briceno LD, Siegel HJ. Utility functions and resource management in an oversubscribed heterogeneous computing environment. IEEE Trans Comput Aug 2015;64:2394–407.10.1109/TC.2014.2360513Search in Google Scholar

12. Stirling WC. Social utility functions – Part I: Theory. IEEE Trans Syst Man Cybern Part C: Appli Rev Nov 2005;35:522–32.10.1109/TSMCC.2004.843198Search in Google Scholar

13. Samadi P, Mohsenian-Rad AH, Schober R, Wong VWS, Jatskevich J. Optimal real-time pricing algorithm based on utility maximization for smart grid. 1st IEEE Int. Conf. on Smart Grid Communications (SmartGridComm). Gaithersburg, MD, Oct 2010.10.1109/SMARTGRID.2010.5622077Search in Google Scholar

14. Song X, Qu J. An improved real-time pricing algorithm based on utility maximization for smart grid. Intelligent Control and Automation (WCICA), 2014 11th World Congress on. Shenyang, China, 2014.Search in Google Scholar

15. Baharlouei Z, Hashemi M, Narimani H, Mohsenian-Rad H. Achieving optimality and fairness in autonomous demand response: Benchmarks and billing mechanisms. IEEE Trans Smart Grid Jun 2013;4(2):968–75.10.1109/TSG.2012.2228241Search in Google Scholar

16. Zhang W, Chen G, Su Y, Dong Z, Li J. A dynamic game behavior: Demand side management based on utility maximization with renewable energy and storage integration. Power Engineering Conference (AUPEC), 2014 Australasian Universities. Perth, WA, Oct 2014.10.1109/AUPEC.2014.6966581Search in Google Scholar

17. Mohsenian-Rad AH, Wong VWS, Jatskevich J, Schober R, Leon-Garcia A. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid Dec 2010;1(3):320–31.10.1109/TSG.2010.2089069Search in Google Scholar

18. Tardos E, Vazirani VV. Basic solution concepts and computational issues. In N Nisan, T Roughgarden, E Tardos, VV Vazirani editors. Algorithmic game theory. New York, NY: Cambridge University Press, 2007:12–13.10.1017/CBO9780511800481Search in Google Scholar

19. Salinas S, Li M, Li P. Multi-objective optimal energy consumption scheduling in smart grids. IEEE Trans Smart Grid Mar 2013;4(1):341–8.10.1109/TSG.2012.2214068Search in Google Scholar

20. Sechilariu M, Wanga BC, Locmenta, and F, Jougletb A. DC microgrid power flow optimization by multi-layer supervision control. Design and experimental validation. Energy Convers Manage Mar 2014;82:1–10.10.1016/j.enconman.2014.03.010Search in Google Scholar

21. Dos Santos LT, Sechilariu M, Locment F. Day-ahead microgrid optimal self-scheduling. comparison between three methods applied to isolated DC microgrid. Proceedings of the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON 2014). Dallas, TX, Nov 2014:2010–16.10.1109/IECON.2014.7048778Search in Google Scholar

22. Carpinelli G, Celli G, Mocci S, Mottola F, Pilo F, Proto D. Optimal integration of distributed energy storage devices in smart grids. IEEE Trans Smart Grid Jun 2013;4:985–95.10.1109/TSG.2012.2231100Search in Google Scholar

23. Chen J, Yang X, Zhu L, Zhang M. Microgrid multi-objective economic operation optimization considering reactive power. New York: Springer, 2014.10.1007/978-1-4614-4981-2_56Search in Google Scholar

24. Parvizimosaed M, Farmani F, Rahimi-Kian A. A multi-objective optimization for energy management in a renewable micro-grid system: A data mining approach. J Renewable Sustainable Energy 2014;6:1–5.10.1063/1.4873997Search in Google Scholar

25. Liang H, Zhuang W. Stochastic modeling and optimization in a microgrid: A survey. Energies 2014;7:2027–50.10.3390/en7042027Search in Google Scholar

26. Ross M, Abbey C, Bouffard F, Joos G. Multiobjective optimization dispatch for microgrids with a high penetration of renewable generation. IEEE Trans Sustainable Energy Oct 2015;6:1306–14.10.1109/TSTE.2015.2428676Search in Google Scholar

27. Liu H, Ji Y, Zhuang H, Wu H. Multi-objective dynamic economic dispatch of microgrid systems including vehicle-to-grid. Energies 2015;8:4476–95.10.3390/en8054476Search in Google Scholar

28. Herold R, Hertzog C. Data privacy for the smart grid. Boca Raton, FL: CRC Press, 2015.10.1201/b18005Search in Google Scholar

29. Wang Z, Yang K, Wang X. Privacy-preserving energy scheduling in microgrid systems. IEEE Trans Smart Grid Dec 2013;4:1810–20.10.1109/TSG.2013.2274466Search in Google Scholar

30. Boroyevich D, Cvetkovic I, Burgos R, Dong D. Intergrid: A future electronic energy network. IEEE J Emerg Sel Top Power Electron Sep 2013;1:127–38.10.1109/JESTPE.2013.2276937Search in Google Scholar

31. Abegaz BW, Mahajan SM. Optimal dispatch control of energy storage systems using forward-backward induction. International Conference on Clean Electrical Power (ICCEP). Taormina, Italy, Jun 2015. 731–6.10.1109/ICCEP.2015.7177572Search in Google Scholar

32. Nieuwenhout F, Brand A. The impact of wind power on day-ahead electricity prices in the Netherlands. IEEE Int. Conf. on the European Energy Market (EEM). Zagreb, Croatia, May 2011. 226–30.10.1109/EEM.2011.5953013Search in Google Scholar

33. NEMS. RELOAD database documentation and evaluation and use in national energy modeling system. Jul 9, 2001. Available at: www.onlocationinc.com/LoadShapesReload2001.pdf. Accessed Dec 2014.Search in Google Scholar

34. VHK. Elektrische Apparatuur in Nederlandse Huishoudens. Delft, Netherlands: Van Holsteijn en Kemna BV voor Senter Novem, 2008.Search in Google Scholar

35. ZicoKolter J, Johnson MJ. REDD: A public dataset for energy disaggregation research. Cambridge, MA: Massachusetts Institute of Technology, Aug 2011:94–8.Search in Google Scholar

36. Qurrent. Energy Prices. Qurrent B.V. 2015. Available at: https://www.qurrent.nl/energietarieven. Accessed 6 Sept 2015.Search in Google Scholar

37. IBM. IBM ILOG CPLEX Studio. Available at: http://www-01.ibm.com/software/info/ilog/. Accessed 2015.Search in Google Scholar

Received: 2015-9-6

Revised: 2016-2-13

Accepted: 2016-2-13

Published Online: 2016-4-21

Published in Print: 2016-6-1

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https://doi.org/10.1515/ijeeps-2015-0154

Keywords for this article

energy efficiency; energy management; energy welfare; prosumer; energy community; smart-grid