Share This Article:

Multi-Energy Simulation of a Smart Grid with Optimal Local Demand and Supply Management

Full-Text HTML XML Download Download as PDF (Size:1074KB) PP. 303-315
DOI: 10.4236/sgre.2015.611025    3,159 Downloads   3,646 Views   Citations

ABSTRACT

A simulation approach of a smart grid by cooperative bargaining is presented in this paper. Each participant of the smart grid determines its optimal schedule to meet its power and heating demand at minimal costs employing solar panels, fuel cells and batteries. This is done by solving a quadratic optimisation problem which takes the energy prices and the available devices into account. The energy prices are related to the demand and supply in the smart grid, so that a lower demand yields lower prices. The cooperative bargaining game is used to tune the participants’ optimal solution to obtain a Nash equilibrium. The computed solutions of the participants are validated against the capacities and structure of the smart grid by solving a multi-commodity flow problem. The presented model features multiple types of energy, so that they may be substituted to meet the participants’ demand. Furthermore, the participants may also act as supplier and not only as consumer, which allows decentralised generation of energy. The approach is validated in several experiments where effects like negative energy prices if generated energy exceeds the smart grid’s total demand and peak-shaving with even small-capacity batteries are exhibited.

Cite this paper

Kuschel, C. , Köstler, H. and Rüde, U. (2015) Multi-Energy Simulation of a Smart Grid with Optimal Local Demand and Supply Management. Smart Grid and Renewable Energy, 6, 303-315. doi: 10.4236/sgre.2015.611025.

References

[1] Biswas, M.M., Azim, M.S., Saha, T.K.S., Zobayer, U. and Urmi, M.C. (2013) Towards Implementation of Smart Grid: An Updated Review on Electrical Energy Storage Systems. Smart Grid and Renewable Energy, 4, 122-132.
http://dx.doi.org/10.4236/sgre.2013.41015
[2] Karl, J. (2006) Dezentrale Energiesysteme: Neue Technologien im liberalisierten Energiemarkt. Oldenbourg.
http://dx.doi.org/10.1524/9783486593341
[3] Ekanayake, J., Jenkins, N., Liyanage, K., Wu, J. and Yokoyama, A. (2012) Smart Grid: Technology and Applications. Wiley, New York.
http://dx.doi.org/10.1002/9781119968696
[4] Monti, A. and Ponci, F. (2010) Power Grids of the Future: Why Smart Means Complex. Proceedings of the Complexity in Engineering, COMPENG’10, Rome, 22-24 February 2010, 7-11.
[5] Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R. and Leon-Garcia, A. (2010) Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid. IEEE Transactions on Smart Grid, 1, 320-331.
http://dx.doi.org/10.1109/tsg.2010.2089069
[6] Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P. and Fonollosa, J.R. (2013) Demand-Side Management via Distributed Energy Generation and Storage Optimization. IEEE Transactions on Smart Grid, 4, 866-876.
http://dx.doi.org/10.1109/TSG.2012.2206060
[7] Li, N., Chen, L. and Low, S.H. (2011) Optimal Demand Response Based on Utility Maximization in Power Networks. Proceedings of the IEEE Power & Energy Society General Meeting, Detroit, 24-29 July 2011, 1-8.
http://dx.doi.org/10.1109/pes.2011.6039082
[8] Lo, C.-H. and Ansari, N. (2013) Decentralized Controls and Communications for Autonomous Distribution Networks in Smart Grid. IEEE Transactions on Smart Grid, 4, 66-77.
http://dx.doi.org/10.1109/tsg.2012.2228282
[9] Arif, M.T., Oo, A.M. and Ali, A.S. (2013) Role of Energy Storage on Distribution Transformer Loading in Low Voltage Distribution Network. Smart Grid and Renewable Energy, 4, 237-251.
http://dx.doi.org/10.4236/sgre.2013.42029
[10] van de Ven, P.M., Hegde, N., Massoulie, L. and Salonidis, T. (2013) Optimal Control of End-User Energy Storage. IEEE Transactions on Smart Grid, 4, 789-797.
[11] Giuntoli, M. and Poli, D. (2013) Optimized Thermal and Electrical Scheduling of a Large Scale Virtual Power Plant in the Presence of Energy Storages. IEEE Transactions on Smart Grid, 4, 942-955.
[12] Gross, J.L., Yellen, J. and Zhang, P. (2013) Handbook of Graph Theory. 2nd Edition, Chapman & Hall/CRC, Boca Raton.
[13] Bayernwerk, A.G. (2013) Netzrelevante Daten. https://www.bayernwerk.de/pages/eby_de/Netz/Stromnetz/Netzinformationen/Netzrelevante_Daten
[14] Houwing, M., Negenborn, R.R. and De Schutter, B. (2011) Demand Response with Micro-CHP Systems. Proceedings of the IEEE, 99, 200-213.
http://dx.doi.org/10.1109/JPROC.2010.2053831
[15] Hansen, A.D., RisØ, F., SØrensen, P., Hansen, L.H., Bindner, H. (2000) Models for a Stand-Alone PV System. RISØ-R-1219, RisØ National Laboratory, Roskilde.
[16] Tsai, H.-L., Tu, C.-S. and Su, Y.-J. (2008) Development of Generalized Photovoltaic Model Using Matlab/Simulink. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, 22-24 October 2008.
[17] Weather Underground (2015) Weather Forecast & Reports.
http://www.wunderground.com
[18] Doering, E., Schedwill, H. and Dehli, M. (2008) Grundlagen der Technischen Thermodynamik: Lehrbuch für Studierende der Ingenieurwissenschaften: Mit 303 Abbildungen, 45 Tabellen sowie 56 Aufgaben mit Lösungen: Studium. Vieweg + Teubner.
[19] Baharlouei, Z., Hashemi, M., Narimani, H. and Mohsenian-Rad, H. (2013) Achieving Optimality and Fairness in Autonomous Demand Response: Benchmarks and Billing Mechanisms. IEEE Transactions on Smart Grid, 4, 968-975.
http://dx.doi.org/10.1109/TSG.2012.2228241
[20] Rosen, J.B. (1965) Existence and Uniqueness of Equilibrium Points for Concave n-Person Games. Econometrica, 33, 520-534.
http://dx.doi.org/10.2307/1911749
[21] Nash, J.F. (1950) Equilibrium Points in N-Person Games. Proceedings of the National Academy of Sciences of the United States of America, 36, 48-49.
http://dx.doi.org/10.1073/pnas.36.1.48
[22] Fukushima, M. and Lin, G.-H. (2004) Smoothing Methods for Mathematical Programs with Equilibrium Constraints. Proceedings of the International Conference on Informatics Research for Development of Knowledge Society Infrastructure, Kyoto, 1-2 March 2004, 206-213.
http://dx.doi.org/10.1109/icks.2004.1313426
[23] Reinhardt, R., Homann, A. and Gerlach, T. (2013) Nichtlineare Optimierung. Theorie, Numerik und Experimente, Springer-Lehrbuch, Springer-Spektrum, Berlin Heidelberg.
[24] Bouza Allende, G. and Still, G. (2007) Mathematical Programs with Complementarity Constraints: Convergence Properties of a Smoothing Method. Mathematics of Operations Research, 32, 467-483.
http://dx.doi.org/10.1287/moor.1060.0245
[25] Hu, X. and Ralph, D. (2004) Convergence of a Penalty Method for Mathematical Programming with Complementarity Constraints. Journal of Optimization Theory and Applications, 123, 365-390.
http://dx.doi.org/10.1007/s10957-004-5154-0
[26] Bertsekas, D.P. (1999) Nonlinear Programming. Athena Scientific, Belmont.
[27] Jaggi, M. (2013) Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization. Proceedings of the 30th International Conference on Machine Learning, Atlanta, 16-21 June 2013, 427-435.
[28] Frank, M. and Wolfe, P. (1956) An Algorithm for Quadratic Programming. Naval Research Logistics Quarterly, 3, 95-110.
http://dx.doi.org/10.1002/nav.3800030109
[29] GLPK (2015) GNU Linear Programming Kit. www.gnu.org/software/glpk

  
comments powered by Disqus

Copyright © 2017 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.