Abstract
We study an offline scheduling problem arising in demand response management in smart grid. Consumers send in power requests with a flexible set of timeslots during which their requests can be served. For example, a consumer may request the dishwasher to operate for one hour during the periods 8am to 11am or 2pm to 4pm. The grid controller, upon receiving power requests, schedules each request within the specified duration. The electricity cost is measured by a convex function of the load in each timeslot. The objective of the problem is to schedule all requests with the minimum total electricity cost. As a first attempt, we consider a special case in which the power requirement and the duration a request needs service are both unit-size. For this problem, we present a polynomial time offline algorithm that gives an optimal solution and show that the time complexity can be further improved if the given set of timeslots is a contiguous interval.
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References
Albers, S.: Energy-efficient algorithms. Communication ACM 53(5), 86–96 (2010)
Azar, Y.: On-line load balancing. In: Fiat, A., Woeginger, G.J. (eds.) Online Algorithms 1996. LNCS, vol. 1442, pp. 178–195. Springer, Heidelberg (1998)
Caron, S., Kesidis, G.: Incentive-based energy consumption scheduling algorithms for the smart grid. In: IEEE Smart Grid Comm., pp. 391–396 (2010)
Chen, C., Nagananda, K.G., Xiong, G., Kishore, S., Snyder, L.V.: A communication-based appliance scheduling scheme for consumer-premise energy management systems. IEEE Trans. Smart Grid 4(1), 56–65 (2013)
Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19(2), 248–264 (1972)
European Commission. Europen smartgrids technology platform (2006), ftp://ftp.cordis.europa.eu/pub/fp7/energy/docs/smartgrids_en.pdf
Hamilton, K., Gulhar, N.: Taking demand response to the next level. IEEE Power and Energy Magazine 8(3), 60–65 (2010)
Hochbaum, D.S., Shanthikumar, J.G.: Convex separable optimization is not much harder than linear optimization. J. ACM 37(4), 843–862 (1990)
Ipakchi, A., Albuyeh, F.: Grid of the future. IEEE Power and Energy Magazine 7(2), 52–62 (2009)
Kannberg, L.D., Chassin, D.P., DeSteese, J.G., Hauser, S.G., Kintner-Meyer, M.C., Pratt, R.G., Schienbein, L.A., Warwick, W.M.: GridWiseTM: The benefits of a transformed energy system. CoRR, nlin/0409035 (September 2004)
Karzanov, A.V., McCormick, S.T.: Polynomial methods for separable convex optimization in unimodular linear spaces with applications. SIAM J. Comput. 26(4), 1245–1275 (1997)
Koutsopoulos, I., Tassiulas, L.: Control and optimization meet the smart power grid: Scheduling of power demands for optimal energy management. In: Proc. e-Energy, pp. 41–50 (2011)
Krishnan, R.: Meters of tomorrow (in my view). IEEE Power and Energy Magazine 6(2), 96–94 (2008)
Li, H., Qiu, R.C.: Need-based communication for smart grid: When to inquire power price? CoRR, abs/1003.2138 (2010)
Li, Z., Liang, Q.: Performance analysis of multiuser selection scheme in dynamic home area networks for smart grid communications. IEEE Trans. Smart Grid 4(1), 13–20 (2013)
Martin, L.: SEELoadTMSolution, http://www.lockheedmartin.co.uk/us/products/energy-solutions/seesuite/seeload.html
Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)
Lui, T., Stirling, W., Marcy, H.: Get smart. IEEE Power and Energy Magazine 8(3), 66–78 (2010)
Ma, C.Y.T., Yau, D.K.Y., Rao, N.S.V.: Scalable solutions of markov games for smart-grid infrastructure protection. IEEE Trans. Smart Grid 4(1), 47–55 (2013)
Maharjan, S., Zhu, Q., Zhang, Y., Gjessing, S., Basar, T.: Dependable demand response management in the smart grid: A stackelberg game approach. IEEE Trans. Smart Grid 4(1), 120–132 (2013)
Minoux, M.: A polynomial algorithm for minimum quadratic cost flow problems. European Journal of Operational Research 18(3), 377–387 (1984)
Minoux, M.: Solving integer minimum cost flows with separable convex cost objective polynomially. In: Gallo, G., Sandi, C. (eds.) Netflow at Pisa. Mathematical Programming Studies, vol. 26, pp. 237–239. Springer, Heidelberg (1986)
Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R.: Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Innovative Smart Grid Technologies (ISGT) (2010)
REGEN Energy Inc. ENVIROGRIDTMSMART GRID BUNDLE., http://www.regenenergy.com/press/announcing-the-envirogrid-smart-grid-bundle/
Salinas, S., Li, M., Li, P.: Multi-objective optimal energy consumption scheduling in smart grids. IEEE Trans. Smart Grid 4(1), 341–348 (2013)
Sokkalingam, P.T., Ahuja, R.K., Orlin, J.B.: New polynomial-time cycle-canceling algorithms for minimum-cost flows. Networks 36(1), 53–63 (2000)
Toronto Hydro Corporation. Peaksaver Program, http://www.peaksaver.com/peaksaver_THESL.html
UK Department of Energy & Climate Change. Smart grid: A more energy-efficient electricity supply for the UK (2013), https://www.gov.uk/smart-grid-a-more-energy-efficient-electricity-supply-for-the-uk
US Department of Energy. The Smart Grid: An Introduction (2009), http://www.oe.energy.gov/SmartGridIntroduction.htm
Végh, L.A.: Strongly polynomial algorithm for a class of minimum-cost flow problems with separable convex objectives. In: Proceedings of the 44th Symposium on Theory of Computing, STOC 2012, pp. 27–40. ACM, New York (2012)
Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced CPU energy. In: Proceedings of IEEE Symposium on Foundations of Computer Science (FOCS), pp. 374–382 (1995)
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Burcea, M., Hon, WK., Liu, HH., Wong, P.W.H., Yau, D.K.Y. (2013). Scheduling for Electricity Cost in Smart Grid. In: Widmayer, P., Xu, Y., Zhu, B. (eds) Combinatorial Optimization and Applications. COCOA 2013. Lecture Notes in Computer Science, vol 8287. Springer, Cham. https://doi.org/10.1007/978-3-319-03780-6_27
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DOI: https://doi.org/10.1007/978-3-319-03780-6_27
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