Abstract
The goal of this chapter is to provide prior work done with linear-programming approaches for the resource-allocation problem. Operations-research (OR) modeling often concerns finding the best quantitative solution for management problems [HL01, Mom01]. The OR methods include mathematical-optimization modeling as simulation, and using OR methods has grown significantly since their origination during World War II. Templeman [Tem91] describes quantitative OR methods for designing and controlling industrial and economical operations. Many private and government organizations have improved their operations by successfully using mathematical programming [Wad83, Aro02, Chv83, Dan63, SS85]. This book focuses on a resource-allocation problem and applies linear programming for the solution approach.
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Arora, S. (2006). Proving integrality gaps without knowing the linear program. Proceedings of the 43rd Symposium on Foundations of Computer Science (pp. 313–322).
Bradley, G., Brown, G., and Graves, G (1977). Design and implementation of large scale primal transshipment algorithms. Management Science, 1–34.
Bignucoloa, F., Caldona, R., & Prandonib, V. (2008). Radial MV networks voltage regulation with distribution management system coordinated controller. Electric Power Systems Research, 78(4), 634–664.
Boutilier, C. (2001). Planning and programming with first-order Markov decision processes: Insights and challenges. City, MA: Morgan Kaufmann.
Boutilier, C. (2002). A POMDP formulation of preference elicitation problems. Proceedings of the Eighteenth National Conference on Artificial Intelligence, City, CA (pp. 239–246).
Chvatal, V. (1983). Linear programming. New York, NY: Freeman.
Dantzig, G. B. (1963). Linear programming and extensions. Princeton, NJ: Princeton University Press.
Dantzig, G. B. (1983). Reminiscences about the origins of linear programming, mathematical programming: The state of the art springer lecture notes (pp. 78–86). Berlin, Germany: Springer.
Dobson, I., Carreras, B., & Newman, D. E. (2004). Probabilistic load-dependent cascading failure with limited component interactions. IEEE International Symposium on Circuits and Systems, City, Canada (pp. 15–32).
Dwyer, A., Nielsen, R., Stangl, J., & Markushevich, N. (1995). Load to voltage dependency tests at B.C. hydro, IEEE Transactions on Power Systems, 10(2), 709–715.
Farag, E., El-Saadany, F., & Seethapathy, R. (2012). A two ways communication-based distributed control for voltage regulation in smart distribution feeders. IEEE Transaction on Smart Grid, 766–772.
Greitzer, F., Podmor, R., Robinson, M., & Ey, P. (2009). Naturalistic decision making for power system operators. In International Conference on Naturalistic Decision Making (NDM). London, England.
Gueret, C., Prins, C., & Sevaux, M. (2000). Programmation lineaire. Paris: Editions Eyrolles.
Hillier, F., & Lieberman, G. (2001). Introduction to operations research. City, England: McGraw-Hill.
Khattam, W., Hegazy, Y., & Salama, Y. (2005). An integrated distributed generation optimization model for distribution system planning. IEEE Transactions on Power Systems, 20(2), 1158–1165.
Momoh, J. (2001). Electric power system Applications of optimization. City, NY: Marcel Dekker.
Moore, R. (1991). Global optimization to prescribed accuracy. Computers and Mathematics with Applications, 21(6), 25–39.
Nguyen, C., & Flueck, A. (2012). Agent based restoration with distributed energy storage support in smart grids. IEEE Transactions on Smart Grid, 3(2), 1029–1038.
Pipattanasomporn, M., Feroze, H., & Rahman, S. Multi-agent systems in a distributed smart grid: Design and implementation. Proceedings of IEEE PES 2009 Power Systems Conference and Exposition, Seattle, Washington, USA, March 2009 (pp. 1–6).
Ranganathan, P., & Nygard, K. A smart agent oriented linear programming control in electric grid. Annual Electric Power and Energy Conference, Canada, October 2012 (pp. 102–106).
Powell, W., Sheffi, Y., Nickerson, S., Butterbaugh, K., & Atherton, S. (1988). Maximizing profits for North American Van Lines truckload division: A new framework for pricing and operations. Interfaces, 18(1), 21–41.
PowerGen plc. (1998). Private communication.
Ranganathan, P., & Nygard, K. (2010). An optimal resource assignment problem in Smart grid. The Second International Conference on Future Computational Technologies and Applications, Portugal, November 26 (pp. 75–82).
Salam, S. (2004). Comparison of Lagrangian relaxation and truncated dynamic programming methods for solving hydrothermal coordination problems. Proceedings of International Conference on Intelligent Sensing and Information Processing (pp. 265–270).
Song, Y. (1999). Modern optimization techniques in power systems. City, ST: Kluwer Academic Publishers.
Sullivan, R., & Secrest, C. (1985). A simple optimization DSS for production planning at Dairymans Cooperative Creamery Association. Interfaces, 15(5), 46–53.
Templeman, B. (1991). Optimization and decision support systems in civil engineering. City, ST: Routledge, Gordon and Breach Science Publishers Ltd.
Waddell, R. (1983). A model for equipment replacement decision and policies. Interfaces, 13(4), 1–7.
Wang, H. (2001). Multi-agent co-ordination for the secondary voltage control in power system contingencies. Proceedings of IEEE Generation, Transmission and Distribution, 148(1), 61–66.
Williams, P. (1993). Model building in mathematical programming (3rd revised edition). Chichester, England: Wiley.
Wright, S. (1997). Primal-dual interior point methods. SIAM, 145–157.
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Ranganathan, P., Nygard, K.E. (2017). Literature Review. In: Distributed Linear Programming Models in a Smart Grid. Power Electronics and Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-52617-1_2
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