Abstract
The implementation of intelligent power grids, in form of smart grids, introduces new challenges to the optimal dispatch of power. Thus, optimization problems need to be solved that become more and more complex in terms of multiple objectives and an increasing number of control parameters. In this paper, a simulation based optimization approach is introduced that uses metaheuristic algorithms for minimizing several objective functions according to operational constraints of the electric power system. The main idea is the application of simulation for computing the fitness- values subject to the solution generated by a metaheuristic optimization algorithm. Concerning the satisfaction of constraints, the central concept is the use of a penalty function as a measure of violation of constraints, which is added to the cost function and thus minimized simultaneously. The corresponding optimization problem is specified with respect to the emerging requirements of future smart electric grids.
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References
Amin, S.M., Wollenberg, B.F.: Toward a Smart Grid: Power Delivery for the 21st Century. IEEE Power and Energy Magazine 3, 34–41 (2005)
Potter, C.W., Archambault, A., Westrick, K.: Building a Smarter Grid Through Better Renewable Energy Information. Power Systems Conference and Exposition (2009)
Guille, C., Gross, G.: A Conceptual Framework for the Vehicle-To-Grid (V2G) Implementation. Energy Policy (2009)
Wood, A.J., Wollenberg, B.: Power Generation, Operation, and Control, 2nd edn. Wiley Interscience, Hoboken (1996)
Mo, N., Zou, Z.Y., Chan, K.W., Pong, T.Y.G.: Transient stability constrained optimal power flow using particle swarm optimization. IET Generation Transmission and Distribution 1(3), 476–483 (2007)
Bakare, G. A., Krost, G., Venayagomoorthy, G. K., Aliyu, U. O.: Comparative Application of Differential Evolution and Particle Swarm Techniques to Reactive Power and Voltage Control. In: International Conference on Intelligent Systems Applications to Power Systems (2007)
Werbos, P.J.: Putting More Brain-Like Intelligence into the Electric Power Grid: What We Need and How to Do It. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2009 (2009)
Panta, S., Premrudeepreechacharn, S., Nuchprayoon, S., Dechthummarong, C.: Optimal Economic Dispatch for Power Generation Using Artificial Neural Network. In: 8th International Power Engineering Conference (2007)
Mohammadi, A., Mohammad, H., Kheirizad, I.: Online Solving of Economic Dispatch Problem Using Neural Network Approach And Comparing It With Classical Method. In: International Conference on Emerging Technologies (2006)
Tangpatiphan, K., Yokoyama, A.: Adaptive Evolutionary Programming With Neural Network for Transient Stability Constrained Optimal Power Flow. In: 15th International Conference on Intelligent Applications to Power Systems (2009)
Abido, M.A.: Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem. IEEE Transactions on Evolutionary Computation 10(3) (2006)
Chan, K.Y., Ling, S.H., Chan, K.W., Lu, H.H.C., Pong, T.Y.G.: Solving Multi- Contingency Transient Stability Constrained Optimal Power Flow Problems with an Improved GA. In: Proceedings IEEE Congress on Evolutionary Computation, pp. 2901–2908 (2007)
Calderon, F., Fuerte-Esquivel, C.R., Flores, J.J., Silva, J.C.: A Constraint-Handling Genetic Algorithm to Power Economic Dispatch. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 371–381. Springer, Heidelberg (2008)
Kempton, W., Tomic, J.: Vehicle-to-grid power implementation: From stabilizing the grid to supporting large- scale renewable energy. Article in press, Science Direct, Journal of Power Sources (2005)
Bruno, S., et al.: Load control through smart-metering on distribution networks. In: IEEE Bucharest Power Tech Conference (2009)
Hirst, D.: Settlement issues for advanced metering with retail competition. In: CIRED Seminar: SmartGrids for Distribution Paper (2008)
Momoh, J.A.: Electric Power System Applications of Optimization, 2nd edn. CRC Press, Boca Raton (2009)
Han, S., Han, S., Sezaki, K.: Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation. IEEE Transactions on Smart Grid 1(1) (June 2010)
Law, A.M., McComas, M.G.: Simulation-Based Optimization. In: Proceedings of the 2002 Winter Simulation Conference, San Diego, CA, USA (2002)
Wagner, S., Affenzeller, M.: HeuristicLab: A Generic and Extensible Optimization Environment. In: Adaptive and Natural Computing Algorithms. Springer Computer Science, pp. 538–541. Springer, Heidelberg (2005), http://www.heuristiclab.com
Beham, A., Affenzeller, M., Wagner, S., Kronberger, G.K.: Simulation Optimization with HeuristicLab. In: Proceedings of the 20th European Modelling and Simulation Symposium (EMSS 2008), Campora San Giovanni, Italy (2008)
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming. In: Modern Concepts and Practical Applications. Chapman and Hall, Boca Raton (2009)
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Hutterer, S., Auinger, F., Affenzeller, M., Steinmaurer, G. (2010). Overview: A Simulation Based Metaheuristic Optimization Approach to Optimal Power Dispatch Related to a Smart Electric Grid. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_41
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DOI: https://doi.org/10.1007/978-3-642-15597-0_41
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