Abstract
General optimal power flow (OPF) is an important problem in the operation of electric power grids. Solution methods to the OPF have been studied extensively that mainly solve steady-state situations, ignoring uncertainties of state variables as well as their near-future. Thus, in a dynamic and uncertain power system, where the demand as well as the supply-side show volatile behavior, optimization methods are needed that provide solutions very quickly, eliminating issues on convergence speed or robustness of the optimization. This paper introduces a policy-based approach where optimal control policies are learned offline for a given power grid based on evolutionary computation, that later provide quick and accurate control actions in volatile situations. With such an approach, it’s no more necessary to solve the OPF in each new situation by applying a certain optimization procedure, but the policies provide (near-) optimal actions very quickly, satisfying all constraints in a reliable and robust way. Thus, a method is available for flexible and optimized power grid operation over time. This will be essential for meeting the claims for the future of smart grids.
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References
Affenzeller, M., Wagner, S., Winkler, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. CRC Press (2009)
Alsac, O., Stolt, B.: Optimal load flow with steady-state security. IEEE Transactions on Power Apparatus and Systems PAS-93(2), 745–751 (1974)
Beyer, H.G., Schwefel, H.P.: Evolution strategies - a comprehensive introduction. Natural Computing 1 (2002)
Fu, M.C.: Feature article: Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing 14, 192–215 (1977)
Hutterer, S., Auinger, F., Affenzeller, M.: Evolutionary optimization of multi-agent control strategies for electric vehicle charging. In: Companion Publication of the 2012 Genetic and Evolutionary Computation Conference (2012)
Hutterer, S., Auinger, F., Affenzeller, M.: Metaheuristic optimization of electric vehicle charging strategies in uncertain environment. In: International Conference on Probabilistic Methods Applied to Power Systems (2012)
Liang, J., Venayagamoorthy, G.K., Harley, R.G.: Wide-area measurement based dynamic stochastic optimal power flow control for smart grids with high variability and uncertainty. IEEE Transactions on Smart Grid 3, 59–69 (2012)
Momoh, J.A.: Toward dynamic stochastic optimal power flow. In: Si, J., Barto, A., Powell, W., Wunsch, D. (eds.) Handbook of Learning and Approximate Dynamic Programming, pp. 561–598. Wiley Interscience (2004)
Momoh, J.A.: Electric Power System Applications of Optimization, 2nd edn. CRC / Taylor & Francis (2009)
Momoh, J.A., Zivi, E.: Control, optimization, security, and self-healing of benchmark power systems. In: Si, J., Barto, A., Powell, W., Wunsch, D. (eds.) Handbook of Learning and Approximate Dynamic Programming, pp. 599–637. Wiley Interscience (2004)
Venayagamoorthy, G.K., Harley, G., Wunsch, D.: Applications of approximate dynamic programming in power systems control. In: Si, J., Barto, A., Powell, W., Wunsch, D. (eds.) Handbook of Learning and Approximate Dynamic Programming, pp. 479–515. Wiley Interscience (2004)
Wang, H., Murillo-SĂ¡nchez, C.E., Zimmerman, R.D., Thomas, R.J.: On computational issues of market-based optimal power flow. IEEE Transactions on Power Systems 22(3), 1185–1193 (2007)
Werbos, P.J.: Adp: Goals, opportunities and principles. In: Si, J., Barto, A., Powell, W., Wunsch, D. (eds.) Handbook of Learning and Approximate Dynamic Programming, pp. 3–44. Wiley Interscience (2004)
Werbos, P.J.: Computational intelligence for the smart grid - history, challenges, and opportunities. IEEE Computational Intelligence Magazine 6(3), 14–21 (2011)
Wood, A.J., Wollenberg, B.F.: Power Generation, Operation, and Control, 2nd edn. Wiley Interscience (1996)
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Hutterer, S., Affenzeller, M., Auinger, F. (2013). Evolutionary Algorithm Based Control Policies for Flexible Optimal Power Flow over Time. In: Esparcia-AlcĂ¡zar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_16
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DOI: https://doi.org/10.1007/978-3-642-37192-9_16
Publisher Name: Springer, Berlin, Heidelberg
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