Abstract
This paper proposes two meta-heuristics (Genetic Algorithm and Evolutionary Particle Swarm Optimization) for solving a 15 bid-based case of Ancillary Services Dispatch in an Electricity Market. A Linear Programming approach is also included for comparison purposes. A test case based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is used to demonstrate that the use of meta-heuristics is suitable for solving this kind of optimization problem. Faster execution times and lower computational resources requirements are the most relevant advantages of the used meta-heuristics when compared with the Linear Programming approach.
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References
Motamedi, A., Fotuhi-Firuzabad, M.: Ancillary Service Markets Design. In: 2007 Large Engineering Systems Conference on Power Engineering (LESCOPE07), Montreal, Quebec, Canada, October 10-12, pp. 316–320 (2007)
Burade, P.G., Helonde, J.B.: A Novel Approach for Optimal Power Dispatch Using Artificial Intelligence (AI) Methods. In: International Conference on Control Automation, Communication and Energy Conservation, ICCACEC 2009, June 4-6 (2009)
Papalexopoulos, A., Singh, H.: On the Various Design Options for Ancillary Services Markets. In: 34th Annual Hawaii International Conference on System Sciences, Maui, Hawaii, Janaury 2002, pp. 798–805 (2002)
Pereira, A.J.C., Vale, Z.A., Machado e Moura, A., Dias Pinto, J.A.: Provision and Costs of Ancillary Services in a Restructured Electricity Market. In: International Conference on Renewable Energy and Power Quality (ICREPQ’04), March 31-April 02 (2004)
Vale, Z., Ramos, C., Faria, P., Soares, J.P., Canizes, B.R., Khodr, H.M.: Ancillary Service Market Simulation. In: IEEE T&D Asia, Seoul, Korea (October 2009)
Li, C., Johnson, R.B., Svoboda, A.J.: A new unit commitment method. IEEE Trans. Power Syst. 12(1), 113–119 (1997)
Ouyang, Z., Shahidehpour, S.M.: An intelligent dynamic-programming for unit commitment application. IEEE Trans. Power Syst. 6(3), 1203–1209 (1991)
Medina, J., Quintana, V.H., Conejo, A.J.: A clipping-off interior point technique for medium-term hydro-thermal coordination. IEEE Trans. Power Syst. 14(1), 266–273 (1999)
Ongsakul, W., Petcharaks, N.: Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Trans. Power Syst. 19(1), 620–628 (2004)
Purushothama, G.K., Jenkins, L.: Simulated annealing with local search—A hybrid algorithm for unit commitment. IEEE Trans. Power Syst. 18(1), 273–278 (2003)
Rajan, C.C.A., Mohan, M.R.: An evolutionary programming-based tabu search method for solving the unit commitment problem. IEEE Trans. Power Syst. 19(1), 577–585 (2004)
Houck, C., Joines, J., Kay, M.: A Genetic Algorithm for Function Optimization: A MatLab Implementation, NCSU-IE TR 95-09 (1995)
Chaturvedi, K.T., Pandit, M., Srivastava, L.: Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Transactions on Power Systems 23(3), 1079–1087 (2008)
Wu, J.K., Zhu, J.Q., Chen, G.T., et al.: A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming. IEEE Transactions on Power Systems 23(4), 1570–1579 (2008)
Huang, C.M., Wang, F.L.: An RBF network with OLS and EPSO algorithms for real-time power dispatch. IEEE Transactions on Power Systems 22(1), 96–104 (2007)
Lee, T.Y.: Optimal spinning reserve for a wind-thermal power system using EIPSO. IEEE Transactions on Power Systems 22(4), 1612–1621 (2007)
Leite, H., Barros, J., Miranda, V.: Evolutionary algorithm EPSO helping doubly-fed induction generators in ride-through-fault. In: PowerTech, 2009 IEEE Bucharest, June 28-July 2, pp. 1–8 (2009)
Azevedo, F., Vale, Z., Moura Oliveira, P.: A Decision-Support System Based on Particle Swarm Optimization for Multiperiod Hedging in Electricity Markets. IEEE Transactions on Power Systems 22(3) (August 2007)
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Vale, Z.A. et al. (2010). Comparison between Deterministic and Meta-heuristic Methods Applied to Ancillary Services Dispatch. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_73
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DOI: https://doi.org/10.1007/978-3-642-13022-9_73
Publisher Name: Springer, Berlin, Heidelberg
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