Abstract
Conventional power system optimization problems deal with the power demand and spinning reserve through real values. In this research, we employ fuzzy variables to better characterize these values in uncertain environment. In building the fuzzy power system reliable model, fuzzy Value-at-Risk (VaR) can evaluate the greatest value under given confidence level and is a new technique to measure the constraints and system reliability. The proposed model is a complex nonlinear optimization problem which cannot be solved by simplex algorithm. In this paper, particle swarm optimization (PSO) is used to find optimal solution. The original PSO algorithm is improved to straighten out local convergence problem. Finally, the proposed model and algorithm are exemplified by one numerical example.
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References
Pathom, A., Hiroyuki, K., Eiichi, T.: A hybrid LR-EP for solving new profit-based UC problem under competitive environment. IEEE Trans. on Power System 18, 229–237 (2003)
Yuan, X., Nie, H., Su, A., Wang, L., Yuan, Y.: An improved binary particle swarm optimization for unit commitment problem. Expert Systems with Applications 36(4), 8049–8055 (2009)
Ruiz, P.A., Philbrick, C.R., Cheung, K.W., Sauer, P.W.: Uncertainty management in the unit commitment problem. IEEE Trans. on Power System 24(2), 642–651 (2009)
Rahman, H.A., Shahidehpour, S.M.: A fuzzy based optimal reactive power control. IEEE Trans. on Power System 8(2), 662–670 (1993)
Khare, R., Christie, R.D.: Prioritizing emergency control problem with fuzzy set theory. IEEE Trans. on Power System 12(3), 1237–1242 (1997)
Abou El-Ela, A.A., Bishr, M.A., Allam, S.M., Ei-Sehiemy, R.A.: An emergency power system control based on the multi-stage fuzzy based procedure. Electric Power Systems Research 77(5-6), 421–429 (2007)
Liu, B., Liu, Y.K.: Expected value of fuzzy variable and fuzzy expected value models. IEEE Transaction on Fuzzy Systems 10(4), 445–450 (2002)
Nahmias, S.: Fuzzy variable. Fuzzy Sets and Systems 1(2), 97–101 (1978)
Wang, S., Liu, Y., Dai, X.: On the continuity and absolute continuity of credibility functions. Journal of Uncertain System 1(2), 185–200 (2007)
Duffie, D., Pan, J.: An overview of value-at-risk. Journal of Derivatives 4(3), 7–49 (1997)
Wang, S., Watada, J., Pedrycz, W.: Value-at-Risk-Based two-satge fuzzy facility location problems. IEEE Transactions on Industrial Informatics 5(4), 465–482 (2009)
Kennedy, J., Eberhaart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neual Network, IV, pp. 1942–1948 (1995)
Sharma, K.D., Chatterjee, A., Rakshit, A.: A Hybrid Approach for Design of Stable Adaptive Fuzzy Controllers Employing Lyapunov Theory and Particle Swarm Optimization. IEEE Transactions on Fuzzy Systems 17(2), 329–342 (2009)
Zhao, L., Yang, Y.: PSO-based single multiplicative neuron model for time series prediction. Expert Systems with Applications 36(2), 2805–2812 (2009)
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Wang, B., Li, Y., Watada, J. (2010). Fuzzy Power System Reliability Model Based on Value-at-Risk. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15390-7_46
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DOI: https://doi.org/10.1007/978-3-642-15390-7_46
Publisher Name: Springer, Berlin, Heidelberg
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