Short-term Scheduling of Non-Cascaded Hydro-thermal System with Transmission Losses using Accelerated Particle Swarm Optimization Algorithm

Hafiz Zaheer Hussain, Aun Haider, Muhammad Salman Fakhar, Jameel Ahmad, Muhammad Asim Butt, Khawar Siddique Khokhar


This paper presents the implementation of accelerated particle swarm optimization (APSO) algorithm for a non-cascaded hydro-thermal scheduling and economic dispatch problem with hydel power transmission losses. APSO is a single step position updating variant of PSO and due to its single step updating of particles, it is very fast in converging towards global optimization solution of non-linear economic dispatch problems, as compared to the other variants of PSO. Convergence rates of this implementation are compared with approaches presented in literature for the same problem. Our solution outperforms other solutions despite additional constraint of transmission losses.

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Wood AJ, Wollenberg BF, Sheble GB. Power Generation, Operation and Control. IEEE & John Wiley, December 2013.

Momoh JA, Adapa R, El-Hawary ME. A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches. IEEE Transactions on Power System, 1999; 14(1): 96–104.

Momoh JA, El-Hawary ME, Adapa R. A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods. IEEE Transactions on Power System, 1999; 14(1): 105–11.

Sinha N, Chakrabarti R, Chattopadhyay P. Fast evolutionary programming techniques for short-term hydro-thermal scheduling. Electric Power System Research, 2003; 66(2): 97–103.

Tavakoli HB, Mozafari B, Soleymani S. Short-term hydro-thermal scheduling via honey bee mating optimization algorithm. In: Proceedings of the Asia pacific power and energy engineering conference (APPEEC), Shanghai (China, 2012: p. 1–5.

Chen P, Chang H. Genetic aided scheduling of hydraulically coupled plants in hydro-thermal coordination. IEEE Transactions on Power Systems, 1996; 11 (2): 975–981.

Chang W. Optimal scheduling of hydro-thermal system based on improved particle swarm optimization. In: Proceedings of the Asia pacific power and energy engineering conference (APPEEC), 2010: 1–4.

Thakur S, Boonchay C, Ongsakul W. Optimal hydro-thermal generation scheduling using self-organizing hierarchical PSO. In: Proceedings of the IEEE power and energy society general meeting, Minneapolis (USA), 2010: 1–6.

M. Basu. Artificial bee colony optimization for short-term hydro-thermal scheduling. Journal of the institute of engineers (India), Series B, December 2014, 95(4): 319-328.

Kennedy J, Eberhart R. Particle swarm optimization. In: Neural Networks, 1995. Proceedings, IEEE International Conference on; 4: 1942–1948.

Eberhart R, Kennedy J. A new optimizer using particle swarm theory, In: Micro Machine and Human Science 1995. MHS95. Proceedings of the IEEE Sixth International Symposium on: 39–43.

Kennedy J, Mendes R. Neighborhood topologies in fully informed and best of neighborhood particle swarms. IEEE Transactions System Man Cybernetics, Part C: Application and Review, 2006; 36(4): 515-519.

Kennedy J, Eberhart R. Population structure and particle swarm performance. IEEE computer Society, May-2002.

Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Transactions Evolutionary Computation, 2004; 8 (3): 204–10.

Nallasivan C, Suman D, Henry J, Ravichandran S. A novel approach for short-term hydro-thermal scheduling using hybrid technique. In: Proceedings of the IEEE power India conference, New Delhi (India), 2006.

Wong K, Wong Y. Short-term hydro-thermal scheduling part. I. Simulated annealing approach. IEEE Proceeding- Generation, Transmission and Distribution, 1994; 141(4): 497–501.

Padmini S, Rajan C. Improved PSO for short-term hydro-thermal scheduling. In: Proceedings of the international conference on sustainable energy and intelligent systems, Chennai (India), 20–22 July, 2011: 332–334.

Samudi C, Das G, Ojha P, Sreeni T, Cherian S. Hydel thermal scheduling using particle swarm optimization. In: IEEE/PES Proceedings and exposition of the transmission and distribution conference, 2008:1–5.

Sinha N, Lai LL. Meta heuristic search algorithms for short-term hydro-thermal scheduling. In: Proceedings of the international conference on machine learning and cybernetics, Dalian (China), August, 2006:4050–4056.

Fakhar MS, Kashif SAR, Saqib MA, Hassan T. Non cascaded short-term hydro-thermal scheduling using fully-informed particle swarm optimization. International Journal of Electrical Power and Energy Systems, 2015; 73: 983–990.

Alrashidi MR, El-Hawary E.A survey of Particle Swarm Optimization Applications in Electric Power system. Electric Power Component Systems, August 2009; 13(4): 913–918.

Farhat IA, El-Hawary ME. Optimization methods applied for solving the short-term hydro-thermal coordination problem. Electric Power Systems Research, 2009; 79(9):1308–1320.

M. S. Fakhar1, S.A. R.Kashif2, H.Z. Hussain3, B.A.Ahmad4 “Implementation of PSO and FIPSO with consideration of constant and linearly decreasing weight strategies on Michaelwicz 3-D function”, Sci.Int.(Lahore), 27(5),4097-4100,2015, ISSN 1013-5316; CODEN: SINT.

Yang XS. Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons, 2010.

Fergany A EL. Accelerated Particle Swarm Optimization-based Approach to the Optimal Design of Substation Grounding Grid. PRZEGLĄD ELEKTROTECHNICZNY. ISSN 0033-2097, R. 89 NR 7/2013.

Talatahari S, Khalili E, Alavizadeh SM. Accelerated Particle Swarm for Optimum Design of Frame Structures. Hindawi Publishing Corporation Mathematical Problems in Engineering, Volume 2013, Article ID 649857:6

Prajna K, Rao GSB, Reddy K, Maheswari RU. A New Dual Channel Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO). I.J. Intelligent Systems and Applications, March 2014, DOI: 10.5815/ijisa.2014.04.01; 04: 1-10.

Rahman I, Vasant PM , Singh BSM, Al-Wadud A. On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles. Alexandria Engineering Journal, March 2016; 55(1): 419–426.

Yang, XS, Deb S, Fong S. Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications, in: NDT2011, Springer, 2011; 136: 53-66.

Gandomi AH, Yun GJ, Yang XS, Talatahari S. Combination of chaos and accelerated particle swarm optimization. Communications in Nonlinear Science and Numerical Simulations; February 2013; 18 (2): 327–340.

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