TY - GEN
T1 - Economic scheduling of combined heat and power systems using whale optimization algorithm
AU - Prashanth, G. Rahul
AU - Joshi, Siddharth Suhas
AU - Jadoun, Vinay Kumar
AU - Agarwal, Anshul
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7/10
Y1 - 2020/7/10
N2 - Scheduling the Combined Heat and Power systems in the most economical manner is a very hard and complex problem. As the interconnected units in a system rise, conventional optimization techniques cannot be used for the task of optimal scheduling of the electrical generator, CHP and heat generator units. Minimization of the cost of fuel used in the CHP systems is the aim of the CHPED problem in which various operational and practical constraints are considered. The non-convex nature along with the nonlinear characteristics of the objective function makes the problem an arduous one. In this paper, the Whale Optimization Algorithm is used for the economic scheduling of CHP systems. The WOA is introduced recently which mimics how the humpback whales behave socially while existing under water. Their exploitation strategy, which is also known as the bubble net hunting strategy is used to form the base of the algorithm. A sinusoidal term for the electrical generator units is appended in the polynomial cost function to consider the valve-point loading effect. Three test systems -one small scale system with four units; one medium scale with seven units and with transmission losses; and one large scale system with forty-eight units are solved using the WOA, which helps establish the efficiency and robustness of the technique. Further, the results obtained using WOA after 100 trials are compared with the results published in existing literature using various other meta-heuristic techniques, to show the supremacy of WOA in terms of obtaining a feasible and better solution, satisfying all the constraints and better convergence characteristics.
AB - Scheduling the Combined Heat and Power systems in the most economical manner is a very hard and complex problem. As the interconnected units in a system rise, conventional optimization techniques cannot be used for the task of optimal scheduling of the electrical generator, CHP and heat generator units. Minimization of the cost of fuel used in the CHP systems is the aim of the CHPED problem in which various operational and practical constraints are considered. The non-convex nature along with the nonlinear characteristics of the objective function makes the problem an arduous one. In this paper, the Whale Optimization Algorithm is used for the economic scheduling of CHP systems. The WOA is introduced recently which mimics how the humpback whales behave socially while existing under water. Their exploitation strategy, which is also known as the bubble net hunting strategy is used to form the base of the algorithm. A sinusoidal term for the electrical generator units is appended in the polynomial cost function to consider the valve-point loading effect. Three test systems -one small scale system with four units; one medium scale with seven units and with transmission losses; and one large scale system with forty-eight units are solved using the WOA, which helps establish the efficiency and robustness of the technique. Further, the results obtained using WOA after 100 trials are compared with the results published in existing literature using various other meta-heuristic techniques, to show the supremacy of WOA in terms of obtaining a feasible and better solution, satisfying all the constraints and better convergence characteristics.
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U2 - 10.1109/SCES50439.2020.9236704
DO - 10.1109/SCES50439.2020.9236704
M3 - Conference contribution
AN - SCOPUS:85096356471
T3 - 2020 IEEE Students' Conference on Engineering and Systems, SCES 2020
BT - 2020 IEEE Students' Conference on Engineering and Systems, SCES 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Students' Conference on Engineering and Systems, SCES 2020
Y2 - 10 July 2020 through 12 July 2020
ER -