Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

Alan Abdu Robbi Afifi(1), Sarjiya Sarjiya(2), Yusuf Susilo Wijoyo(3*)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author


Unit Commitment or generator scheduling is one of complex combination issues aiming to obtain the cheapest generating power total costs. Ant Colony Optimization is proposed as a method to solve Unit Commitment issues because it has a better result convergence according to one of journals that reviews methods to solve Unit Commitment issues. Ant Colony Optimization modification into Nodal Ant Colony Optimization as well as addition of several elements are also conducted to overcome Ant Colony Optimization limitations in resolving Unit Commitment issues. Nodal Ant Colony Optimization simulations are then compared with Genetic Algorithm and Simulated Annealing methods which previously has similar simulations. Reliability index combination in a form of Loss of Load Probability and Expected Unserved Energy are also added as reliability constraints in the system. Comparison of three methods shows that Nodal Ant Colony Optimization is able to provide better results up to 0.08% cheaper than Genetic Algorithm or Simulated Annealing methods.


Generator scheduling; Nodal Ant Colony Optimization; Reliability Constraints

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T. Logenthiran and D. Srinivasan, “Formulation of Unit Commitment (UC) Problems and Analysis of Available Methodologies Used for Solving the Problems,” 2010 IEEE Int. Conf. Sustain. Energy Technol. ICSET 2010, 2010, pp. 1-6.

M.Y. El-Sharkh, N.S. Sisworahardjo, A. Rahman, and M.S. Alam, “An Improved Ant Colony Search Algorithm for Unit Commitment Application,” 2006 IEEE PES Power Syst. Conf. Expo., 2006, pp. 1741-1746.

T. Sum-im and W. Ongsakul, “Ant Colony Search Algorithm for Unit Commitment,” IEEE Int. Conf. Ind. Technol. 2003, 2003, pp. 72-77.

K. Vaisakh and L.R. Srinivas, “Unit Commitment by Evolving Ant Colony Optimization,” World Congr. Nat. Biol. Inspired Comput., Vol. 3, pp. 67-77, 2010.

C.C. Columbus, K. Chandrasekaran, and S.P. Simon, “Nodal Ant Colony Optimization for Solving Profit Based Unit Commitment Problem for GENCOs,” Appl. Soft Comput. J., Vol. 12, No. 1, pp. 145-160, 2012.

A.Y. Saber and A.M. Alshareef, “Scalable Unit Commitment by Memory-Bounded Ant Colony Optimization with A* Local Search,” Int. J. Electr. Power Energy Syst., Vol. 30, No. 6–7, pp. 403-414, 2008.

D.R. Wijayanti, “Penjadwalan Pembangkit dengan Constraint Keandalan Menggunakan Algoritma Genetika Mempertimbangkan Ketidakpastian Beban”, Bachelor thesis, Universitas Gadjah Mada, Yogyakarta, Indonesia, 2014.

D.N. Simopoulos, S.D. Kavatza, and C.D. Vournas, “Reliability Constrained Unit Commitment Using Simulated Annealing,” IEEE Trans. Power Syst., Vol. 21, No. 4, pp. 1699-1706, 2006.

D.N. Simopoulos, S.D. Kavatza, and C.D. Vournas, “Unit Commitment by an Enhanced Simulated Annealing Algorithm,” IEEE Trans. Power Syst., Vol. 21, No. 1, pp. 68-76, 2006.

J. Heinonen and F. Pettersson, “Hybrid Ant Colony Optimization and Visibility Studies Applied to a Job-Shop Scheduling Problem,” Appl. Math. Comput., Vol. 187, No. 2, pp. 989-998, 2007.

(2015) "Ant Colony Optimization" [Online],, access date: 15-Feb-2017.


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