Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization

• Autorzy: Suresh Vishnu , Janik Przemyslaw , Jasinski Michal

Identyfikatory

DOI

10.24425/aee.2020.133029

• Języki publikacji: EN

Abstrakty

EN

This paper presents the application of an improved ant colony optimization algorithm called mixed integer distributed ant colony optimization to optimize the power flow solution in power grids. The results provided indicate an improvement in the reduction of operational costs in comparison with other optimization algorithms used in optimal power flow studies. The application was realized to optimize power flow in the IEEE 30 and the IEEE 57 bus test cases with the objective of operational cost minimization. The optimal power flow problem described is a non-linear, non-convex, complex and heavily constrained problem.

Słowa kluczowe

EN

ant colony optimization; IEEE 30 bus; IEEE 57 bus; metaheuristic algorithm; mixed integer distributed ant colony optimization; optimal power flow

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2020
• Tom: Vol. 69, nr 2
• Strony: 335--348
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wz.

Twórcy

autor

Suresh Vishnu

• Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Janik Przemyslaw

• Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Jasinski Michal

• Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• [1] Reddy S.S., Rathnam C.S., Optimal Power Flow using Glowworm Swarm Optimization, International Journal of Electrical Power and Energy Systems, vol. 80, pp. 128–139 (2016).
• [2] Attia H., El Sehiemy R.A., Hasanien H.M., Optimal power flow solution in power systems using a novel Sine-Cosine algorithm, International Journal of Electrical Power and Energy Systems, vol. 8, pp. 331–343 (2018).
• [3] El-Fergany A.A, Hasaniem H.M., Tree-seed algorithm for solving optimal power flow problem in largescale power systems incorporating validations and comparisons, Applied Soft Computing Journal, vol. 64, pp. 307–316 (2018).
• [4] Augustine N., Suresh S., Moghe P., Sheikh K., Economic dispatch for a microgrid considering renewable energy cost functions, 2012 IEEE PES Innovative Smart Grid Technollogies (ISGT), pp. 1–7 (2012).
• [5] Ghasemi E.A.M., Ghavidel S., Gitizadeh M., An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow, International Journal of Electrical Power and Energy Systems, vol. 65, pp. 375–384 (2015).
• [6] Abido M.A., Optimal power flow using tabu search algorithm, Electric Power Components and Systems, vol. 30, pp. 469–483 (2002).
• [7] Bahmanyar A., Estebsari A., Bahmanyar A., Bompard E., Nonsy Load Flow?: Smart Grid Load Flow Using Non-Synchronized Measurements, 2017 IEEE International Conference on Environment and Electrical Engineering, 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe), pp. 1–5 (2017), 10.1109/EEEIC/ICPSEurope40099.2017.
• [8] Yard W.N., Distributed optimal active power dispatch with energy storage units and power flow limits in smart grids, pp. 1–9 (2019).
• [9] Rao B.V., Kumar G.V.N., Electrical Power and Energy Systems Optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller, International Journal of Electrical Power and Energy Systems, vol. 68, pp. 81–88 (2015).
• [10] Yan X., Quintana V.H., Improving an interior-point-based off by dynamic adjustments of step sizes and tolerances, IEEE Transactions on Power Systems, vol. 14, pp. 709–716 (1999).
• [11] Momoh J.A., Zhu J.Z., Improved interior point method for off problems, IEEE Transactions on Power Systems, vol. 14, pp. 1114–1120 (1999).
• [12] Alsac O., Stott B., Optimal load flow with steady-state security, IEEE Transactions on Power Apparatus Systems (1974).
• [13] Shoults R., Sun D., Optimal power flow based on P–Q decomposition, IEEE Transactions on Power Apparatus Systems, vol. 101, no. 2, pp. 397–405 (1982).
• [14] Wibowo R.S., Maulana R., Taradini A., Pamuji F.A., Soeprijanto A., Penangsang O., Quadratic programming approach for security constrained optimal power flow, Proc. – 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 200–203 (2015).
• [15] Aoki K., Nishikori A., Yokoyama R., Constrained Load Flow Using Recursive Quadratic Programming, IEEE Transactions on Power Systems, vol. 2, no. 1, pp. 8–16 (1987).
• [16] Suresh V.S.,Comparison of Solvers Performance for Load Flow Analysis, Transactions on Environment and Electrical Engineering, vol. 3, no. 1, pp. 26–32 (2019).
• [17] Rahli M., Pirotte P., Optimal load flow using sequential unconstrained minimization technique (SUMT) method under power transmission losses minimization, Electric Power System Research, vol. 52, no. 1, pp. 61–64 (1999).
• [18] Surender Reddy S., Bijwe P.R., Efficiency improvements in meta-heuristic algorithms to solve the optimal power flow problem, International Journal of Electrical Power and Energy Systems, vol. 82, pp. 288–302 (2016).
• [19] Surender Reddy S., Bijwe P.R., Abhyankar A.R., Faster evolutionary algorithm based optimal power flow using incremental variables, International Journal of Electrical Power and Energy Systems, vol. 54,pp. 198–210 (2014).
• [20] Shaheen A.M., Farrag S.M., El-sehiemy R.A., MOPF solution methodology, IET Generation, Transmission & Distribution, vol. 11, pp. 570–581 (2017).
• [21] Chaib A.E., Bouchekara H.R.E.H., Mehasni R., Abido M.A., Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm, International Journal of Electrical Power and Energy Systems, vol. 81, pp. 64–77 (2016).
• [22] Krishnanand K.N., Ghose D., Glowworm swarm optimization for simultaneous capture of multipl local optima of multimodal functions, Swarm Intell., vol. 3, pp. 87–124 (2009).
• [23] Hemamalini S., Simon S.P., Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions, Electric Power Components and Systems, vol. 38, pp. 786–803 (2010).
• [24] Yi Y., A Novel Artificial Bee Colony Algorithm, Electric Power Components and Systems, vol. 38, (2014).
• [25] Yu X., Chen W., Zhang X., An Artificial Bee Colony Algorithm for Solving Constrained Optimization Problems, 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 2663–2666 (2018).
• [26] Ghasemi M., Ghavidel S., Rahmani S., Roosta A., Falah H., A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with nonsmooth cost functions, Engineering Applications of Artificial Intelligence, vol. 29, pp. 54–69 (2014).
• [27] Rao R.V., Savsani V.J., Vakharia D.P., Teaching–Learning–Based Optimization: An optimization method for continuous non-linear large scale problems, Information Sciences: an International Journal, vol. 183, no. 1, pp. 1–15 (2012).
• [28] Bland J.A., Dawson G.P., Tabu search and design optimization, Computer-Aided Design, vol. 23, no. 1, pp. 195–201 (1991).
• [29] Duman S., Güvenç U., Sönmez Y., Yörükeren N., Optimal power flow using gravitational search algorithm, Energy Conversion and Management, vol. 51, pp. 86–95 (2012).
• [30] Glavitsch H., Bacher R., Optimal power flow problem. The ordinary power flow, Swiss Federal Institute of Technology, Zurich, Switzerland, pp. 1–72 (n.d.).
• [31] Dorigo M., Optimization, learning and natural algorithms, PhD Thesis, Dip. Di Elettron, Politecnico di Milano, Computer Science (1992).
• [32] Dorigo M., Birattari M., Stutzle T., Ant Colony Optimization, Universitée Libre de Bruxelles, BELGIUM (2006).
• [33] Dorigo M., Stützle T., Ant colony optimization: Overview and recent advances, Part of the International Series in Operations Research and Management Science, vol. 146, pp. 227–263 (2019).
• [34] Yilmaz N.K., Evangelinos C., Lermusiaux P.F.J., Patrikalakis N.M., Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming, IEEE Journal of Oceanic Engineering, vol. 33, no. 4 (2008).
• [35] Jovanovic R., Tuba M., Voß S., An efficient ant colony optimization algorithm for the blocks relocation problem, European Journal of Operational Research, vol. 274, no. 1, pp. 78–90 (2019).
• [36] Schlueter M., Nonlinear Mixed Integer Based Optimization Technique for Space Applications, School of Mathematics, The University of Birmingham (2012).
• [37] Schlüter M., Egea J.A., Banga J.R., Extended ant colony optimization for non-convex mixed integer nonlinear programming, Computers and Operational Research, vol. 36, no. 6, pp. 2217–2229 (2009).
• [38] Dorigo M., Di Caro G., Gambardella L.M., Ant Algorithms for Discrete Optimization, n.d. http://people.idsia.ch/∼luca/ij_23-alife99.pdf, accessed May 5 2019.
• [39] Socha K., Dorigo M., Ant colony optimization for continuous domains, European Journal of Operational Research, vol. 185, no. 3, pp. 1155–1173 (2008).
• [40] Socha K., ACO for Continuous and Mixed-Variable Optimization, International Workshop on Ant Colony Optimization and Swarm Intelligence (2004).
• [41] Abido M.A., Optimal power flow using particle swarm optimization, International Journal of Electrical Power and Energy Systems, vol. 24, no. 7, pp. 564–571 (2002).
• [42] Bhattacharya A., Chattopadhyay P.K., Application of biogeography-based optimisation to solve different optimal power flow problems, IET Generation Transmission and Distribution, vol. 5, no. 1, pp. 70–80 (2011)
• [43] Abou El Ela A.A., Abido M.A., Spea S.R., Optimal power flow using differential evolution algorithm, Electric Power Systems Research, vol. 80, no. 7, pp. 878–885 (2010).
• [44] Sayah S., Zehar K., Modified differential evolution algorithm for optimal power flow with non-smooth cost functions, Energy Convers. Manag., vol. 49, pp. 3036–3042 (2008).
• [45] Mohagheghi E., Geletu A., Bremser N., Alramlawi M., Gabash A., Li P., Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach, Proc. – 2018 IEEE International Conference on Environment and Electrical Engineering (2018), DOI: 10.1109/EEEIC.2018.8494530.
• [46] Mohagheghi E., Gabash A., Li P., A framework for real-time optimal power flow under wind energy penetration, Energies, vol. 10 (2017), DOI:10.3390/en10040535.
• [47] Mohagheghi E., Gabash A., Li P., Real-time optimal power flow under wind energy penetration – Part I: Approach, EEEIC 2016 – International Conference on Environment and Electrical Engineering (2016), DOI: 10.1109/EEEIC.2016.7555464.
• [48] Mohagheghi E., Alramlawi M., Gabash A., Li P., A survey of real-time optimal power flow, Energies, vol. 11 (2018), DOI: 10.3390/en11113142.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).