Interactive artificial ecosystem algorithm for solving power management optimizations
DOI:
https://doi.org/10.20998/2074-272X.2022.6.09Keywords:
artificial ecosystem based optimization, power management, intensification and diversification, FACTS devicesAbstract
Introduction. Power planning and management of practical power systems considering the integration and coordination of various FACTS devices is a vital research area. Recently, several metaheuristic methods have been developed and applied to solve various optimization problems. Among these methods, an artificial ecosystem based optimization has been successfully proposed and applied to solve various industrial and planning problems. The novelty of the work consists in creating an interactive process search between diversification and intensification within the standard artificial ecosystem based optimization. The concept of the introduced variant is based on creating dynamic interaction between production operator and consumer operator during search process. Purpose. This paper introduces an interactive artificial ecosystem based optimization to solve with accuracy the multi objective power management optimization problems. Methods. The solution of the problem was carried out using MATLAB program and the developed package is based on combining the proposed metaheuristic method and the power flow tool based Newton-Raphson algorithm. Results. Obtained results confirmed that the proposed optimizer tool may be suitable to solve individually and simultaneously various objective functions such as the total fuel cost, the power losses and the voltage deviation. Practical value. The efficiency of the proposed variant in terms of solution quality and convergence behavior has been validated on two practical electric test systems: the IEEE-30-bus, and the IEEE-57-bus. A statistical comparative study with critical review is elaborated and intensively compared to various recent metaheuristic techniques confirm the competitive aspect and particularity of the proposed optimizer tool in solving with accuracy the power management considering various objective functions.
References
Carpentier J. Contribution á l’étude du dispatching économique. Bulletin de la Société Francaise des électriciens, 1962, vol. 3, pp. 431-447. (Fra).
Ullah Z., Elkadeem M.R., Wang S., Azam M., Shaheen K., Hussain M., Rizwan M. A Mini-review: Conventional and Metaheuristic Optimization Methods for the Solution of Optimal Power Flow (OPF) Problem. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, 2020, vol. 1151. Springer, Cham. doi: https://doi.org/10.1007/978-3-030-44041-1_29.
Frank S., Steponavice I., Rebennack S. Optimal power flow: a bibliographic survey II. Energy Systems, 2012, vol. 3, no. 3, pp. 259-289. doi: https://doi.org/10.1007/s12667-012-0057-x.
Mahdad B., Srairi K. A new interactive sine cosine algorithm for loading margin stability improvement under contingency. Electrical Engineering, 2018, vol. 100, no. 2, pp. 913-933. doi: https://doi.org/10.1007/s00202-017-0539-x.
Mahdad B. Improvement optimal power flow solution under loading margin stability using new partitioning whale algorithm. International Journal of Management Science and Engineering Management, 2019, vol. 14, no. 1, pp. 64-77. doi: https://doi.org/10.1080/17509653.2018.1488225.
Bouchekara H. Solution of the optimal power flow problem considering security constraints using an improved chaotic electromagnetic field optimization algorithm. Neural Computing and Applications, 2020, vol. 32, no. 7, pp. 2683-2703. doi: https://doi.org/10.1007/s00521-019-04298-3.
Kotb M.F., El-Fergany A.A. Optimal Power Flow Solution Using Moth Swarm Optimizer Considering Generating Units Prohibited Zones and Valve Ripples. Journal of Electrical Engineering & Technology. 2019, vol. 15, pp. 179-192. doi: https://doi.org/10.1007/s42835-019-00144-7.
Taher M.A., Kamel S., Jurado F., Ebeed M. Modified grasshopper optimization framework for optimal power flow solution. Electrical Engineering, 2019, vol. 101, no. 1, pp. 121-148. doi: https://doi.org/10.1007/s00202-019-00762-4.
Warid W. Optimal power flow using the AMTPG-Jaya algorithm. Applied Soft Computing, 2020, vol. 91, art. no. 106252. doi: https://doi.org/10.1016/j.asoc.2020.106252.
El-Fergany A.A., Hasanien H.M. Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons. Applied Soft Computing, 2018, vol. 64, pp. 307-316. doi: https://doi.org/10.1016/j.asoc.2017.12.026.
Radosavljević J., Klimenta D., Jevtić M., Arsić N. Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm. Electric Power Components and Systems, 2015, vol. 43, no. 17, pp. 1958-1970. doi: https://doi.org/10.1080/15325008.2015.1061620.
Youssef H., Kamel S., Ebeed, M. Optimal Power Flow Considering Loading Margin Stability Using Lightning Attachment Optimization Technique. 2018 Twentieth International Middle East Power Systems Conference (MEPCON), 2018, pp. 1053-1058. doi: https://doi.org/10.1109/MEPCON.2018.8635110.
Berrouk F., Bounaya K. Optimal Power Flow For Multi-FACTS Power System Using Hybrid PSO-PS Algorithms. Journal of Control, Automation and Electrical Systems, 2018, vol. 29, no. 2, pp. 177-191. doi: https://doi.org/10.1007/s40313-017-0362-7.
Shabanpour-Haghighi A., Seifi A.R., Niknam T. A modified teaching–learning based optimization for multi-objective optimal power flow problem. Energy Conversion and Management, 2014, vol. 77, pp. 597-607. doi: https://doi.org/10.1016/j.enconman.2013.09.028.
Mugemanyi S., Qu Z., Rugema F.X., Dong Y., Bananeza C., Wang L. Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm. IEEE Access, 2020, vol. 8, pp. 65830-65867. doi: https://doi.org/10.1109/ACCESS.2020.2982988.
Nguyen T.T. A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy, 2019, vol. 171, pp. 218-240. doi: https://doi.org/10.1016/j.energy.2019.01.021.
Kahourzade S., Mahmoudi A., Mokhlis H. Bin. A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electrical Engineering, 2015, vol. 97, no. 1, pp. 1-12. doi: https://doi.org/10.1007/s00202-014-0307-0.
Mahdad B., Srairi K. Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Applied Soft Computing, 2016, vol. 46, pp. 501-522. doi: https://doi.org/10.1016/j.asoc.2016.05.027.
Mahdad B., Kamel S. New strategy based modified Salp swarm algorithm for optimal reactive power planning: a case study of the Algerian electrical system (114 bus). IET Generation, Transmission & Distribution, 2019, vol. 13, no. 20, pp. 4523-4540. doi: https://doi.org/10.1049/iet-gtd.2018.5772.
Bentouati B., Javaid M.S., Bouchekara H.R.E.H., El-Fergany A.A. Optimizing performance attributes of electric power systems using chaotic Salp swarm optimizer. International Journal of Management Science and Engineering Management, 2020, vol. 15, no. 3, pp. 165-175. doi: https://doi.org/10.1080/17509653.2019.1677197.
Pulluri H., Naresh R., Sharma V. A solution network based on stud krill herd algorithm for optimal power flow problems. Soft Computing, 2018, vol. 22, no. 1, pp. 159-176. doi: https://doi.org/10.1007/s00500-016-2319-3.
Li S., Gong W., Wang L., Yan X., Hu C. Optimal power flow by means of improved adaptive differential evolution. Energy, 2020, vol. 198, art. no. 117314. doi: https://doi.org/10.1016/j.energy.2020.117314.
El-Fergany A.A., Hasanien H.M. Salp swarm optimizer to solve optimal power flow comprising voltage stability analysis. Neural Computing and Applications, 2020, vol. 32, no. 9, pp. 5267-5283. doi: https://doi.org/10.1007/s00521-019-04029-8.
Sakthivel V.P., Suman M., Sathya P.D. Squirrel search algorithm for economic dispatch with valve-point effects and multiple fuels. Energy Sources, Part B: Economics, Planning, and Policy, 2020, vol. 15, no. 6, pp. 351-382. doi: https://doi.org/10.1080/15567249.2020.1803451.
Meng A., Zeng C., Wang P., Chen D., Zhou T., Zheng X., Yin H. A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem. Energy, 2021, vol. 225, art. no. 120211. doi: https://doi.org/10.1016/j.energy.2021.120211.
Mehdi M.F., Ahmad A., Ul Haq S.S., Saqib M., Ullah M.F. Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function. Electrical Engineering & Electromechanics, 2021, no. 2, pp. 64-69. doi: https://doi.org/10.20998/2074-272X.2021.2.09.
Kouadri R., Slimani L., Bouktir T. Slime mould algorithm for practical optimal power flow solutions incorporating stochastic wind power and static var compensator device. Electrical Engineering & Electromechanics, 2020, no. 6, pp. 45-54. doi: https://doi.org/10.20998/2074-272X.2020.6.07.
Djabali C., Bouktir T. Simultaneous allocation of multiple distributed generation and capacitors in radial network using genetic-salp swarm algorithm. Electrical Engineering & Electromechanics, 2020, no. 4, pp. 59-66. doi: https://doi.org/10.20998/2074-272X.2020.4.08.
Zhao W., Wang L., Zhang Z. Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 2020, vol. 32, no. 13, pp. 9383-9425. doi: https://doi.org/10.1007/s00521-019-04452-x.
Mouassa S., Jurado F., Bouktir T., Raja M.A.Z. Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid. Neural Computing and Applications, 2021, vol. 33, no. 13, pp. 7467-7490. doi: https://doi.org/10.1007/s00521-020-05496-0.
Shaheen A., Elsayed A., Ginidi A., El-Sehiemy R., Elattar E. Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study. Alexandria Engineering Journal, 2022, vol. 61, no. 8, pp. 6105-6118. doi: https://doi.org/10.1016/j.aej.2021.11.035.
Khasanov M., Kamel S., Tostado-Veliz M., Jurado F. Allocation of Photovoltaic and Wind Turbine Based DG Units Using Artificial Ecosystem-based Optimization. 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2020, pp. 1-5. doi: https://doi.org/10.1109/EEEIC/ICPSEurope49358.2020.9160696.
Eid A., Kamel S., Korashy A., Khurshaid T. An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 2020, vol. 8, pp. 178493-178513. doi: https://doi.org/10.1109/ACCESS.2020.3027654.
Zimmerman R.D., Murillo-Sanchez C.E., Thomas R.J. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 2011, vol. 26, no. 1, pp. 12-19. doi: https://doi.org/10.1109/TPWRS.2010.2051168.
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