A Metaheuristic Approach for the Optimal Allocation of Distributed Energy Resources in a Distribution System
DOI:
https://doi.org/10.69650/rast.2025.261307Keywords:
Distributed Energy Resources, Distribution Systems, Optimization, Particle Swarm Optimization, Renewable EnergyAbstract
In this paper, a variant of the Chimp optimisation algorithm, namely, Chimp Particle Swarm Optimisation Algorithm (ChPSO), a metaheuristic optimisation technique, is recommended to optimise the placement of Distributed Energy Resources (DER) in electric power distribution systems. By integrating the Chimp Optimization Algorithm with Particle Swarm Optimization, ChPSO enhances the efficiency and reliability of power distribution networks. The algorithm's effectiveness is evaluated using the IEEE-33 bus distribution system, a well-established benchmark in power system research. The primary goal is to strategically position DERs to minimize real power losses, and reduce voltage deviations across all nodes. Results indicate significant improvements in these performance metrics, showcasing ChPSO's capability to tackle complex optimization challenges. Specifically, the implementation of three optimally placed DERs achieved a remarkable 92.20% reduction in real power loss, while positioning four DERs resulted in an even greater reduction of 92.90%. Additionally, reactive power losses decreased by 90.55% and 91.59% for three and four DERs, respectively. These findings highlight the potential of the ChPSO algorithm as a decent solution for optimized DER placement, significantly enhancing the operational efficiency and reliability of modern distribution systems and emphasizing the need for innovative optimization strategies in sustainable energy solutions.
References
Rezaei,O., Mirzapour, O.,Panahazari, M. and Gholami, H., Hybrid AC/DC Provisional Microgrid Planning Model Considering Converter Aging. Electricity. 3 (2022) 236-250, doi: https://doi.org/10.3390/electricity3020014.
Marnay, C., Blanco, R., Hamachi, K. S., Kawaan, C. P., Osborn, J. G. and Rubio, F. J., Integrated assessment of dispersed energy resources deployment. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States), LBNL-46082 (2000), doi: https://doi.org/10.2172/776601.
Guzman, C. P., Arias, N. B., Franco, J. F., Rider, M. J. and Romero, R., Enhanced Coordination Strategy for an Aggregator of Distributed Energy Resources Participating in the Day-Ahead Reserve Market. Energies. 13 (2020) 1965, doi: https://doi.org/10.3390/en13081965.
Shahzad, S. and Jasińska, E., Renewable Revolution: A Review of Strategic Flexibility in Future Power Systems. Sustainability. 16 (2024) 5454, doi: https://doi.org/10.3390/su16135454.
Sułek, A. and Borowski, P. F., Business Models on the Energy Market in the Era of a Low-Emission Economy. Energies. 17 (2024) 3235, doi: https://doi.org/10.3390/en17133235.
Paliwal, P., Comprehensive analysis of distributed energy resource penetration and placement using probabilistic framework. IET Renewable Power Generation. 15 (2021) 794-808, doi: https://doi.org/10.1049/rpg2.12069.
Funde, N. and Yoon, S.-G., Rank-based energy scheduling strategy of networked microgrids in distribution systems. IET Generation, Transmission & Distribution. 16 (2022) 84–98, doi: https://doi.org/10.1049/gtd2.12279.
Divya, B. and Devarapalli, R. Estimation of sensitive node for IEEE-30 bus system by load variation. in 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). (2014), 1–4, doi: https://doi.org/10.1109/ICGCCEE.2014.6922357.
Rajan, C. C. A., An Evolutionary Programming Algorithm for Solving Unit Commitment Problem in Smart Grid Environment. Journal of Renewable Energy and Smart Grid Technology. 16 (2021) 51-65, doi: https://ph01.tci-thaijo.org/index.php/RAST/article/view/243534.
Xu, G., Yu, W., Griffith, D., Golmie, N. and Moulema, P., Toward Integrating Distributed Energy Resources and Storage Devices in Smart Grid. IEEE Internet of Things Journal. 4 (2017) 192–204, doi: https://doi.org/10.1109/jiot.2016.2640563.
Gautam, M., Bhusal, N. and Benidris, M. A Cooperative Game Theory-based Approach to Sizing and Siting of Distributed Energy Resources in 2021 North American Power Symposium (NAPS). (2021), 1–6, doi: https://doi.org/10.1109/NAPS52732.2021.9654578.
Leng, R., Li, Z. and Xu, Y. A Comprehensive Literature Review for Optimal Planning of Distributed Energy Resources in Distribution Grids. in 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia). (2022), 369–373, doi: https://doi.org/10.1109/ISGTAsia54193.2022.10003621.
Gill, A., Singh, P., Jobanputra, J. H. and Kolhe, M. L., Placement analysis of combined renewable and conventional distributed energy resources within a radial distribution network. AIMSE Energy. 10 (2022) 1216-1229, doi: https://doi.org/10.3934/energy.2022057.
Gill, A., Bali, H. and Choudhary, A., Placement of Renewable Distributed Energy Resources in the Radial Distribution Network to Overcome the Losses and Air Pollution. Asian Journal of Water, Environment and Pollution. 19 (2022) 119–125, doi: https://doi.org/10.3233/AJW220096.
Netisak, W. and Ketjoy, N., Agent based energy management system for microgrid. Journal of Renewable Energy and Smart Grid Technology. 10 (2015) 55-62, doi: https://ph01.tci-thaijo.org/index.php/RAST/article/view/48631.
Galgali, V. S., Ramachadran, M. and Vaidya, G. A., Techniques for Optimal Placement and Sizing of Distributed Energy Resources: a Review. International Review of Automatic Control (IREACO). 10 (2017), doi: https://doi.org/10.15866/ireaco.v10i6.14154.
Pombo, A. V., Pires, V. F. and Pina, J. M. Multiobjective switching devices placement considering environmental constrains in distribution networks with distributed energy resources. in 2015 IEEE International Conference on Industrial Technology (ICIT). (2015) 2789–2793, doi: https://doi.org/10.1109/ICIT.2015.7125509.
Chihota, M. J. and Bekker, B. Modelling and Simulation of Uncertainty in the Placement of Distributed Energy Resources for Planning Applications. in 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). (2020), 1–6, doi: https://doi.org/10.1109/PMAPS47429.2020.9183657.
Alarcon-Rodriguez, A., Ault, G. and Galloway, S., Multi-objective planning of distributed energy resources: A review of the state-of-the-art. Renewable and Sustainable Energy Reviews. 14 (2010) 1353–1366, doi: https://doi.org/10.1016/j.rser.2010.01.006.
Cavalheiro, E. M. B., Vergílio, A. H. B. and Lyra, C., Optimal configuration of power distribution networks with variable renewable energy resources. Computers & Operations Research. 96 (2018) 272–280, doi: https://doi.org/10.1016/j.cor.2017.09.021.
Vais, R. I.,Sahay, K., Chiranjeevi, T., Devarapalli, R. and Knypiński, Ł., Parameter Extraction of Solar Photovoltaic Modules Using a Novel Bio-Inspired Swarm Intelligence Optimisation Algorithm. Sustainability. 15 (2023) 8407, doi: https://doi.org/10.3390/su15108407.
Manasvi, K., Venkateswararao, B., Devarapalli, R. and Prasad, U. PSO Based Optimal Reactive Power Dispatch for the Enrichment of Power System Performance. in Recent Advances in Power Systems. (2020), 267–276. doi: https://doi.org/10.1007/978-981-15-7994-3_24.
Knypiński, Ł., Devarapalli, R. and Gillon, F., The hybrid algorithms in constrained optimization of the permanent magnet motors. IET Science, Measurement & Technology. 18 (2024) 455–461, doi: https://doi.org/10.1049/smt2.12185.
Babu, N. R., Chiranjeevi, T., Devarapalli, R., Knypiński, Ł. and Márquez, F. P. G., Real-time validation of an automatic generation control system considering HPA-ISE with crow search algorithm optimized cascade FOPDN-FOPIDN controller. Archives of Control Sciences. 33 (2023) 371-390, doi: https://doi.org/10.24425/acs.2023.146280.
Nagadurga, T., Devarapalli, R. and Knypiński, Ł. Comparison of Meta-Heuristic Optimization Algorithms for Global Maximum Power Point Tracking of Partially Shaded Solar Photovoltaic Systems. Algorithms. 16 (2023) 376, doi: https://doi.org/10.3390/a16080376.
Bathina, V., Devarapalli, R. and Márquez, F. P. G., Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems. Journal of Electrical Engineering & Technology. 18 (2023) 1637–1653, doi: https://doi.org/10.1007/s42835-022-01301-1.
Khishe, M. and Mosavi, M. R., Chimp optimization algorithm. Expert Systems with Applications. 149 (2020) 113338, doi: http://dx.doi.org/10.1016/j.eswa.2020.113338.
Daravath, R. and Bali, S. K., Optimal Distributed Energy Resources Placement to Reduce Power Losses and Voltage Deviation in a Distribution System. Iranian Journal of Science and Technology, Transactions of Electrical Engineering. 49 (2025) 725-744, doi: https://doi.org/10.1007/s40998-024-00780-4.
Devarapalli, R., Bhattacharyya, B., Sinha, N. K. and Dey, B., Amended GWO approach based multi-machine power system stability enhancement. ISA Transactions. 109 (2021) 152–174, doi: https://doi.org/10.1016/j.isatra.2020.09.016.
Devarapalli, R. and Bhattacharyya, B., A novel hybrid AGWO-PSO algorithm in mitigation of power network oscillations with STATCOM. NACO. 11 (2021) 579–611, doi: https://doi.org/10.3934/naco.2020057.
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