Multi-Objective Optimization of Electric Distribution Systems with integrated distributed Generation using Deep Reinforcement Learning
Received: 26 January 2025 | Revised: 28 February 2025 | Accepted: 6 March 2025 | Online: 3 April 2025
Corresponding author: Trieu Ngoc Ton
Abstract
This paper proposes a method for optimizing the placement and capacity of Distributed Generators (DGs) in distribution systems based on Deep Reinforcement Learning (DRL). The objective of the method is to minimize power losses, investment costs, voltage deviations, and CO2 emissions while ensuring strict compliance with system operating constraints. The proposed approach leverages the robust capabilities of DRL to handle nonlinear and complex-constrained problems, making it highly adaptable to various operational scenarios. Experimental results on standard distribution systems demonstrate that the proposed method outperforms traditional algorithms, significantly improving operational efficiency and enhancing the integration of renewable energy sources. This contributes to the development of smart grid systems and promotes sustainable energy solutions.
Keywords:
distributed generator, reinforcement learning, multi-objective optimization, carbon emission reduction, loss minimizationDownloads
References
R. P. Payasi, A. K. Singh, and D. Singh, “Review of distributed generation planning: objectives, constraints, and algorithms,” International Journal of Engineering, Science and Technology, vol. 3, no. 3, pp. 133-153, Jul. 2011.
W. L. Theo, J. S. Lim, W. S. Ho, H. Hashim, and C. T. Lee, “Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods,” Renewable and Sustainable Energy Reviews, vol. 67, pp. 531–573, Jan. 2017.
G. S. Naik, D. K. Khatod, and M. P. Sharma, “Distributed Generation Impact on Distribution Networks: A Review,” International Journal of Electronics and Electical Engineering, vol. 2, no. 3, Jan. 2014, Art. no. 10.
J. Pierezan and L. Dos Santos Coelho, “Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems,” in 2018 IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1–8.
S. S. Kola and Jayabarathi, “Optimal Allocation of Renewable Distributed Generation and Capacitor Banks in Distribution Systems using Salp Swarm Algorithm,” International Journal of Renewable Energy Research (IJRER), vol. 9, no. 1, pp. 96–107, Mar. 2019.
D. Singh, D. Singh, and K. S. Verma, “GA based energy loss minimization approach for optimal sizing & placement of distributed generation,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 12, no. 2, pp. 147–156, May 2008.
F. Tooryan, E. R. Collins, A. Ahmadi, and S. S. Rangarajan, “Distributed generators optimal sizing and placement in a microgrid using PSO,” in 2017 IEEE 6th International Conference on Renewable Energy Research and Applications, San Diego, CA, USA, 2017, pp. 614–619.
P. Paliwal, N. P. Patidar, and R. K. Nema, “Planning of grid integrated distributed generators: A review of technology, objectives and techniques,” Renewable and Sustainable Energy Reviews, vol. 40, pp. 557–570, Dec. 2014.
T. T. Nguyen, N. D. Nguyen, P. Vamplew, S. Nahavandi, R. Dazeley, and C. P. Lim, “A multi-objective deep reinforcement learning framework,” Engineering Applications of Artificial Intelligence, vol. 96, Nov. 2020, Art. no. 103915.
V.-H. Bui, A. Hussain, and H.-M. Kim, “Double Deep Q -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties,” IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 457–469, Jan. 2020.
Z. Yuan, W. Wang, H. Wang, and A. Yildizbasi, “A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction,” Journal of Energy Storage, vol. 29, Jun. 2020, Art. no. 101368.
W. Sheng, K.-Y. Liu, Y. Liu, X. Meng, and Y. Li, “Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II,” IEEE Transactions on Power Delivery, vol. 30, no. 2, pp. 569–578, Apr. 2015.
M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Transactions on Power Delivery, vol. 4, no. 2, pp. 1401–1407, Apr. 1989.
T. N. Ton, T. T. Nguyen, A. V. Truong, and T. P. Vu, “Optimal Location and Size of Distributed Generators in an Electric Distribution System based on a Novel Metaheuristic Algorithm,” Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5325–5329, Feb. 2020.
Downloads
How to Cite
License
Copyright (c) 2025 Trieu Ngoc Ton, Loc Huu Pham, Phong Minh Le, Tan Minh Le
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
