Journal of Green Energy Research and Innovation
Quarterly

Optimizing Distributed Energy Resources for Sustainable Solutions: A Multi-Objective Approach Based on Harmony Search Algorithm

Document Type : Research article

Authors

Fardad Rastgou 1
Saman Hosseini-Hemati 1
Ashkan Mohammadi 2

1 Department of Electrical Engineering, Ker.C., Islamic Azad University, Kermanshah, Iran

2 Department of Electrical Engineering, IsG.C., Islamic Azad University, Eslamabad-E-Gharb, Kermanshah, Iran

10.61882/jgeri.3.1.42
Abstract
This study introduces a comprehensive multi-objective harmony search algorithm designed to ‎simultaneously minimize total monetary costs and pollutant emissions while explicitly accounting ‎for uncertainties associated with electrical load demand and electricity market prices. To ‎effectively capture and model these inherent uncertainties, a Monte Carlo simulation (MCS) ‎framework is employed, enabling a probabilistic assessment of system behavior under varying ‎operating conditions. The formulated optimization problem integrates six distinct types of ‎distributed energy resources (DER), namely wind turbines, photovoltaic systems, fuel cells, ‎micro-turbines, gas turbines, and diesel generators. This diverse portfolio of DER technologies ‎allows the model to accurately reflect the operational flexibility and heterogeneity of modern ‎distributed energy systems. Within the proposed multi-objective harmony search framework, a ‎non-dominated sorting mechanism is applied to systematically classify candidate solutions and ‎extract the Pareto-optimal front, thereby revealing the trade-offs between economic and ‎environmental objectives. To further support practical decision-making, a fuzzy decision-making ‎methodology is incorporated to identify the most suitable compromise solution from the set of ‎Pareto-optimal alternatives, taking into account decision-makers’ preferences and system ‎priorities. The simulation results demonstrate that higher penetration of renewable energy sources ‎plays a crucial role in reducing energy losses, mitigating environmental impacts, and improving ‎overall system efficiency. These findings highlight the effectiveness of the proposed optimization ‎framework in enhancing the economic and environmental performance of distributed energy ‎systems under uncertainty.‎‎

Graphical Abstract

Optimizing Distributed Energy Resources for Sustainable Solutions: A Multi-Objective Approach Based on Harmony Search Algorithm

Highlights

❖ A multi-objective harmony search algorithm with Monte Carlo simulation minimizes costs and emissions under load and price uncertainties.
❖ The model optimizes six diverse energy resources to balance economic and environmental trade-offs using fuzzy decision-making.
❖ Results confirm that higher renewable energy penetration significantly reduces losses, cuts emissions, and improves system efficiency. 

Keywords

Harmony search algorithm
optimization in power system
distributed energy resources
Monte Carlo simulation

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

Credit Authorship Contribution Statement

Fardad Rastgou: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization . Saman Hosseini-Hemati: Software, Writing-original draft, Writing-review & editing.  Ashkan Mohammadi: Software, Writing-original draft, Writing-review & editing. 

Bibliography

Fardad Rastgou holds a Bachelor's, Master's, and Ph.D. degree in Power Electrical Engineering. He completed his Bachelor's degree at Tabriz University and went on to earn both his Master's and Ph.D. degrees with honors from Kurdistan University in Sanandaj. Dr. Rastgou has a keen interest in power system planning, bi-level planning, and renewable resource planning. His academic journey reflects a strong commitment to advancing the field of electrical engineering, particularly in optimizing power systems for sustainability and efficiency.

Saman Hosseini-Hemati  Assistant Professor and Electrical Engineer with over 12 years of combined experience in power systems engineering, academic teaching, and research. Skilled in power system analysis, MV/HV design, and grid integration. Experienced in teaching, supervising students, and leading research in smart grids and renewable energy.

 Ashkan Mohammadi  received the M.Sc. degree in Electrical Engineering from Azad Islamic University, Science and Research Branch, Tehran, Iran, in 2015. He obtained his Ph.D. from the University of Science and Research, Tehran. Currently, he is a faculty member at Azad Islamic University, Islamabad West Branch, Kermanshah.

Citation

A. Rastgou, S. Hosseini-Hemati, and A. Mohammadi, "Optimizing Distributed Energy Resources for Sustainable Solutions: A Multi-Objective Approach Based on Harmony Search Algorithm," Journal of Green Energy Research and Innovation, vol. 3, no. 1, pp. 42-55, 2026.

[1]  A. Rastgou, "Distribution Network Expansion Planning: An Updated Review of Current Methods and New Challenges," Renewable and Sustainable Energy Reviews, vol. 189, 114062, 2024.
[2]  P. Hajiamoosha, A. Rastgou, and H. Afshar, "A Multi-Objective Framework for Smart Energy Hubs: Leveraging Compressed Air Storage and Demand Response," Journal of Green Energy Research and Innovation, vol. 2, no. 2, pp. 1–25, 2025.
[3]  B. Ahmadi, O. Ceylan, A. Ozdemir, and M. Fotuhi-Firuzabad, "A Multi-Objective Framework for Distributed Energy Resources Planning and Storage Management," Applied Energy, vol. 314, 118887, 2022.
[4]  R. S. F. Ferraz, R. S. F. Ferraz, V. F. S. Júnior, and A. C. Rueda-Medina, "Multi-Objective Approach for Distribution System Planning Considering Stochastic Customer-Owned Distributed Energy Resources," IEEE Access, vol. 13, pp. 40561–40576, 2025.
[5]  A. K. ALAhmad, R. Verayiah, H. Shareef, A. Ramasamy, and S. Ba-swaimi, "Enhancing Renewable Energy Integration Through Strategic Stochastic Optimization Planning of Distributed Energy Resources (Wind/PV/SBESS/MBESS) in Distribution Systems," Energy Strategy Reviews, vol. 59, 101683, 2025.
[6]  A. Ali, A. Bughio, et al., "Optimization of Distributed Energy Resources Planning and Battery Energy Storage Management Via Large-Scale Multi-Objective Evolutionary Algorithm," Energy, vol. 311, 133463, 2024.
[7]  K. E. Adetunji, I. W. Hofsajer, A. M. Abu-Mahfouz, and L. Cheng, "An Optimization Planning Framework for Allocating Multiple Distributed Energy Resources and Electric Vehicle Charging Stations in Distribution Networks," Applied Energy, vol. 322, 119513, 2022.
[8]  K. Twaisan, and N. Barışçı, "Integrated Distributed Energy Resources (DER) and Microgrids: Modeling and Optimization of DERs," Electronics, vol. 11, no. 18, 2816, 2022.
[9]  P. Hajiamoosha, A. Rastgou, S. Bahramara, and S. M. Bagher Sadati, "Stochastic Energy Management in a Renewable Energy-Based Microgrid Considering Demand Response Program," International Journal of Electrical Power & Energy Systems, vol. 129, 106791, 2021.
[10] A. Rastgou, J. Moshtagh, and S. Bahramara, "Improved Harmony Search Algorithm for Electrical Distribution Network Expansion Planning in the Presence of Distributed Generators," Energy, vol. 151, pp. 178–202, 2018.
[11] M. J. Chihota, and B. Bekker, "New Planning Principles for Distribution Networks with Penetration of Distributed Energy Resources," 2020 6th IEEE International Energy Conference (ENERGYCon), pp. 643–648, 2020.
[12] E. Kianmehr, S. Nikkhah, V. Vahidinasab, D. Giaouris, and P. C. Taylor, "A Resilience-Based Architecture for Joint Distributed Energy Resources Allocation and Hourly Network Reconfiguration," IEEE Transactions on Industrial Informatics, vol. 15, no. 10, pp. 5444–5455, 2019.
[13] Y. Wang, Y. Xu, and H. Sun, "Multi-Objective Planning of Distributed Energy Resources Based on Enhanced Adaptive Weighted-Sum Algorithm," IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4624–4637, 2024.
[14] Y. Wang, Y. Xu, and H. Sun, "Three-Objective Capacity Planning of Distributed Energy Resources in Distribution Network," 2024 IEEE Power & Energy Society General Meeting (PESGM), pp. 1–5, 2024.
[15] M. Adham, S. Keene, and R. B. Bass, "Distributed Energy Resources: A Systematic Literature Review," Energy Reports, vol. 13, pp. 1980–1999, 2025.
[16] A. Rastgou, J. Moshtagh, and S. Bahramara, "Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm," Journal of Operation and Automation in Power Engineering, vol. 6, no. 1, pp. 111–125, 2018.
[17] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
[18] A. Rastgou, S. Bahramara, and J. Moshtagh, "Flexible and Robust Distribution Network Expansion Planning in the Presence of Distributed Generators," International Transactions on Electrical Energy Systems, vol. 28, no. 12, e2637, 2018.
[19] X. Yang, "Harmony Search as a Metaheuristic Algorithm," Studies in Computational Intelligence, pp. 1–14, n.d.
[20] A. Rastgou, and J. Moshtagh, "Improved Harmony Search Algorithm for Transmission Expansion Planning with Adequacy–Security Considerations in the Deregulated Power System," International Journal of Electrical Power & Energy Systems, vol. 60, pp. 153–164, 2014.
[21] V. Vahidinasab, "Optimal Distributed Energy Resources Planning in a Competitive Electricity Market: Multiobjective Optimization and Probabilistic Design," Renewable Energy, vol. 66, pp. 354–363, 2014.
[22] R. N. Allan, Reliability Evaluation of Power Systems, Springer Science & Business Media, 2013.
[23] Lei Wu, M. Shahidehpour, and Tao Li, "Cost of Reliability Analysis Based on Stochastic Unit Commitment," IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 1364–1374, 2008.
[24] ZZ. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer Science & Business Media, 2013.
[25] M. Haghifam, H. Falaghi, and O. Malik, "Risk-Based Distributed Generation Placement," IET Generation, Transmission & Distribution, vol. 2, no. 2, pp. 252–260, 2008.
Journal of Green Energy Research and Innovation
Volume 3, Issue 1
Spring 2026
Pages 42-55

  • Receive Date 02 April 2025
  • Revise Date 15 May 2025
  • Accept Date 20 June 2025
  • Publish Date 01 March 2026

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us