International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 7 | Year 2026 | Article Id. IJETT-V74I7P104 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I7P104

Optimal Battery Storage Sizing and Strategic Operation in Renewable Energy Integrated Grid using Roulette Chaotic Pelican Optimisation Algorithm


Ifeoluwa Titilayo Akinola, Yanxia Sun, Isaiah Gbadegesin Adebayo, Zenghui Wang

Received Revised Accepted Published
21 Feb 2025 08 Jan 2026 03 Jun 2026 28 Jul 2026

Citation :

Ifeoluwa Titilayo Akinola, Yanxia Sun, Isaiah Gbadegesin Adebayo, Zenghui Wang, "Optimal Battery Storage Sizing and Strategic Operation in Renewable Energy Integrated Grid using Roulette Chaotic Pelican Optimisation Algorithm," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 7, pp. 34-54, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I7P104

Abstract

The incorporation of Renewable Energy Sources (RESs) into the power grids poses a considerable challenge due to the intermittent nature of their production. Battery Energy Storage Systems (BESS) eliminate such problems by absorbing excess generation during peaks and providing stored charge at troughs. This leads to the fact that identifying a suitable BESS size is important to ensure a trade-off between cost-efficiency and grid stability, as inappropriately sized systems would lead to inefficiency, high capital and operating costs, and reduced reliability. This study uses the Roulette Chaotic Pelican Optimisation Algorithm (RCPOA) to optimise BESS sizing in RES-integrated grids. RCPOA outperforms at least five out of seven algorithms across 23 classical benchmarks. It excels in the CEC 2019 benchmarks, ranking first in the Friedman rank test, demonstrating its robustness compared to other state-of-the-art algorithms. Its success extends to the 'tension spring design' constraint problem, underscoring its versatility in different optimisation contexts. Simulations validate RCPOA's capability to determine optimal BESS sizes, enhancing grid performance and cost efficiency. Furthermore, sensitivity analyses show that changes in State of Charge (SOC) minimum limits, battery efficiency, and range of capacities all affect the optimal battery capacity greatly. By comparison, minimum capital costs are relatively stable and are only highly sensitive to the costs of operations and maintenance, a shortfall in demand, and the cost of grid energy. These results confirm the usefulness of RCPOA to optimise BESS setup in renewable energy solutions, indicating potential to promote economic and operational goals in current power systems.

Keywords

Battery sizing, Energy Storage, Grid Stability, Optimisation algorithm, Algorithm modification.

References

[1] Ruixiaoxiao Zhang et al., “Optimization of Battery Energy Storage System (BESS) Sizing in Different Electricity Market Types Considering BESS Utilization Mechanisms and Ownerships,” Journal of Cleaner Production, vol. 470, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Yuqing Yang et al., “Battery Energy Storage System Size Determination in Renewable Energy Systems: A Review,” Renewable and Sustainable Energy Reviews, vol. 91, pp. 109-125, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Dlzar Al kez et al., “A Critical Evaluation of Grid Stability and Codes , Energy Storage and Smart Loads in Power Systems with Wind Generation,” Energy, vol. 205, pp. 1-22, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Amela Ajanovic, Albert Hiesl, and Reinhard Haas, “On the Role of Storage for Electricity in Smart Energy Systems,” Energy, vol. 200, pp. 1-18, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Apar Chitransh, and Ranjit Kumar Bindal, “A Review on Energy Storage System of Smart Grid System,” International Journal of Science and Research, vol. 10, no. 6, pp. 38-43, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Olalekan Kunle Ajiboye et al., “A Review of Hybrid Renewable Energies Optimisation: Design, Methodologies, and Criteria,” International Journal of Sustainable Energy, vol. 42, no. 1, pp. 648-684, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Yijie Zhang, Tao Ma, and Hongxing Yang, “A Review on Capacity Sizing and Operation Strategy of Grid-Connected Photovoltaic Battery Systems,” Energy and Built Environment, vol. 5, no. 4, pp. 500-516, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Pavel Trojovský, and Mohammad Dehghani, “Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications,” Sensors, vol. 22, no. 3, pp. 1-34, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[9] K. Anjani Parvathi, N.C. Kotaiah, and K. Radha Rani, “Pelican Optimization Algorithm for Optimal Demand Response in Islanded Active Distribution Network Considering Controllable Loads,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 6, pp. 132-141, 2022.
[CrossRef] [Google Scholar]

[10] Anuj Kumar et al., “Modified Wild Horse Optimizer for Constrained System Reliability Optimization,” Axioms, vol. 12, no. 7, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Asmita Ajay Rathod, and S. Balaji, “Optimization of Stand-Alone Hybrid Renewable Energy System based on Techno-Socio-Enviro-Financial Perspective using Improved Red-Tailed Hawk Algorithm,” Applied Energy, vol. 376, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Roya AhmadiAhangar et al., “Impacts of Grid-Scale Battery Systems on Power System Operation, Case of Baltic Region,” IET Smart Grid, vol. 7, no. 2, pp. 101-119, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Saman Korjani et al., “Battery Management for Energy Communities-Economic Evaluation of an Artificial Intelligence-Led System,” Journal of Cleaner Production, vol. 314, pp. 1-36, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Abed Kazemtarghi, and Ayan Mallik, “Techno-Economic Microgrid Design Optimization Considering Fuel Procurement Cost and Battery Energy Storage System Lifetime Analysis,” Electric Power Systems Research, vol. 235, pp. 1-31, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Mohammed Atta Abdulgalil, Muhammad Khalid, and Fahad Alismail, “Optimal Sizing of Battery Energy Storage for a Wind Uncertainties,” Energies, vol. 12, no. 12, pp. 1-29, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Umar T. Salman, Fahad Al-Ismail  Saleh, and Khalid Muhammad, “Optimal Sizing of Battery Energy Storage for Grid-Connected and Isolated Wind-Penetrated Microgrid,” IEEE Access, vol. 8, pp. 91129-91138, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Sharmistha Sharma, Subhadeep Bhattacharjee, and Aniruddha Bhattacharya, “Grey Wolf Optimisation for Optimal Sizing of Battery Energy Storage Device to Minimise Operation Cost of Microgrid,” IET Generation, Transmission and Distribution, vol. 10, no. 3, pp. 625-637, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Mamood Gholizadeh, and Martin Wolter, “Cost-Beneficial Analysis of Utilizing a Combination of Renewable and Non-Renewable Energy Sources,” 2020 55th International Universities Power Engineering Conference, Turin, Italy, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Polamarasetty P. Kumar, and Rajeshwer Prasad Saini, “Optimization of an Off-Grid Integrated Hybrid Renewable Energy System with Various Energy Storage Technologies using Different Dispatch Strategies,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 47, no. 1, pp. 274-303, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Mohammad Amini et al., “Optimal Sizing of Battery Energy Storage in a Microgrid Considering Capacity Degradation and Replacement Year,” Electric Power Systems Research, vol. 195, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[21] M. Faisal et al., “Particle Swarm Optimised Fuzzy Controller for Charging-Discharging and Scheduling of Battery Energy Storage System in MG Applications,” Energy Reports, vol. 6, pp. 215-228, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[22] MohammadReza AkbaiZadeh, Taher Niknam, and Abdollah Kavousi-Fard, “Adaptive Robust Optimization for the Energy Management of the Grid-Connected Energy Hubs based on Hybrid Meta-Heuristic Algorithm,” Energy, vol. 235, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Qi Xiong, Jincheng She, and Jinkun Xiong, “A New Pelican Optimization Algorithm for the Parameter Identification of Memristive Chaotic System,” Symmetry, vol. 15, no. 6, p. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[24] K.M. Ang et al., “Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems,” Mekatronika, vol. 3, no. 1, pp. 35-43, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Feyza Altunbey Özbay, “A Modified Seahorse Optimization Algorithm based on Chaotic Maps for Solving Global Optimization and Engineering Problems,” Engineering Science and Technology, an International Journal, vol. 41, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Kamini Shahare, “Performance Analysis and Comparison of Various Techniques for Short-Term Load Forecasting,” Energy Reports, vol. 9, pp. 799-808, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Khaidem Bidyanath, Sanasam Dhanabanta Singh, and Shuma Adhikari, “Implementation of Genetic and Particle Swarm Optimization Algorithm for Voltage Profile Improvement and Loss Reduction using Capacitors in 132 KV Manipur Transmission System,” Energy Reports, vol. 9, pp. 738-746, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[28] Cristina Bianca Pop et al., “Review of Bio-Inspired Optimization Applications in Renewable-Powered Smart Grids : Emerging Population-based Metaheuristics,” Energy Reports, vol. 8, pp. 11769-11798, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[29] Peiyao Yang et al., “Application of Improved Chimp Optimization Algorithm Integrating Multiple Strategies in Reservoirs Optimization Operation,” Expert Systems with Applications, vol. 285, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[30] Kumeshan Reddy, and Akshay K. Saha, “A Modified Whale Optimization Algorithm for Exploitation Capability and Stability Enhancement,” Heliyon, vol. 8, no. 10, pp. 1-11, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[31] Vamsi Krishna Reddy Aala Kalananda, and Venkata Lakshmi Narayana Komanapalli, “A Combinatorial Social Group Whale Optimization Algorithm for Numerical and Engineering Optimization Problems,” Applied Soft Computing, vol. 99, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[32] Heming Jia et al., “Improved Sandcat Swarm Optimization Algorithm for Solving Global Optimum Problems,” Artificial Intelligence Review, vol. 58, no. 1,  pp. 1-38, 2024.
[
CrossRef] [Google Scholar] [Publisher Link]

[33] S. Vikram Singh et al., “Enhancing Microgrid Stability: Optimal BESS Sizing via Modified PSO for Frequency Control and Cost Efficiency,” 2024 7th International Conference on Contemporary Computing and Informatics, Greater Noida, India, pp. 627-632, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[34] Sahar Saeedi et al., “Improved Many-Objective Particle Swarm Optimization Algorithm for Scientific Workflow Scheduling in Cloud Computing,” Computers and Industrial Engineering, vol. 147, 2020.
[
CrossRef] [Google Scholar] [Publisher Link]

[35] Sumit Kumar et al., “Chaotic Marine Predators Algorithm for Global Optimization of Real-World Engineering Problems,” Knowledge-based Systems, vol. 261, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[36] Hamed Khosravi et al., “An Improved Group Teaching Optimization Algorithm based on Local Search and Chaotic Map for Feature Selection in High-Dimensional Data,” Expert Systems with Applications, vol. 204, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[37] Esin Ayşe Zaimoğlu et al., “A Binary Chaotic Horse Herd Optimization Algorithm for Feature Selection,” Engineering Science and Technology, an International Journal, vol. 44, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[38] Ruba Abu Khurma et al., “A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem,” Mathematics, vol. 10, no. 3, pp. 1-45, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[39] Hao Liu, Hongbin Dong, and Jing Zhou, “Feature Selection Method based on Hybrid Multi-Strategy Pelican Optimization Algorithm,” 2023 6th International Conference on Data Storage and Data Engineering, Mianyang, China, pp. 59-63, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[40] Hao-Ming Song et al., “Improved Pelican Optimization Algorithm with Chaotic Interference Factor and Elementary Mathematical Function,” Soft Computing, vol. 27, no. 15, pp. 10607-10646, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[41] Ziqiang Zhang, and Yixin Zhao, “Mixed Strategy-based Improved Pelican Optimization Algorithm,” 2023 4th International Symposium on Computer Engineering and Intelligent Communications, Nanjing, China, pp. 428-433, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[42] Purba Daru Kusuma, and Anggunmeka Luhur Prasasti, “Guided Pelican Algorithm,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 6, pp. 179-190, 2022.
[CrossRef] [Google Scholar]

[43] Chun Qing Li, Zheng Feng Jiang, and Yong Ping Huang, “Multi-Strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning,” Information Technology and Control, vol. 53, no. 2, pp. 372-389, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[44] Xiaohan Zhao et al., “Enhancement Method based on Multi-Strategy Improved Pelican Optimization Algorithm and Application to Low-Illumination Forest Canopy Images,” Forests, vol. 15, no. 10, pp. 1-30, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[45] Lingzhi Yi et al., “A Multi-Objective Pelican Optimization Algorithm for Dynamic Reconfiguration of Multi-Type Rural Rooftop PV Array,” Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, vol. 47, no. 5-6, pp. 393-409, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[46] Y. Ramu Naidu, “Multi-objective Pelican Optimization Algorithm for Engineering Design Problems,” Distributed Computing and Intelligent Technology: 19th International Conference, ICDCIT 2023, Bhubaneswar, India, pp. 362-368, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[47] Xizhai Ge et al., “A Hyperchaotic Map with Distance-Increasing Pairs of Coexisting Attractors and its Application in the Pelican Optimization Algorithm,” Chaos, Solitons and Fractals, vol. 173, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[48] Amit Raj, Parul Punia, and Pawan Kumar, “A Novel Hybrid Pelican-Particle Swarm Optimization Algorithm (HPPSO) for Global Optimization Problem,” International Journal of System Assurance Engineering and Management, vol. 15, no. 8, pp. 3878-3893, 2024.
[
CrossRef] [Google Scholar] [Publisher Link]

[49] SeyedDavoud SeyedGarmroudi et al., “Improved Pelican Optimization Algorithm for Solving Load Dispatch Problems,” Energy, vol. 289, pp. 1-32, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[50] Gokulakrishnan Sriram et al., “Dynamics of a Novel Chaotic Map,” Journal of Computational and Applied Mathematics, vol. 436, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[51] Rasika B. Naik, and Udayprakash Singh, “A Review on Applications of Chaotic Maps in Pseudo-Random Number Generators and Encryption,” Annals of Data Science, vol. 11, no. 1, pp. 25-50, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[52] Herman Herman et al., “Scheduling using Genetic Algorithm and Roulette Wheel Selection Method Considering Lecturer Time,” Journal Information of Technology and its Utilization, vol. 2, no. 1, p. 24-28, 2019.
[Google Scholar] [Publisher Link]

[53] Rosshairy Abd Rahman et al., “Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation,” Mathematical Problems in Engineering, vol. 2016, no. 1, pp. 1-10, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[54] G.S. Geethamani, and M. Mayilvaganan, “Analysis of Genetic Crossover Techniques based on Roulette Wheel Selection Algorithm and Steady State Selection Algorithm,” International Journal of Engineering and Computer Science, vol. 5, no. 1, pp. 15428-15431, 2016.
[CrossRef] [Google Scholar]

[55] Asaad Shakir Hameed et al., “Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm,” Applied Computational Intelligence and Soft Computing, vol. 2023, no. 1, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[56] I.M. El-Desoky et al., “A Hybrid Genetic Algorithm for Job Shop Scheduling Problems,” International Journal of Advancement in Engineering, Technology and Computer Sciences, vol. 3, no. 1, pp. 6-17, 2016.
[
Google Scholar]

[57] Warrick Pierce, and Monique Le Roux, “Statistics of Utility-Scales Power Generation in South Africa,” CSIR Energy Center, pp. 1-122, 2023.
[Google Scholar]

[58] Chnoor M. Rahman, and Tarik A. Rashid, “A New Evolutionary Algorithm: Learner Performance based Behavior Algorithm,” Egyptian Informatics Journal, vol. 22, no. 2, pp. 213-223, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[59] Jaza Mahmood Abdullah, and Tarik Ahmed, “Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process,” IEEE Access, vol. 7, pp. 43473-43486, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[60] Janez Brest, Mirjam Sepesy Maučec, and Borko Bošković, “The 100-Digit Challenge: Algorithm jDE100,” 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand, pp. 19-26,  2019.
[CrossRef] [Google Scholar] [Publisher Link]

[61] Gulin Zeynep Oztas, and Sabri Erdem, “A Penalty-based Algorithm Proposal for Engineering Optimization Problems,” Neural Computing and Applications, vol. 35, no. 10, pp. 7635-7658, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[62] Saeed Rafee Nekoo, José Ángel Acosta, and Anibal Ollero, “A Search Algorithm for Constrained Engineering Optimization and Tuning the Gains of Controllers,” Expert Systems with Applications, vol. 206, pp. 1-27, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[63] Chalermjit Klansupar, and Surachai Chaitusaney, “Optimal Sizing of Grid-Scaled Battery with Consideration of Battery Installation and System Power-Generation Costs,” Energies, vol. 15, no. 13, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[64] Jonas Engels, Bert Claessens, and Geert Deconinck, “Optimal Combination of Frequency Control and Peak Shaving with Battery Storage Systems,” IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3270-3279, 2020.
[CrossRef] [Google Scholar] [Publisher Link]