Published on: June 2026
COST-AWARE ENERGY MANAGEMENT STRATEGY FOR MICROGRID SYSTEMS USING HYBRID OPTIMIZATION TECHNIQUES
Najimuddin Sharad Kumar Sanjeev Kumar Kulshrestha
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Abstract
The increasing integration of renewable energy resources, distributed generation units, energy storage systems, and intelligent loads has transformed conventional microgrids into highly dynamic and complex energy ecosystems. While these advancements improve sustainability and energy independence, they also introduce significant challenges related to energy scheduling, operational cost minimization, resource utilization, and system reliability. Traditional energy management approaches often rely on fixed scheduling policies and single-objective optimization methods, which may be insufficient for handling the uncertainty associated with renewable energy generation and fluctuating load demands. To address these challenges, this paper proposes a Cost-Aware Energy Management Strategy for Microgrid Systems Using Hybrid Optimization Techniques. The proposed framework integrates real-time monitoring, intelligent energy profiling, demand forecasting, renewable energy coordination, battery storage management, and hybrid optimization mechanisms to determine optimal energy allocation strategies under varying operating conditions. The hybrid optimization approach combines the strengths of multiple optimization methods to balance energy supply and demand while minimizing operational costs and maximizing resource utilization. Experimental results demonstrate that the proposed strategy significantly improves energy efficiency, reduces operational costs, enhances renewable energy utilization, and strengthens overall system reliability compared with conventional energy management approaches.
How to Cite this Paper
Najimuddin, , Kumar, S. & Kulshrestha, S. K. (2026). Cost-Aware Energy Management Strategy for Microgrid Systems using Hybrid Optimization Techniques. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.141
Najimuddin, , et al.. "Cost-Aware Energy Management Strategy for Microgrid Systems using Hybrid Optimization Techniques." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.141.
Najimuddin, ,Sharad Kumar, and Sanjeev Kulshrestha. "Cost-Aware Energy Management Strategy for Microgrid Systems using Hybrid Optimization Techniques." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.141.
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References
- Cai et al., "TEMP: Cost-Aware Two-Stage Energy Management for Electrical Vehicles Empowered by Blockchain," in IEEE Internet of Things Journal, vol. 11, no. 23, pp. 38246-38261, 1 Dec.1, 2024, doi: 10.1109/JIOT.2024.3445601.
- Nadeem, M. Zaery, S. M. Amrr, S. M. S. Hussain and M. A. Abido, "AI-Driven Energy Cost Optimization for Mixed-Use Buildings Using Machine Learning and Renewable Integration," 2025 IEEE International Conference on Energy Technologies for Future Grids (ETFG), Wollongong, Australia, 2025, pp. 1-6, doi: 10.1109/ETFG61999.2025.11401097.
- R. Tamma, S. Shamsheer, R. Venigalla, C. Akash, S. S. A. Begum and S. K. Medishetti, "Cost Aware Scheduling in Hybrid Cloud-Fog Environment Using Earthworm Optimization Algorithm," 2024 International Conference on Advancement in Renewable Energy and Intelligent Systems (AREIS), Thrissur, India, 2024, pp. 1-9, doi: 10.1109/AREIS62559.2024.10893670.
- Qi, H. Moore, D. Milojicic, C. Bash and S. Pasricha, "SHIELD-EB: Sustainable Hybrid Evolutionary-Boosting Framework for Carbon, Wastewater, and Cost-Aware Datacenter Management," in IEEE Access, vol. 14, pp. 40878-40898, 2026, doi: 10.1109/ACCESS.2026.3674066.
- Yuan et al., "Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence," in IEEE Transactions on Mobile Computing, vol. 24, no. 3, pp. 1530-1549, March 2025, doi: 10.1109/TMC.2024.3484158.
- Shayeghi, B. Mohamadi, P. Zare and N. Bizon, "Hydrogen-Oriented Cost-Aware Dispatch Modeling of RES-Coupling Systems Under Stepped Carbon Trading Constraints," 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Targoviste, Romania, 2025, pp. 1-7, doi: 10.1109/ECAI65401.2025.11095580.
- Kumar, K. K. Gautam, V. Sharma, B. Samania, T. K. Vashishth and S. Chaudhary, "Enhancing Cloud Computing Performance: A Novel Approach for Optimizing Energy Efficiency through AI- Based Load Balancing Algorithm," 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE), Bengaluru, India, 2025, pp. 1-6, doi: 10.1109/ICICKE65317.2025.11136754.
- Cheng, J. Zheng and Y. Li, "JointCloud LLM Inference Deployment Framework: A Cost-Aware Optimization Approach Based on Hybrid Genetic Reinforcement Learning," 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA), Xi'an, China, 2025, pp. 1615-1618, doi: 10.1109/AIITA65135.2025.11047742.
- Burda, D. Bitner, F. Bestehorn, C. Kirches and M. Grotjahn, "Mixed-Integer Real-Time Control of a Building Energy Supply System," in IEEE Control Systems Letters, vol. 7, pp. 907-912, 2023, doi: 10.1109/LCSYS.2022.3229159.
- Sharma, A. Chaudhary, S. Kumari, T. K. Vashishth, K. Choudhary and P. Chauhan, "Design and Evaluation of AI-Based Energy-Efficient Load Balancing Algorithm in Cloud Data Centers," 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM), Bhopal, India, 2025, pp. 1-6, doi: 10.1109/RCSM67767.2025.11506712.
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- •Published on: Jun 13 2026
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