Smart grids are a major improvement on the conventional electrical grids as they incorporate digital communications, automated control, and sophisticated sensing systems in order to maximize power generation, distribution, and utilization. The paper will examine how Artificial Intelligence (AI) can be used to optimize energy systems in smart grid systems. AI, as a result of machine learning, reinforcement learning, and genetic algorithms, can solve the major problems of energy distribution, load balancing, fault detection, and renewable energy integration. The primary task of the paper is to review how the AI-oriented optimization methods can be used to improve the efficiency, stability, and cost-efficiency of smart grids. The approach implies the use of several AI methods on real-time energy usage data, renewable energy data, and grid performance indicators. The findings indicate that there is an essential improvement in the energy efficiency, cost decrease, and stability of the systems. The AI-optimized grid realized the 15 % energy efficiency, 12 % operational cost reduction, and a 20 % grid stability. Moreover, the percentage of renewable energy sources integration was increased by 18 %, which demonstrates the capabilities of AI to cope with the fluctuations in the generation of renewable energy. The results indicate that AI can transform smart grid management by making the systems more adaptive and efficient. Nevertheless, there are still difficulties, especially with the quality of the data, generalization of the model, and scalability. The future discipline of AI-based energy systems must focus on how to enhance the strength of AI models, how to integrate AI models with other emerging technologies like blockchain, and how to consider the policy implications of AI-based energy systems.
Arévalo P, Jurado F. Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. Energies. 2024 Sep 8;17(17):4501.
2.
Khan MA, Saleh AM, Waseem M, Sajjad IA. Artificial intelligence enabled demand response: Prospects and challenges in smart grid environment. Ieee Access. 2022 Dec 21; 11:1477-505.
3.
Ukoba K, Olatunji KO, Adeoye E, Jen TC, Madyira DM. Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment. 2024 Nov;35(7):3833-79.
4.
Noviati ND, Maulina SD, Smith S. Smart grids: Integrating ai for efficient renewable energy utilization. International Transactions on Artificial Intelligence. 2024 Nov 1;3(1):1-0.
5.
Iyaniwura AA, Mayaki CS. Artificial Intelligence-enabled smart grid systems for real-time load forecasting, fault detection, renewable energy integration and optimization. Global Journal of Engineering and Technology Advances. 2025;24(03):191-208.
6.
Liu Z, Gao Y, Liu B. An artificial intelligence-based electric multiple units using a smart power grid system. Energy Reports. 2022 Nov 1; 8:13376-88.
7.
Nnajiofor CA, Eyo DE, Adegbite AO, Abdullahi I, Odoguje EW, Folorunsho FE, Adeyeye AA. Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability. environment. 2024 Jan; 24:25.
8.
Kumar A, Alaraj M, Rizwan M, Nangia U. Novel AI based energy management system for smart grid with RES integration. IEEE Access. 2021 Nov 30; 9:162530-42.
9.
Joshan A. Emerging trends and advanced techniques in power system optimization for future smart grids. Power, Control, and Data Processing Systems. 2025 Jun 1;2(2):26-38.
10.
Choudhary EA, Pathania EA. Artificial Intelligence and Optimization Techniques for Intelligent Power Systems: Fault Detection, Energy Management, and Grid Stability. Artificial Intelligence. 2025 Jun;12(06).
11.
Chen J, Li L. Optimization of renewable energy integration in smart grids using AI and data analytics. In2nd International Conference on Power, Communication, Computing and Networking Technologies (PCCNT 2024) 2024 Oct 25 (Vol. 2024, pp. 111-116).
12.
Omitaomu OA, Niu H. Artificial intelligence techniques in smart grid: A survey. Smart Cities. 2021 Apr 22;4(2):548-68.
13.
Khosrojerdi F, Akhigbe O, Gagnon S, Ramirez A, Richards G. Integrating artificial intelligence and analytics in smart grids: a systematic literature review. International Journal of Energy Sector Management. 2022 Jan 19;16(2):318-38.
14.
Rao BN, Praveen M, Babu DR. A review on the role of AI in optimizing renewable energy grid management. International Journal of Scientific Research in Engineering and Management. 2024;8(11):1- 3.
15.
Balamurugan M, Narayanan K, Raghu N, Arjun Kumar GB, Trupti VN. Role of artificial intelligence in smart grid–a mini review. Frontiers in Artificial Intelligence. 2025 Feb 4; 8:1551661.
16.
Ali SS, Choi BJ. State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics. 2020 Jun 22;9(6):1030.
17.
Fathollahi A. Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review. Energies. 2025 Jun 30;18(13):3431.
18.
Kobra MJ, Rahman MO, Hossain ZM. AI-Powered Smart Grid for Sustainable Energy Distribution: A Comprehensive Simulation and Optimization Framework. Middle East Research Journal of Engineering and Technology. 2025 Oct;5(05):122-34.
19.
Sankarananth S, Karthiga M, Bavirisetti DP. AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids. Energy Reports. 2023 Nov 1; 10:1299-312.
20.
Hsu CC, Jiang BH, Lin CC. A survey on recent applications of artificial intelligence and optimization for smart grids in smart manufacturing. Energies. 2023 Nov 20;16(22):7660.
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