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Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review

Author

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  • Lei Zhang

    (Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
    Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking, Ministry of Education, Shenyang 110819, China)

  • Yuxing Yuan

    (Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
    Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking, Ministry of Education, Shenyang 110819, China)

  • Su Yan

    (Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
    Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking, Ministry of Education, Shenyang 110819, China)

  • Hang Cao

    (Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
    Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking, Ministry of Education, Shenyang 110819, China)

  • Tao Du

    (Key Laboratory of Eco-Industry, Ministry of Ecology and Environment, Northeastern University, Shenyang 110819, China
    Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking, Ministry of Education, Shenyang 110819, China)

Abstract

With the increasing liberalization of energy markets, the penetration of renewable clean energy sources, such as photovoltaics and wind power, has gradually increased, providing more sustainable energy solutions for energy-intensive industrial sectors or parks, such as iron and steel production. However, the issues of the intermittency and volatility of renewable energy have become increasingly evident in practical applications, and the economic performance and operational efficiency of localized microgrid systems also demand thorough consideration, posing significant challenges to the decision and management of power system operation. A smart microgrid can effectively enhance the flexibility, reliability, and resilience of the grid, through the frequent interaction of generation–grid–load. Therefore, this paper will provide a comprehensive summary of existing knowledge and a review of the research progress on the methodologies and strategies of modeling technologies for intelligent power systems integrating renewable energy in industrial production.

Suggested Citation

  • Lei Zhang & Yuxing Yuan & Su Yan & Hang Cao & Tao Du, 2025. "Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review," Energies, MDPI, vol. 18(10), pages 1-50, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2465-:d:1653434
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    as
    1. Frate, Guido Francesco & Ferrari, Lorenzo & Desideri, Umberto, 2021. "Energy storage for grid-scale applications: Technology review and economic feasibility analysis," Renewable Energy, Elsevier, vol. 163(C), pages 1754-1772.
    2. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Qiao, Jinpeng & Mi, Yang & Ma, Siyuan & Han, Yunhao & Wang, Peng, 2025. "Trilayer stackelberg game scheduling of active distribution network based on microgrid group leasing shared energy storage," Applied Energy, Elsevier, vol. 382(C).
    5. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    6. Wang, Richard & Hsu, Shu-Chien & Zheng, Saina & Chen, Jieh-Haur & Li, Xuran Ivan, 2020. "Renewable energy microgrids: Economic evaluation and decision making for government policies to contribute to affordable and clean energy," Applied Energy, Elsevier, vol. 274(C).
    7. Feng, Wei & Jin, Ming & Liu, Xu & Bao, Yi & Marnay, Chris & Yao, Cheng & Yu, Jiancheng, 2018. "A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools," Applied Energy, Elsevier, vol. 228(C), pages 1656-1668.
    8. Qiao, Jinpeng & Mi, Yang & Shen, Jie & Lu, Changkun & Cai, Pengcheng & Ma, Siyuan & Wang, Peng, 2025. "Optimization schedule strategy of active distribution network based on microgrid group and shared energy storage," Applied Energy, Elsevier, vol. 377(PD).
    9. Xiao, Guowei & Zhang, Miao & Huang, Weiqiang & Mo, Zihao & Xie, Haishun & Tang, Xiongmin, 2025. "A data-driven hybrid robust optimization approach for microgrid operators in the energy reserve market considering different wind power producers’ strategies," Applied Energy, Elsevier, vol. 386(C).
    10. Yao, Haowei & Qu, Pengyu & Qin, Hengjie & Lou, Zhen & Wei, Xiaoge & Song, Huaitao, 2024. "Multidimensional electric power parameter time series forecasting and anomaly fluctuation analysis based on the AFFC-GLDA-RL method," Energy, Elsevier, vol. 313(C).
    11. Xiyun Yang & Guo Fu & Yanfeng Zhang & Ning Kang & Feng Gao, 2017. "A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization," Energies, MDPI, vol. 10(11), pages 1-15, November.
    12. Burhan U Din Abdullah & Shahbaz Ahmad Khanday & Nair Ul Islam & Suman Lata & Hoor Fatima & Sarvar Hussain Nengroo, 2024. "Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation," Energies, MDPI, vol. 17(7), pages 1-21, March.
    13. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    14. Ryu, Hanee & Dorjragchaa, Shonkhor & Kim, Yeonbae & Kim, Kyunam, 2014. "Electricity-generation mix considering energy security and carbon emission mitigation: Case of Korea and Mongolia," Energy, Elsevier, vol. 64(C), pages 1071-1079.
    15. Li, Shenglin & Zhu, Jizhong & Dong, Hanjiang & Zhu, Haohao & Fan, Junwei, 2022. "A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids," Applied Energy, Elsevier, vol. 309(C).
    16. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    17. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    18. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    19. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    20. Zhou, Sixun & Yan, Rujing & Zhang, Jing & He, Yu & Geng, Xianxian & Li, Yuanbo & Yu, Changkun, 2025. "Optimizing interaction in renewable-vehicle-microgrid systems: Balancing battery health, user satisfaction, and participation," Renewable Energy, Elsevier, vol. 245(C).
    21. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    22. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    23. Boyu Zhu & Dazhi Wang, 2024. "Master–Slave Game Optimal Scheduling for Multi-Agent Integrated Energy System Based on Uncertainty and Demand Response," Sustainability, MDPI, vol. 16(8), pages 1-27, April.
    24. Xinhe Liu & Wenmin Wang, 2024. "Deep Time Series Forecasting Models: A Comprehensive Survey," Mathematics, MDPI, vol. 12(10), pages 1-33, May.
    25. Habib, Md. Ahasan & Hossain, M.J., 2024. "Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework," Renewable Energy, Elsevier, vol. 235(C).
    26. Yang, Mao & Wang, Jinxin & Cao, Xudong & Gu, Dake, 2024. "Economic dispatch of microgrid generation-load-storage based on dynamic bi-level game of multiple stakeholders," Energy, Elsevier, vol. 313(C).
    27. Yang, Xiaohui & Wang, Xiaopeng & Leng, Zhengyang & Deng, Yeheng & Deng, Fuwei & Zhang, Zhonglian & Yang, Li & Liu, Xiaoping, 2023. "An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG," Renewable Energy, Elsevier, vol. 211(C), pages 307-325.
    28. Aneke, Mathew & Wang, Meihong, 2016. "Energy storage technologies and real life applications – A state of the art review," Applied Energy, Elsevier, vol. 179(C), pages 350-377.
    29. Wang, Can & Wang, Mingchao & Wang, Aoqi & Zhang, Xiaojia & Zhang, Jiaheng & Ma, Hui & Yang, Nan & Zhao, Zhuoli & Lai, Chun Sing & Lai, Loi Lei, 2025. "Multiagent deep reinforcement learning-based cooperative optimal operation with strong scalability for residential microgrid clusters," Energy, Elsevier, vol. 314(C).
    30. Li, Ling-Ling & Ji, Bing-Xiang & Li, Zhong-Tao & Lim, Ming K. & Sethanan, Kanchana & Tseng, Ming-Lang, 2025. "Microgrid energy management system with degradation cost and carbon trading mechanism: A multi-objective artificial hummingbird algorithm," Applied Energy, Elsevier, vol. 378(PA).
    31. Mariam, Lubna & Basu, Malabika & Conlon, Michael F., 2016. "Microgrid: Architecture, policy and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 477-489.
    32. Ariyaluran Habeeb, Riyaz Ahamed & Nasaruddin, Fariza & Gani, Abdullah & Targio Hashem, Ibrahim Abaker & Ahmed, Ejaz & Imran, Muhammad, 2019. "Real-time big data processing for anomaly detection: A Survey," International Journal of Information Management, Elsevier, vol. 45(C), pages 289-307.
    33. Mah, Angel Xin Yee & Ho, Wai Shin & Hassim, Mimi H. & Hashim, Haslenda & Ling, Gabriel Hoh Teck & Ho, Chin Siong & Muis, Zarina Ab, 2021. "Optimization of a standalone photovoltaic-based microgrid with electrical and hydrogen loads," Energy, Elsevier, vol. 235(C).
    34. Zhao, Xiancong & Bai, Hao & Shi, Qi & Lu, Xin & Zhang, Zhihui, 2017. "Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing," Applied Energy, Elsevier, vol. 195(C), pages 100-113.
    35. Olabi, A.G. & Onumaegbu, C. & Wilberforce, Tabbi & Ramadan, Mohamad & Abdelkareem, Mohammad Ali & Al – Alami, Abdul Hai, 2021. "Critical review of energy storage systems," Energy, Elsevier, vol. 214(C).
    36. Yuan, Yuxing & Na, Hongming & Chen, Chuang & Qiu, Ziyang & Sun, Jingchao & Zhang, Lei & Du, Tao & Yang, Yuhang, 2024. "Status, challenges, and prospects of energy efficiency improvement methods in steel production: A multi-perspective review," Energy, Elsevier, vol. 304(C).
    37. Banihabib, Reyhaneh & Fadnes, Fredrik Skaug & Assadi, Mohsen, 2024. "Techno-economic optimization of microgrid operation with integration of renewable energy, hydrogen storage, and micro gas turbine," Renewable Energy, Elsevier, vol. 237(PC).
    38. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    39. Wu, Nan & Wang, Zhipeng & Li, Xinyan & Lei, Lin & Qiao, Yupeng & Linghu, Jinqing & Huang, Jie, 2025. "Research on real-time coordinated optimization scheduling control strategy with supply-side flexibility in multi-microgrid energy systems," Renewable Energy, Elsevier, vol. 238(C).
    40. Chen, J.J. & Qi, B.X. & Rong, Z.K. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Multi-energy coordinated microgrid scheduling with integrated demand response for flexibility improvement," Energy, Elsevier, vol. 217(C).
    41. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    42. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    43. Chen, Zhengganzhe & Zhang, Bin & Du, Chenglong & Yang, Chunhua & Gui, Weihua, 2025. "Outlier-adaptive-based non-crossing quantiles method for day-ahead electricity price forecasting," Applied Energy, Elsevier, vol. 382(C).
    44. Abdul Ghani Olabi & Tabbi Wilberforce & Mohammad Ali Abdelkareem & Mohamad Ramadan, 2021. "Critical Review of Flywheel Energy Storage System," Energies, MDPI, vol. 14(8), pages 1-33, April.
    45. Sgouridis, Sgouris & Ali, Mohamed & Sleptchenko, Andrei & Bouabid, Ali & Ospina, Gustavo, 2021. "Aluminum smelters in the energy transition: Optimal configuration and operation for renewable energy integration in high insolation regions," Renewable Energy, Elsevier, vol. 180(C), pages 937-953.
    46. Guo, Shiliang & He, Jianqi & Ma, Kai & Yang, Jie & Wang, Yaochen & Li, Pengpeng, 2025. "Robust economic dispatch for industrial microgrids with electric vehicle demand response," Renewable Energy, Elsevier, vol. 240(C).
    47. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    48. Coelho, Francisco C.R. & Assis, Fernando A. & Castro, José Filho C. & Donadon, Antonio R. & Roncolatto, Ronaldo A. & Andrade, Vittoria E.M.S. & Rosas, Pedro A.C. & Barcelos, Silvangela L.S.L. & Saaved, 2025. "Monte Carlo simulation of community microgrid operation: Business prospects in the Brazilian regulatory framework," Utilities Policy, Elsevier, vol. 92(C).
    49. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    50. Patteeuw, Dieter & Bruninx, Kenneth & Arteconi, Alessia & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2015. "Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems," Applied Energy, Elsevier, vol. 151(C), pages 306-319.
    51. Zhao, Zhuoli & Xu, Jiawen & Lei, Yu & Liu, Chang & Shi, Xuntao & Lai, Loi Lei, 2025. "Robust dynamic dispatch strategy for multi-uncertainties integrated energy microgrids based on enhanced hierarchical model predictive control," Applied Energy, Elsevier, vol. 381(C).
    52. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    53. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    54. Wang, Peng-Tao & Xu, Qing-Chuang & Wang, Fei-Yin & Xu, Mao, 2025. "Study on the coupling of the iron and steel industry with renewable energy for low-carbon production: A case study of matching steel plants with photovoltaic power plants in China," Energy, Elsevier, vol. 320(C).
    55. Yu, Bolin & Fang, Debin & Xiao, Kun & Pan, Yuling, 2023. "Drivers of renewable energy penetration and its role in power sector's deep decarbonization towards carbon peak," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    56. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    57. Fuhrlaender, David & Vermeulen, Ben & Schnuelle, Christian, 2025. "Green hydrogen transformation of the iron and steel production system: An integrated operating concept for system-internal balance, lower emissions, and support for power system stability," Applied Energy, Elsevier, vol. 381(C).
    58. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    59. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    60. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Missing data imputation using mixture factor analysis for building electric load data," Applied Energy, Elsevier, vol. 304(C).
    61. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    62. Misaghian, M.S. & Saffari, M. & Kia, M. & Heidari, A. & Shafie-khah, M. & Catalão, J.P.S., 2018. "Tri-level optimization of industrial microgrids considering renewable energy sources, combined heat and power units, thermal and electrical storage systems," Energy, Elsevier, vol. 161(C), pages 396-411.
    63. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    64. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    65. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    66. Qamar, Hafiz Ghulam Murtza & Guo, Xiaoqiang & Ahmad, Fareed, 2024. "Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique," Renewable Energy, Elsevier, vol. 237(PB).
    67. Aguilar, Diego & Quinones, Jhon J. & Pineda, Luis R. & Ostanek, Jason & Castillo, Luciano, 2024. "Optimal scheduling of renewable energy microgrids: A robust multi-objective approach with machine learning-based probabilistic forecasting," Applied Energy, Elsevier, vol. 369(C).
    68. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
    69. Jani, Ali & Karimi, Hamid & Jadid, Shahram, 2022. "Two-layer stochastic day-ahead and real-time energy management of networked microgrids considering integration of renewable energy resources," Applied Energy, Elsevier, vol. 323(C).
    70. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    71. Maysam Abbasi & Ehsan Abbasi & Li Li & Ricardo P. Aguilera & Dylan Lu & Fei Wang, 2023. "Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods," Energies, MDPI, vol. 16(1), pages 1-36, January.
    72. Ferahtia, Seydali & Houari, Azeddine & Cioara, Tudor & Bouznit, Mohammed & Rezk, Hegazy & Djerioui, Ali, 2024. "Recent advances on energy management and control of direct current microgrid for smart cities and industry: A Survey," Applied Energy, Elsevier, vol. 368(C).
    73. Zhang, Yiwen & Ren, Yifan & Liu, Ziyun & Li, Haoqin & Jiang, Huaiguang & Xue, Ying & Ou, Junhui & Hu, Renzong & Zhang, Jun & Gao, David Wenzhong, 2025. "Federated deep reinforcement learning for varying-scale multi-energy microgrids energy management considering comprehensive security," Applied Energy, Elsevier, vol. 380(C).
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