{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:11:38Z","timestamp":1772187098807,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>With the increasing complexity of power grid operations, operator training requires timely feedback and objective assessment. Traditional approaches based on lectures and scripted simulations provide limited personalization and weak explainability. This paper presents AI Instructors, an intelligent simulation training system for power-grid control and dispatching. The system is organized into learning, training, assessment, and analysis modules, and is built around two core technical components: (i) parameterized item generation from rule\/knowledge bases using a phrase-enhanced transformer (PET), and (ii) solver-grounded, topology-aware grading with hierarchical feedback for both numeric and free-text responses. A voice interaction module is integrated to simulate telephone-based dispatch orders. We validate the system through a pilot deployment with licensed dispatch operators and scenario experiments on benchmark cases. Compared with a conventional scripted DTS workflow, AI Instructors achieves higher stepwise procedure accuracy (68%\u219290%), a lower topology-violation rate (32%\u219211%), and shorter response time (120 s\u219272 s), while increasing the proportion of parameterized questions and accelerating skill acquisition. These results suggest that combining adaptive sequencing with topology-safe, explainable evaluation can improve training effectiveness and operational safety.<\/jats:p>","DOI":"10.3390\/bdcc10030068","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:31:19Z","timestamp":1772181079000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Simulation Training System for Power Grid Control and Operations"],"prefix":"10.3390","volume":"10","author":[{"given":"Sheng","family":"Yang","sequence":"first","affiliation":[{"name":"Electric Power Dispatching and Control Center, Guangxi Power Grid Co., Ltd., Nanning 530000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Electric Power Dispatching and Control Center, Guangxi Power Grid Co., Ltd., Nanning 530000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Fu","sequence":"additional","affiliation":[{"name":"Electric Power Dispatching and Control Center, Guangxi Power Grid Co., Ltd., Nanning 530000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Guangzhou Novasein Digital Technology Co., Ltd., Guangzhou 510700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0960-4447","authenticated-orcid":false,"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109450","DOI":"10.1016\/j.ress.2023.109450","article-title":"A human operator model for simulation-based resilience assessment of power grid restoration operations","volume":"238","author":"Kottmann","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.egyr.2022.03.158","article-title":"Comprehensive evaluation of power transmission and transformation project based on electric power big data","volume":"8","author":"Chen","year":"2022","journal-title":"Energy Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"459","DOI":"10.17775\/CSEEJPES.2018.01010","article-title":"Framework for artificial intelligence analysis in large-scale power grids based on digital simulation","volume":"4","author":"Tang","year":"2018","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112128","DOI":"10.1016\/j.rser.2022.112128","article-title":"Data-driven probabilistic machine learning in sustainable smart energy\/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm","volume":"160","author":"Ahmad","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_5","unstructured":"Liu, J., Wang, Y., Lin, Z., Chen, M., Hao, Y., and Hu, L. (2024). Natural Language Fine-Tuning. arXiv."},{"key":"ref_6","unstructured":"Huo, D., Zhang, H., Hao, Y., Ye, Y., Hu, L., Wang, R., and Chen, M. (2024). DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training. arXiv."},{"key":"ref_7","first-page":"2151","article-title":"Developing trend of power system simulation and analysis technology","volume":"34","author":"Tian","year":"2014","journal-title":"Proc. CSEE"},{"key":"ref_8","first-page":"167","article-title":"Analysis and lessons of the blackout in Indian power grid on July 30 and 31, 2012","volume":"Volume 32","author":"Tang","year":"2012","journal-title":"Zhongguo Dianji Gongcheng Xuebao (Proceedings of the Chinese Society of Electrical Engineering)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, N., Dong, L., Hu, L., Feng, W., Tang, Y., and Ye, L. (2024). Design of an Electricity Knowledge QA System Incorporating Engineering Ethics Analysis. 2024 4th International Conference on Electronic Information Engineering and Computer (EIECT), IEEE.","DOI":"10.1109\/EIECT64462.2024.10866119"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Z., Jia, J., Luan, H., and Sun, M. (2016). Representation learning of knowledge graphs with entity descriptions. Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence.","DOI":"10.1609\/aaai.v30i1.10329"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, J., Yu, J., Jiang, Q., and Fan, Q. (2025). Adaptive Transfer Learning Assisted Multimodal Multi-objective Optimization Algorithm Based on Zoning Search. 2025 IEEE Congress on Evolutionary Computation (CEC), IEEE.","DOI":"10.1109\/CEC65147.2025.11043031"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dong, A., Lai, X., Lin, C., Lin, C., Jin, W., and Wen, F. (2023). A Brief Survey on the Development of Intelligent Dispatcher Training Simulators. Energies, 16.","DOI":"10.3390\/en16020706"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1145\/3643818","article-title":"Immersive Multimedia Service Caching in Edge Cloud with Renewable Energy","volume":"20","author":"Hossain","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TETCI.2024.3442872","article-title":"MASER: Multi-Order Attention and Semantic-Enhanced Representation Model for Complex Text Recommendation","volume":"9","author":"Lai","year":"2025","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.enbuild.2016.06.082","article-title":"Performance evaluation of conventional demand response at building-group-level under different electricity pricings","volume":"128","author":"Shen","year":"2016","journal-title":"Energy Build."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Das, R., Ray, A., Mondal, S., and Das, D. (2016). A rule based question generation framework to deal with simple and complex sentences. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE.","DOI":"10.1109\/ICACCI.2016.7732102"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Minn, S., Yu, Y., Desmarais, M.C., Zhu, F., and Vie, J.J. (2018). Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing. 2018 IEEE International Conference on Data Mining (ICDM), IEEE.","DOI":"10.1109\/ICDM.2018.00156"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3896","DOI":"10.1080\/10447318.2023.2202574","article-title":"Parallel or cross? Effects of two collaborative modes on augmented reality co-located operations","volume":"40","author":"Feng","year":"2024","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.jmsy.2024.09.012","article-title":"A transfer learning method in press hardening surrogate modeling: From simulations to real-world","volume":"77","author":"Abio","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"ref_21","unstructured":"Chung, C.A. (2003). Simulation Modeling Handbook: A Practical Approach, CRC Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.procir.2014.10.032","article-title":"Simulation in manufacturing: Review and challenges","volume":"25","author":"Mourtzis","year":"2014","journal-title":"Procedia Cirp"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hermann, M., Pentek, T., and Otto, B. (2016). Design principles for industrie 4.0 scenarios. 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE.","DOI":"10.1109\/HICSS.2016.488"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.enbuild.2015.10.039","article-title":"A GA-based coordinated demand response control for building group level peak demand limiting with benefits to grid power balance","volume":"110","author":"Gao","year":"2016","journal-title":"Energy Build."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"120393","DOI":"10.1016\/j.apenergy.2022.120393","article-title":"A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective","volume":"331","author":"Zou","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100020","DOI":"10.1016\/j.caeai.2021.100020","article-title":"Artificial intelligence in education: The three paradigms","volume":"2","author":"Ouyang","year":"2021","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100005","DOI":"10.1016\/j.caeai.2020.100005","article-title":"A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers","volume":"1","author":"Chen","year":"2020","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100001","DOI":"10.1016\/j.caeai.2020.100001","article-title":"Vision, challenges, roles and research issues of Artificial Intelligence in Education","volume":"1","author":"Hwang","year":"2020","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s41239-018-0109-y","article-title":"More than tools? Making sense of the ongoing digitizations of higher education","volume":"15","author":"Selwyn","year":"2018","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1111\/bjet.12771","article-title":"Technology-mediated learning theory","volume":"50","author":"Bower","year":"2019","journal-title":"Br. J. Educ. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s41239-019-0171-0","article-title":"Systematic review of research on artificial intelligence applications in higher education\u2013where are the educators?","volume":"16","author":"Bond","year":"2019","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.caeai.2020.100002","article-title":"Application and theory gaps during the rise of artificial intelligence in education","volume":"1","author":"Chen","year":"2020","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TPAS.1974.293985","article-title":"Fast decoupled load flow","volume":"PAS-93","author":"Stott","year":"1974","journal-title":"IEEE Trans. Power Appar. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/59.141737","article-title":"The continuation power flow: A tool for steady state voltage stability analysis","volume":"7","author":"Ajjarapu","year":"1992","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109921","DOI":"10.1016\/j.epsr.2023.109921","article-title":"Interval Holomorphic Embedding Load Flow Method: A novel approach to power flow solution considering uncertainties","volume":"229","author":"Lima","year":"2024","journal-title":"Electr. Power Syst. Res."},{"key":"ref_36","unstructured":"Latimer, J.R., and Masiello, R.D. (1977, January 24\u201327). Design of a dispatcher training system. Proceedings of the Conference on Power Industry Computer Applications (PICA), Toronto, ON, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mondrag\u00f3n Bernal, I.F., Lozano-Ram\u00edrez, N.E., Puerto Cort\u00e9s, J.M., Valdivia, S., Mu\u00f1oz, R., Arag\u00f3n, J., Garc\u00eda, R., and Hern\u00e1ndez, G. (2022). An Immersive Virtual Reality Training Game for Power Substations Evaluated in Terms of Usability and Engagement. Appl. Sci., 12.","DOI":"10.3390\/app12020711"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5087","DOI":"10.1109\/TII.2023.3331113","article-title":"Human\u2013Machine Collaborative Reinforcement Learning for Power Line Flow Regulation","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_39","unstructured":"Woo, J.H., Xiao, Q., Paduani, V.D., and Lu, N. (2024). A Two-Stage Optimization Method for Real-Time Parameterization of PV-Farm Digital Twin. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106536","DOI":"10.1016\/j.jobe.2023.106536","article-title":"A new framework integrating reinforcement learning, a rule-based expert system, and decision tree analysis to improve building energy flexibility","volume":"71","author":"Zhou","year":"2023","journal-title":"J. Build. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106547","DOI":"10.1016\/j.epsr.2020.106547","article-title":"Neural networks for power flow: Graph neural solver","volume":"189","author":"Donnot","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4644","DOI":"10.1109\/TPWRS.2020.2990179","article-title":"A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning","volume":"35","author":"Wang","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108412","DOI":"10.1016\/j.epsr.2022.108412","article-title":"Physics-Informed Neural Networks for AC Optimal Power Flow","volume":"212","author":"Nellikkath","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"110661","DOI":"10.1016\/j.epsr.2024.110661","article-title":"Dual conic proxies for AC optimal power flow","volume":"229","author":"Qiu","year":"2024","journal-title":"Electr. Power Syst. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/TPWRS.2010.2051168","article-title":"MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education","volume":"26","author":"Zimmerman","year":"2011","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_46","unstructured":"Babaeinejadsarookolaee, S., Birchfield, A., Christie, R.D., Coffrin, C., DeMarco, C., Diao, R., Ferris, M., Fliscounakis, S., Greene, S., and Huang, R. (2019). The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms. arXiv."},{"key":"ref_47","first-page":"138","article-title":"Learning to Run a Power Network Challenge: A Retrospective Analysis","volume":"Volume 133","author":"Marot","year":"2021","journal-title":"Proceedings of the NeurIPS 2020 Competition and Demonstration Track"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"24325","DOI":"10.1109\/ACCESS.2024.3363615","article-title":"HELICS: A Co-Simulation Framework for Scalable Multi-Domain Modeling and Analysis","volume":"12","author":"Hardy","year":"2024","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_50","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems 30 (NIPS 2017), Curran Associates, Inc."},{"key":"ref_51","unstructured":"Liu, J., and Chen, M. (2024). FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with Autonomous Human-Machine Collaboration. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). Neural Architectures for Named Entity Recognition. Proceedings of NAACL-HLT 2016, Association for Computational Linguistics (ACL).","DOI":"10.18653\/v1\/N16-1030"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/3\/68\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:04:46Z","timestamp":1772183086000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/3\/68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["bdcc10030068"],"URL":"https:\/\/doi.org\/10.3390\/bdcc10030068","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]}}}