The real-world implementation of DRL-based operation and control methods is facing the following fundamental challenges: 1) performance guarantee under modeling and parameter uncertainties between simulated and real-world inverter-connected energy systems; 2) safety guarantee (do not violate the energy system's physical constraints) during the learning and decision-making processes; and 3) adaptability against the dynamic system operating conditions. To this end, we propose a series of physics-informed Adaptive and Safe-Certified DRL (AdapSafe) algorithms for system operation and control to address the aforementioned challenges simultaneously.

• X. Wan, M. Sun*(Corresponding Author), etc., “AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-neutral Power Systems”, in AAAI-23, Washington, US, 2023.
• X. Wan, M. Sun*(Corresponding Author), “AdapSafe2: Prior-free Safe-certified Reinforcement Learning for Multi-Area Frequency Control”, in IEEE Transactions on Power Systems, Under Revision.
• L. Zeng, M. Sun*(Corresponding Author), “Bridge the Sim-to-Real Gap for Deep Reinforcement Learning-based Frequency Control”, in IEEE Transactions on Power Systems, Under Revision.
• X. Wan, C. Yang, C. Yang, J. Song, M. Sun*(Corresponding Author), “Fuzzify Uncertainty: Leverage Fuzzy-Logic for Robust Safe Reinforcement Learning”, in NeurIPS 2024, Under Revision.
• Y. Wang, D. Qiu, M. Sun, G. Strbac, Z. Gao, “Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach”, in Applied Energy, vol. 335, 120759, 2023.

The ongoing decarbonization of modern electricity systems has led to a substantial increase in operational uncertainty, particularly due to the large-scale integration of renewable energy generation. However, the expanding space of possible operating points renders necessary the development of novel data-driven probabilistic security assessment approaches. Furthermore, a better understanding of confidence in the security assessment result is of key importance for Transmission System Operators (TSOs) to use and rely on these deep learning methods with high interpretability.

• M. Sun, I. Konstantelos and G. Strbac, "A Deep Learning-Based Feature Extraction Framework for System Security Assessment," in IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5007-5020, Sep. 2019.
• I. Konstantelos, M. Sun*(Corresponding Author), etc., "Using Vine Copulas to Generate Representative System States for Machine Learning," in IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 225-235, Jan. 2019.
• T. Zhang, M. Sun*（Corresponding Author), etc., "A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment," in IEEE Transactions on Power Systems, vol. 36, no. 5, pp. 3907-3920, Sept. 2021.
• F. Bellizio, J. Cremer, M. Sun*（Corresponding Author), G. Strbac, "A Causality Based Feature Selection Approach for Data-Driven Dynamic Security Assessment", in Electric Power Systems Research, vol. 201, 107537, 2021.
• Z. Zhang, M. Sun*（Corresponding Author), etc., "Physics-Constrained Robustness Evaluation of Intelligent Security Assessment for Power Systems," in IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 872-884, Jan. 2023.

Although the benefits of using Machine learning (ML) approaches to realize the power system autonomous operation and control have been partially uncovered, the vulnerabilities or risks of ML-based power system operation and control models have not been fully explored. It has been recognized that the ML model is vulnerable to adversarial examples, and the power system communication network is prone to cyber-attacks. Moreover, most of the existing works neglect the bad data detection mechanism and energy system physical constraints among the state variables.

• L. Zeng, M. Sun*(Corresponding Author), etc., "Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF," in IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2690-2704, May 2023.
• Y. Chen, M. Sun*(Corresponding Author), etc., "Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack," in IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 2275-2287, May 2023.
• K. Zuo, M. Sun*(Corresponding Author), etc., "Transferability-Oriented Adversarial Robust Security-Constrained Optimal Power Flow," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3397654.
• Z. Zhang, K. Zuo, R. Deng, F. Teng and M. Sun*(Corresponding Author), "Cybersecurity Analysis of Data-Driven Power System Stability Assessment," in IEEE Internet of Things Journal, vol. 10, no. 17, pp. 15723-15735, 1 Sept.1, 2023.
• L. Zeng, D. Qiu, M. Sun*（Corresponding Author）, “Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks,” in Applied Energy, vol. 324, 119688, 2022.

The wide deployment of Advanced Metering Infrastructures (AMIs) in smart energy systems provides valuable opportunities to characterize energy consumers and fully understand their consumption behaviours by exploiting the massive amount of fine-grained data and cutting-edge artificial intelligence technologies. On the other hand, consumers may not want to share their actual data information with the system operators for decision-making due to privacy concerns.

• M. Sun, T. Zhang, Y. Wang, G. Strbac and C. Kang, "Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting," in IEEE Transactions on Power Systems. vol. 35, no. 1, pp. 188-201, Jan. 2020.
• M. Sun, I. Konstantelos and G. Strbac, "C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data," in IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 2382-2393, May 2017.
• M. Sun, Y. Wang, G. Strbac and C. Kang, "Probabilistic Peak Load Estimation in Smart Cities Using Smart Meter Data," in IEEE Transactions on Industrial Electronics, vol. 66, no. 2, pp. 1608-1618, Feb. 2019.
• M. Sun, Y. Wang, F. Teng, Y. Ye, G. Strbac and C. Kang, "Clustering-Based Residential Baseline Estimation: A Probabilistic Perspective," in IEEE Transactions on Smart Grid. vol. 10, no. 6, pp. 6014-6028, Nov. 2019.
• Q. Yuan, M. Sun*(Corresponding Author), Y. Sheng and Q. Guo, "PrivCPM: Privacy-Preserving Cooperative Pricing Mechanism in Coupled Power-Traffic Networks," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2024.3406572.

Energy system investment planning problems become intractable due to the vast variability that characterizes system operation and the increasing complexity of the optimization model to capture the characteristics of renewable energy sources (RES). In this context, making optimal investment decisions by considering every operating period is unrealistic and inefficient. Therefore, one of the most effective solutions is to select a limited number of representative scenarios. To this end, three major research questions are pointed out and investigated: 1) Which variables should the clustering be based on? The choice can include combinations of variables in the input domain (e.g. demand and/or renewable injection) and in the decision domain (e.g. lines built or investment cost); 2) Which is the most appropriate clustering technique to be applied for a chosen set of variables (e.g. centroid methods, mixture models, etc.); 3) After clustering the different scenarios, how to select the representative profile of each cluster (e.g. mean value or median point).

• M. Sun, F. Teng, X. Zhang, G. Strbac and D. Pudjianto, "Data-Driven Representative Day Selection for Investment Decisions: A Cost-Oriented Approach," in IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 2925-2936, Jul. 2019.
• M. Sun, F. Teng, I. Konstantelos, G. Strbac, "An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources," in Energy, vol. 145, pp. 871-885, Feb. 2018.
• M. Sun, G. Strbac, P. Djapic, D. Pudjianto, "Preheating Quantification for Smart Hybrid Heat Pumps Considering Uncertainty," in IEEE Transactions on Industrial Informatics, vol. 15, no. 8, pp. 4753-4763, Aug. 2019.