This work develops a robust adaptive control strategy for discrete-time systems using Control Barrier Functions (CBFs) to ensure safety under parametric model uncertainty and disturbances. A key contribution of this work is establishing a barrier function certificate in discrete time for general online parameter estimation algorithms. This barrier function certificate guarantees positive invariance of the safe set despite disturbances and parametric uncertainty without access to the true system parameters. In addition, real-time implementation and inherent robustness guarantees are provided. The proposed robust adaptive safe control framework demonstrates that the parameter estimation module can be designed separately from the CBF-based safety filter, simplifying the development of safe adaptive controllers for discrete-time systems. The resulting safe control approach guarantees that the system remains within the safe set while the controller adapts to model uncertainties, making it a promising strategy for discrete-time safety-critical systems.
Max-pressure (MP) control stands out among real-time network traffic signal control methods due to its simplicity, decentralized nature, and theoretical stability. However, existing MP control methods have limited consideration of public transportation and do not address the network stability problem of transit-prioritized MP in partially connected vehicle (CV) environments. In this study, we propose Transit-MP, which realizes transit-prioritized MP control in partially CV environments by considering real-time vehicle occupancy and the impact of transit dwell at stations. Theoretically, we demonstrate that Transit-MP, while using different traffic state measures for upstream and downstream links for pressure calculation, still achieves road network stability even in partially CV environments. Note that for MP controllers in sparse CV environments, some movements may have missing CV observations, leading to link spillovers, which create the queue starvation phenomenon: a movement no longer receives the green phase despite the queue spillover. Therefore, we further propose a modified Transit-MP (mTransit-MP) that uses historical traffic data as a fallback under zero-CV-observation conditions to address this issue. We rigorously prove that the proposed mTransit-MP can effectively avoid the queue starvation phenomenon. Experimental results on a real-world corridor in Amsterdam with 15 transit lines and 31 stations show that our method significantly reduces the real-time vehicle and spillover count, and improves delays for both private vehicles and transit vehicles compared to a state-of-the-art MP controller for transit signal priority. In sparse CV environments, our mTransit-MP is effective in mitigating link spillovers while enhancing the overall performance of multi-modal traffic.
To raise awareness of the environmental impact of deep learning (DL), numerous studies have estimated the energy consumption of DL systems. However, energy estimates during DL training often rely on unverified assumptions. This work addresses that gap by investigating how model architecture and training environment affect energy consumption. We train a variety of computer vision models and collect energy consumption and accuracy metrics to analyze their trade-offs across configurations. Our results show that selecting the right model-training environment combination can reduce training energy consumption by up to 80.68% with less than 2% loss in F1 score. We find a significant interaction effect between model and training environment: energy efficiency improves when GPU computational power scales with model complexity. Moreover, we demonstrate that common estimation practices, such as using FLOPs or GPU TDP, fail to capture these dynamics and can lead to substantial errors. To address these shortcomings, we propose the Stable Training Epoch Projection (STEP) and the Pre-training Regression-based Estimation (PRE) methods. Our evaluation demonstrates that STEP and PRE achieve reductions in Root Mean Squared Error (RMSE) up to 97% and 84%, respectively, when compared to existing estimation tools.
Distributed energy resources (DERs) are transforming power networks, challenging traditional operational methods, and requiring new coordination mechanisms. To address this challenge, this paper introduces SecuLEx (Secure Limit Exchange), a market-based paradigm for allocating and trading power injection and withdrawal limits, known as dynamic operating envelopes (DOEs). Under this paradigm, distribution system operators (DSOs) first assign initial DOEs to customers through a fair allocation mechanism. These limits can be exchanged afterward through a market, allowing customers to reallocate them according to their needs while ensuring network operational constraints. We formalize SecuLEx and illustrate DOE allocation and market exchanges on a small-scale low-voltage (LV) network. In this case study, SecuLEx reduces renewable curtailment and improves grid utilization and social welfare compared to traditional approaches.
Transmission System Operators routinely use transmission switching as a tool to manage congestion and ensure system security. Motivated by sub-transmission operations at RTE, this paper considers the Optimal Transmission Switching with De-energization (OTSD), which captures potential loss of connectivity (and therefore localized blackout) following loss of transmission elements. While directly relevant to real-life operations, this problem has received very little attention in the literature. The paper proposes a new mixed-integer linear programming formulation for OTSD that represents post-contingency loss of connectivity without requiring additional binary variables. This new formulation provides the foundation for a fast, iterative heuristic algorithm. Computational experiments confirms that state-of-the-art optimization solvers struggle to solve the extensive formulation of OTSD, often failing to find even trivial solutions within reasonable time. In contrast, numerical results demonstrate the efficiency of the proposed heuristic, which finds high-quality feasible solutions 100-1000x faster than using Gurobi.
