Peter W. Sauer

Dariush Fooladivanda, Alejandro D. Domínguez-García, Peter W. Sauer

Renewable surplus power is increasing due to the increasing penetration of these intermittent resources. In practice, electric grid operators either curtail the surplus energy resulting from renewable-based generations or utilize energy storage resources to absorb it. In this paper, we propose a framework for utilizing water pumps and tanks in water supply networks to absorb the surplus electrical energy resulting from renewable-based electricity generation resources in the electrical grid. We model water supply networks analytically, and propose a two-step procedure that utilizes the water tanks in the water supply network to harvest the surplus energy from an electrical grid. In each step, the water network operator needs to solve an optimization problem that is non-convex. To compute optimal pump schedules and water flows, we develop a second-order cone relaxation and an approximation technique that enable us to transform the proposed problems into mixed-integer second-order cone programs. We then provide the conditions under which the proposed relaxation is exact, and present an algorithm for constructing an exact solution to the original problem from a solution to the relaxed problem. We demonstrate the effectiveness of the proposed framework via numerical simulations.

Thuy Nuong Tran, Tim Adler, Amine Yamlahi et al.

Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concrete effects of common and rare pitfalls on specific applications. We address this gap in the literature in the context of colon cancer screening. Our contribution is twofold. Firstly, we present the winning solution of the Endoscopy computer vision challenge (EndoCV) on colon cancer detection, conducted in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2022. Secondly, we demonstrate the sensitivity of commonly used metrics to a range of hyperparameters as well as the consequences of poor metric choices. Based on comprehensive validation studies performed with patient data from six clinical centers, we found all commonly applied object detection metrics to be subject to high inter-center variability. Furthermore, our results clearly demonstrate that the adaptation of standard hyperparameters used in the computer vision community does not generally lead to the clinically most plausible results. Finally, we present localization criteria that correspond well to clinical relevance. Our work could be a first step towards reconsidering common validation strategies in automatic colon cancer screening applications.

Olaoluwapo Ajala, Alejandro Dominguez-garcia, Peter Sauer et al.

This paper presents a library of second-order models for synchronous machines that can be utilized in power system dynamic performance analysis and control design tasks. The models have a similar structure to the classical model in that they consist of two dynamic states, the power angle and the angular speed. However, unlike the classical model, the models find applications beyond first swing stability analysis; for example, they can also be utilized in transient stability studies. The models are developed through a systematic reduction of a nineteenth-order model, using singular perturbation techniques, and they are validated by comparing their voltage, frequency, and phase profiles with that of the high-order model and that of the classical model.

Hanchen Xu, Alejandro D. Domínguez-García, Peter W. Sauer

In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in radial power distribution systems under uncertain load dynamics. The objective is to find a policy to determine the tap positions that only uses measurements of voltage magnitudes and topology information so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a batch reinforcement learning (RL) algorithm to solve it. By taking advantage of a linearized power flow model, we propose an effective algorithm to estimate the voltage magnitudes under different tap settings, which allows the RL algorithm to explore the state and action spaces freely offline without impacting the system operation. To circumvent the "curse of dimensionality" resulted from the large state and action spaces, we propose a sequential learning algorithm to learn an action-value function for each LTC, based on which the optimal tap positions can be directly determined. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders.