Topology control algorithms in power systems

This research focuses on improving the efficiency of power market operations by providing system operators additional tools for managing the costs of supplying and delivering electricity. A transmission topology control (TC) framework for production cost reduction based on a shift factor (SF) representation of branch and breaker flows is proposed. The framework models topology changes endogenously while maintaining linearity in the overall Mixed Integer Linear Programming (MILP) formulation. This work develops the DC lossless, and loss-adjusted TC formulations that can be used in a Day Ahead or intra-day market framework as well as an AC-based model that can be used in operational settings. Practical implementation choices for the Shift Factor formulation are discussed as well as the locational marginal prices (LMPs) under the TC MIP setting and their relation to LMPs without TC. Compared to the standard B-theta alternative used so far in TC research, the shift factor framework has significant computational complexity advantages, particularly when a tractably small switchable set is optimized under a representative set of contingency constraints. These claims are supported and elaborated by numerical results

Shift factor-based SCOPF topology control MIP formulations with substation configurations

Topology control (TC) is an effective tool for managing congestion, contingency events, and overload control. The majority of TC research has focused on line and transformer switching. Substation reconfiguration is an additional TC action, which consists of opening or closing breakers not in series with lines or transformers. Some reconfiguration actions can be simpler to implement than branch opening, seen as a less invasive action. This paper introduces two formulations that incorporate substation reconfiguration with branch opening in a unified TC framework. The first method starts from a topology with all candidate breakers open, and breaker closing is emulated and optimized using virtual transactions. The second method takes the opposite approach, starting from a fully closed topology and optimizing breaker openings. We provide a theoretical framework for both methods and formulate security-constrained shift factor MIP TC formulations that incorporate both breaker and branch switching. By maintaining the shift factor formulation, we take advantage of its compactness, especially in the context of contingency constraints, and by focusing on reconfiguring substations, we hope to provide system operators additional flexibility in their TC decision processes. Simulation results on a subarea of PJM illustrate the application of the two formulations to realistic systems.The work was supported in part by the Advanced Research Projects Agency-Energy, U.S. Department of Energy, under Grant DE-AR0000223 and in part by the U.S. National Science Foundation Emerging Frontiers in Research and Innovation under Grant 1038230. Paper no. TPWRS-01497-2015. (DE-AR0000223 - Advanced Research Projects Agency-Energy, U.S. Department of Energy; 1038230 - U.S. National Science Foundation Emerging Frontiers in Research and Innovation)http://buprimo.hosted.exlibrisgroup.com/primo_library/libweb/action/openurl?date=2017&issue=2&isSerivcesPage=true&spage=1179&dscnt=2&url_ctx_fmt=null&vid=BU&volume=32&institution=bosu&issn=0885-8950&id=doi:10.1109/TPWRS.2016.2574324&dstmp=1522778516872&fromLogin=truePublished versio

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

The Management of Direct Material Cost During New Product Development: A Case Study on the Application of Big Data, Machine Learning, and Target Costing

This dissertation thesis investigates the application of big data, machine learning, and the target costing approach for managing costs during new product development in the context of high product complexity and uncertainty. A longitudinal case study at a German car manufacturer is conducted to examine the topic. First, we conduct a systematic literature review, which analyzes use cases, issues, and benefits of big data and machine learning technology for the application in management accounting. Our review contributes to the literature by providing an overview about the specific aspects of both technologies that can be applied in managerial accounting. Further, we identify the specific issues and benefits of both technologies in the context management accounting. Second, we present a case study on the applicability of machine learning and big data technology for product cost estimation, focusing on the material costs of passenger cars. Our case study contributes to the literature by providing a novel approach to increase the predictive accuracy of cost estimates of subsequent product generations, we show that the predictive accuracy is significantly larger when using big data sets, and we find that machine learning can outperform cost estimates from cost experts, or produce at least comparable results, even when dealing with highly complex products. Third, we conduct an experimental study to investigate the trade-off between accuracy (predictive performance) and explainability (transparency and interpretability) of machine learning models in the context of product cost estimation. We empirically confirm the oftenimplied inverse relationship between both attributes from the perspective of cost experts. Further, we show that the relative importance of explainability to accuracy perceived by cost experts is important when selecting between alternative machine learning models. Then, we present four factors that significantly determine the perceived relative importance of explainability to accuracy. Fourth, we present a proprietary archival study to investigate the target costing approach in a complex product development context, which is characterized by product design interdependence and uncertainty about target cost difficulty. We find that target cost difficulty is related to more cost reduction performance during product development based on archival company data, and thereby complement results from earlier studies, which are based on experimental studies. Further, we demonstrate that in a complex product development context, product design interdependence and uncertainty about target cost difficulty may both limit the effectiveness of target costing