Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach

Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design

Bringing Analytics into Practice: Evidence from the Power Sector

Across industries, the increasing availability of sensor data has created business opportunities for the application of analytical information systems. We shed light on the analytics implementation in practice in the context of a case study in the power sector. Following a design science approach, we present a case study on the implementation of a decision support system (DSS) for grid planning at a large utility. Given the very large number of grids, process automation through analytics promises significant efficiency gains for labor-intensive planning tasks. We demonstrate how the DSS leads to process improvements regarding speed, accuracy, and flexibility. Apart from the benefits for the company, this work contributes to IS practice by deriving general lessons for IS executives facing analytics challenges

Online Mechanism Design for Scheduling Non-Preemptive Jobs under Uncertain Supply and Demand

ABSTRACT We design new algorithms for the problem of allocating uncertain, flexible, and multi-unit demand online given uncertain supply, in order to maximise social welfare. The algorithms can be seen as extensions of the expectation and consensus algorithms from the domain of online scheduling. The problem is especially relevant to the future smart grid, where uncertain output from renewable generators and conventional supply need to be integrated and matched to flexible, non-preemptive demand. To deal with uncertain supply and demand, the algorithms generate multiple scenarios which can then be solved offline. Furthermore, we use a novel method of reweighting the scenarios based on their likelihood whenever new information about supply becomes available. An additional improvement allows the selection of multiple non-preemptive jobs at the same time. Finally, our main contribution is a novel online mechanism based on these extensions, where it is in the agents' best interest to truthfully reveal their preferences. The experimental evaluation of the extended algorithms and different variants of the mechanism show that both achieve more than 85% of the offline optimal economic efficiency. Importantly, the mechanism yields comparable efficiency, while, in contrast to the algorithms, it allows for strategic agents

Online mechanism design for scheduling non-preemptive jobs under uncertain supply and demand

We design new algorithms for the problem of allocating uncertain flexible, and multi-unit demand online given uncertain supply, in order to maximise social welfare. The algorithms can be seen as extensions of the expectation and consensus algorithms from the domain of online scheduling. The problem is especially relevant to the future smart grid, where uncertain output from renewable generators and conventional supply need to be integrated and matched to flexible, non-preemptive demand. To deal with uncertain supply and demand, the algorithms generate multiple scenarios which can then be solved offline. Furthermore, we use a novel method of reweighting the scenarios based on their likelihood whenever new information about supply becomes available. An additional improvement allows the selection of multiple non-preemptive jobs at the same time. Finally, our main contribution is a novel online mechanism based on these extensions, where it is in the agents' best interest to truthfully reveal their preferences. The experimental evaluation of the extended algorithms and different variants of the mechanism show that both achieve more than 85% of the offline optimal economic efficiency. Importantly, the mechanism yields comparable efficiency, while, in contrast to the algorithms, it allows for strategic agents

Micronutrient Status of Recreational Runners with Vegetarian or Non-Vegetarian Dietary Patterns

Vegetarian diets have gained popularity in sports. However, few data exist on the status of micronutrients and related biomarkers for vegetarian and vegan athletes. The aim of this cross-sectional study was to compare the micronutrient status of omnivorous (OMN, n = 27), lacto-ovo-vegetarian (LOV, n = 26), and vegan (VEG, n = 28) recreational runners. Biomarkers of vitamin B12, folate, vitamin D, and iron were assessed. Additionally, serum levels of calcium, magnesium, and zinc were examined. Lifestyle factors and supplement intake were recorded via questionnaires. About 80% of each group showed vitamin B12 adequacy with higher levels in supplement users. Mean red blood cell folate exceeded the reference range (>340 nmol/L) in all three groups (OMN: 2213 ± 444, LOV: 2236 ± 596, and VEG: 2354 ± 639 nmol/L; not significant, n.s.). Furthermore, vitamin D levels were comparable (OMN: 90.6 ± 32.1, LOV: 76.8 ± 33.7, and VEG: 86.2 ± 39.5 nmol/L; n.s.), and we found low prevalence (<20%) of vitamin D inadequacy in all three groups. Less than 30% of each group had depleted iron stores, however, iron deficiency anemia was not found in any subject. Our findings suggest that a well-planned, health-conscious lacto-ovo-vegetarian and vegan diet, including supplements, can meet the athlete’s requirements of vitamin B12, vitamin D and iron

Quantification of the influence of parameters determining radiative heat transfer in an oxy-fuel operated boiler

Radiative heat transfer is a very important heat transfer mechanism in pulverized coal combustion. To identify the influence of parameters determining radiatve heat transfer and to give recommendations on the required accuracy of corresponding submodels, a 3D-periodic oxy-fuel pulverized coal combustion test case is investigated. Measurement values determined by the authors or elaborate submodels are applied for each parameter and compared to simplified models or empirical constants. To investigate the interaction between particle radiation and the strong spectral dependence of gas radiation in oxy-fuel scenarios, a comparison between spectrally averaged and spectrally resolved calculations performed. To the best knowledge of the authors, for the first time the contribution of the parameters determining radiative heat transfer are quantified and compared in one comprehensive study. The results indicate a strong influence of coal particle emissivity and scattering phase function as well as the projected particle surface on the radiative source term. For the wall heat flux, the largest influences were found for ash and coal particle emissivity, projected particle surface and the scattering phase function. Additionally, the difference between coal particle and gas temperature was found to have a significant influence on wall heat flux. A comparison of spectrally averaged to spectrally resolved results and the corresponding models for gas radiation (WSGGM and SNBM) yielded similar trends for the influence of each parameter. Thus, based on the models and parameters involved in this study, a spectrally averaged approach seems to be of sufficient accuracy to describe radiative heat transfer in oxy-fuel combustion systems