Decision Making under Uncertainty and Competition for Sustainable Energy Technologies

This dissertation addresses the main challenges faced in the transition to a more sustainable energy sector by applying modelling tools that could design more effective managerial responses and provide policy insights. To mitigate the impact of climate change, the electric power industry needs to reduce markedly its emissions of greenhouse gases. As energy consumption is set to increase in the foreseeable future, this can be achieved only through costly investments in more efficient conventional generation or in renewable energy resources. While more energy-efficient technologies are commercially available, the deregulation of most electricity industries implies that investment decisions need to be taken by private investors with government involvement limited to setting policy measures or designing market rules. Thus, it is desirable to understand how investment and operational decisions are to be made by decentralised entities that face uncertainty and competition. One of the most efficient thermal power technologies is cogeneration, or combined heat and power (CHP), which can recover heat that otherwise would be discarded from conventional generation. Cogeneration is particularly efficient when the recovered heat can be used in the vicinity of the combustion engine. Although governments are supporting on-site CHP generation through feed-in tariffs and favourable grid access, the adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. While deterministic and real options studies have revealed distributed generation to be both economical and effective at reducing CO2 emissions, these analyses have not addressed the aspect of risk management. In order to overcome the barriers of financial uncertainties to investment, it is imperative to address the decision-making problems of a risk-averse energy consumer. Towards that end, we develop a multi-stage, stochastic mean-risk optimisation model for the long-term and medium-term risk management problems of a large consumer. We first show that installing a CHP unit not only results in both lower CO2 emissions and expected running cost but also leads to lower risk exposure. In essence, by investing in a CHP unit, a large consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing significantly its financial risk over the investment period. To provide further insights into risk management strategies with on-site generation, we examine also the medium-term operational problem of a large consumer. In this model, we include all available contracts from electricity and gas futures markets, and analyse their interactions with on-site generation. We conclude that by swapping the volatile electricity spot price for the less volatile gas spot price, on-site generation with CHP can lead to lower risk exposure even in the medium term, and it alters a risk-averse consumer’s demand for futures contracts. While extensive subsidies have triggered investments in renewable generation, these installations need to be accompanied by transmission expansion. The reason for this is that solar and wind energy output is intermittent, and attractive solar and wind sites are often located far away from demand centres. Thus, to integrate renewable generation into the grid system and to maintain a reliable and secure electricity supply, a vastly improved transmission network is crucial. Finding the optimal transmission line investments for a given network is already a very complex task since these decisions need to take into account future demand and generation configurations, too, which now depend on private investors. To address these concerns, our third study models the problem of wind energy investment and transmission expansion jointly through a stochastic bi-level programming model under different market designs for transmission line investment. This enables the game-theoretic interaction between distinct decision makers, i.e., those investing in power plants and those constructing transmission lines, to be addressed directly. We find that under perfect competition only one of the wind power producers, the one with lower capital cost, makes investment and to a lower degree under a profit-maximising merchant investor (MI) than under a welfare-maximising transmission system operator (TSO), as the MI reduces the transmission capacity to increase congestion rent. In addition, we note that regardless of whether the grid expansion is carried out by the TSO or by the MI, a higher proportion of wind energy is installed when power producers exercise market power. In effect, strategic withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidising wind and the MI creates more flow to maximise profit. Under perfect competition, a higher level of wind generation can be achieved only through mandating renewable portfolio standards (RPS), which in turn results also in increased transmission investment

Optimal Operation of Combined Heat and Power under Uncertainty and Risk Aversion

Despite the proven benefits of combined heat and power (CHP) and recently introduced subsidies to support it, CHP adoption has not met its targets. One of the possible reasons for this is risk from uncertain electricity and gas prices. To gain insights into the risk management of a CHP unit, we develop a multi-stage stochastic mean-risk optimisation model for the medium-term management of a distributed generation system with a gas-fired microturbine with heat recovery and a boiler. The model adopts the perspective of a large consumer that procures gas (for on-site generation) and electricity (for consumption) on the spot and futures markets. The consumer's risk aversion is incorporated into the model through the conditional value-at-risk (CVaR) measure. We show that CHP not only decreases the consumer's expected cost and risk exposure by 10% each but also improves expected energy efficiency by 4 percentage points and decreases expected CO2 emissions by 16%. The risk exposure can be further mitigated through the use of financial contracts

Transmission and wind investment in a deregulated electricity industry

Adoption of dispersed renewable energy technologies requires transmission network expansion. Besides the transmission system operator (TSO), restructuring of electricity industries has introduced a merchant investor (MI), who earns congestion rents from constructing new lines. We compare these two market designs via a stochastic bi-level programming model that has either the MI or the TSO making transmission investment decisions at the upper level and power producers determining generation investment and operation at the lower level while facing wind power variability. We find that social welfare is always higher under the TSO because the MI has incentive to boost congestion rents by restricting capacities of transmission lines. Such strategic behaviour also limits investment in wind power by producers. However, regardless of the market design (MI or TSO), when producers behave a la Cournot, a higher proportion of energy is produced by wind. In effect, withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidising wind and the MI creates more flow to maximise profit

Optimal Selection of Distributed Energy Resources under Uncertainty and Risk Aversion

The adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. In order to overcome this barrier to investment, we develop a mean-risk optimization model for the long-term risk management problem of an energy consumer using stochastic programming. The consumer can invest in a number of generation technologies, and also has access to electricity and gas futures to reduce its risk. We examine the role of on-site generation in the consumer’s risk management strategy, as well as interactions between on-site generation and financial hedges. Our study shows that by swapping electricity (with high price volatility) for gas (with low price volatility), even relatively inefficient technologies reduce risk exposure and CO _2 emissions. The capability of on-site generation is enhanced through the use of combined heat and power (CHP) applications. In essence, by investing in a CHP unit, a consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing its financial risk. Finally, in contrast to the extant literature, we demonstrate that on-site generation affects the consumer’s decision to purchase financial hedges. In particular, while on-site generation and electricity futures may act as substitutes, on-site generation and gas futures can function as complements

Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques

Radiologic images are vast three-dimensional data sets in which each voxel of the underlying volume represents distinct physical measurements of a tissue-dependent characteristic. Advances in technology allow radiologists to image pathologies with unforeseen detail, thereby further increasing the amount of information to be processed. Even though the imaging modalities have advanced greatly, our interpretation of the images has remained essentially unchanged for decades. We have arrived in the era of precision medicine where even slight differences in disease manifestation are seen as potential target points for new intervention strategies. There is a pressing need to improve and expand the interpretation of radiologic images if we wish to keep up with the progress in other diagnostic areas. Radiomics is the process of extracting numerous quantitative features from a given region of interest to create large data sets in which each abnormality is described by hundreds of parameters. From these parameters datamining is used to explore and establish new, meaningful correlations between the variables and the clinical data. Predictive models can be built on the basis of the results, which may broaden our knowledge of diseases and assist clinical decision making. Radiomics is a complex subject that involves the interaction of different disciplines; our objective is to explain commonly used radiomic techniques and review current applications in cardiac computed tomography imaging.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/

The year in cardiology: imaging. The year in cardiology 2019.

No abstract available

A coronariarendszer komputertomográfiás vizsgálata - Országos Plakk Regiszter és Adatbázis (OPeRA)

INTRODUCTION AND AIM: Coronary computer tomography angiography is a unique non-invasive imaging technique with the capability to provide information regarding plaque quantity, burden and structure. A reliable registry is required to use the data of these examinations in research projects. The difficulty is that registries need double data entry simultaneously to the hospital information system. METHOD: Our registry solves this problem through a structured reporting tool, which generates clinical report and stores all data simultaneously. The automatically generated report is based on international guidelines. RESULTS: Between August 1. 2014 and September 1. 2015 we registered the data of 2866 patients. Coronary plaque was observed in 77.03% of the patients, 33.18% of the plaques were calcified. Severe stenosis was present in 13.71% of the patients. CONCLUSIONS: The structured reporting decreases reporting time, eliminates double data entry related errors. Our goal is to initiate a nationwide, unified registry, the National Plaque Registry and Database. Orv. Hetil., 2017, 158(3), 106-110