Electricity Industry Competition and Market Power with High Renewable Penetrations

Globally, energy systems are expected to undergo a complete transition from fossil- fuelled generation to renewable energy in the coming decades, with a majority of energy supplied by wind and solar in many countries. In much of the developed world, this transition will take place in the context of restructured electricity markets. This thesis examines whether electricity markets, which are intended to be the key drivers of electricity industry operation and investment, are suitably designed and implemented for transitioning to high penetrations of renewable energy. Of particular interest is the role of competition in delivering efficient market outcomes, the potential for exertion of market power in high-penetration renewable energy scenarios, and whether current auction designs to incentivise efficient behaviour will be effective in the context of energy delivered at near-zero marginal cost. Previous work on electricity market competition in Australia has focused on measuring market concentration, a commonly used indicator of competitiveness, on short-term time horizons, based on historical data. However, competitiveness in Australia’s National Electricity Market (NEM) in the long term has not been assessed, nor how it might change as a result of the transition to high penetrations of variable renewable energy (VRE). This may be due in part to lack of suitable measures of competition in markets with multiple interconnected regions, but also the theory and evidence around VRE bidding patterns now and into the future has not yet been confirmed. Assessing competitiveness of future markets requires new methods for modelling and assessing potential market dynamics that affect market power. While capacity expansion modelling has been used for understanding the future technical and economic performance of electricity systems with different generation technologies, there have been very few attempts to relate these models back to the concepts of competition and market concentration. Machine learning techniques may also have the potential to provide new insights into the strategic behaviour of participants in future energy systems and have been used for modelling and solving many other complex multi-agent interactions, but to date a straightforward method for applying modern machine learning techniques to models of competitive electricity markets has not been proposed. Furthermore, significant changes that are under consideration to facilitate the energy transition, such as the introduction of a new two-sided market design in the NEM that would require all demand-side participants to submit bids, have not been considered in modelling to date. This thesis aims to investigate competition and market power in restructured electricity markets as well as their role in the clean energy transition. It investigates whether the Australian NEM has been and will continue to be a competitive market through the transition to renewable energy and how renewable generators participate in electricity auctions now and into the future. Additionally, it examines the way new tools and frameworks might further understandings of incentives and behaviour to enable more efficient and stable market designs. In order to establish a theoretical base and explore what causes market mechanism failure, a literature review and case study are undertaken into episodes of the exercise of market power globally, with a specific focus on the Californian electricity crisis. To establish how well market mechanisms are currently working, a range of competition metrics are applied to historical datasets in order to study the level of competitiveness of the Australian National Electricity Market. This leads to new answers to the question of whether the NEM is currently a competitive market, showing that current market concentration indicators provide conflicting results depending on how they are applied. A new measure of competition is provided which demonstrates that most regions are generally competitive, but some, such as Queensland, have notable periods of constraint. In order to determine how the transition to renewables might impact competition in the NEM, new indicators of competitiveness are also applied to simulations of future high-penetration renewable energy scenarios. These analyses demonstrate that swings between surplus and constraint can lead to an increase in the frequency of opportunities to exercise market power. This is an important result that shows how high-penetration renewables may significantly disrupt the function of wholesale electricity spot markets. To understand both the underlying incentives acting on renewable generators in the NEM and the current bidding strategies of these generators a case study of these generators in the NEM is undertaken. It is seen that these participants generally offer energy at or below $0/MWh, but are occasionally seen to bid at very high prices, possibly in an attempt to push up the spot price. Following this analysis, in order to examine what strategic incentives might be present in future high-penetration renewable energy grids, new equilibria for near-zero marginal cost generators are proposed. Following on from these investigations, the performance of a two-sided market in a 99% renewable energy grid is explored. In a two-sided market, flexible demand-side participants would be required to enter bids into the wholesale market. Based on forecasts of flexible demand response and renewable energy performance in a 99% renewable energy scenario, this modelling showed that demand response was, counterintuitively, less likely to be present in a two-sided market; additionally, the two-sided market was seen to mitigate the impacts of the exercise of market power because the more elastic supply curve placed upper limits on strategic generator offers. In order to develop a new modelling framework for renewable bidding behaviour in recognition of the difficulties in modelling competitive equilibria for future high- penetration renewable electricity market conditions, a market simulator is developed for the OpenAI platform that can be used to train deep learning models of electricity market bidding. Such models may be extremely useful in the context of the transition to high-penetration renewables, because competitive dynamics could be accurately predicted and understood before new capacity is built and operated. There are several key contributions of this work; it presents a new method for calculating and estimating levels of competition in electricity markets such as the NEM, which are comprised of multiple regions with constrained interconnectors, provides and applies a new methodology for exploring thresholds of competitiveness in simulations of future energy systems, develops the first long-term exploration of renewable bidding behaviour in Australia’s NEM, gives a new tool for running market behaviour experiments with emerging AI tools, and provides an early analysis of the impact of implementing a two-sided market mechanism, as proposed by Australia’s Energy Security Board. Together, these contributions may help to significantly enhance current understandings of the opportunities and challenges associated with transitioning to high-penetration renewable energy within a wholesale electricity market

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

Introduction: Ways of Machine Seeing

How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. This 'editorial' is for a special issue of AI & Society, which includes contributions from: María Jesús Schultz Abarca, Peter Bell, Tobias Blanke, Benjamin Bratton, Claudio Celis Bueno, Kate Crawford, Iain Emsley, Abelardo Gil-Fournier, Daniel Chávez Heras, Vladan Joler, Nicolas Malevé, Lev Manovich, Nicholas Mirzoeff, Perle Møhl, Bruno Moreschi, Fabian Offert, Trevor Paglan, Jussi Parikka, Luciana Parisi, Matteo Pasquinelli, Gabriel Pereira, Carloalberto Treccani, Rebecca Uliasz, and Manuel van der Veen

Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969

Four Paradigms in Comparative Psychology

The following examines the development of comparative psychology and the evidence, arguments, and epistemological challenges that have characterized its approach to the question of animal rationality. I distinguish between four modes of research that come to prominence at different points in its history, the natural historical, strict behavioral, cognitive, and neurophysiological, analyzing each through a critical episode in its development and the set of claims associated with the approach. The first study concerns the field’s Darwinian origins and its early commitment to the fundamental similarity of human and animal minds. I argue from a close reading of Darwin’s notebooks that the critical break for the nascent field came not from an antecedent endorsement to evolutionary theory, as commonly supposed, but a set of political and philosophical commitments inherited from the Enlightenment. Next, I show how this approach proved vulnerable to attack from younger and more positivistic psychologists in the twentieth century. I analyze why the Darwinians were accused of employing less than scientific methods, explaining how this fact helped precipitate a shift toward more conservative standards of evidence and strictly lab-based research. From there, I consider how the behavioral tools of this era have left modern ‘cognitive’ research with nagging underdetermination issues. I argue that strictly behavioral methods cannot tell us what the nature of animal thought is but that other methods may. Finally, I consider the state of the rationality debate at present. Drawing on the most recent evidence from systems neuroscience, I argue that animals as distant as rats have the capacity to engage in basic forms of reasoning ventured by Darwin and suspected but never quite shown in the cognitive era

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding