Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Multi-agent system for modelling the restructured energy market

Master'sMASTER OF ENGINEERIN

A Robust Optimization Approach to the Self-scheduling Problem Using Semidefinite Programming

In deregulated electricity markets, generating companies submit energy bids which are derived from a self-schedule. In this thesis, we propose an improved semidefinite programming-based model for the self-scheduling problem. The model provides the profit-maximizing generation quantities of a single generator over a multi-period horizon and accounts for uncertainty in prices using robust optimization. Within this robust framework, the price information is represented analytically as an ellipsoid. The risk-adversity of the decision maker is taken into account by a scaling parameter. Hence, the focus of the model is directed toward the trade-off between profit and risk. The bounds obtained by the proposed approach are shown to be significantly better than those obtained by a mean-variance approach from the literature. We then apply the proposed model within a branch-and-bound algorithm to improve the quality of the solutions. The resulting solutions are also compared with the mean-variance approach, which is formulated as a mixed-integer quadratic programming problem. The results indicate that the proposed approach produces solutions which are closer to integrality and have lower relative error than the mean-variance approach

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

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