327 research outputs found

    Building and investigating generators' bidding strategies in an electricity market

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    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

    Optimizing energy market participation with batteries

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    Due to the fact that the energy sector is in transition, there are goals for lowering the energy cost with the use of renewables and batteries. This presents challenges to the system and the solution is the issuing of energy communities that can be used to make electricity provision more clean and secure. It is also to see how energy flexibility elements or elements on the consumption side can make the system more efficient and cheaper, which is being done in this paper concerning the day-ahead bid and batteries. Traditional day-ahead bidding methods have become costly, mainly when the forecasted energy consumption differs from the actual consumption, which has to be resolved by penalizing with an imbalance cost. This thesis is part of a more significant project (Layered Energy System) that is to be deployed in Spain. Applying such changes to the electricity system first requires becoming familiar with and understanding Spain's context. The first part of this thesis provides research to understand the Spanish regulatory framework, how the market works, and the status of these technologies in Spain. Following that, this thesis's primary work is to explore how day-ahead market bid could be improved through the use of batteries for better planning and error assumptions. It mentions several day-ahead bidding strategies in the context of energy and batteries. And then selects a subset (three) of the studied strategies and implements them, comparing their performance on actual electricity data. Finally, selects the one that best fits various scenarios and requirements. A particular objective function is opted to be minimized with respect to the battery constraints that involve the variables. A linear program will find the values that best fits those variables at every time step tt of a single day. The methodology is an improvement over traditional predictive models. After comparing different strategies, Results show that strategy one, namely "Stochastic Chance-constraint optimization", yields the best results. In this strategy, the battery would have the freedom to maximize profit even if it sometimes increases imbalance. The preferred error distribution for this strategy is the Gamma distribution. Using a battery to offset imbalances can help to minimize total energy cost for a whole day (up to 26%). The last part of the thesis is ongoing research about capacity traders and market performance. It surveys the literature on trading strategies in various contexts and markets relevant to capacity traders. The market performance in capacity trading needs to consider how well the buildings can reach their desired capacity through bidding and selling. Performance metrics that are typically used to evaluate those trading strategies were documented. This feature is being worked on with python, but it will not be able to be shown

    Quantitative Models in Life Science Business

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    This open access book explores the field of life science business from a multidisciplinary perspective. Applying statistical, mathematical, game-theoretic, and data science tools to pharmaceutical and biotechnology business endeavors, the book describes value creation, value maintenance, and value realization in the life sciences as a sequence of processes using the quantitative language of applied mathematics. Written by experts from a variety of fields, the contributions illustrate the shift from a deterministic to a stochastic view of the processes involved, offering a new perspective on life sciences economics. The book covers topics such as valuing and managing intellectual property in life science, licensing in the pharmaceutical business, outsourcing pharmaceutical R&D, and stochastic modelling of a pharmaceutical supply chain. The book will appeal to scholars of economics and the life sciences, as well as to professionals in chemical and pharmaceutical industries

    Quantitative Models in Life Science Business

    Get PDF
    This open access book explores the field of life science business from a multidisciplinary perspective. Applying statistical, mathematical, game-theoretic, and data science tools to pharmaceutical and biotechnology business endeavors, the book describes value creation, value maintenance, and value realization in the life sciences as a sequence of processes using the quantitative language of applied mathematics. Written by experts from a variety of fields, the contributions illustrate the shift from a deterministic to a stochastic view of the processes involved, offering a new perspective on life sciences economics. The book covers topics such as valuing and managing intellectual property in life science, licensing in the pharmaceutical business, outsourcing pharmaceutical R&D, and stochastic modelling of a pharmaceutical supply chain. The book will appeal to scholars of economics and the life sciences, as well as to professionals in chemical and pharmaceutical industries

    Catchment Care - Developing an Auction Process for Biodiversity and Water Quality Gains. Volume 1 - Report

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    This report describes the design, development and trial of catchment care. Catchment Care is an auction-based system which aims to increase the cost effectiveness of funds for private on-ground natural resource management actions.Water;Australia;Natural Resource Management;Catchment Care; auction.

    Hierarchical reinforcement learning for trading agents

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    Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets
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