Risk management in electricity markets: hedging and market incompleteness

The high volatility of electricity markets gives producers and retailers an incentive to hedge their exposure to electricity prices by buying and selling derivatives. This paper studies how welfare and investment incentives are affected when markets for derivatives are introduced, and to what extent this depends on market completeness. We develop an equilibrium model of the electricity market with risk-averse firms and a set of traded financial products, more specifically: forwards and an increasing number of options. Using this model, we first show that aggregate welfare in the market increases with the number of derivatives offered. If firms are concerned with large negative shocks to their profitability due to liquidity constraints, option markets are particularly attractive from a welfare point of view. Secondly, we demonstrate that increasing the number of derivatives improves investment decisions of small firms (especially when firms are risk-averse), because the additional financial markets signal to firms how they can reduce the overall sector risk. Also the information content of prices increases: the quality of investment decisions based on risk-free probabilities, inferred from market prices, improves as markets become more complete Finally, we show that government intervention may be needed, because private investors may not have the right incentives to create the optimal number of markets.

Risk management in electricity markets: hedging and market incompleteness.

The high volatility of electricity markets gives producers and retailers an incentive to hedge their exposure to electricity prices by buying and selling derivatives. This paper studies how welfare and investment incentives are affected when markets for derivatives are introduced, and to what extent this depends on market completeness. We develop an equilibrium model of the electricity market with riskaverse firms and a set of traded financial products, more specifically: forwards and an increasing number of options. Using this model, we first show that aggregate welfare in the market increases with the number of derivatives offered. If firms are concerned with large negative shocks to their profitability due to liquidity constraints, option markets are particularly attractive from a welfare point of view. Secondly, we demonstrate that increasing the number of derivatives improves investment decisions of small firms (especially when firms are risk-averse), because the additional financial markets signal to firms how they can reduce the overall sector risk. Also the information content of prices increases: the quality of investment decisions based on risk-free probabilities, inferred from market prices, improves as markets become more complete Finally, we show that government intervention may be needed, because private investors may not have the right incentives to create the optimal number of markets.

Market completeness: how options affect hedging and investments in the electricity sector.

The high volatility of electricity markets gives producers and retailers an incentive to hedge their exposure to electricity prices by buying and selling derivatives. This paper studies how welfare and investment incentives are affected when an increasing number of derivatives are introduced. It develops an equilibrium model of the electricity market with risk averse firms and a set of traded financial products, more specifically: a forward contract and an increasing number of options. We first show that aggregate welfare (the sum of individual firms' utility) increases with the number of derivatives offered, although most of the benefits are captured with one to three options. Secondly, power plant investments typically increase because additional derivatives enable better hedging of investments. However, the availability of derivatives sometimes leads to ‘crowding-out’ of physical investments because capital is being used more profitably to speculate on financial markets. Finally, we illustrate that players basing their investment decisions on risk-free probabilities inferred from market prices, may significantly overinvest when markets are not sufficiently complete.

Models and algorithms for dominance-constrained stochastic programs with recourse

In der vorliegenden Dissertationsschrift befassen wir uns mit stochastischen Optimierungsproblemen unter Nebenbedingungen, die mithilfe stochastischer Ordnungen formuliert sind. Hierbei konzentrieren wir uns auf stochastische Dominanz erster Ordnung und die steigende konvexe Ordnung, wobei beide Ordnungen in unserem Fall auf Zufallsgrößen operieren, welche Optimalwerten zweistuger stochastischer Optimierungsprobleme mit Kompensation entsprechen. Wir stellen die theoretische Relevanz der vorliegenden Problemklasse heraus und tragen zur Entwicklung von effizienten Lösungsverfahren bei. Um Letzteres zu erreichen untersuchen und erweitern wir bestehende gemischt-ganzzahlige lineare Repräsentationen dieser Probleme und entwickeln maßgeschneiderte Dekompositionsverfahren. Der Schwerpunkt dieser Arbeit liegt dabei auf der Entwicklung und Implementierung besonders effizienter Lösungsansätze für den Fall mit linearer Kompensation.We consider optimization problems with stochastic order constraints of first and second order posed on random variables coming from two-stage stochastic programs with recourse. We clarify the theoretical relevance of these specific problems, and contribute to improving their computational tractability. For the latter, we review and enhance mixed-integer linear programming (MILP) equivalents. These exist for either mixed-integer or continuous variables in the second stage. Algorithmically, our focus is on developing tailored cutting-plane decomposition methods for these models

Heuristic optimization of electrical energy systems: Refined metrics to compare the solutions

Many optimization problems admit a number of local optima, among which there is the global optimum. For these problems, various heuristic optimization methods have been proposed. Comparing the results of these solvers requires the definition of suitable metrics. In the electrical energy systems literature, simple metrics such as best value obtained, the mean value, the median or the standard deviation of the solutions are still used. However, the comparisons carried out with these metrics are rather weak, and on these bases a somehow uncontrolled proliferation of heuristic solvers is taking place. This paper addresses the overall issue of understanding the reasons of this proliferation, showing a conceptual scheme that indicates how the assessment of the best solver may result in the unlimited formulation of new solvers. Moreover, this paper shows how the use of more refined metrics defined to compare the optimization result, associated with the definition of appropriate benchmarks, may make the comparisons among the solvers more robust. The proposed metrics are based on the concept of first-order stochastic dominance and are defined for the cases in which: (i) the globally optimal solution can be found (for testing purposes); and (ii) the number of possible solutions is so large that practically it cannot be guaranteed that the global optimum has been found. Illustrative examples are provided for a typical problem in the electrical energy systems area – distribution network reconfiguration. The conceptual results obtained are generally valid to compare the results of other optimization problem

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

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