[[alternative]]An Investigation of the Option Value of Electric Power

計畫編號：NSC92-2415-H032-014研究期間：200308~200407研究經費：381,000[[sponsorship]]行政院國家科學委員

The Value of IS-Enabled Flexibility in Electricity Demand - a Real Options Approach

As the transition to renewable energy sources progresses, their integration makes electricity production increasingly fluctuating, also causing amplified volatility in electricity prices on energy markets. To contribute to power grid stability, utilities need to balance volatile supply through shifting demand. This measure of demand side management creates flexibility, being enabled as the integration of IS in the power grid grows. The flexibility of deferring consumption to times of lower demand or higher supply bears an economic value. We show how to quantify this value in order to support decisions on short-term consumer compensation. We adapt real options theory, which has been widely used in IS research for valuation under uncertainty. Addressing a prerequisite, we develop a stochastic process, which realistically replicates intraday electricity spot price development. We employ it in a binomial tree model to assess the value of IS-enabled flexibility in electricity demand

Providing Utility to Utilities: The Value of Information Systems Enabled Flexibility in Electricity Consumption

As the transition to renewable energy sources progresses, the integration of such sources makes electricity production increasingly fluctuate. To contribute to power grid stability, electric utilities must balance volatile supply by shifting demand. This measure of demand response depends on flexibility, which arises as the integration of information systems in the power grid grows. The option to shift electric loads to times of lower demand or higher supply bears an economic value. Following a design science research approach, we illustrate how to quantify this value to support decisions on short-term consumer compensation. We adapt real options theory to the design—a strategy that IS researchers have used widely to determine value under uncertainty. As a prerequisite, we develop a stochastic process, which realistically replicates intraday electricity spot price development. With this process, we design an artifact suitable for valuation, which we illustrate in a plug-in electric vehicle scenario. Following the artifact’s evaluation based on historical spot price data from the electricity exchange EPEX SPOT, we found that real options analysis works well for quantifying the value of information systems enabled flexibility in electricity consumption

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

Financial Engineering for Energy System Capital Budgeting

The United State energy industry is experiencing a major paradigm shift. This conventional vertically integrated energy industry is gradually transformed to a competitive market environment—a deregulated energy market. The market and regulatory frameworks are expected to continue to evolve in the future. Market participants are emphasizing more on profit maximization as returns on investment are no longer guaranteed. Therefore, risk management and capital budgeting play critical roles in energy system planning. Planning always involves uncertainties. When there are uncertainties, there are risks involve. This dissertation concentrates on the application of Real Options Analysis, ROA, especially lattice method, to energy system capital budgeting. Lattice method has one major weakness: massive bush of lattice. This dissertation proposes a method known as Binomial Lattice-Value at Risk approach to solve the curse of lattice dimensionality. Due to deregulation, market participants\u27 incentives have changed. Generation companies, GENCOs, are no longer willing to release their cost information or strategic plans. Thus, this dissertation introduces the implementation of Profit at Risk ideology into decision analysis, which created an efficient approach known as Binomial Lattice-Profit at Risk, BL-PaR. With the price of fuels soaring and environmental concerns growing larger, the expansion of ROA into renewable energy sector is desirable. Renewable energy has significant advantages as it does not contribute to greenhouse gases. This research focuses on wind energy, which is uncontrollable and unpredictable. A decision based solution of incorporating wind energy with pump storage hydro, PSH, and financial contract hedging is introduced. This energy technology integration is capable of increasing the available-capability of wind energy to be as effective as thermal unit. A physical asset hedging known as the Look Ahead Optimization, LAO, method is then applied to both wind unit and PSH system. This optimization method minimizes the size of hedging and maximizes profit by obtaining the optimal energy storage. The combination of the LAO method with BL-PaR approach achieves several critical goals. Together with the inclusion of financial contract hedging via financial transmission rights, FTRs, a double-protections mechanism is established. The evaluation of FTRs portfolio using ROA enables the risk management process to run efficiently