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A Real Options Approach to Valuing Flexibility in Demand-Side Response Operations and Investments under Uncertainty

Schachter, Jonathan

[Thesis]. Manchester, UK: The University of Manchester; 2016.

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Abstract

This thesis investigates methodologies for valuing the flexibility of demand-side response (DSR) in its ability to respond to future uncertainties. The ability to quantify this flexibility is especially important for energy systems investments given their large and irreversible capital costs. The consideration of uncertainty in electricity markets and energy networks requires solutions that allow decision makers to quickly respond to unexpected events, such as extreme short-term electricity price variations in an operational setting, or incorrect long-term demand projections in planning. This uncertainty, coupled with the irreversibility of energy network investments, results in the need for viable 'wait-and-see' investment strategies that can help hedge electicity price risk in the short-term while hedging planning risk in the long-term, until at least some, if not all, uncertainty is resolved. In both cases, this leads to an added value in the case of temporary flexible investment options like DSR, which may otherwise be considered unattractive under a deterministic analysis setting. A number of significant contributions to power systems research are offered in this work, focusing on valuation methods for quantifying the flexibility value of DSR under both short-term and long-term uncertainty. The first outcome of this research is an extensive review of current real options (RO) methods that clarifies the assumptions and utilization of RO for decision-making in engineering applications. It suggests that many of the assumptions used contribute to a misuse of the models when applied to physical systems. A framework for investing under uncertainty is proposed, where the methodologies, steps, inputs, assumptions, limitations and advantages of different RO models are described so as to offer a practical guide to decision makers for selecting the most appropriate RO model for their valuation purposes. The second outcome is the design of a probabilistic RO framework and operational model for DSR that quantifies its benefits as an energy service for hedging different market price risks. A mathematical formulation for applying “real options thinking” is presented that provides decision makers with a means of quantifying the value of DSR when both operational and planning decisions are subject to uncertainty. In particular, DSR contracts can have tremendous value as an arbitrage or portfolio-balancing tool, helping hedge almost entirely electricity price risk in day-ahead and real-time markets, especially when prices are highly volatile. This value is quantified using a novel RO framework that frees the decision maker from the assumptions needed in financial option models. A new load forecasting and price simulation model is also developed to forecast load profiles and simulate new price series with different average values, higher volatilities and extreme price spikes to represent potential future market scenarios and to determine under which conditions DSR has the most value. The valuation of a DSR investment is then presented to show how the physical characteristics of a system, in this case the physical load recovery effect of loads after a DSR activation, can tremendously affect the profitability of an investment when uncertainty is taken into account. The third outcome of this work is the development of a complete, general and practical tool for making long-term multi-staged investment decisions in future power networks under multiple uncertainties. It is argued throughout this work that many of the current methods are either unsuitable for long-term investment valuation or are too complex for practical application and implementation at the industry level. A strategic spreadsheet-based tool for making long-term investment decisions under uncertainty is therefore created and tested in collaboration with industry for solving real network planning problems.