A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems

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Abstract

This paper aims at serving as a critical analysis of Real Options (RO) methodologies that have so far been applied to the flexible evaluation of smart grid developments and as a practical guide to understanding the benefits but more importantly the limitations of RO methodologies. Hence, future research could focus on developing more practical RO tools for application to the energy industry, thus making the utilization of powerful “real options thinking” for decision making under uncertainty more widespread. This is particularly important for applications in low carbon power and energy systems with increasing renewable and sustainable energy resources, given the different types of uncertainty they are facing in the transition towards a truly Smart Grid. In order to do so, and based on an extensive relevant literature review, the analogies with financial options are first presented, with various assumptions and their validity being clearly discussed in order to understand if, when, and how specific methods can be applied. It is then argued how option theory is in most cases not directly applicable to investment in energy systems but requires the consideration of their physical characteristics. The paper finally gives recommendations for building practical RO approaches to energy system (and potentially all engineering) project investments under uncertainty, regardless of the scale, time frame, or type of uncertainty involved.

Introduction

The energy and power system sectors are currently seeing a significant shift in the way energy is generated and transmitted to customers. Led by increasing environmental concerns and an ever-increasing dependence on energy, traditional high carbon-emitting plants are being replaced by low-carbon ones, and in many cases, are based on renewable sources such as wind, solar, wave energy, and so on. The fast and efficient integration of these renewable sources requires large infrastructure investments in new electricity generation, transmission and demand as well as flexible network management systems (the so-called “Smart Grid”) [1]. These investments are usually subject to a number of different types of uncertainty, including techno-economic and policy changes brought about by deregulation and high levels of competitiveness [2], uncertainties in energy and carbon prices, demand evolution, technological advances, capital funding, financing models and an ever changing regulatory environment. All these uncertainties contribute to making strategic decisions extremely difficult and, as a result, make development of business cases uneasy for investments in low carbon energy systems. In particular, the irreversibility of these large capital investments increases the risk of asset stranding in the event that high uncertainty causes unexpected market conditions to materialize. A solution to this is to promote investment in non-network technologies such as storage [3], [4], [5], demand-side management (DSM) and demand response (DR) [6], [7], [8], [9], FACTS [10], [11], phase-shifters [12] and so on. These can help alleviate congestion by either shifting flexible loads from periods of high-energy demand and congestion to off-peak ones, by controlling the flow of power over the network, or act as a post-fault corrective action, thus enhancing the ability of the system to accommodate intermittent renewables [12]. In the context of planning under uncertainty, these flexible solutions can provide tremendous value in helping defer large irreversible investments until at least some uncertainty is resolved and the need for large capacity reinforcements is fully established [12], [13]. Another such example of flexible investment to facilitate the development of high-efficiency low-carbon distributed energy systems can be found in [14]; in that work, it is shown how in order to optimally invest into complex smart multi-energy systems [15] and maximize the benefits that the interplay of several energy vectors can bring in terms of flexibility, it is critical to take into account different types of uncertainty (energy prices and loads, in primis) and respond by suitable investment over time, while uncertainties are resolved. Developing investment tools that can account for flexibility and uncertainty in such evolving energy scenarios as of today is therefore essential to encourage the deployment of cleaner and smarter technologies.

Real Options (RO) theory, based on financial option pricing theory, is an investment tool that has been proposed to specifically deal with investment planning under uncertainty. The recent application of RO theory to the profitability assessment of engineering projects, and more specifically to energy systems investment, is hence by no means a coincidence, as discussed in [16]. In fact, a robust RO approach is extremely valuable in a fast-changing energy context as it enables to identify possible flexible investment directions in the light of various uncertainties. It allows building a dynamic roadmap to decision-making in the case of future unforeseen events, thus minimizing exposure to risk, by considering the fact that management will act if conditions change over time. Nonetheless, RO are derived from financial theory, whose assumptions are often neither clear, nor understood or fully applicable when dealing with engineering problems. For instance, as it will be widely discussed later, the assumption that the uncertainty must follow a Wiener process limits the application of RO methods to more general problems, where the uncertainty may not follow a particular stochastic process. Another example involves the assumption of risk-neutrality and that the asset being valued is required to be traded on financial markets, thus limiting its application to non-traded assets. These all contribute to a misuse of “RO thinking” on the one hand, and to its limited adoption in industry due to lack of clarity on the other [17].

This work presents a critical review of current RO methods applied to energy systems projects and, more importantly, explains their underlying assumptions so to create a framework of RO methods for a practical application to engineering. This is a key contribution to add clarity on many RO papers that often do not clarify the validity or sensitivity of their assumptions within the context discussed. The review is built on considering a framework based on the three levels of flexibility that, in our opinion, engineering RO are able to quantify, namely:

  • i)

    The value in having the flexibility over the project lifetime to actively adjust decisions based on future conditions.

  • ii)

    The value in having the flexibility to wait until at least some uncertainty is resolved before making an investment.

  • iii)

    The value in the ability to adapt to varying conditions by changing the system design of the project or investment [18], [19], [20], [21], [22]. This third type of flexibility is what truly separates RO from financial ones in an engineering context since the feature is simply not found in financial options.

As a result, this work aims at serving as a practical guide to any decision maker wishing to clearly understand how a RO approach can be useful and under what particular conditions it can be practically applied to engineering and in particular energy system investment projects, thus contributing to its wider adoption in industry.

Section snippets

Limitations of DCF

New energy infrastructure projects usually involve a large initial investment for research, development and construction, which in many cases can never be recovered [23]. Correctly valuing these projects and assessing their profitability is therefore crucial. This assessment is traditionally done using capital budgeting techniques based on discounted cash flows (DCF) analysis and using indicators such as the net present value (NPV) or internal rate of return (IRR) [24]. However these techniques

Early research on Real Options

According to Myers [42], a firm’s value should not only include the real assets it has in place, but also the present value of “options” to make further investments in the future and, exactly as in finance, these investments should be made only when they are profitable. Teisberg and Trigeorgis [52], Mason and Merton [53], and Slade [54] were amongst the first to turn option theory into a method for valuing investments, where corporate investment opportunities were considered as financial

“Options Thinking” in energy systems projects

As widely discussed earlier, extending financial option valuation methods to a “real” options context and in particular for energy system investment applications potentially allows quantifying the value arising from having the flexibility to better consider and deal with uncertainty, make decisions based on the evolution of this uncertainty, and possibly exploit this uncertainty to gain additional benefits. However, in addition to the critical analysis on the RO methodologies provided in the

Concluding remarks

This paper has presented a critical assessment of current real options (RO) methods for energy (and more generally engineering) systems investment assessment under uncertainty, with the objective to clearly present and explain the assumptions of various possible methods. The consideration of uncertainty and irreversibility in energy network and energy system investments results in the need for viable׳wait-and-see׳ strategies until at least some, if not all, uncertainty is resolved. This leads

Acknowledgments

The authors wish to thank the Autonomic Power Systems project within the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/I031650/1 for their financial support.

References (156)

  • R. Madlener et al.

    Power plant investments in the Turkish electricity sector: a real options approach taking into account market liberalization

    Appl Energy

    (2012)
  • S.C. Myers

    Determinants of corporate borrowing

    J Financ Econ

    (1977)
  • M.E. Slade

    Valuing managerial flexibility: an application of real-option theory to mining investments

    J Environ Econ Manag

    (2001)
  • L.G. Chorn et al.

    Real options for risk management in petroleum development investments

    Energy Econ

    (2006)
  • M.A.G. Dias

    Valuation of exploration and production assets: an overview of real options models

    J Pet Sci Eng

    (2004)
  • F.A. Felder

    Integrating financial theory and methods in electricity resource planning

    Energy Policy

    (1996)
  • J. Frayer et al.

    What is it worth? application of real options theory to the valuation of generation assets

    Electr J

    (2001)
  • E. Näsäkkälä et al.

    Flexibility and technology choice in gas fired power plant investments

    Rev Financ Econ

    (2005)
  • S.-E. Fleten et al.

    Gas-fired power plants: Investment timing, operating flexibility and CO2 capture

    Energy Econ

    (2010)
  • G. Kumbaroğlu et al.

    A real options evaluation model for the diffusion prospects of new renewable power generation technologies

    Energy Econ

    (2008)
  • S.-J. Deng et al.

    Exotic electricity options and the valuation of electricity generation and transmission assets

    Decis Support Syst

    (2001)
  • J.D.M. Marreco et al.

    Flexibility valuation in the Brazilian power system: a real options approach

    Energy Policy

    (2006)
  • G.A. Davis et al.

    Optimizing the level of renewable electric R&D expenditures using real options analysis

    Energy Policy

    (2003)
  • S. Fleten et al.

    Optimal investment strategies in decentralized renewable power generation under uncertainty

    Energy

    (2007)
  • O. Sezgen et al.

    Option value of electricity demand response

    Energy

    (2007)
  • S.-E. Fleten et al.

    Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer

    Eur J Oper Res

    (2007)
  • R. Geske

    The valuation of compound options

    J Financ Econ

    (1979)
  • J.C. Cox et al.

    Option pricing: a simplified approach

    J Financ Econ

    (1979)
  • Department of Energy and Climate Change (DECC), Planning Our Electric Future: Technical Update,” London, UK; Dec....
  • Department of Energy and Climate Change (DECC), Planning our Electric Future: A White Paper for Secure, Affordable and...
  • A.D. Lamont

    Assessing the economic value and optimal structure of large-scale electricity storage

    IEEE Trans Power Syst

    (2013)
  • Schofield J, Stanojevic V, Bilton M, Strbac G, Dragovic J. Application of demand side response and energy storage to...
  • J.A. Schachter et al.

    Demand response contracts as real options: a probabilistic evaluation framework under short-term and long-term uncertainties

    IEEE Trans Smart Grid

    (2015)
  • J. Mutale et al.

    Transmission network reinforcement versus FACTS: an economic assessment

    IEEE Trans Power Syst

    (2000)
  • G. Blanco et al.

    Real option valuation of FACTS investments based on the least square Monte Carlo method

    IEEE Trans Power Syst

    (2011)
  • I. Konstantelos et al.

    Valuation of flexible transmission investment options under uncertainty

    IEEE Trans Power Syst

    (2015)
  • Cesena EAM, Mancarella P. Distribution network reinforcement planning considering demand response support. In:...
  • E.A. Martinez Cesena et al.

    Flexible distributed multienergy generation system expansion planning under uncertainty

    IEEE Trans Smart Grid

    (2015:)
  • A. Triantis

    Realizing the potential of real options : does theory meet practice?

    J Appl Corp Financ

    (2005)
  • E. Fricke et al.

    Design for changeability (DfC): principles to enable changes in systems throughout their entire lifecycle

    Syst Eng

    (2005)
  • De Neufville R. Architecting/configuring/designing engineering systems using real options. In: Proceedings of the ESD...
  • H.B. Nembhard et al.

    Real options in engineering, operations and management

    (2010)
  • R. Hassan et al.

    Design of engineering systems under uncertainty via real options and heuristic optimization

    (2007)
  • E.A. Martínez Ceseña et al.

    Wind power projects planning considering real options for the wind resource assessment

    IEEE Trans Sustain Energy

    (2012)
  • Rothwell G, Gómez T. The Cost of Capital. In: Electricity Economics: Regulation and Deregulation, First. Wiley-IEEE...
  • S.C. Myers et al.

    Capital budgeting and the capital asset pricing model : good news and bad news

    J Financ

    (1977)
  • Avinash K. Dixit et al.

    Investment under uncertainty

    (1994)
  • R. McDonald et al.

    The value of waiting to invest

    Quaterly J Econ

    (1986)
  • Boyle G, Guthrie G, Meade R. Real Options and Transmission Investment: The New Zealand Grid Investment Test;...
  • Buygi M, Shahidepour M. Market based transmission planning under uncertainties. In: Proceedings of the 8th...
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