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

Journal

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 56, Issue -, Pages 261-271

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2015.11.071

Keywords

Real Options; Flexibility; Renewable energy; Energy systems investment; Low carbon technologies; Smart Grid

Funding

  1. Autonomic Power Systems project within the Engineering and Physical Sciences Research Council (EPSRC) [EP/I031650/1]
  2. EPSRC [EP/I031650/1] Funding Source: UKRI
  3. Engineering and Physical Sciences Research Council [EP/I031650/1] Funding Source: researchfish

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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. (C) 2015 Elsevier Ltd. All rights reserved.

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