The Influence of Policy Regime Risks on Investments in Innovative Energy Technology.

AuthorGarnier, Ernesto
  1. INTRODUCTION

    This paper addresses the effects of policy regime risks in interaction with wholesale market dynamics on investments in innovative energy technology. It is motivated by two observations. First, there is an ongoing debate about power market design reforms in numerous countries, with potentially drastic implications for the economic attractiveness of different energy technologies (see various contributions to this special issue of The Energy Journal). Second, there is a gap between required and actual investment levels with respect to innovative technologies and business models for low-carbon, smart energy infrastructure. We seek to contribute to a better understanding of this subject matter in light of increasing shares of renewable electricity and distributed power generation.

    The current annual investments in clean energy technology are far from sufficient to attain climate policy goals; globally, they have to double by 2020 and to multiply six fold until 2030 (International Energy Agency, 2014). Today, in many countries investment activity in innovative energy technology is tightly coupled to subsidies aimed at fueling their market diffusion. Most notably, global installed capacities of the widely subsidized photovoltaic (PV) and wind power technologies have soared to about 180 GW and 370 GW by 2014, respectively (Global Wind Energy Council, 2015; Masson et al., 2014). In Germany, these two sources alone account for about 16% of electricity consumption (Nieder et al., 2015). While this still ranges far from the envisaged market shares in the medium- to longer term, less subsidized technologies have fared much worse. Perhaps most notably, virtual power plants (VPP) are far from fully leveraged, despite the importance assigned to them for the further integration of DER into power markets. (1) VPPs connect and coordinate geographically dispersed DER to operate them as an integrated power plant (Asmus, 2010). This is done based on information and communication technology (ICT), including a control center and remote communication devices, in combination with steering, optimization, trading and back office processes. VPPs increase the transparency over available DER capacities, enable higher efficiency in DER operations and support system stability in view of intermittent renewable energy supply. Thus, there is the view shared by many that VPPs will be essential to integrate the increasing DER capacities into power markets and leverage their full value potential (Wille-Haussmann et al., 2010; Kok, 2009; You et al., 2009; Pudjianto et al., 2007). Nevertheless, recent estimates indicate that only 5 GW, or less than 1%, of DER are aggregated and optimized within VPPs (Martin, 2014). The importance of the technology to support energy system transformation is thus today not reflected in its current market penetration.

    A simple explanation might be the challenge of low profitability in wholesale power markets. In recent years, liberalization and the presence of increasing amounts of renewable energy have put considerable pressure on day-ahead spot market prices (e.g. Hirth, 2013). Further, we know from investment theory that market uncertainties, i.e. in our case volatile fuel and electricity prices and other stochastic market-related processes, might lead to investment delays (e.g. Cox et al., 1979). But beyond that, policy regime risk is frequently cited in the energy economics literature as a reason for investment gaps (e.g. Fan et al., 2012; Yang et al., 2008; Ishii and Yan, 2004; Jaffe and Stavins, 1994). Moreover, it is considered as the key investment barrier by virtually all top managers of leading utilities in countries affected by clean energy expansion targets. In 2013, the CEOs of 8 major European utilities (2) drafted a letter to the European Council, calling for more political stability regarding power market design to support investments in promising new energy technologies (Wetzel, 2013).

    Strikingly, the ways in which policy regime risk interacts with and influences innovative energy technology investments have not been investigated in much detail to date. Given the supposed relevance of the issue, we consider several (to our knowledge) unanswered questions worth addressing. Namely, is the effect of policy regime risk primarily driven by actual differences between policy designs and the impact on power market dynamics, i.e. a policy content effect?Or is it determined by (uncertainty about) the process of change, i.e. the speed and probability, i.e. a policy process effect? Do policy regime change and uncertainty affect different innovative energy technologies differently? How do investment levels for (partly) subsidized technologies respond to regime change and uncertainty, as opposed to non-subsidized technologies? Ultimately, what should policymakers do about the issue?

    In the following, we attempt to add to the existing literature by looking at policy regime risk from a micro-level perspective. We observe the strategy of a risk-neutral, rational investor under different conditions of policy regimes and related uncertainty. Our focus is on the implications of different policy conditions on his investment valuation and timing. In this context, the notion of policy regime risks is scrutinized beyond the existing literature, by distinguishing between content and process. The investments considered are a VPP platform and DER assets that could potentially be operated more effectively within a VPP. We will explicitly discuss the different responses to policy regime risk between technologies, depending on value pools and degrees of subsidization of technologies. Our aim is to ultimately be able to draw conclusions for policymakers on how to dampen negative investment repercussions of policy regime changes and related uncertainty in a differentiated way. Further, we want to offer a better understanding of how VPPs may eventually create additional value in their role as a platform technology, or rather some additional capability to do business.

    As an instrument for investigation, we develop a valuation framework that extends from the traditional discrete-time real options (RO) methodology (Cox et al., 1979). The framework formalizes the opportunity to build a VPP by first investing in a platform, and second integrating DER assets. (3) The sequential nature of these investment steps is incorporated through a compound approach following the principles of Majd and Pindyck (1987). Here, each step is considered as a discrete investment step, which unlocks access to the option to enact the following investment step. Further, we incorporate market prices and subsidies as drivers of cash flows as correlated stochastic processes. Correlation is included by applying the multinomial approach from Rohlfs and Madlener (2014), but with standard as well as alternative stochastic processes (e.g., Alexander et al., 2012). We have first applied this approach in a different context--balancing forecast errors--in Garnier and Madlener (2015).

    Policy regime risk is introduced through market design alternatives and corresponding probabilities of design change; if change occurs, (stochastic) market parameters are being structurally overhauled. The probability of regime change occurrence is modeled by means of a binomial process picking up elements from Poisson processes. This approach is distinct from works in which policy regime risk presents a (further) stochastic process similar to prices. For instance, Yang et al. (2008) model uncertain climate policy effects on energy asset investments by including a volatile carbon price. Effects of climate policy on other market parameters or possible correlations are not modeled. Ishii and Yan (2004) investigate empirical electricity generation investment data for the U.S. between 1996 and 2000. They provide evidence for an impact of regulatory uncertainty on investment levels. Further, they suggest that an option value exists due to regulatory uncertainty, i.e. that regulatory uncertainty increases the value of delaying investments relative to investing immediately. Interestingly, Ishii and Yan (2004) find it difficult to separate the effect of actual uncertainty about a regulatory change process (resembling our policy process effect) from the effects of the market design changes resulting from policy change (resembling our policy content effect).

    Our paper proceeds as follows. We formulate the model in section 2. In section 3, we apply the model to the German market, including information retrieved from actual players. In section 4, we discuss the implications of the model and its application for energy policymakers and investors in innovative energy technology, and then conclude.

  2. INVESTMENT VALUATION MODEL

    Assume a risk-neutral, rational actor interested in entering the DER market. His desire is to profitably operate some DER (e.g., wind power plants, PV power plants, biogas power plants, storage units, flexible loads) in some value pools (e.g., wholesale power markets, subsidy schemes, balancing market). Some DER can be operated in some value pools in an isolated manner without a VPP platform. For instance, building a local wind power plant and marketing the production either through a feed-in tariff or simple direct marketing does not require a sophisticated operation and optimization infrastructure. Contrarily, the capacity or production of some DER can only be placed in certain markets if operated through a VPP platform. In general, this applies whenever some form of asset pooling and/or coordinated dispatching based on optimization algorithms is needed. Examples would be the placement of biogas power plant capacities in electricity reserve markets, or the pooling of forecast errors from geographically dispersed wind / PV power plants before balancing them in the market. If an actor is keen on maximizing value from DER by pooling them, optimizing their...

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