Renewable Energy Technologies and Electricity Forward Market Risks.

AuthorKoolen, Derek

    One of the remarkable new features of wind and solar technologies is the capability to be introduced at large or small scale, and by consumers as well as conventional generators. This raises research questions regarding whether the same renewable technology, e.g. solar, has a different impact on wholesale prices if it is introduced by consumers or by upstream generators, and indeed if there is such an effect, does it vary between renewable technologies, e.g. wind and solar. This technology and supply chain specificity has not been researched elsewhere and is the novel theme of this paper. Thus, we investigate how the integration of renewable energy sources, both large-scale and distributed energy resources, affect price formation and, as a consequence, the market participants' risk-related trading behaviour in short-term wholesale power markets. The large scale installations are typically controlled by large utilities, often able to make relatively good short-term predictions on the renewable production levels in their own portfolio. In contrast, small scale installations (e.g. domestic solar) are owned by residential customers with little benefit to their retailer's perspective, who as a consequence face higher demand uncertainties as it becomes even less clear what is taking place behind their meters.

    We have chosen to focus on the forward risk premium between the forward and spot markets for electricity, rather than the spot market processes by itself, as the premium reveals the balance of risk-averse behaviour between producers and retailers (Bunn and Chen, 2013). In their seminal paper, Bessembinder and Lemmon (2002) indicate via a general equilibrium model that forward prices are biased predictors of spot prices, and account the emergence of risk premiums from the heterogeneous hedging pressure of producers and retailers. Thereby, the market forward premium in essence reflects the net hedging cost of all market participants against spot price uncertainty in competitive market settings. Speculative arbitrage is however observed to be not fully efficient in wholesale power markets as persistent premia are observed, which have been related to considerations of market power (Borenstein et al., 2008), trading periods (Longstaff and Wang, 2004) or operational market characteristics (Daskalakis et al., 2015). Furthermore, other factors such as limited arbitrage participation (Jha and Wolak, 2015), trading inefficiencies (Borenstein et al., 2008) and strategic behaviour (Murphy and Smeers, 2010; Peura and Bunn, 2016) also play a role in the emergence of the forward premium.

    By modelling the emergence of competitive forward premia via a two-stage equilibrium approach, for forward and spot trading, we find evidence for a technology-varying risk premium, related to the respective risk preferences of producers and retailers. Our simulation results indicate that technological production characteristics affect market agents' risk related hedging pressure, which may result in non-monotonic behaviour of the forward premium with an increasing market share of renewable energy sources. We validate our model with short-term power market data from 1 January 2015 to 31 December 2017 in Great Britain and California. The empirical results show evidence for the distinct effect of these sources on the short-term risk premium.

    The findings are directly relevant for practitioners and policy makers. Due to environmental policies, electricity generated by renewable energy sources has experienced a sharp increase with specific annual growth rates as high as 35% (International Energy Agency, 2018). As investments in wind and solar PV become increasingly cost competitive with fossil-fuel based production sources, it is key for policymakers to devise measures carefully and ensure that markets provide adequate price signals for assets and investments. Thereby, understanding the role of different renewable technologies on market dynamics (Campisi et al., 2015) and investment decisions (Wagner, 2019) becomes increasingly important. In doing so, this work ultimately engages policy makers and regulators to efficiently evaluate subsidies and other market initiatives to understand the role of renewable technologies, both large-scale and smaller scale distributed renewable energy sources, on power market dynamics and price implications.

    The rest of the paper is organized as follows. We first review related work on the role of renewable technologies on power price dynamics and propose a multi-stage competitive equilibrium model with both large-scale and distributed renewable energy technologies. We next elaborate with a set of simulations, which lay the foundation for our multi-factor propositional framework on the effects of technological, fundamental and dynamic factors on short-term power price dynamics. In the following section, we validate the model by conducting an empirical econometric analysis with data from the power markets of Britain and California. Finally, we discuss results and implications and reflect by pointing out directions for future work.


    Forward markets help with efficient allocation of resources for energy commodity goods like electricity, coal, oil and gas, that face uncertainty in price or quantity at delivery (Ito and Reguant, 2016; Pindyck, 2001).This analysis considers a model with a forward and a spot market. Both markets trade the same commodity, electricity, physically delivered at a specific time slot in the future. With electricity not (yet) economically storable on a mass scale, the fundamental costof-carry relationship that links spot prices to forward prices in most commodities cannot be used directly for electricity and so it is typical to model forward prices based on expectations of spot prices and forward risk premiums. Focusing on the latter, it is usual to translate risk premium behaviour to risk-related factors of market agents (Koolen et al., 2017; Aid et al., 2011).

    Bessembinder and Lemmon (2002) model the electricity forward price as a result of an expectation over the spot price plus a premium, modelling the supply and demand side of electricity markets in a closed economy. They provide empirical evidence for their results from the US PJM market, but empirical support is mixed with applications to other markets (and during different time periods). For example, Longstaff and Wang (2004) conduct an empirical analysis of the above model by using hourly prices and find that the risk premiums are time-varying and directly related to economic risk factors, such as the volatility of unexpected changes in demand, spot prices, total revenues and the risk that the electricity transmission system reaches its capacity limit. Further empirical studies have suggested the emergence of positive ex-post risk premia, with forward prices higher than realized spot prices for example, for the German EEX market (Wilkens and Wimschulte, 2007) and the NordPool market (Botterud et al., 2010). Others find evidence for negative forward premia, for example Cartea and Villaplana (2008) indicate backwardation in the Nordic, British and PJM market. Douglas and Popova (2008) empirically relate forward risk premiums to indirect storability, associating the forward risk premium back to gas inventory levels. Redl and Bunn (2013) conduct a multi-factor analysis and show that the forward premium in electricity is dependent on fundamental, behavioural, dynamic and shock components. Huisman and Kilic (2012) also find evidence for the dependence of the risk premium on the level of (in)direct storability in the market, comparing the Dutch (mainly gas) and Scandinavian (mainly hydro) markets. They state that one cannot apply the same model to all electricity markets, as forward risk premium behaviour heavily depends on the technology mix of the underlying production sources. Summarising, the sign and size of the forward premium is found to depend on market structure characteristics and demand, supply and price uncertainty.

    The influence of the integration of renewable energy sources on power market price dynamics is recently attracting more attention (Neuhoff et al., 2016), with a large stream of empirical work focusing on short-term market decisions (Karanfil and Li, 2017; Gianfreda et al., 2016), but very few studies modelling the effect of (renewable) technology on forward power trading have appeared. However, it has been shown that, under various market conditions, the forward and spot price formations can depend upon the market agents' operational and technological constraints (Peura and Bunn, 2016). With the increase of (intermittent) renewable energy sources in worldwide power markets still only a recent phenomenon, to the best of our knowledge the research documented in this paper is one of the first to combine analytic modelling and empirical validation in order to analyze the effects of both large-scale and distributed renewable technologies on short-term forward and spot pricing in wholesale power markets.

    2.1 Equilibrium Model

    In order to assess short-term forward and spot power trading with increasing intermittent supply, we model optimal forward and spot positions in a two-stage equilibrium approach, following Bessembinder and Lemmon (2002). Modelling the supply and demand side of power markets in a closed system, allows us to relate forward and spot price formation processes back to the risk preferences of producers and retailers. For a more detailed discussion on the model, see Koolen (2019). We summarise the features of the model here, the complete derivation can be found in the Appendix A. 1.

    There are N power producers /' and M power retailers j that trade the homogeneous, non-storable commodity electricity in a competitive electricity wholesale market. Retailers are required to meet the demand of end-consumers and sell it against a fixed price [p.sub.c]. The demand of...

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