OverviewIt is widely agreed that renewable electricity policies, such as feed-in tariffs, that encourage siting of renewabledevelopments irrespective of the marginal value of their output, promote inefficient investment in terms of maximizingthe net economic and environmental value. Instead, the EU and its member states are moving towards feed-inpremiums, curtailment requirements, and other policies that result in profitability better reflecting the market value ofelectric energy. Development may therefore be encouraged where resources produce fewer annual MWh, but wherethe increased market value more than makes up for that decrease, due to timing or transmission availability.However, although such policies might decrease the net economic cost of achieving renewable energy targets, it hasbeen argued that they are still inefficient in achieving the goal of promoting technology improvement. In particular, iflearning-by-doing occurs through cumulative MW investment rather than through cumulative MWh production, thenpolicies that are tied to investment rather than output might be more effective in reducing technology costs (Newberyet al., 2017). These policies may take the form of straight-forward per MW investment subsidies. A more sophisticatedvariant, promoted by Newbery et al. (ibid.), would pay a per MWh subsidy, but only up to a maximum number ofMWh per MW of capacity.In this paper, we compare the impact of energy-focused (feed-in premium) and capacity-focussed (investmentsubsidies) renewable policies upon the EU-wide electric power market in 2030 using a market equilibrium model. Inparticular, we ask the following question: How do the different policies impact the mix of renewable and non-renewable generation investment,electricity costs, renewable output, the amount of subsidies, and consumer prices? Specifically, do capacitybasedpolicies result in significantly more investment and possibly learning?Capacity versus energy subsidies may also have a strong effect on the economics of “system friendly” wind turbines,which have been recently promoted as having lower integration costs and more valuable power output profiles (Hirthand Müller, 2015; May, 2017). But because such turbines have lower capacity per unit output (which may be achievedsimply by using smaller electric power generators for a given tower size and rotor diameter), they may bedisadvantaged by capacity subsidy programs. We investigate whether this is indeed the case.Moreover, we also evaluate the efficiency of national policy targets for renewable electricity production (as a wholeor per technology) and compare these with a cost-effective allocation of renewable enegy production, given resourcequality, network constraints and the structure of the electricity system in the various EU countries.To address these issues, we use COMPETES, an EU-wide transmission-constrained power market model, which weenhanced to simulate both generation investment and operations decisions (Özdemir et al., 2013, 2016). In contrast,other analyses of renewable electric energy policies in Europe have often identified best locations and technologiesbased on levelized costs or other metrics that disregard the space- and timing-specific value of their electricity output.COMPETES uses linear programming to simulate the equilibrium in a market in which generation decisionssimultaneously consider the effect of development costs, subsidies, and energy market revenues on profitability.MethodsA market equilibrium assuming a perfectly competitive market has two characteristics. First, each market partypursues its own objective (its profit), and believes that it cannot increase its surplus by deviating from the equilibriumsolution. The second characteristic is that the market clears where supply equals demand for electricity at each nodein the network. One approach to modeling market equilibria is to concatenate the first-order conditions for each marketparty's problem with market clearing equalities, yielding a complementarity problem. Complementarity problems canbe solved either by specialized algorithms or, in special cases, by instead formulating and solving an equivalent singleoptimization model. The version of COMPETES applied here adopts the latter approach. It uses a single linearprogram that is equivalent to a market with profit maximizing generators who invest and operate to maximize profitsand a transmission operator who minimizes dispatch costs, all subject to policy constraints such as renewable energyor capacity targets and carbon prices. For practicality, this version of COMPETES uses a sample of 1200 (out of 8760)hours to capture load and renewable output variability within a year, and a static (single year) equilibrium is calculatedfor the year 2030 rather than for a multiple year time horizon. Also, this version represents the EU 28 country market with 22 nodes, considering net transmission capability constraints between countries or regions.ResultsAn initial comparison of four policies (no renewable subsidies, which results in 46.8% renewable production share inannual EU demand; a MWh feed-in premium that achieves a 65% renewable goal, and two MW investment subsidiespolicies that also achieve 65% renewable energy) is shown in the first four columns of the table below. The renewablepolicies we simulated assume a single EU-wide target without country-specific mandates, and furthermore assumethat the same level of subsidy applies to all renewable sources. Of course, the reality of EU policy is that there aredistinct programs for wind, solar, biomass, and hydropower, and each country has their own targets, with relativelylimited opportunities for countries to satisfy their renewable requirements elsewhere. However, these simplificationsallow us to to explore the general impact of energy versus capacity policies.Our simulations also explored the impact of country-specific targets (last column). This is a MW-based policy witha minimum amount of renewable solar, wind onshore and offshore capacity by country based on targets reported byENTSO-E’s Sustainable Transition (ST) scenario (ENTSO-E, 2018). The cost of achieving a 52.7% EU-widerenewable energy goal using the specific country goals was 8,5 Billion Euro per year. This is about six times higherthan than the incremental cost of achieving the same level by using the most cost-effective locations and technologiesin the EU, and almost as high as the cost of achieving a much more ambitious 65% target by the most cost-efficientmeans. Thus, country-specific targets without renewable energy credit trading greatly increase the cost of renewablepolicies.ConclusionsAssuming that policy makers adjust capacity targets to meet a 65% energy target, the basic capacity-based policy mustincrease costs of achieving that target (by 58%), since directly constraining (and paying) the product that directlycontributes to a desired target is the first-best way of meeting that target. But the capacity policy does have the benefitof increasing the GW of renewable investment compared to the no-policy case (446 additional GW, which is 63%higher than the 273 GW additional capacity in the energy target case). In contrast, the Newbery et al. proposal’s resultsfall in-between these cases, as it has characteristics of both capacity and energy policies; compared to no policy, itincreases the incremental GW capacity investment (by 36%, 372 GW vs. 273 GW) at a somewhat lower cost perincremental GW unit (incremental cost of achieving the target of 28%).On the other hand, we note Newbery et al. (2017)’s observation that if the objective is to promote technologyimprovement through capacity installation, then it can be significantly less expensive to use capacity subsidymechanisms to achieve a given capacity installation goal than to use an approach based on renewable energy subsidy.In particular, in other runs (not shown), we have found that the 377.3 GW of new renewables that results from the65% feed-in premium policy (second column of table) could also be achieved directly by a 47614€/MW/yr subsidy,at an incremental cost that is 26% lower than the cost of the feed-in premium policy. On the other hand, the capacitypolicy also achieves only 59,9% renewable penetration, and has higher carbon emissions.Meanwhile, other results (not shown) indicate that “system friendly” wind turbines with half the generator capacitymight be financially attractive under MWh subsidies because the generator cost savings might exceed foregonerevenues and subsidies during the times of highest wind. Whether this is the case or not depends on the assumptionsmade concerning the cost savings from reduced electric generator and associated machinery expenses. However,foregone revenue is several times larger if instead the capacity-based policy is in place, greatly decreasing the valueof the system friendly design.Overall, our analysis shows that there is considerable room for coordinating and improving renewable energy policieswithin Europe which will help reduce the total costs of realizing renewable energy production.
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