CC BY
33Real Options Model of RES Policies Benefits in Russian Federation*
Denis ZELENTSov
International Finance Faculty, Financial University, Moscow denis.zelentsov@yandex.ru
Inna LUKASHENKo
Head, International Financial Laboratory, International Finance Faculty, Financial University, Moscow IVLukashenko@fa.ru
Alexandra AKHMETCHINA, Ph.D.
Deputy Dean, International Finance Faculty, Financial University, Moscow AAkhmetchina@fa.ru
Abstract. Shift of energy consumption structure towards increase of Renewable Energy Resources (RES) share is one of the goals of national energy strategy of Russian Federation. While such shift could bring many positive implications, all of them falling in one of the standard bins of sustainability triad, a need for proactive position of government in RES promotion is undeniable, as egoistic rational motivation of individual economic agents stops them from spending resources on altruistic goals of sustainable development. Rigorous cost-benefit analysis of RES support strategies could be cumbersome if possible at all, as assessment model should address numerous intricacies of policy design and uncertainties of innovation process, energy market and new technology adoption. We develop real options model to address at least several mentioned complexities, and analyse RES support policy options to recommend the best for Russian Federation.
Аннотация. Изменение структуры потребления энергии в части увеличения доли возобновляемых источников энергии (ВИЭ) является одной из целей национальной энергетической политики России.
Несмотря на то что от такого изменения, вписывающегося в перспективу повышения устойчивости развития, выиграют все потенциальные стейкхолдеры, государство должно занимать более активную позицию в продвижении ВИЭ, так как рационально действующие экономические агенты эгоистичны, а следовательно не могут тратить свои ресурсы на альтруистические цели. Точное измерение эффекта государственной политики поддержки ВИЭ сложно, если вообще возможно, c учетом всех тонкостей механизмов поддержки и неопределенностей, связанных с инновационными процессами и рынками энергии. В нашем исследовании мы разрабатываем модель реальных опционов, которая учитывает некоторые из перечисленных моментов, и анализируем различные альтернативные политики поддержки ВИЭ в Российской Федерации для того, чтобы определить лучшую.
Key words: real options, policy impact assessment, cost-benefit analysis, RES support, energy policy.
introduction
Shift of energy consumption from non-renewable to renewable energy sources are intensively discussed during several last decades. Implications of such shift might include stabilizing world energy market, higher energy security level, decreased GHG emissions, increased environment protection, positive impact on unemployment due to creation of new industry sector, and further decrease in costs of RES implementation, representing society adaptation and optimization.
To answer these questions, the policymakers and representatives of business and academia should concentrate on direct economic costs and benefits for economic agents, economic externalities, and other effects beyond purely economic rationale. Generally, all these costs and benefits fall into well-known triad of sustainable development (economic, environmental and social factors). It is considered that government should take more proactive role in balancing these factors, as individual agents, rationally acting on their own, may lack altruistic
* Модель реального опциона для оценки эффективности государственной поддержки ВИЭ в Российской Федерации
44
motivation to spend additional resources. Taking into account the advantages of RE, many countries design dedicated energy policies aimed at business, households and overall society. Possible policy palette includes feed-in tariffs (FiT) and R&D subsidies. FiT promotes RES penetration by providing incentives from benefit side; R&D subsidies, on the opposite,— by providing incentives on the costs side.
There are various ways of implementation of FiT. Generally speaking, FiT is any artificial positive externality, created by government for suppliers and/or consumers of RES. In some cases, consumers may pay for gross consumption of energy to energy suppliers, and are compensated by government for using energy from RES; in other cases suppliers are paid for energy generated from certified RES sources; and, finally, there are implementation cases combining features of the first two (as in case of household, deploying wind farm or solar array, supplying excess energy to the grid on windy/sunny days, and covering lack of energy from the grid on less windy/sunny days).
From the purely economic point of view, costs on RE mainly depend on initial investments on installation, maintenance cost and climate factor. We may consider RE as dependent on the level of implemented technology, but free from finite resources prices. NRE costs, on the opposite, depend both on prices of resources and on technology. At present RE is generally more expensive than NRE. Additional investments in R&D should be endeavored to lower NRE costs. Benefit from implementing new RE is not an externality. Investments in R&D could be enormous and unpredictable. In this research we consider direct governmental subsidies for R&D to lower RE (electricity) generation costs. The cumbersome issue here is in choice between investments in R&D of new RE technology to decrease RE costs, and continuation of NRE usage.
Let’s assume that every year the government invests in R&D. Through some years government may choose between abandoning the project, or continuing R&D, or deploying R&D results. Result of deployment depends very much on market penetration of deployed RE technology. It means that RE costs, decreased as result of R&D and FiT offered by government, allow the potential RE generator to obtain benefit. So we obtain the significant condition of RE diffusion: under concrete FiT level technology will be used by private entity only if it will be profitable.
The subject of this research is the overall benefit of FiT-based RES policy in presence of R&D expenses. There are some works in this area. In Lee & Shih (2010) overall value of policy in Taiwan has been evaluated, experience curves have been taken into
consideration, but no direct spending on R&D. We will not consider such curves, instead using levelized costs and taking that after deployment, without R&D, they cannot be reduced. From one side, this is for model simplification; from other hand for public sector (business, homeowners, and smaller plant) it may be omitted. In Siddiqui, Marnay & Wiser (2005) the case of United States has been considered. Experience curves are not taken into consideration. This work rather assesses policy for private investor, not for economy overall. Both of these works use real options analysis for policy valuation. In current research we will use results from these two papers, augmenting it with condition of RE diffusion.
In subsequent sections, we will briefly review literature on the topic, develop the mathematical model for financial valuation using real options approach, and assess policy alternatives for renewable energy in Russian Federation. In final section we conclude and discuss implications.
literature review
Energy efficiency, including RE and NRE comparison. Advantages and disadvantages of RE are provided, for example, in: Ardente et al. (2008), and International Energy Agency (2005). RE allows reducing CO2 emissions to the environment and may be generated anywhere depending only on natural limits, which leads to cost reduction of energy delivery, avoidance of energy loss and improved energy efficiency. Also energy diversification leads to higher energy security. From other hand, some traditional energy should be saved as reserve and technological reserve, and RE overall is most expensive. The main disadvantages of NRE: costs highly depend on price of non-renewable resources. The separate part of RE costs is technology and experience curves (International Energy Agency, 2000). It means that during implementation and continuous use the costs have potential to be reduced “automatically” as effects of “learning-by-doing” and “learning-by-searching” (Lee & Shih, 2010). This may also be considered as “self-optimization” of technology and society together.
In International Energy Agency (2005), many questions related to energy policy efficiency have been described with the following recommendations to energy policy-makers, program managers and analysts:
• Take into account the direct and economy-wide rebound effects when estimating the energy savings resulting from energy efficiency improvements;
• Maximize the number of households and businesses that participate in energy efficiency policies
and programs, and ensure that low-income households are well-served and benefit;
• Continue to analyse the cost-effectiveness of energy efficiency policies and programs using discount rates that are used to analyse other government or utility investment options, typically real discount rates in the range of 4 to 8%;
• Analyse the full costs and benefits of energy efficiency policies and programs, including the transaction costs and non-energy benefits.
Policy design on the basis of Feed-in-Tariff. Regarding FiT-based RE policy design here are information and recommendations: Cory et al. (2009), Couture et al. (2010), and Couture, Cory et al. (2010). These resources describe FiT-based RE policy as one of the most effective one with reference to the best practices in United States, Germany, Spain, Italy and other European Union countries. In these articles various schemes of FiT implementations are described with conclusion that FiT level assignment, which is based on levelized RE costs, is most popular because of its simplicity and transparency. Also here various ways of FiT funding are provided. Ratepayer scheme is most effective and therefore will be considered in detail in this work.
So we have reviewed literature for policy definition, design and implementation. In this resources RE and NRE are compared, technology and experience curves are considered. And we can continue with RE policy evaluation.
Methodologies for RE policy evaluation. Methodologies of RE policy evaluation may be divided in two main categories: first, which is based on extension of traditional discounted cash flow (DCF) analysis, and second, which is based on real options analysis (ROA).
In Bode-Greuel et al. (2005), DCF has been expanded to evaluate project with consideration of uncertainties in business. Quantitative financial evaluation of drug development and technology platforms in biotechnology companies have been evaluated by taking into consideration the probabilities of successful completion of various stages of the project. The suggested evaluation approach, as noted, may be useful for internal project prioritization purposes, for licensing negotiations and for investors, who wish to facilitate financing discussions and to support the definition of exit strategies.
Use of real options analysis for RE policy evaluation. General information about ROA valuation may be found, for example, in Han & Lenos (2004), Mun (2002, 2003).
There are resources related to RE policy assessment using ROA: Fan et al. (2010), Lee et al. (2010), Siddiqui et al. (2005).
In Fan et al. (2010) ROA is used for analyzing the effects of government climate policy uncertainty on private investors’ decision-making in the power sector. It presents an analysis undertaken by the International Energy Agency (IEA) that implements ROA within a dynamic programming approach for technology investment choice.
Lee et al. (2010) considers the case of Taiwan. The significant moment of this work is that the policy is assessed in connection with overall policy value for society. Feed-in-Tariff and experience curves are taken in consideration. But there are no investments in R&D directly in this article. Obtained result has been compared with result given by methodology of BodeGreuel et al. (2005). Difference is high because the extension of traditional DCF is insufficient to model non-renewable resources prices.
In Siddiqui et al. (2005), some variants of yearly decisions are taken into consideration. But from other hand, no FiT and experience curves in this work and valuation are done generally for plant, the RE generator. This work uses results of Brennan et al. (1985), where difficulties of evaluation of mining and other natural resource projects have been indicated because of high degree of uncertainty. By extending the set of decisions at each period to include the possibility of abandonment, Brennan et al. (1985) applies the options pricing method to their copper mine problem. Siddiqui et al. (2005) have generalized mining problem to benefits analysis of US Federal government funded R&D programs for RE technology improvement.
Both Lee et al. (2010) and Siddiqui et al. (2005) use discrete binomial lattice variant of ROA. But there is more general variant based on Partial Differential Equations (PDE) (Davis et al., 2003). Siddiqui et al. specify that for financial managers, policy makers, and other users of the model it is not possible to use “black-box” model based on PDE. Of course, PDE model has high scientific value, but risks related with solution of such equations are high, and risks are increased during complication of the model, if necessary (and in this work we will do this). So, discrete binomial lattice variant seems like the middle between scientific and practical.
As during strategy preparation various possible scenarios should be analyzed, there is a problem to use traditional evaluation approach such as Discounted Cash Flow (DCF) because of its lack of flexibility and failure to account for variety of scenarios. Real options are well-known for their ability to overcome above mentioned difficulties, and have been used in similar context several times: Fan et al. (2010); Siddiqui et al. (2005, 2010); Szolgayova et al. (2008); Kumbaroglu et al. (2004, 2008); Lee et al. (2010).
THE MODEL
Real options method has been chosen in this research as the most effective model for valuation with uncertainty. Binomial lattice variant of real options analysis is applied for modeling. Assumptions of the model are:
• The policy is designed for normal energy users, such as households and businesses.
• FiT is simply a premium over RE levelized cost.
• FiT is shared between non-RE consumers and collected via bills.
• This additional charge has no significant impact on total electricity price.
• No technical risks, and effect from switching costs.
Under these assumptions a financial model for valuation of RE policy, based on FiT introduction, has been created. Significant feature of this model is: model takes into consideration conditional market penetration of new RE technology.
All results of the model are obtained as decision tree, which may be helpful for controlling further policy implementation.
Let’s define that t means year, m means number of NRE price (RUB/kWh) movement. For example, NRE (t, m) | t=3 m =2 means NRE price in year 3, if scenario of possible price movement in this year, 2, will be realized.
Vwill be overall policy value, RUB, V (t, m) means policy value at time moment t and NRE price movement m. So V(1,1) means policy value at start.
Other financial model parameters: I(t), D(t), A(t)— all in RUB and, accordingly, investments, deployment costs, abandonment costs in year t.
Pmeans discount factor, p and q are probabilities for binomial tree of NRE price volatility, and up — maximum of one up-movement of NRE price according to binomial lattice.
FiT — Feed-in-Tariff, RUB/kWh, RE — cost of RE technology, RUB/kWh, L — number of years with fixed Feed-in-Tariff (guarantee of FiT), T — life-time of policy in years. One more variable is used in the model: d means time when technology has been deployed.
There is significant condition of successful policy realization: FiT should be profitable for users. In Siddiqui et al. (2005) and Lee et al. (2010) this is considered as unconditional market penetration of new RE technology. We will consider this as a function:
G (t, m, d ) = g ■ 0O (up2m-l-t ■ Z - B -(RE (d) + max {FiT - RE (d ),0})),
where g means the maximum of penetration, kWh/a, and d0 (x) — Heaviside step function:
qo(*) = {°’
10, x < 0; [l, * >i
Next, we will introduce overall policy value for fixed FiT:
W(t, m, d) = [up2m 1 1 • Z1— B • (RE(d) — max{FiT — RE(d), 0})] • G (t, m, d) + 60(T — t) • p • (p • W(t + l,m + 1, d) + q • W(t + l,m, d)^.
This function is highly dependent on previous functions, meaning that if FiT is profitable for consumers it is reasonable to introduce it.
Using these parameters, variables and functions we have constructed the following model for policy value as function of time and price of NRE:
-I(t) + 90(T - t) • 0 -(p • V(t + l,m + 1) + q • V(t + l,m)),''
V(t,m) = max\ -D(t)+ W(t,m,t),
-m
t = 1,T, m = l,t,
where
W (t, m, d) = [up2m 1 t - Z1 — B - (RE (d) — max{FiT — RE (d), 0})] -G (t, m, d) + 0O(T — t) - ß - (p - W(t + l,m + 1, d) + q - W(t + 1,m,d)),
G(t,m, d) = g - 60(up2m~1~t - Z1 — B - (RE(d) + max{FiT — RE(d), 0})),
t = d,T, m = 1,t, d = 1,T for W(t,m,d) and G(t,m,d),
L
Zi=Z(1,1), B=^ßt~1,
t=i
Z(t,m) = up2t~1~m • NRE1 + 0O(L — t) • p • (p • Z(t + l,m + 1) + q • Z(t + 1,m)^,
t = 1,L, m = 1,t
and 00 (x) — Heaviside step function:
*<■«=£ HZ
So, from final formalization above, we obtain overall policy value at start:
f-/(1) + d0(T-1)-p -(p • 7(2,2) + q - 7(2,1)), 7(1,1) =maxl -D(1)+ W(1,1,1),
I -4(1)
where
W(1,1,1) = [Z1-B ■ (RE(1) - max{FiT - RE(1),0})] ■ G(1,1,1) + 60(T -1)-p-(p- W(2,2,1) + q • W(2,1,1)),
G(1,1,1) = g-60(Z1-B ■ (RE(1) + max{FiT - RE(1), 0})),
L
Zi=Z(1,1), B = YuPt~1> t=1
Z(1,1) = NRE1 + e0(L - 1) -p-(p- Z(2,2) + q • Z(2,1)), and d0 (x) — Heaviside step function:
,w={?;
0, x < 0; x > 0.
In other points, for example, in year 2, if NRE prices have been decreased:
V(2,1) = max
{-m + dQ(T F(3,2) + q • V(3, 1)),]
-D(1) + W(2,1,2),
-4(1)
where
W(2,1,2) = [Zi — B • (RE(2)~ max{FiT- RE(2), 0})] • G(2,1,2) + 60(T - 1 )-/3-(p- W(C,2,2) + q • W(3,1,2)), G(2,1,2) = g • 90(Zi - B • (RE(2) + max{FiT - RE(2), 0»),
L
Z1=Z(l,1l B=YdPt~1> t=l Z( 1,1) = NRE1 + e0(L -1 )-p-{p- Z(2,2) + q • Z(2,^,
and 0O (x) — Heaviside step function:
*«=£ lit:
ASSESSING RES POLICY ALTERNATivES FOR Russian federation
In the previous section we have developed a model for defining current benefit in monetary units and other parameters of RES promotion policy, given its lifetime, year since introduction, inflation effect, current price of NRE source, efficiency of R&D expenses (decrease of the cost of installing RES) and the amount of feed-in-tariff (monetary incentives for clean energy use). Other parameters, that are determined for each year of policy realisation, are further possible paths for price and RES capacity, which will be achieved till the end of policy.
The model accounts for flexibility of energy users to shift from NRE to RES using pure economic rationale and the speed of new RES technology penetration. This instrument could be further used for many applications in energy policy design.
Official goal of Russian government in the area of RE is to achieve 4,5% of electricity generated from RES by the year 2020, which is translated to no less than 22 billion gWh, according to EBRD estimates. Considering this goal, our further research would concentrate on evaluation and comparing possible economic benefit of several policy options and sce-
narios for national economy up to year 2020. Our business-as-usual (BAU) scenario would assume RE costs are increased by 2% per year, and there is no FiT. RE cost increase is due to the effect of inflation, breaking even positive influence of new technology on the price of generation: R&D leads to decrease of RE costs while inflation increases it. As a result, RE costs are rising. We will check which set of joint parameters of two policy instruments — FiT and R&D subsidies — would lead to the best outcome, satisfying the “4.5% by 2020” strategic goal set by government.
In all subsequent scenarios (except for BAU) we assume that FiT is unchanged during policy lifetime and applied during 15 or 20 (depending on scenario) years after capacity was installed. Thus capacity owner might decide relying on guaranteed FiT to shift or not to shift to RES generation. Initial cost of RES capacity is defined by R&D efforts, therefore our model accounts for four factors, directly influencing capacity of owner’s investment decision: guaranteed FiT amount and lifetime, current cost of capacity installation, inflation, and current NRE cost.
The options considered are as follows:
1. Investments decrease RE costs by 0% per year, high FiT during 15 years.
Decisions Tree
Deploy V: 1,6E+12 RUB G:1,0E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB
W Deploy V: 2,3E+12 RUB G:2,0E+10 kWh/a R&D: 0,0E+00RU FiT: 0,0E+00RUB Deploy V: 7,5E+11 RUB G:1,0E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB
r* Deploy V: 2,4E+12 RUB G:3,0E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 9,9E+11 RUB G:2,0E+10 kWh/a R&D: 0,0E+00RU FiT: 0,0E+00RUB Deploy V: 2,4E+11 RUB G:1,0E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB
Deploy V: 2,2E+12 RUB G:3,9E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 8,9E+11 RUB G:2,8E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 2,1E+11 RUB G:1,6E+10 kWh/a R&D: 0,0E+00RU FiT: 0,0E+00RUB Abandon V: 0,0E+00 RUB
r~ Deploy V: 1,7E+12 RUB G:4,5E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 6,4E+11 RUB G:3,0E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 1,2E+11 RUB G:9,1E+09 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB
Deploy V: 1,2E+12 RUB G:4,4E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 3,9E+11 RUB G:2,0E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 6,9E+10 RUB G:5,2E+09 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB
Deploy V: 7,8E+11 RUB G:3,1E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 2,3E+11 RUB G:1,3E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 4,0E+10 RUB G:3,0E+09 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB
Deploy V: 4,9E+11 RUB G:2,1E+10 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 1,4E+11 RUB G:8,0E+09 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Deploy V: 2,3E+10 RUB G:1,7E+09 kWh/a R&D: 0,0E+00RUB FiT: 0,0E+00RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB Abandon V: 0,0E+00 RUB
8
6
5
3
2
Figure 1.
2. Investments decrease RE costs by 0% per year, high FiT during 20 years.
3. Investments decrease RE costs by 2% per year, low FiT during 15 years.
4. Investments decrease RE costs by 2% per year, low FiT during 20 years.
5. Investments decrease RE costs by 2% per year, medium FiT during 15 years.
6. Investments decrease RE costs by 2% per year, medium FiT during 20 years.
Data regarding investments and current NRE price has been obtained from “The final report on the results of expert work on the issues of socio-economic strategy of Russia until 2020, Strategy 2020: New Growth Model — a new social policy” (2011), “Energy efficiency and energy development for the period 2013-2020” (2013), and website of Ministry of Energy of Russian Federation. Data regarding RE prices have been obtained from International Energy Agency resources. Other parameters are the assumptions.
results and discussion
Simulation shows, that in BAU for the rational economic agents (homeowners, businesses) it is better to start deploying RES immediately under current NRE prices. But if these prices will hold to 2015, it will be more reasonable for owners to choose to abandon deployment and continue installing only if NRE prices would rise. Finally, in 2020 only 3 (out of 8 possible in our model) NRE levels would lead to RES capacities continue to be installed (see Figure 1).
Moreover, under any conditions additional RES capacities would achieve not more than 21 billion kWh/a in 2020, which is below policy goal of 4.5% RES generation in 2020. Total economic benefit generated in this scenario will be 490 billion RUB. Thus, our simulation shows that if government will not introduce enough economic incentives and R&D subsidies to promote RES, the only possible option would be to rely on C&C policies to achieve stated strategic goal.
Decisions Tree
Deploy
V: 2,1E+12 RUB G:2,0E+10 kWh/a R&D: 6,0E+08RUB FiT: 1,4E+11RUB
Deploy
V: 1,5E+12 RUB G:1,0E+10 kWh/a R&D: 7,0E+08RUB FiT: 7,0E+10RUB
Deploy
V: 6,3E+11 RUB G:1,0E+10 kWh/a R&D: 7,0E+08RUB FiT: 7,0E+10RUB
___
jDeploy
|v: 9,9E+11 RUB |G:5,0E+10 kWh/a
Ir&D: 3,0E+08RUB
\
[FiT: 3,5E+11RUB
Deploy
V: 1,6E+12 RUB G:4,0E+10 kWh/a R&D: 4,0E+08RUB FiT: 2,8E+11RUB
R&D
V: 2,7E+11 RUB
Deploy
V: 2,0E+12 RUB G:3,0E+10 kWh/a R&D: 5,0E+08RUB FiT: 2,1E+11RUB
Deploy
V: 4,8E+11 RUB G:3,0E+10 kWh/a R&D: 5,0E+08RUB FiT: 2,1E+11RUB
R&D
V: 3,3E+10 RUB
Deploy
V: 7,3E+11 RUB G:2,0E+10 kWh/a R&D: 6,0E+08RUB FiT: 1,4E+11RUB
R&D
V: 6,2E+10 RUB
Abandon V: -1,0E+08 RUB
Deploy
V: 1,1E+11 RUB G:1,0E+10 kWh/a R&D: 7,0E+08RUB FiT: 7,0E+10RUB
Abandon V: -1,0E+08 RUB
Aba ndon V: -1,0E+08 RUB
- 2013 2014 2015 2016 2017 2018 2019 2020
8
6
5
3
2
Figure 2.
Table below shows BAU inputs and outcomes in comparison with other 6 scenarios.
Our 6 scenarios incorporate both instruments (R&D and FiT) with different level of use. In the first scenario FiT is guaranteed during 15 year from any point of new RE installation. Tree on Figure 2 shows that in this case R&D would be continued till 2015 under any NRE market conditions. In 2016
Table 1.
the following variants are possible: deployment of achieved R&D or rejection of policy in total. In 1 state of 5 (very high NRE prices) there will be first installations of new capacities; otherwise, almost in every state of the NRE market government should continue to subsidize R&D and only in one state — very low price — it should completely abandon using RES and shift toward NRE. If NRE costs would
Scenario R&D effect, % of yearly decrease of RES cost 4 U_ CÛ FiT policy lifetime, years R&D lifetime, years Policy benefit, bn RuB Final RES capacity, bn kWh/a
BAU -2% 0 0 0 490 21
1 0% 7.0 15 2 990 50
2 0% 7.0 20 3 1800 50
3 2% 5.3 15 1 1200 54
4 2% 5.3 20 2 1900 58
5 2% 5.7 15 3 1600 50
6 2% 5.8 20 2 1700 58
'l£l Decisions Tree
8 I i I I I i ! Deploy V: 1,7E+12 RUB G:1,0E+10 kWh/a R&D: 1,5E+09RUB FiT: 4,8E+10RUB ! § § § § §
7 6 i: i I I Deploy V: 2,4E+12 RUB G:2,0E+10 kWh/a R&D: 1,2E+09RUB FiT: 9,9E+10RUB Deploy V: 8,4E+11 RUB G:1,0E+10 kWh/a R&D: 1,5E+09RUB FiT: 4,8E+10RUB § § § S
\ I I I I Deploy V: 2,6E+12 RUB G:3,0E+10 kWh/a R&D: 9,0E+08RUB FiT: 1,5E+11RUB Deploy V: 1,1E+12 RUB G:2,0E+10 kWh/a R&D: 1,2E+09RUB FiT: 9,9E+10RUB Deploy V: 3,3E+11 RUB G:1,0E+10 kWh/a R&D: 1,5E+09RUB FiT: 4,8E+10RUB § § § § §
5 i i i I I I Deploy V: 2,3E+12 RUB G:4,0E+10 kWh/a R&D: 6,0E+08RUB FiT: 2,0E+11RUB Deploy V: 1,0E+12 RUB G:3,0E+10 kWh/a R&D: 9,0E+08RUB FiT: 1,5E+11RUB Deploy V: 3,1E+11 RUB G:2,0E+10 kWh/a R&D: 1,2E+09RUB FiT: 9,9E+10RUB Deploy V: 1,1E+10 RUB G:1,0E+10 kWh/a R&D: 1,5E+09RUB FiT: 4,8E+10RUB j § § § § §
4 I I I I Deploy V: 1,8E+12 RUB G:4,9E+10 kWh/a R&D: 3,0E+08RUB FiT: 2,5E+11RUB Deploy V: 7,1E+11 RUB G:3,7E+10 kWh/a R&D: 6,0E+08RUB FiT: 1,9E+11RUB R&D V: 1,7E+11 RUB R&D V: 5,7E+09 RUB Aba ndon V: -3,0E+08 RUB | § §
3 i I I I I t Deploy V: 1,2E+12 RUB G:5,4E+10 kWh/a R&D: 2,0E+08RUB FiT: 2,9E+11RUB R&D V: 4,2E+11 RUB R&D V: 9,2E+10 RUB R&D V: 2,7E+09 RUB Abandon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB i § § i
2 I I I I R&D V: 7,3E+11 RUB R&D V: 2,4E+11 RUB R&D V: 5,0E+10 RUB R&D V: 1,0E+09 RUB Abandon V: -3,0E+08 RUB Abandon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB j ! §
1 Tr&d |v: 4,5E+11 RUB I R&D V: 1,4E+11 RUB R&D V: 2,7E+10 RUB R&D V: 1,3E+08 RUB Abandon V: -3,0E+08 RUB Abandon V: -3,0E+08 RUB Abandon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB | § § § § I
- 2013 2014 2015 2016 2017 2018 2019 2020
Figure 3.
continue to rise, it will be profitable to start deploying existing technology under FiT 7.00 RUB/kWh. With this tariff the agents will install about 50 billion kWh/a during policy lifetime, which is far above government goals, and total economic benefit for society will be 990 billion RUB.
Second scenario is also built on the assumption that R&D investments have no results. The only difference from the first one is that FiT is guaranteed during next 20 years, starting from any point of new RE installation. Extending policy lifetime only for five extra years almost doubles policy benefit from 990 to 1800 billion RUB, with new capacities volume remaining the same. Decision tree configuration also remains congruent to the scenario of 15-year high FiT.
In the third policy we have assumed that investments in R&D have very large effect and it is greater than inflation effect. As a result RE costs are decreased by 2% per year. Our results suggest that in this case depending on NRE price dynamics, installations could start as early as in 2015 (tree on Figure 3).
If NRE price would rise, it will be profitable to deploy RES technology and offer FiT, which is equal to 5.30 RUB/kWh in this scenario. Using this tariff economy would install about 54 billion kWh/a before 2020, which is higher than in scenario 2, and total economic benefit of the policy for the society will be 1200 billion RUB. From this result we can conclude, that while early introduction of new RES technology could bring more installations, the price for earlier adoption would be lower policy benefit even when FiT expenses are low for the government. Sometimes, even if from the point of view of individual agent it is rational to adopt current technology, for the economy as a whole more it is rational to abstain from immediate installations and continue investing in further decrease of RES installation cost. Further we would call such leeway left in benefit by early adoption “the productivity loss’ of benefit.
Fourth scenario again bets on very large effect of R&D on RES cost, and low FiT is applied during
Decisions Tree
8 i \ I \ I \ ? Deploy |v: 1,6E+12 RUB ]G:1,0E+10 kWh/a ! R&D: 1,5E+09RUB j FiT: 5,3E+10RUB ? i
7 6 I \ I | Deploy V: 2,4E+12 RUB G:2,0E+10 kWh/a R&D: 1,2E+09RUB FiT: 1,1E+11RUB jDeploy Iv: 7,9E+11 RUB |g:1,0E+10 kWh/a | R&D: 1,5E+09RUB FiT: 5,3E+10RUB
; \ \ I \ Deploy V: 2,4E+12 RUB G:3,0E+10 kWh/a R&D: 9,0E+08RUB FiT: 1,7E+11RUB Deploy V: 1,0E+12 RUB G:2,0E+10 kWh/a R&D: 1,2E+09RUB FiT: 1,1E+11RUB ¡Deploy | |v: 2,8E+11 RUB ¡G:1,0E+10 kWh/a j R&D: 1,5E+09RUB j FiT: 5,3E+10RUB
5 I \ I \ \ Deploy V: 2,1E+12 RUB G:4,0E+10 kWh/a R&D: 6,0E+08RUB FiT: 2,2E+11RUB Deploy V: 8,8E+11 RUB G:3,0E+10 kWh/a R&D: 9,0E+08RUB FiT: 1,7E+11RUB Deploy V: 2,2E+11 RUB G:2,0E+10 kWh/a R&D: 1,2E+09RUB FiT: 1,1E+11RUB fkbandon | |V: -3,0E+08 RUB
4 I \ I ? I Deploy V: 1,6E+12 RUB G:5,0E+10 kWh/a R&D: 3,0E+08RUB FiT: 2,8E+11RUB R&D V: 5,2E+11 RUB R&D V: 1,2E+11 RUB Aba ndon V: -3,0E+08 RUB jAbandon | V: -3,0E+08 RUB i
3 i \ I I I ? R&D V: 9,6E+11 RUB R&D V: 3,0E+11 RUB R&D V: 6,2E+10 RUB Abandon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB
2 | ; i I I R&D V: 5,9E+11 RUB R&D V: 1,8E+11 RUB R&D V: 3,3E+10 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB
1 Tr&d |v: 3,6E+11 RUB i i i L R&D V: 1,0E+11 RUB R&D V: 1,8E+10 RUB | J Abandon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB Aba ndon V: -3,0E+08 RUB
- 2013 2014 2015 2016 2017 2018 2019 2020
Figure 4.
prolonged 20-years period. First installation of newly born RES technology again appears no earlier than 2015. Extension of low FiT period by 5 years results in even higher volume of installations than in scenario 3 (58 gWh/a vs. 54 gWh/a), with policy benefit significantly higher than in BAU, and scenarios 1, 2 and 3-1900 billion RUB. That might be read as recommendation to policymaker, when confronting early adoption of fresh (and possibly suboptimal) RES technology, to provide extended period for low FiT policy, so that the market, forming comparatively higher penetration rate as an answer to enjoying more FiT, would install more and compensate the “productivity loss” of benefit with higher volume of capacities installed.
Finally, fifth and sixth scenarios were simulated to answer the following question: what is the scale of impact of FiT amount on policy benefit and penetration rate. Tree on Figure 4 shows that R&D should be continued at least till 2015. Applying “average” FiT incentive of 5.71 RUB/kWh is enough to stimulate in-
stallation of capacities close to scenario with high FiT an low R&D efficiency (scenarios 1 and 2), and total economic benefit for society overall will be 1600 billion RUB. Extending FiT period by five years (scenario 6) would give 100 billion RUB in policy benefit and 8 gWh/a capacities.
Comparing outcomes of scenarios 1-6 to BAU we have to note that FiT policy offers huge advantage over hands-off policy. The main recommendation is: if policymaker would like to increase market penetration of RE technology, he needs to increase FiT. In this case optimal points could be found using model, introduced in research. If policymaker aims to maximize revenue of policy, he may consider decreasing FiT, which would in turn decrease probability of technology diffusion. This is significant property of specified model: it takes into consideration conditions of successful market penetration as profitability for user of this technology. The model allows calibrating policy according to policymaker’s strategic goals.
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