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A real options approach to renewable electricity generation in the Philippines
Energy, Sustainability and Society volume 8, Article number: 1 (2018)
Abstract
Background
The Philippines is making a significant stride to become energy independent by developing more sustainable sources of energy. However, investment in renewable energy is challenged by competitive oil prices, very high investment cost for renewable energy, and high local electricity prices. This paper evaluates the attractiveness of investing in renewable energy sources over continue using oil for electricity generation.
Methods
This paper uses the real options approach to analyze how the timing of investment in renewable energy depends on volatility of diesel price, electricity price, and externality for using oil.
Results
The result presents a positive net present value for renewable energy investment. Under uncertainty in oil prices, dynamic optimization describes how waiting or delaying investment in renewables incurs loses. Decreasing the local electricity price and incorporating negative externality favor investment in renewable energy over continuing the use of oil for electricity generation.
Conclusions
Real options approach highlights the flexibility in the timing of making investment decisions. At the current energy regime in the Philippines, substituting renewable energy is a better option than continue importing oil for electricity generation. Policies should aim at supporting investment in more sustainable sources of energy by imposing externality for using oil or decreasing the price of electricity.
Background
Environmental problems associated with emissions from fossil fuel, along with limited supply, volatile prices, and energy security, prompted developed and developing countries to find more reliable and sustainable sources of energy. Renewable energy (RE) sources, being abundant, inexhaustible, cleaner, and readily available, emerge as a promising alternative energy source. According to International Energy Agency (IEA), RE accounted to 13.7% of the world energy generation mix in 2015 [1]. With a rapid decline in RE costs, this percentage mix is expected to double by 2040 [2]. In the Philippines, the development and optimal use of RE resources is an essential part of the country’s low emission strategy and is vital to addressing climate change, energy security, and access to energy [3]. In 2015, renewable energy accounts to 25% of the country’s total electricity generation mix, only 1% from wind and solar energy [4]. Since the country is highly dependent on imported fossil fuels, sudden changes in the price of fuels in the world market may eventually affect the country’s energy security. Renewable energy serves as a longterm solution by introducing localized RE sources. However, despite the country’s huge potential for RE generation, investments in RE projects are challenged by competitive prices of fossil fuels, more mature technology for fossil fuels, and very high investment cost for renewable energy. These give us the motivation to make a study that analyzes the attractiveness of RE investments to address the country’s concern on energy sufficiency and sustainability.
One of the most common techniques in analyzing investment projects is the net present value (NPV). This technique is widely used by developers, financial institutions, and government agencies under the condition of definite cash flow. Since RE investment in emerging economies involves high risk from volatile energy prices and changing RE technologies, NPV undervalues investment opportunities and thus considered inappropriate for assessing RE projects in developing countries including the Philippines [5]. Real options approach (ROA) overcomes this limitation as it combines risks and uncertainties with flexibility in the timing of investment as a potential factor that gives additional value to the project [6]. Recent studies use ROA renewable energy investment particularly for wind, solar photovoltaic (PV), hydropower, concentrated solar power (CSP), and combination (hybrid) of RE with uncertainties in nonRE cost, certified emission reduction (CER), feedin tariff (FIT), energy production, operations and maintenance (O&M) cost, research and development (R&D) grants, production tax credit (PTC), RE credit (REC), among others (see Table 1).
This paper contributes to the existing literature by proposing a ROA framework for analyzing RE projects for developing countries, particularly, island countries that are highly dependent on imported oil for electricity generation. While previous studies proposed a full system switch to RE [7] or applied the ROA model to largescale RE projects [8,9,10,11], this study takes the case of Palawan island in the Philippines and focuses on a smaller scale project which is particularly more realistic to developing countries. Whereas previous works’ approaches used coal and gas for fuel price uncertainty [7, 9, 10, 12], this work uses uncertainty in oil prices as the world energy mix is dominated by liquid fuel, more developing countries are dependent on imported oil, and that investments in renewable energy is affected more by volatility in oil prices than coal prices. Finally, this paper proposes an externality tax for using fossil fuels as it more applicable in developing countries than introducing CER price, PTC, REC, CO_{2} price, and emission/externality cost as proposed in previous works [7, 9, 10, 13, 14].
Applying ROA, this study aims to evaluate whether investing in RE is a better option than continue using diesel for electricity generation by considering various uncertainties in diesel fuel price, local electricity prices, and imposing externality tax for using diesel. This finally aims to recommend various government actions to address environmental problem, supply chain, and national security regarding energy.
Methods
Real options approach
Myers [15] first referred ROA or real options valuation as the application of option pricing theory to valuate nonfinancial or “real” assets. Real option itself is “as the right, but not the obligation, to take an action (e.g., deferring, expanding, contracting or abandoning) at a predetermined cost, called exercise price, for a predetermined period of time – the life of the option” [16]. Investment decisions, in the real world, have main characteristics: irreversible, high risk and uncertain, and flexible [17]. These characteristics are not captured by traditional methods of valuation, such as NPV, discounted cash flow (DCF), internal rate of return (IRR), and return on investment (ROI) leading to poor policy and investment decisions. ROA, on the other hand, combines uncertainty and option flexibility which characterize many investment decisions in the energy sector.
This research applies ROA to analyze investment decisions whether to continue using diesel for electricity generation or invest in RE. We use the uncertainty in diesel prices as a main factor that affects investment decisions. Using dynamic optimization, we evaluate the maximized value of investment at each price of diesel, identify the trigger price for shifting technology from dieselbased electricity to RE, and analyze the value of waiting or delaying to invest in RE. Finally, we incorporate sensitivity analyses with respect to electricity prices and externality tax for using diesel.
Dynamic optimization
We follow the method described by Dixit and Pindyck [18] and adopt the work of Detert and Kotani [7] on optimizing investment decision under uncertainty using dynamic programming. In this research, we describe a model of an investor that identifies the optimal value of either investing in RE or continue using diesel for electricity generation as shown in Eq. 1 (see Table 2 for the list of variables and parameters).
where
Using this model, we determine the option value, V_{D, t}, by maximizing the investment at each price of diesel, D, from 0 to US$1000/barrel, for each investment period, t. We set the dynamic optimization process to 40 years which represent a situation where an investor is given a period to make an investment decision. After that period, he has no other option but to continue using diesel for electricity generation. The choice is valued for another 25 years to represent the lifetime of power plant using diesel. We set the value of T_{ R } to 25 years to represent the number of years of electricity generation using RE. Finally, we solve the problem backwards using dynamic programming from terminal period [7, 19]. The uncertainty in diesel prices in Eqs. 2 and 3 as well as the Monte Carlo simulation in the dynamic optimization process is discussed in the next subsection.
Stochastic prices and Monte Carlo simulation
In line with the previous studies, we assume that the price of diesel is stochastic and follow geometric Brownian motion (GBM) [20,21,22]. Dixit and Pindyck [18] present the stochastic price process as
where α and σ represent the mean and volatility of diesel price, dt is the time increment, and dz is the increment of Wiener process equal to \( {\varepsilon}_t\sqrt{dt} \) such that ε_{ t }~N(0, 1). Using Ito’s lemma, we arrive at
We approximate Eq. 6 in discrete time as
To determine the drift and variance of P, we use the Augmented DickeyFuller (ADF) unit root test using the following regression equation
where \( c(1)=\left(\alpha \frac{1}{2}{\sigma}^2\right)\Delta t \) and \( {e}_t=\sigma {\varepsilon}_t\sqrt{\Delta t} \). We then estimate the maximum likelihood of the drift \( \alpha =\mu +\frac{1}{2}{s}^2 \) and variance σ = s, where α is the mean and s is the standard deviation of the series p_{ t } − p_{t + 1} [23].
In this research, we use the annual prices of diesel from 1980 to 2016. The result of ADF test as shown in Table 2 implies that the null hypothesis that p_{ t } has a unit root at all significant levels cannot be rejected. Therefore, P conforms GBM. We estimate the parameters α = 0.007614 and σ = 0.358889 and use in identifying stochastic prices of diesel under GBM (Table 3).
We use the Monte Carlo simulation to compute the expected net present value of electricity generation using diesel in Eqs. 2 and 3. First, we approximate a vector of potential prices of diesel using the stochastic prices of GBM as follows:
This equation illustrates that the previous price affects the current price of diesel. Second, from the initial price of diesel, P_{D, 0}, we estimate the succeeding prices of diesel in each period using Eq. 9. We incorporate these prices in Eq. 2 and calculate the present values of using diesel for electricity generation. Finally, we estimate the expected net present value at each initial price node i and repeat the whole process in a sufficiently large number of J = 10000 times and take the average as given by the equation
Trigger price of diesel
Dynamic optimization process in the previous sections generates the maximized option values of investment. From these simulation results, we identify the trigger price of diesel for switching to RE as follows
where \( {\widehat{P}}_D \) is the trigger price of diesel or the minimum price where the option value in the initial period V_{0}(P_{D, t}) is equal to the option value in the terminal period of investment \( {V}_{{\mathrm{T}}_R}\left({P}_{D,\mathrm{t}}\right) \) [7, 18, 24]. From the given equation, we define trigger price as the minimum price of diesel that maximizes the profit of shifting the source of electricity from diesel power plant to RE.
Data and scenarios
To determine a suitable set of parameter values for the baseline scenario, we use data from various sources that nearly reflects the investment environment for renewable energy project in Palawan. This is the largest island province in the Philippines composed of 1780 islands and islets that are currently not connected to the national grid and only depend on imported diesel and bunker fuel. The recent Calatagan Solar Farm project in Batangas is set as a benchmark of the data for investment in RE, as this project is the latest RE project in the Philippines and has similar geographic features with Palawan; hence, investment cost estimations are uptodate and relatively comparable [25]. This 63.3 MW solar farm, covering a total area of 160 ha, projects to generate 88,620 MWh of electricity per year. It costs US$120 million and will operate for at least 25 years. We use the data from Palawan Electric Cooperative (PALECO) [26] to approximate the local electricity price and the quantity and costs of generating electricity from diesel.
Electricity prices in the Philippines varies from island to island depending on the source of energy, as well as various charges including the generation, transmission, distribution, metering, and loss. In Palawan, effective power rates also vary across different municipalities [26]. We employ these variations in the electricity price scenario by changing the electricity price in the baseline model. In this scenario, we aim to describe how policy in imposing electricity price ceiling or price floor affects the investment decisions particularly in introducing RE as a source for electricity generation.
Lastly, we consider the externality tax of electricity generation from diesel. This value represents the negative externality including, but not limiting to, health and environmental problems associated with combustion of diesel. We use the data of the estimated average external costs for electricity generation technologies from European Environmental Agency (EEA) [27]. For this scenario, we include externality costs, tax for estimating the net present value of using diesel in Eqs. 2 and 3. We arbitrarily assign values, between 0 (for baseline) to US$ 80/MWh, which are lower than those reported in literature to describe a more realistic condition. We assume that RE source, particularly solar PV, produces minimal or nearly no externality.
Results and discussion
Baseline scenario
Figure 1 and Table 4 show the result of dynamic optimization at the baseline scenario. The first point of interest is the positive net present value of RE. This implies that, using the traditional valuation method, renewable project is a good investment in the island of Palawan. This result is evident as the installation of solar energy projects grows rapidly in the recent years. In 2016, there are already 538.45 MW installed capacity of solar projects from the 4399.71 potential capacity in the whole country [25]. Caution must be applied as net present value is not the sole determinant of investment in ROA. The optimal timing that maximizes the value of investment opportunity under uncertainty must also be accounted for [18].
Figure 1 shows the dynamics of the option values at different initial prices of diesel. Result shows that the option values decrease over diesel price as the cost of generating electricity increases with fuel price. The trigger price as indicated by the intersection of option value curves indicates the minimum price of diesel that maximizes the decision of shifting from diesel based to RE generation. The result in the baseline scenario at US$168/barrel is higher than the current price at US$101.6/barrel. Intuitively, this implies that waiting to invest in RE is a better option than investing at the current price of diesel. However, the value of waiting to invest as describe by the distance between option value curves from initial to terminal period is negative. As seen in Table 4, the option value at the current price of diesel at the initial period of investment is US$141.38 million and decreases to 104.97 million at the terminal period. This results to a US$36.41 million loss from delaying or waiting to invest. This implies that waiting to invest in RE incurs losses.
Electricity price scenario
This scenario describes how adjusting the local electricity price affects the option values and the trigger price. Figures 2 and 3 show the dynamics of option values with increasing and decreasing electricity prices decreasing electricity prices (see Additional file 1 Table S2 for dynamic optimization result). Result shows that the option values shift upwards with increasing electricity prices. This shows that at higher electricity prices, the value of either renewable energy or dieselbased electricity both increases. However, the trigger prices of diesel also increase to US$172/barrel at US$220/MWh and US$185/barrel at US$250/MWh from the baseline electricity price of US$202/MWh. This suggests that increasing the electricity price encourages waiting or delaying to invest in RE.
On the other hand, decreasing electricity prices shifts the option value curves downwards and decreasing the trigger price of diesel. This result is apparent as decreasing electricity price results to a lower revenue and thus lower profit for both options. The trigger prices of diesel decrease to US$160/barrel at US$180/MWh, US$150/barrel at US$150/MWh, and US$139/barrel at US$120/MWh price of electricity (Figs. 3 and 4). This suggests that lowering the electricity price decreases the timing to invest in renewable energy. Further, the option values become negative at electricity price below US$120/MWh. This implies that policy makers or power producers must not set an electricity price below US$120/MWh, as this will result to a loss for producing electricity from diesel as well as a negative investment for RE.
Externality scenario
This scenario describes how inclusion of externality tax from combustion of diesel affects the option values and triggers prices in investment in RE projects. The result in Fig. 5 (see Additional file 1 Table S3 for dynamic optimization result) shows that option values shift to the left. First, this implies that imposing externality tax decreases the revenue from electricity generation using diesel and thus decreasing the option values. Second, the unchanged lower boundary of the curves implies externality does not affect the value of investment in renewable energy. This is due to our assumption that electricity generation from RE produces no externality.
With externality, the trigger prices of diesel decrease to US$140/barrel at US$20/MWh, US$111/barrel at US$40/MWh, US$82/barrel at US$60/MWh, and US$54/barrel at US$80/MWh externality cost (Figs. 5 and 6). This implies that imposing externality tax for diesel makes investment in RE more optimal than continue using diesel. Finally, the threshold of externality cost is US$46.55/MWh at the current diesel price of US$101.64/barrel. This is the minimum externality cost that favors immediate investment in RE than continue using diesel.
Conclusions
We evaluate investment environments and decisionmaking process for substituting diesel power plant with RE for electricity generation in the Philippines. Using real options approach under uncertainty in diesel prices, we identify the option values, trigger prices of diesel, and value of waiting to invest in RE. We analyze the sensitivity of investment decisions with respect to various electricity prices and addition of externality tax for using diesel.
ROA highlights the flexibility in the timing of making investment decisions. Our analyses conclude that for a developing country that is highly dependent on imported fuel, shifting to RE is a better option than continue using imported diesel. Policies should aim at supporting investment in more sustainable sources of energy by imposing externality for using fossilbased fuel or decreasing the price of electricity. This may negatively affect the power producers but encourage them to shift from diesel to renewable energy.
We summarized a unique approach to energy investment by replacing diesel with RE for electricity generation. We believe that the ROA framework introduced in this research is a good benchmark for further application. First, ROA may take account of environmental and social costs. This may include the cost of deforestation for solar farm, wildlife and habitat loss, air and water pollution, damage to public health, and loss of jobs. Finally, analyzing investment decisions with several RE resources includes dynamic optimization with different scenarios of generation mix from various RE sources. We are optimistic that this research becomes onestep forward for further analysis of investment in more sustainable sources of energy.
Abbreviations
 ADF:

Augmented DickeyFuller
 CER:

Certified emission reduction
 CSP:

Concentrated solar power
 DCF:

Discounted cash flow
 EEA:

European Environmental Agency
 FIT:

Feedin tariff
 GBM:

Geometric Brownian motion
 IEA:

International Energy Agency
 IRR:

Internal rate of return
 NPV:

Net present value
 O&M:

Operations and maintenance
 PALECO:

Palawan Electric Cooperative
 PTC:

Production tax credit
 PV:

Solar photovoltaic
 R&D:

Research and development
 RE:

Renewable energy
 REC:

Renewable energy credit
 ROA:

Real options approach
 ROI:

Return on investment
References
 1.
IEA (2017) Key world energy statistics. International Energy Agency. https://www.iea.org/publications/freepublications/publication/KeyWorld2017.pdf Accessed 12 Oct 2017
 2.
BNEF (2017) New energy outlook 2017. Bloomberg New Energy Finance. https://data.bloomberglp.com/bnef/sites/14/2017/06/BNEF_NEO2017_ExecutiveSummary.pdf?elqTrackId=431b316cc3734996abdb55ddbbca0249&elq=0714ab8b3c51467a8b29e864d6fff67a&elqaid=7785&elqat=1&elqCampaignId= Accessed 12 Oct 2017
 3.
DOE (2012) Philippine Energy Plan 20122030. Philippines’ Department of Energy. https://www.doe.gov.ph/sites/default/files/pdf/pep/20122030_pep.pdf Accessed 09 Sept 2017
 4.
DOE (2016) Philippine Power Statistics 2015. Philippines’ Department of Energy. https://www.doe.gov.ph/sites/default/files/pdf/energy_statistics/power_statistics_2015_summary.pdf Accessed 01 Jan 2017
 5.
Kim K, Park H, Kim H (2017) Real options analysis for renewable energy investment decisions in developing countries. Renew Sust Energ Rev 75:918–926. https://doi.org/10.1016/j.rser.2016.11.073
 6.
Brach MA (2003) Real options in practice. John Wiley & Sons, Inc., Hoboken, New Jersey
 7.
Detert N, Kotani K (2013) A real options approach to energy investments in Mongolia. Energy Policy 56:136–150. https://doi.org/10.1016/j.enpol.2012.12.003
 8.
Weibel S, Madlener R (2015) Costeffective design of ringwall storage hybrid power plants: a real options analysis. Energy Convers Manag 103:871–885. https://doi.org/10.1016/j.enconman.2015.06.043
 9.
Wesseh PK Jr, Lin B (2015) Renewable energy technologies as beacon of cleaner production: a real options valuation analysis for Liberia. J Clean Prod 90:300–310. https://doi.org/10.1016/j.jclepro.2014.11.062
 10.
Zhang MM, Zhou P, Zhou DQ (2016) A real options model for renewable energy investment with application to solar photovoltaic power generation in China. Energy Econ 59:213–226. https://doi.org/10.1016/j.eneco.2016.07.028
 11.
Kitzing L, Juul N, Drud N, Boomsma TK (2017) A real options approach to analyse wind energy investments under different support schemes. Appl Energy 188:83–96. https://doi.org/10.1016/j.apenergy.2016.11.104
 12.
Kim KT, Lee DJ, Park SJ (2014) Evaluation of R&D investments in wind power in Korea using real option. Renew Sust Energ Rev 40:335–347. https://doi.org/10.1016/j.rser.2014.07.165
 13.
Lee H, Park T, Kim B, Kim K, Kim H (2013) A real optionbased model for promoting sustainable energy projects under the clean development mechanism. Energy Policy 54:360–368. https://doi.org/10.1016/j.rser.2014.07.165
 14.
Tian et al. (2017). The valuation of photovoltaic power generation under carbon market linkage based on real options. Appl Energy, 201:354362. doi: https://doi.org/10.1016/j.apenergy.2016.12.092
 15.
Myers SC (1977) The determinants of corporate borrowing. J Financ Econ 5:147–175. https://doi.org/10.1016/0304405X(77)900150
 16.
Copeland T, Antikarov V (2003) Real options: a practitioner’s guide. Cen gage Learning, New York
 17.
Baecker PN (2007) Real options and intellectual property: capital budgeting under imperfect patent protection. Springer Berlin Heidelberg
 18.
Bertsekas DP (2012) Dynamic programming and optimal control, Vol. 2, fourth ed. Athena Scientific.
 19.
Dixit AK, Pindyck RS (1994) Investment under uncertainty. Princeton University Press, New Jersey
 20.
Fonseca MN et al (2017) Oil price volatility: a real option valuation approach in an African oil field. J Pet Sci Eng 150:297–304. https://doi.org/10.1016/j.petrol.2016.12.024
 21.
Guedes J, Santos P (2016) Valuing an offshore oil exploration and production project through real options analysis. Energy Econ 60:377–386. https://doi.org/10.1016/j.eneco.2016.09.024
 22.
Postali FAS, Picchetti P (2006) Geometric Brownian motion and structural breaks in oil prices: a quantitative analysis. Energy Econ 28(4):506–522. https://doi.org/10.1016/j.eneco.2006.02.011
 23.
Insley M (2002) A real options approach to the valuation of a forestry investment. J Environ Econ Manag 44(3):471–492. https://doi.org/10.1006/jeem.2001.1209
 24.
Davis GA, Cairns RD (2012) Good timing: the economics of optimal stopping. J Econ Dyn Control 36(2):255–265. https://doi.org/10.1016/j.jedc.2011.09.008.
 25.
DOE (2016) Awarded Solar Grid 2016. Philippines’ Department of Energy https://www.doe.gov.ph/sites/default/files/pdf/renewable_energy/awarded_solar_grid_20160630.pdf Accessed: 16 Jan 2017
 26.
Paleco (2016) Status of electrification. Palawan Electric Cooperative Accessed: 16 Jan 2017
 27.
EEA (2010). Estimated average EU external costs for electricity generation technologies in 2005. European Environmental Agency. http://www.eea.europa.eu/dataandmaps/figures/estimatedaverageeuexternalcosts Accessed 20 March 2017
 28.
Abadie LM, Chamorro JM (2014) Valuation of wind energy projects: a real options approach. Energies 7:3218–3255. https://doi.org/10.3390/en7053218
 29.
Jeon C, Lee J, Shin J (2015) Optimal subsidy estimation method using system dynamics and the real option model: photovoltaic technology case. Appl Energy 142:33–43. https://doi.org/10.1016/j.apenergy.2014.12.067
 30.
Barrera GM, Ramírez CZ, González JMG (2016) Application of real options valuation for analysing the impact of public R&D financing on renewable energy projects: a company’s perspective. Renew Sust Energ Rev 63:292–301. https://doi.org/10.1016/j.rser.2016.05.073
 31.
Eryilmaz D, Homans R (2016) How does uncertainty in renewable energy policy affect decisions to invest in wind energy? Electr J 29(3):64–71. https://doi.org/10.1016/j.tej.2015.12.002
 32.
Ritzenhofen I, Spinler S (2016) Optimal design of feedintariffs to stimulate renewable energy investments under regulatory uncertainty—a real options analysis. Energy Econ 53:76–89. https://doi.org/10.1016/j.eneco.2014.12.008
Acknowledgements
We acknowledge the support by the DFG Open Access Publication Funds of the RuhrUniversität Bochum.
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CA conceptualized the research objectives and modeling scenarios. All authors contributed to the data analysis and writing of the final manuscript. All authors read and approved the manuscript.
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Correspondence to Casper Boongaling Agaton.
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Additional file
Additional file 1:
Table S1. ADF unit root test result of oil prices from 19812016. Table S2. Note: elec+2_0: option values at 25% higher electricity price than the base at the initial period; elec+2_T: option values at 25% higher electricity price than the base at the terminal period elec+1_0: option values at 10% higher electricity price than the base at the initial period; elec+1_T: option values at 10% higher electricity price than the base at the terminal period; base_0: option values of energy investment at the initial period; base_T: option values of energy investment at the terminal period; elec1_0: option values at 10% lower electricity price than the base at the initial period; elec1_T: option values at 10% lower electricity price than the base at the terminal period; elec2_0: option values at 25% lower electricity price than the base at the initial period; elec2_T: option values at 25% lower electricity price than the base at the terminal period; elec3_0: option values at 40% lower electricity price than the base at the initial period; elec3_T: option values at 40% lower electricity price than the base at the terminal period. Table S3. base_0: option values of energy investment with no externality at the initial period; base_T: option values of energy investment with no externality at the terminal period; ex1_0: option values at 20/MWhexternalitycosttheinitialperiod; ex1_{T}: optionvaluesat20/MWh externality cost at the terminal period; ex2_0: option values at 40/MWhexternalitycosttheinitialperiod; ex2_{T}: optionvaluesat40/MWh externality cost at the terminal period; ex3_0: option values at 60/MWhexternalitycosttheinitialperiod;ex3_{T}: optionvaluesat60/MWh externality cost at the terminal period; ex3_0: option values at 80/MWhexternalitycosttheinitialperiod; ex4_{T}: optionvaluesat80/MWh externality cost at the terminal period. (DOCX 95 kb)
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Agaton, C.B., Karl, H. A real options approach to renewable electricity generation in the Philippines. Energ Sustain Soc 8, 1 (2018). https://doi.org/10.1186/s137050170143y
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Keywords
 Dynamic optimization
 Price uncertainty
 Renewable energy
 Externality tax