Real Options-Based Feasibility Evaluation of Offshore Wind Farm Development in Korea’s Idle Coastal Areas

Abstract

This study evaluates the economic feasibility of offshore wind farm development on idle coastal areas in Korea, focusing on the Wando Geumil Offshore Wind Farm (GOWF) as a representative case. Offshore wind farms are increasingly recognized as key contributors to achieving carbon neutrality, and Korea’s coastal idle zones offer strategic potential for large-scale deployment with minimal land-use conflict. To address market uncertainty—particularly the sensitivity of revenues to the Renewable Portfolio Standard (RPS) and Renewable Energy Certificate (REC) weight—this research applies both the Discounted Cash Flow (DCF) method and Real Options Analysis (ROA), incorporating expansion and contraction scenarios. Using eleven years of historical System Marginal Price (SMP) and REC data, we estimate price volatility via a Geometric Brownian Motion (GBM) model (σ = 23.04%). The DCF results indicate a negative Net Present Value (NPV) of −313.7 million USD, suggesting baseline infeasibility. In contrast, ROA adds strategic value, with the expansion option yielding 69.6 million USD and the contraction option 2.1 million USD in additional project value. These findings demonstrate that integrating policy-driven revenue uncertainty into ROA substantially alters investment recommendations, offering practical guidance for optimizing offshore wind farm deployment on Korea’s idle coastal sites.

1. Introduction

The global transition toward low carbon energy systems is accelerating as nations respond to the urgent challenges of climate change, energy security, and industrial decarbonization [1]. Offshore wind farm has emerged as a critical renewable energy source, offering high generation capacity, predictable output, and scalability that can contribute significantly to national carbon neutrality targets [2]. In South Korea, offshore wind farm development has been prioritized in recent policy frameworks, including the “Korean New Deal” and the national Renewable Portfolio Standard (RPS), which mandate a growing share of renewable energy in the generation mix [3]. However, most studies and projects have focused on conventional open sea offshore sites, often located far from shore, where development faces high costs and complex logistical challenges.

A distinctive but underexplored opportunity lies in utilizing idle coastal areas nearshore maritime zones that are unused or underutilized for industrial or environmental purposes. These areas offer several advantages over deep water offshore sites, including shorter grid connection distances, reduced construction and maintenance costs, and greater compatibility with existing port infrastructure [2]. Furthermore, the proximity to local communities creates potential for stakeholder participation, job creation, and regional economic revitalization. Despite these advantages, the development of offshore wind farms in idle coastal areas remains limited, in part due to a lack of robust economic feasibility assessments that account for the unique policy, technical, and market conditions in Korea.

Feasibility evaluations of renewable energy projects in Korea have traditionally relied on the Discounted Cash Flow (DCF) method, which estimates project value based on expected future cash flows discounted at a given rate. While DCF is a standard approach, it is inherently static, assuming fixed project parameters and ignoring the possibility of adaptive decision-making under uncertainty. In reality, offshore wind farm projects are exposed to multiple layers of uncertainty, particularly in Korea’s regulatory and market environment. The revenue streams from such projects are highly sensitive to changes in the System Marginal Price (SMP) and the Renewable Energy Certificate (REC) weight, both of which fluctuate over time due to shifts in fuel prices, supply-demand conditions, and policy adjustments [3]. In the case of REC weights, changes can occur during the multi-year permitting process, significantly altering the expected return profile before operations even begin.

To address these challenges, Real Options Analysis (ROA) has gained increasing attention in energy economics as a complementary or alternative method to DCF. ROA extends traditional valuation techniques by incorporating managerial flexibility, allowing for strategic actions such as project expansion, contraction, postponement, or abandonment in response to evolving market conditions [4]. This approach is particularly suited to capital intensive renewable energy projects in uncertain policy environments, where the option to modify capacity or operation strategies can create substantial value. Despite its growing use internationally, applications of ROA to Korean offshore wind projects especially those in idle coastal areas are scarce, and studies explicitly integrating RPS/REC-related uncertainties into real options frameworks are virtually absent from the literature [4,5].

This study aims to fill this gap by applying both DCF and ROA to evaluate the economic feasibility of the Wando Geumil Offshore Wind Farm (GOWF), a 600 MW project planned in an idle coastal zone of Jeollanam-do, Korea. The analysis integrates eleven years of historical SMP and REC price data, using a Geometric Brownian Motion (GBM) model to estimate price volatility and capture market uncertainty. Two real options scenarios—expansion to 1200 MW and contraction to 300 MW—are assessed alongside the base case to quantify the value of strategic capacity adjustments. By explicitly modeling revenue uncertainty linked to Korea’s RPS framework and embedding managerial flexibility into the valuation process, this study offers new insights into how idle coastal areas can be leveraged for offshore wind farm deployment. The results not only provide practical guidance for investors and developers but also inform policymakers seeking to optimize renewable energy incentives and permitting frameworks to accelerate the sustainable use of Korea’s coastal resources [3,4,5].

2. Literature Review

2.1. ROA Applications in Offshore Wind Farm and Renewable Energy Projects

Recently, ROA has been actively applied to evaluate the economic feasibility of renewable energy projects, such as solar and wind power plants, which require high initial investment and face significant uncertainty in future revenues. Fernandes et al. reviewed this trend and noted the growing adoption of ROA in the energy sector [4]. In studies on the development of solar, wind, and hydroelectric power plants, ROA is used to reflect the value of long-term revenue uncertainty and strategic decision-making factors often overlooked in traditional financial analysis [6]. These projects commonly share characteristics such as large upfront capital costs and high irreversibility once the project is initiated. Therefore, it is crucial to quantify the value of strategic flexibility and incorporate it into economic evaluations [7]. The DCF method has the advantage of being relatively easy to calculate and interpret. However, it does not adequately capture long-term revenue uncertainty or staged decision-making processes, such as the timing and scale of investment [8]. In contrast, ROA enables the quantification of uncertainty and flexibility, allowing for different investment timing or scaling conclusions even when using the same input data [9]. In summary, in the energy sector where projects must consider uncertainty in revenue, irreversible early decisions, and changes in policy and market conditions, ROA is emerging as a practical tool to complement or improve upon traditional DCF methods [10].

In South Korea, there has been an active push to develop offshore wind farm projects. Compared to onshore wind farm, offshore locations benefit from stronger and more stable winds, and face fewer constraints such as land compensation, making them attractive as long-term energy development options [11]. Case studies in Korea show that the profitability of offshore wind projects is highly dependent on power generation output and electricity selling price. To support renewable energy development, the Korean government operates the Renewable Energy Certificate (REC) system, which applies weighted values based on factors such as technology type, distance from shore, and sea depth [3]. Offshore wind farm projects tend to receive higher REC weights. These weights significantly influence project revenue and play a crucial role in determining financial feasibility [12]. Several studies have also pointed out that the REC price in Korea is highly sensitive to market conditions and policy changes, making it an uncertain factor. This uncertainty suggests that changes in policy or REC weighting can strongly affect investment incentives [5]. In fact, these REC weights are clearly defined in official announcements by the Ministry of Trade, Industry and Energy, and include specific criteria such as distance from the coastline and sea depth. Therefore, these regulatory factors must be incorporated as key assumptions in economic feasibility assessments [3].

Despite the importance of these variables, there is still a lack of systematic research in Korea that applies ROA to offshore wind farm project evaluation while simultaneously considering the uncertainty of both REC weighting and electricity price. Most existing studies remain limited to DCF-based scenario comparisons and only provide threshold analysis identifying the REC weight at which the NPV becomes zero. These approaches do not sufficiently capture market uncertainty or the flexibility of strategic decision-making [11,12]. This study aims to fill that gap. It builds a ROA-based economic evaluation model that considers uncertainty by treating REC weight as a policy variable and electricity selling price as a market variable. First, the sensitivity of NPV to the REC weight is analyzed to determine the minimum REC weight required to achieve a positive NPV under the DCF framework. Then, using ROA, the values of expansion and contraction options are calculated. Based on these values, sensitivity analysis is performed to identify regions where expansion is advantageous and where contraction or waiting is preferable, according to REC weight levels. This study is expected to provide a clear numerical threshold and range for the appropriate REC weight required in offshore wind projects, considering future revenue uncertainty and investment flexibility.

2.2. Current Status of Offshore Wind Development in Korea

As of the end of 2024, South Korea’s operational offshore wind capacity remained limited, at approximately 136 MW, across six projects, as shown in

Table 1. Status of offshore wind farm projects in Korea.

Region/Category	Capacity (MW or GW)	Status/Notes	Source
National Total—Operational Installed	136 MW	Six offshore wind farms commissioned by end of 2024 (NRF). Ramboll: ~124.5 MW across 10 projects.	[13,14]
National Total—Permitted (>30 MW projects)	~3.59 GW	19 projects permitted; concentrated in Jeollanam-do and Jeollabuk-do.	[13]
Pipeline (Under Development)	~44 GW	116 offshore wind projects under development across Korea.	[14]
Policy Target (2030)	14.3 GW	National offshore wind goal for 2030, up from 0.13 GW baseline.	[15]
Jeollanam-do	~2.52 GW	Accounts for ~70% of permitted capacity; major cluster development.	[13]
Jeollabuk-do	499 MW	Permitted projects in region.	[13]
Incheon	233.5 MW	Specific permitted project.	[13]
Gyeonggi	200 MW	Permitted project(s) prior to 2024.	[13]
Busan	136 MW	Existing permitted capacity.	[13]
Wando (Case Study)	600 MW	Geumil Offshore Wind Farm (planned, idle coastal site); subject of this study.	[4,13]

[13]. Ramboll further notes that about 10 offshore wind farms were operational, delivering ~124.5 MW, while 116 projects are under development with an expected capacity of ~44 GW [14]. These figures highlight both the modest level of currently installed capacity and the substantial growth potential reflected in the development pipeline. According to NZTE (2025), the South Korean government has set a target of 14.3 GW of offshore wind capacity by 2030, a sharp increase from the current 0.13 GW baseline [15]. This goal is supported by market-oriented instruments, including a 20-year fixed-price Power Purchase Agreement (PPA) mechanism introduced in 2023, which enhances investment certainty.

Regionally, offshore wind farm development is concentrated in Jeollanam-do, which accounts for more than 70% of the permitted capacity (~2.52 GW). Other regions with permitted projects include Jeollabuk-do (499 MW), Incheon (233.5 MW), Gyeonggi (200 MW), and Busan (136 MW) [13]. Within Jeollanam-do, cluster-scale projects are underway in Yeonggwang, Wando, Shinan, Haenam, and Yeosu. Among these, the Wando Geumil Offshore Wind Farm (600 MW) is notable as it is located in an idle coastal zone, making it well suited for analysis of market-policy uncertainties and their implications for economic feasibility.

While most projects are planned in deep-water or open-sea areas, offering stronger wind conditions but higher costs for grid connection, installation, and operation, idle coastal sites present distinct advantages. These include shorter transmission distances, simplified logistics, and improved accessibility for maintenance. They also encourage greater local stakeholder engagement, which can reduce social conflicts during project implementation. Nevertheless, such sites also face constraints, including potential conflicts with fisheries, environmental and visual impacts, and overlapping administrative jurisdictions. Furthermore, site-specific factors such as distance from shore and water depth influence the classification of REC weights, thereby directly affecting economic viability.

Taken together, these characteristics underscore the necessity of evaluation methods that go beyond static financial models. Accordingly, this study applies both the DCF method and ROA to the Wando Geumil Offshore Wind Farm. This dual approach allows quantification of the value of managerial flexibility including expansion, contraction, and deferral options under conditions of market (electricity price) and policy (REC weight) uncertainty.

3. Theoretical Background

3.1. Discounted Cash Flow (DCF) Method

Net Present Value (NPV) is a fundamental metric in DCF analysis, representing the difference between the present value of expected cash inflows and outflows over the project’s operational life. NPV is calculated to determine whether a project’s anticipated benefits outweigh its costs, as shown in Equation (1) [16,17,18,19,20]:

$$\mathrm{NPV}={\displaystyle \sum }_{t=0}^T{\displaystyle \frac{B_t}{{\left(1+r\right)}^t}}-{\displaystyle \sum }_{t=0}^T{\displaystyle \frac{C_t}{{\left(1+r\right)}^t}}$$

(1)

where $B_t$ is the benefit, $C_t$ is the cost, $r$ is the discount rate, $T$ is lifetime.

The Benefit–Cost (B/C) ratio measures economic efficiency by comparing the discounted value of benefits to costs. This ratio helps verify if the project’s benefits justify the investment. Calculated as shown in Equation (2), a B/C Ratio above 1 implies efficiency, while below 1 suggests inefficiency [16,17,18,19,20]:

$$B/C Ratio={\displaystyle \sum }_{t=0}^T{\displaystyle \frac{B_t}{{\left(1+r\right)}^t}}/{\displaystyle \sum }_{t=0}^T{\displaystyle \frac{C_t}{{\left(1+r\right)}^t}}$$

(2)

A positive NPV or a B/C Ratio greater than one indicates economic feasibility, while negative values suggest the opposite. For projects utilizing idle coastal areas, DCF provides a baseline assessment of profitability under static assumptions. However, as these projects often face prolonged permitting, fluctuating REC weights, and volatile electricity prices, DCF alone cannot fully capture the value of strategic flexibility inherent in such developments [16,17,18,19,20].

3.2. Real Options Analysis (ROA) Method

3.2.1. Concept of Real Options

ROA is a method of economic evaluation designed to aid decision-making in uncertain environments by leveraging the principles of financial options applied to real projects. ROA’s primary advantage is its ability to enhance economic viability through flexibility, allowing decisions like expansion, contraction, postponement, or even abandonment based on changing market conditions. Unlike DCF analysis, which is generally based on static assumptions about future values, ROA introduces a dynamic approach that accounts for the option to react as markets change.

ROA is especially valuable in sectors characterized by high uncertainty and rapid evolution, such as construction, pharmaceuticals, and technology, where adaptability can add significant value. By quantifying the benefits of this flexibility, ROA captures the added value of adjusting strategies as new market information becomes available.

For instance, renewable energy projects involve large initial investment cost, uncertain energy outputs due to weather variations, and revenue fluctuations due to price changes in electricity markets. In such cases, ROA facilitates decisions around flexible project sizes, deferring capital intensive investments, or scaling production to match favorable conditions. Thus, ROA is a suitable framework for assessing the economic feasibility and developing adaptive strategies in renewable energy projects, supporting optimal operational and investment decision-making.

3.2.2. Defining Option Parameters

To perform ROA, the option parameters outlined in

Table 2. Parameters of the binomial option model.

Parameters	Description
$\mathrm{S}$	Underlying asset value
$\mathrm{X}$	strike price
$\sigma$	Volatility
$\mathrm{r}$	Risk-free interest rate
$\mathrm{T}$	Option life
$∆t$	Time step
$u$	Up factor
$d$	Down factor
$p$	Risk-neutral probability

must be calculated. Their definitions and explanations are provided in the subsequent section [16,17,18,19,20].

In ROA, the underlying asset value ($\mathrm{S})$represents the current value of the asset or project based on its anticipated cash flows, serving as the foundation for assessing potential growth or decline. The strike price $\left(\mathrm{X}\right)$ is the cost incurred when exercising an option. For example, if the expansion option is exercised, the strike price reflects additional expansion costs; for the contraction option, it denotes the value gained from contracting; and for the deferral option, it reflects construction costs that may escalate over time. Option life $\left(\mathrm{T}\right)$ is the period during which the option can be exercised, determined by project characteristics and specific scenarios that exercise the option. The time step $\left(∆t\right)$ divides this period into intervals, allowing for a step by step evaluation of asset value changes within the binomial lattice model used in ROA. Smaller time steps provide a finer analysis of asset fluctuations, with each step representing a node where asset value shifts are assessed. The up factor $\left(u\right)$ and down factor $\left(d\right)$ signify potential changes in asset value at each node: the up factor indicates an increase, while the down factor reflects a decrease. These are essential in modeling future asset values within the lattice. The risk-neutral probability $\left(p\right)$ represents the likelihood of asset value increases in a neutral framework, crucial for calculating ROA in the binomial lattice. The values of the up factor (u), down factor (d), and probability (p) are calculated using Equation (3) to model this dynamic effectively [16,17,18].

$$u=e^{\sqrt[\sigma ]{∆t}} , d=e^{-\sqrt[\sigma ]{∆t}} , p={\displaystyle \frac{e^{r∆t}-d}{u-d}}$$

(3)

3.2.3. Calculate the Real Option Value

ROA can be performed using the Black-Scholes model or the binomial lattice model, with the choice of model depending on the project’s characteristics and volatility. Below, we outline the concepts, advantages, and limitations of each model, followed by a detailed explanation of the ROA process using the binomial lattice model.

The Black-Scholes model is widely used in the financial sector to calculate Real Option Value (ROV). Its primary advantage lies in its efficiency, allowing for quick calculation of single values, making it ideal for financial options analysis. However, the Black-Scholes model assumes continuous volatility and continuous trading, which limits its applicability in projects with high uncertainty and real assets. This model is less suitable for scenarios where the value of decision flexibility needs to be assessed over time.

The binomial lattice model divides the option life into discrete time steps, calculating the potential asset value at each node to form a “tree” structure. At each stage, the underlying asset value either increases by an upward factor ($u$) or decreases by a downward factor ($d$), creating a framework that includes all possible future asset values. The option value at each node is then determined by working backward calculation from the option’s expiration date to the present. The binomial lattice model provides a useful structure for option calculations and enables observation of changes in the underlying asset value over time. It also more accurately reflects real world scenarios in which decision timing and conditions may evolve.

An expansion option allows for increasing the project size by investing additional funds when the market conditions are favorable. At option expiration, the value is the greater of continuing without expansion or expanding the project, as shown in Equation (4). Before maturity, the value is the greater of withholding the expansion option or exercising it, as shown in Equation (5). If $X_e$ is the expansion cost, $E$ is the project expansion coefficient, and $S_0$ is the current asset value, the value $V_{i,j}$ at each node is defined as follows [16,17,18],

$$V_{n,j}=\max \left[S_0{u}^{n-j}{d}^j , E\left(S_0{u}^{n-j}{d}^j\right)-X_e\right]$$

(4)

$$V_{i,j}=max\left[\left(p{V}_{i+1}+\left(1-p\right)V_{i+1, j+1}\right)e^{-r∆t} , E\left(S_0{u}^{n-j}{d}^j\right)-X_e\right]$$

(5)

The contraction option allows for project size reduction in unfavorable market conditions, potentially recovering value by selling off equipment. At option expiration, the value is the greater of continuing without contracting or proceeding with reduction, as indicated in Equation (6). Prior to maturity, the value is the greater of withholding or exercising the contraction option, as shown in Equation (7). If $C$ is the contraction coefficient, $X_c$ is the benefit from reduction, and $S_0$ is the project’s present value, the value $V_{i,j}$ at each node is determined as follows [16,17,18],

$$V_{n,j}=\max \left[S_0{u}^{n-j}{d}^j , C\left(S_0{u}^{n-j}{d}^j\right)+X_c\right]$$

(6)

$$V_{i,j}=max\left[\left(p{V}_{i+1}+\left(1-p\right)V_{i+1,j+1}\right)e^{-r∆t} , C\left(S_0{u}^{n-j}{d}^j\right)+X_c\right]$$

(7)

4. Case Study

4.1. Project Overview

This case study evaluates the Wando Geumil Offshore Wind Farm (GOWF), a flagship large scale offshore wind initiative utilizing Korea’s idle coastal areas as shown Figure 1. These areas defined as underutilized maritime zones with minimal conflicting uses offer strategic advantages for offshore wind deployment, including reduced land acquisition constraints, proximity to port and grid infrastructure, and favorable wind resource conditions. The GOWF case is particularly significant as it reflects the typical technical characteristics of Korea’s coastal offshore wind projects, such as water depth, distance from shore, and site-specific conditions. Leveraging such areas not only supports Korea’s renewable energy capacity expansion but also fosters balanced regional development.

Situated in the southern waters of Wando-gun, Jeollanam-do, the GOWF is designed to install 75 offshore wind turbines, each rated at 8 MW, over an area of approximately 76 km². The planned total capacity is 600 MW, with a projected total investment of 2,336,429 thousand USD. The development schedule includes a design phase beginning in 2020, construction starting in 2022, commissioning anticipated in late 2025, and an operational lifespan of 20 years.

Given the prolonged permitting processes typical of Korea’s offshore wind sector, the GOWF presents an ideal case for evaluating the application of ROA. By incorporating strategic flexibility—such as capacity expansion or contraction—into the feasibility assessment, the study aims to determine optimal operational strategies that can enhance long-term project value under uncertain market and regulatory conditions. This focus is particularly relevant for idle coastal area projects, where site scalability and exposure to evolving policy environments are significant determinants of success.

4.2. Data Collect for DCF Economic Analysis

In this study, we estimated facility standard data, cost data, benefit data, and financial data, as summarized in

Table 3. Details of the case study.

Items		Value	Method	Sources and Reference
Facility Standard	Facility capacity	600 MW	Wando Geumil offshore wind farm	-
	Utilization rate	31%	Assumed to be 31%	[21,22,23]
	Efficiency reduction rate	0.2%	Assumed to be 0.2%	[21,22]
	Complex loss	5%	Assumed to be 5%	[21,22]
Cost Data	CAPEX (thousand USD)	2,336,429	Wando Geumil offshore wind farm	-
	OPEX	3%	Assumed to be 3% of annual sales	[24]
Benefit Data	SMP (USD/kWh)	0.07	2024 SMP, REC unit price in Korea	[25,26]
	REC (USD/kWh)	0.05
	REC weight	2.71	Offshore wind farm 2.5 +0.4 (composite) for every 5m increase in water depth and 5km connection distance	[27]
Financial Cost	Inflation rate	3.5%	Last 30 years average inflation data in South Korea	[28]
	Discount rate	5%	Recommended by IEA	[29]
	Risk-free interest rate	5%	Average value of the three-year treasury bonds of the South Korea from 2021 to 2023	[30]

. Facility capacity and cost-related data were scaled based on actual cases conducted in Korea and preliminary feasibility studies, with verification from industry experts.

Facility standard data include metrics such as facility capacity, utilization rate (capacity factor), efficiency reduction rate, and complex loss rate. Facility capacity denotes the maximum electrical output the wind farm can produce under ideal conditions, measured in megawatts (MW). For the GOWF project, this involves 75 wind turbines, each with an 8 MW capacity, resulting in a total of 600 MW. However, due to factors such as wind variability and maintenance, actual production typically falls short of this maximum output [21,22]. The Utilization Rate (Capacity Factor), calculated as the percentage of actual energy produced versus potential maximum energy over a specific period, is estimated at 31% based on consultations and a literature review. Since Korea’s current offshore wind power plant construction is in its initial stage, the utilization rate is slightly lower. It is important to adjust and recalculate this rate as the industry matures and technological advancements improve efficiency [21,22,23]. The efficiency reduction rate represents annual performance declines due to mechanical wear, suboptimal maintenance, and environmental influences; this rate is set at 0.2% [21,22]. Additionally, complex loss refers to cumulative losses from various sources, including wake effects, electrical transmission losses, and environmental impacts (e.g., icing or blade soiling) [21,22]. The complex loss rate was set at 5% following expert input.

Cost data encompass CAPEX, OPEX, and other financial costs. The CAPEX for this project is estimated at 2,336,429 thousand USD, covering construction related expenses such as turbine materials, installation, foundation, electrical and control systems, and dedicated transmission line construction. OPEX includes repair and maintenance costs, purchased power costs, and other operational expenses (e.g., insurance, administrative expenses, environmental compliance, and licensing fees). Repair and maintenance costs, due to the offshore environment, are significant and form a large part of the total OPEX. Purchased power costs account for electricity procured during low wind periods or maintenance shutdowns. In this analysis, other costs were estimated at 3% of annual sales [24].

Benefit data include the SMP, REC price, and REC weight. The analysis applied the 2024 SMP and REC values specific to Korea [25,26]. REC weight, which varies depending on location and installation size, was set at 2.5, with an additional composite weight of 0.4 per 5 m increase in water depth or km increase in distance [27]. Recently, the REC weight for the GOWF project was adjusted to 2.71 by government directive.

Financial data encompass inflation rate, interest rate, risk-free rate, and loan repayment period. Based on 30 years of inflation data in Korea, an inflation rate of 3.5% was used [28]. The interest rate was assumed at 5%, aligned with the average yield of 3-year Korean Treasury bonds [29,30]. The loan repayment period for the project was set to 5 years.

4.3. Conduct DCF Economic Analysis

This section evaluates the economic feasibility of the GOWF project using DCF analysis based on cash flow projections derived from project data, as illustrated in Figure 2. The results of the DCF analysis, summarized in

Table 4. DCF results for GOWF project.

NPV	−313,724 thousand USD
B/C Ratio	0.87
Breakeven period	None
Decision	No-Go

, provide a detailed examination of the project’s financial outlook. Through the application of Equation (1), the NPV was calculated to be −313,724 thousand USD. This negative NPV signifies that, under the given assumptions, the project does not meet the economic viability criteria, thus supporting a recommendation against project continuation.

Further analysis employed Equation (2) to calculate the B/C Ratio, resulting in a value of 0.87. As the B/C Ratio falls below 1, it corroborates the conclusion that the project is economically unfeasible. Additionally, there is no identifiable breakeven period, reinforcing the assessment that the project would not generate sufficient returns.

While these results suggest that the project is not economically feasible under existing conditions, it is essential to conduct a broader economic analysis that incorporates potential future uncertainties. This extended analysis should consider the strategic value of adaptive decision-making in response to market dynamics, as this approach may reveal opportunities for future economic viability amid evolving conditions.

4.4. Conduct ROA Economic Analysis

4.4.1. Estimate the Volatility

A review of existing literature identifies electricity selling price as the primary uncertainty factor impacting profitability in offshore wind farm projects. Recent trends indicate a marked decline in electricity selling prices in Korea, coupled with heightened uncertainty. In response, this study quantifies and incorporates price volatility into project analysis by calculating volatility based on historical price data.

Volatility estimation for electricity selling prices was conducted using GBM simulation, leveraging 11 years of historical SMP and REC data. This approach relies on historical data of uncertain factors to forecast future trends [31]. For the GBM model to be applicable in predicting electricity prices, SMP and REC time series data must adhere to a GBM stochastic process, which can be tested via the Augmented Dickey–Fuller (ADF) test. If a unit root is present, the data is deemed non-stationary, validating the use of GBM in time-series empirical analysis.

Our analysis, supported by prior studies, confirmed that both SMP and REC prices are non-stationary, satisfying the conditions for employing ROA under dual uncertainty within a GBM framework. SMP data was sourced annually from the Korea Power Exchange (KPX), while REC data was obtained from KPX’s 2023 electricity market statistics [25,26].

Table 5. Historical data for SMP and REC prices.

Year	SMP (USD/kWh)	REC (USD/kWh)	SMP + REC (USD/kWh)
2023	0.12	0.05	0.17
2022	0.14	0.04	0.18
2021	0.07	0.02	0.09
2020	0.05	0.03	0.08
2019	0.06	0.04	0.11
2018	0.07	0.07	0.14
2017	0.06	0.09	0.15
2016	0.06	0.10	0.15
2015	0.07	0.07	0.14
2014	0.10	0.09	0.19
2013	0.11	0.13	0.24
Average	0.08	0.06	0.15

presents the acquired data, with SMP + REC prices calculated annually.

The expected rate of price increase (μ) and the instantaneous volatility (σ) of the REC and SMP electricity selling prices, which follow the GBM stochastic process, were estimated using the methodology outlined by Tsay (2002) [32]. The governing equation for a process following GBM is represented by Equation (8), where $dw$ denotes the Wiener increment, a stochastic term that accounts for random fluctuations in SMP and REC prices.

$$dp=\mu pdt+\sigma pdw$$

(8)

As shown in Equation (9), we define the period rate of return as $y_t$ and the time interval as $dt$.

$$y_t=\mathit{ln}(p_t-p_{t-1})$$

(9)

For a finite time interval, the change rate $\log p$ for the state variable $p_t$ adheres to a lognormal distribution. Under this distribution, the mean is expressed as $E_{yt}$, and the standard deviation is $S_{yt}$ leading to the calculation of the expected growth rate (μ) and instantaneous volatility (σ) as per Equations (10) and (11).

$$\mu ={\displaystyle \frac{E_{yt}}{dt}}+{\displaystyle \frac{S_{yt}^2}{2dt}}$$

(10)

$$\sigma ={\displaystyle \frac{S_{yt}}{\sqrt{dt}}}$$

(11)

The time interval $dt$ was based on yearly time series data, hence $dt=1$. For monthly time series data, $dt$ would be adjusted to ${\displaystyle \frac{1}{12}}$, and for daily data, it would be ${\displaystyle \frac{1}{365}}$. Following the parameter estimation, the volatility of the SMP + REC price was calculated using 1000 simulations. The results of the GBM simulations predict the fluctuations in SMP + REC prices over the next 20 years (20 × 365 days), as illustrated in Figure 3, showing an average projected price of 0.142 USD/kWh with a volatility of 23.04% [33].

4.4.2. Expansion Option

In this section, we assess the value of the GOWF project by exploring the feasibility of expanding its operational scale. Initial DCF analysis indicated that the project is not economically viable under current conditions, yielding an NPV of −313,724 thousand USD and a B/C Ratio of 0.87. However, as market conditions evolve particularly with potential increases in electricity sales prices or favorable policy shifts the option to expand could offer a strategic avenue for enhanced profitability. This study, therefore, examines the expansion option value while factoring in the uncertainty of future electricity sales prices. The findings may provide insights into the viability of the proposed offshore wind farm model, suggesting expansion as a feasible strategy if the ROA value surpasses the DCF expansion value.

To conduct the ROA, option parameters were defined as shown in

Table 6. Expansion option parameters for ROA.

Parameter	Expansion Option
$\mathrm{S}$	2,022,705 thousand USD
$\mathrm{X}$	4,672,857 thousand USD
E	2
T	5 years
$\sigma$	23.04%
$\mathrm{r}$	5%
$∆\mathrm{t}$	1

, using an American call option approach. Here, X represents the strike price upon exercising the expansion option, covering the costs of both the initial 600 MW facility and an additional 600 MW expansion. E, the expansion variable, indicates a doubling of the project’s size, bringing the total capacity to 1200 MW. T designates the time frame for exercising the option, assuming an extension of the permitting period by five years, which allows for design modifications to accommodate the expanded scale.

A real options analysis based on the binomial lattice model was conducted using these option parameters. The valuation followed a structured sequence. First, a binomial lattice was constructed to represent changes in the underlying asset’s value through forward progression, using the upward factor (u) and downward factor (d) of the underlying asset value (S), as outlined in the theoretical framework. The resultant values at each node are depicted in Figure 4, above each node. Next, using risk-neutral probability (p), the upward (u) and downward (d) factors were applied to calculate the present value at each preceding stage, progressing backward from the t = 5 node values. The corresponding expansion option values are indicated below each node in Figure 4.

The final expansion option value at T = 1 was calculated as 2,092,266 thousand USD. This includes the value of the currently operational GOWF’s underlying asset. To isolate the value of the expansion option itself, we identify the ROV, which amounts to 69,562 thousand USD, as shown in

Table 7. Comparison of NPV and ROV (Expansion).

NPV	−313,724 thousand USD
ROV (Expansion)	69,562 thousand USD

(2,092,266–2,022,705). This ROV highlights the potential added value under market and policy conditions that support expansion.

4.4.3. Contraction Option

This section evaluates the project value by considering the potential reduction in the GOWF project size. According to the DCF analysis, the project was found to lack economic feasibility, with an NPV of −313,724 thousand USD and a B/C Ratio of 0.87. Nevertheless, in a scenario where market conditions become unfavorable, implementing a contraction option could allow for recovery of some benefits by selling off existing assets. In this study, we assess the value of the contraction option, considering uncertainties in future electricity sales prices. These findings can support evaluations of the viability and scalability of the proposed offshore wind farm model. If the value of the contraction option derived through ROA exceeds that of the DCF analysis, exercising the reduction option may be justified.

For this assessment, option parameters were calculated for the ROA using an American put contraction option model, as outlined in

Table 8. Contraction option parameters for ROA.

Parameter	Contraction Option
$\mathrm{S}$	2,022,705 thousand USD
$\mathrm{X}$	467,286 thousand USD
E	0.5
T	5 years
$\sigma$	23.04%
$\mathrm{r}$	5%
$∆\mathrm{t}$	1

. Here, X represents the strike price associated with exercising the contraction option, which reflects the additional recoverable value when contracting a 600 MW facility to 300 MW. It was assumed that 40% of the existing construction costs could be recouped. The variable E represents the degree of reduction, with this study assuming a 50% downscaling, resulting in a total of 300 MW. T denotes the option exercise period, with an as summed five-year extension to the project’s permitting phase, allowing for design changes that support downscaling within the extended timeframe, and additional revenue from equipment sales.

The contraction option values are presented under each node in Figure 5. The final value of the contracting option at T = 1 was calculated at 2,024,827 thousand USD, which includes the underlying asset value of the currently operational facility. Focusing solely on the reduction option’s added value, the ROV for the final contraction option was confirmed at 2122 thousand USD, as shown in

Table 9. Comparison of NPV and ROV (Contraction).

NPV	−313,724 thousand USD
ROV (Contraction)	2122 thousand USD

(2,024,827–2,022,705). This result underscores the potential economic benefit of implementing the contraction strategy if market conditions deteriorate.

4.4.4. Comprehensive Decision-Making

In this section, we conduct a comprehensive analysis of the value generated by expanding or contracting the scale of the GOWF project using ROA to determine the optimal operational strategy. The GOWF project initially aimed to construct a 600 MW offshore wind farm by utilizing idle coastal area. DCF analysis yielded an NPV of −313,724 thousand USD, indicating that the project, in its original form, lacks economic feasibility. However, using ROA to evaluate an expansion option, which would increase capacity to 1200 MW, the project’s value was estimated at 69,562 thousand USD. This result suggests that expanding the project’s capacity could secure additional profits, supporting the feasibility of the proposed offshore wind farm model for broader adoption.

An economic analysis was conducted using ROA to evaluate the contracting option to 300 MW, which confirmed a value of 2122 thousand USD. The conclusion drawn is that contracting yields higher economic feasibility than maintaining the existing scale, while expanding the project proves to be more advantageous than contracting. This indicates a strategic operational preference for scaling up, as it ultimately secures greater profitability compared to both maintaining the current size and reducing capacity.

Table 10. Optimal operation strategies thorough scale adjustment.

	NPV	Contraction	Expansion
Value (thousand USD)	−313,724	2122	69,562

provides a comparison of values for maintaining, expanding, or contracting the scale, showing that reducing rather than maintaining the current scale, and expanding rather than reducing, offers a more rational approach.

5. Discussion

This study further conducted sensitivity analysis of NPV and ROV based on REC weight, and the results of the analysis are shown in Figure 6a,b. Figure 6a shows how NPV changes with different REC weights. In the case study, when the REC weight was 2.71, the NPV was −313,724 thousand USD, indicating that the project was not economically viable. The sensitivity analysis showed that the NPV became positive and economically feasible when the REC weight exceeded 3.01. Therefore, decisions should be made to either not start the business or stop immediately if the REC weight is below 3.01.

Figure 6b presents the changes in ROV for both the expansion options and contraction options according to different REC weights. This suggests the potential to reduce losses or expand opportunities through various strategies, and the results can be divided into three zones based on the REC weight value.

First, as the REC weight decreases, the value of the expansion options becomes zero, while the value of the contraction options increases. Notably, in the contraction zone, where the REC weight falls below 1.5, the value of the contraction options increases rapidly. This indicates that the business cannot be maintained and is no longer viable. While it may be possible to recover some value by reducing the scale of operations, this is not an active investment strategy but rather one aimed at minimizing losses. Therefore, in this zone, it is more reasonable to stop the business or delay entry.

The neutral zone, where the REC weight is between 1.5 and 3.0, represents a situation where it is difficult to choose the best strategy. In this zone, the values of both the expansion options and contraction options are fairly similar. This suggests that decisions should be postponed to observe market changes instead of acting immediately. In other words, the neutral zone suggests waiting for further observations on external factors such as policy changes, SMP and REC prices, and technological developments. This reflects the flexibility of real option analysis, which is one of its key benefits.

Finally, the expansion zone, where the REC weight is above 3.01, shows a sharp increase in the value of the expansion options, indicating that expanding the business maximizes its economic feasibility. Additionally, the NPV sensitivity analysis confirms that the decision to expand becomes more reliable, as the NPV becomes positive when the REC weight exceeds 3.01. Therefore, the expansion zone is the key point where large-scale expansion can maximize the economic potential of the business, provided that the policy and market environment are favorable.

The sensitivity analysis of REC weights in this study provides three strategic decision-making frameworks that go beyond simply assessing the presence of economic feasibility. This analysis clarifies the strategic flexibility that was not addressed by DCF analysis, enabling more refined investment decisions through ROA. Considering that REC weights can change due to policy factors, the results offer strategic guidelines for adjusting the size of the project. These findings can serve as a valuable reference for future investment decisions and policy development.

6. Conclusions

This study conducted a comprehensive economic evaluation of offshore wind farm development in Korea’s idle coastal areas, using the Wando Geumil Offshore Wind Farm (GOWF) as a representative case. Idle coastal areas, defined as nearshore maritime zones with minimal competing uses, offer strategic advantages over conventional deep-water offshore sites, including shorter transmission distances, reduced construction and maintenance costs, and compatibility with existing port infrastructure. These locational benefits are further enhanced by local stakeholder participation, job creation, and economic revitalization, aligning closely with Korea’s broader goals for carbon neutrality and balanced regional development. Despite these advantages, the economics of offshore wind farm projects remain sensitive to fluctuations in market and policy conditions, highlighting the need for an analytical framework that captures the unique positional characteristics and inherent uncertainties of such projects.

In this study, both the DCF method and ROA were applied to assess the economic viability of the GOWF project, with particular focus on market uncertainty due to electricity selling price and REC weight fluctuation. The DCF analysis revealed a negative NPV of −313.724 thousand USD and a B/C Ratio of 0.87, indicating that the project would not be economically viable under static assumptions. However, after introducing managerial flexibility through ROA, additional strategic value emerged. The expansion option, which assumed scaling the project from 600 MW to 1200 MW, resulted in a ROV of 69.562 thousand USD, while the contraction option, reducing capacity to 300 MW, generated an ROV of 2.122 thousand USD. Both scenarios outperformed the static DCF evaluation, demonstrating the potential for adaptive strategies to offset some of the unfavorable baseline conditions.

The sensitivity analysis identified three strategic zones based on REC weight. The contraction zone (REC weight below 1.5), the neutral zone (REC weight between 1.5 and 3.0), and the expansion zone (REC weight above 3.01). In the contraction zone, where the REC weight drops below 1.5, the value of the project rapidly decreases, suggesting that the project is no longer viable. In this case, minimizing losses through a reduction in capacity is the most reasonable strategy. The neutral zone, where the REC weight is between 1.5 and 3.0, shows a balance between the values of expansion and contraction options, making it difficult to determine the optimal strategy. In this zone, further observation of external factors such as policy changes, electricity selling price and REC prices, and technological developments is necessary before making a decision. In the expansion zone, where the REC weight exceeds 3.01, the value of the expansion option increases significantly, indicating that large-scale expansion is the optimal strategy for maximizing the project’s economic feasibility.

The results of this study highlight the critical importance of strategic flexibility in offshore wind farm projects. A flexible project strategy, capable of responding to market and policy uncertainty, is essential for maximizing the economic viability of the project. The use of ROA overcomes the limitations of DCF analysis and enables more sophisticated investment decisions. This study provides policymakers with valuable insights into the importance of establishing a stable and predictable REC weighting system, which could enhance investor confidence and promote large-scale offshore wind farm development. For developers, integrating ROA into project appraisal offers a flexible investment path that adapts to the uncertainties of market and policy conditions, and provides a useful tool for adjusting investment timing and scale based on real-world circumstances.

Future research should expand on this analysis in two key areas. First, a more detailed sensitivity analysis of REC weights is needed to identify the optimal range that balances investor returns with policy objectives. Second, incorporating additional strategic options such as phased development, hybrid renewable energy configurations, or energy storage integration would provide a more comprehensive framework for evaluating the economic potential of offshore wind farm. Furthermore, expanding the analytical framework to include environmental and social impact metrics would strengthen the basis for sustainable decision-making.

This study demonstrates that idle coastal areas have significant untapped potential for offshore wind farm development in Korea. However, realizing this potential requires both strategic flexibility and supportive policy frameworks. By incorporating real option analysis, this research integrates site-specific advantages, market uncertainty, and adaptive operational strategies, offering a replicable analytical framework for optimizing renewable energy investments under uncertainty.