Solar vs. Nuclear Power

A data-driven techno-economic feasibility study for Bangladesh's future energy portfolio, based on Monte Carlo simulations and Multi-Criteria Decision Analysis.

Introduction & Background

Context for Bangladesh

The increasing demand for sustainable, reliable, and affordable electricity in both global and developing country contexts has intensified interest in low-carbon energy technologies. Nuclear and solar power are prominent contenders in the low-carbon electricity generation mix, each exhibiting distinct advantages and challenges. Bangladesh is undergoing rapid industrialization and urbanization, leading to a significant increase in electricity demand. Current national grid capacity is approximately 15,500 MW (as of 2023), but projections estimate this will surpass 50,000 MW by 2041 to meet development goals. The government aims to diversify its energy portfolio by integrating both nuclear and renewable sources to enhance sustainability and energy security.

Solar energy offers considerable promise, with the country's technical potential estimated at over 50 GW—nearly 80% of projected demand. Decentralized solar home systems have already achieved 40% electricity access in off-grid areas, showcasing its scalability and relevance for rural electrification. Parallelly, the Rooppur Nuclear Power Plant; Bangladesh's first comprising two 1,200 MW VVER-1200 reactors, is scheduled to come online by late 2025. This analysis compares their long-term feasibility, focusing on sustainability, levelized cost of electricity (LCOE), and dynamic policy and technological factors.

Literature Review & Research Gaps

A review of existing literature reveals key insights and critical gaps in the current understanding of solar and nuclear power in the context of Bangladesh.

Nuclear LCOE: Existing literature for the Rooppur plant shows a range of cost estimates. Islam and Bhuiyan (2020) found a Levelized Unit Electricity Cost (LUEC) from $43.8 to $82.5/MWh, while other analyses place it closer to $94.8/MWh. This study's Monte Carlo simulation validates this range, yielding a mean LCOE of $47.98/MWh (90% C.I.: $31.66 - $66.96/MWh), confirming that the project's viability is highly sensitive to financing and operational assumptions.

Solar LCOE: In contrast, literature on solar PV projects in Bangladesh highlights a wide LCOE range, from as low as 2.6 cents/kWh ($26/MWh) for microgrids to 5.1 cents/kWh ($51/MWh) for floating solar. While these specific projects demonstrate solar's potential, they may not capture the full scope of a national utility-scale program. This study's simulation of such a program produces a more comprehensive mean LCOE of $81.60/MWh (90% C.I.: $69.42 - $95.03/MWh).

Technology & Risk: This finding challenges the common global narrative of solar being dramatically cheaper. For large-scale deployment in Bangladesh, our analysis reveals the technologies are remarkably competitive, with significantly overlapping cost ranges. This shifts the decision-making focus from pure cost to strategic characteristics: nuclear's reliability versus solar's rapid deployment, intermittency, and degradation risks. A crucial gap in local literature is the lack of dynamic LCOE models that account for aging and lifetime costs, which this analysis helps to fill.

Identified Research Gaps: The existing body of research reveals several key gaps that this analysis aims to address:

  • A lack of comprehensive uncertainty modeling and sensitivity analysis.
  • An absence of scenario planning that incorporates factors like carbon pricing or hybrid energy systems.
  • Sparse inclusion of detailed environmental and socioeconomic metrics.
  • An overreliance on international data due to limited local operational records.

Comparative Analysis: Solar vs. Nuclear

Strategic Comparison

Criteria Solar Energy Nuclear Energy
Capital Cost (per MW) Lower ($1-1.5M) Very High ($6-9M+)
Construction Time 1-2 years 8-10 years
Lifespan 25 years 60+ years
Baseload Supply No (Intermittent) Yes (Reliable)
Land Use High (5-10 acres/MW) Very low (< 1 acre/MW)
Lifecycle Emissions 40-60 gCO₂eq/kWh 12-15 gCO₂eq/kWh
Waste Management Minimal (Recyclable Panels) Complex (Spent Fuel)
Energy Independence Partial (Panels Imported) Low (Uranium & Tech Imported)
Scalability High (Distributed) High (Centralized)
Resilience to Crisis High (Modular) Low (Single Point of Failure)

Multi-Criteria Performance Radar

This chart visualizes the trade-offs. A larger area indicates better overall performance based on normalized data.

Scenario-Based Multi-Criteria Analysis Results

The TOPSIS method was applied independently for each strategic scenario using the weights defined in the methodology. The results reveal that there is no single "best" technology; the optimal choice is contingent on Bangladesh's overarching policy priorities.

Scenario / Core Priority Winner Runner-Up Strategic Implication
1. Energy Security & Industrialization Nuclear Energy Solar Energy When reliable baseload power and minimal land use are paramount for industrial growth, nuclear's high capacity factor makes it the superior strategic choice.
2. Rapid Electrification & Cost Focus Solar Energy Nuclear Energy If the goal is rapid, low-cost capacity expansion, solar's modularity, fast deployment, and lower LCOE make it the undisputed winner.
3. Environmental Sustainability Nuclear Energy Solar Energy When both lifecycle emissions and land use are heavily prioritized, nuclear's vastly smaller physical footprint gives it the edge despite long-term waste concerns.

National Solar LCOE Analysis (459-3000 MW)

Monte Carlo Simulation Results

Mean LCOE

$81.60/MWh

Median LCOE (P50)

$81.24/MWh

Mean Discounted Payback

10.9 years

90% Confidence Interval

$69.42 - $95.03/MWh

Solar LCOE Input Parameters for Monte Carlo Simulation
Parameter Distribution Min Mode/Most Likely Max Unit
Installed Capacity Triangular 459 2,000 3,000 MW
Capital Cost (OCC) Triangular 800 950 1,200 $/kW
Fixed O&M Cost Triangular 12 15 25 $/kW-year
Variable O&M Cost Triangular 2 3.5 5 $/MWh
Capacity Factor Beta-PERT 17 19 21 %
Discount Rate (WACC) Uniform 8 - 12 %
Degradation Rate Uniform 0.5 - 1 %/year
Construction Period Fixed - 2 - years
Plant Lifetime Fixed - 25 - years
Inverter Replacement Fixed - 10 - % of CAPEX at Year 15
Decommissioning Cost Fixed - 50 - M$/GW

Nuclear LCOE Analysis (Rooppur NPP)

Monte Carlo Simulation Results

Mean LCOE

$47.98/MWh

Median LCOE (P50)

$47.20/MWh

Financing

~90% Russian Loan ($11.38B)

90% Confidence Interval

$31.66 - $66.96/MWh

Nuclear LCOE Input Parameters for Monte Carlo Simulation
Parameter Distribution Min Mode/Most Likely Max Unit
Installed Capacity Triangular 1,070 1,818 1,944 MW
Capital Cost (OCC) Triangular 6,000 7,500 9,500 $/kW
Fuel Cost Triangular 4.5 6.38 11.2 mills/kWh
Fixed O&M Cost Triangular 6.5 8.1 14.5 mills/kWh
Variable O&M Cost Triangular 0.95 1.1 1.4 mills/kWh
Capacity Factor Beta-PERT 75 85 90 %
Discount Rate (WACC) Uniform 3 - 10 %
Inflation Rate Uniform 2 - 6 %
Construction Period Fixed - 8 - years
Plant Lifetime Fixed - 60 - years
Decommissioning Cost Fixed - 700 - M$/GW

Interactive Simulators

MCDA Feasibility Simulator

This tool simulates a fuzzy logic-based decision process. Adjust the sliders to assign importance (weight) to each criterion based on your priorities. The feasibility scores below will update in real-time, showing which technology becomes more favorable.

Live Feasibility Scores

Solar Energy 50%
Nuclear Energy 50%

LCOE Sensitivity Calculator

See how key financial inputs affect the final cost of electricity ($/MWh).

Solar LCOE
$0
Nuclear LCOE
$0
LCOE Component Breakdown

Policy Scenario Builder

See how different policies can change the economic viability of each technology.

Hybrid Energy Mix Builder

Design a national energy portfolio and see its overall performance.

Land Use Visualizer

Compare the physical footprint required for the same power output.

Solar Farm Area

Nuclear Plant Area

Daily Power Output & Grid Simulator

Model the 24-hour performance of a Solar and Nuclear mix against a typical demand curve. Add battery storage to see how it improves grid stability.

Project Payback & ROI Calculator

Estimate the financial return of a 1000 MW plant based on key inputs.

Simple Payback

8.0 yrs

25-Year ROI

213%

Dynamic LCOE Over Project Lifetime

Visualize how costs evolve over a project's lifespan due to factors like degradation and major maintenance.

Solar Parameters
Nuclear Parameters

Monte Carlo Simulator

Run a full, in-browser simulation for LCOE, NPV, and IRR.

3. Adjust Input Variable Distributions

Financial Metrics Dashboard

Analyze the long-term profitability of a 1000 MW project.

NPV

$0 M

IRR

0%

Discounted Payback

0 yrs

Project Cash Flow Visualization

Interactive Sensitivity Results (Sobol Analysis)

Explore the results of a Sobol analysis. This shows which variables contribute most to the variance in LCOE, considering both direct effects (First-Order) and interactions (Total-Order).

Methodology, Data & Assumptions

1. Analytical Framework

This study employs a multi-stage methodology to provide a robust comparison, combining quantitative financial modeling with qualitative multi-criteria analysis to account for uncertainty and diverse decision factors. The framework integrates deterministic cost modeling, uncertainty analysis using Monte Carlo simulation, global and local sensitivity analyses, scenario-based projections, and additional financial, environmental, and policy dimensions. The visual flow is as follows:

1. [Define LCOE Model & Assumptions]
↓
2. [Run Monte Carlo Simulation (10,000 iterations)]
↓
3. [Evaluate Financial & Risk Metrics (NPV, IRR, PBP)]
↓
4. [Assess Environmental & Social Impact (LCA, Jobs)]
↓
5. [Apply MCDA using AHP/TOPSIS]
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6. [Integrated Feasibility Results & Policy Implications]

2. Financial, Risk, and Sensitivity Analysis

The core of the quantitative analysis rests on a suite of financial metrics and rigorous sensitivity analyses to navigate uncertainty.

Financial Metric Formulation

LCOE: The analysis begins with a standard financial model to estimate the Levelized Cost of Electricity (LCOE), representing the average cost per megawatt-hour (MWh) over the plant's lifetime. It is calculated using the discounted cash flow (DCF) method:

LCOE = ΣTt=1 [(Iₜ + O&Mₜ + Fₜ + Dₜ) / (1+r)⁶] / ΣTt=1 [Eₜ / (1+r)⁶]
Important Limitation: Grid Integration Costs

It is critical to note that a standard LCOE represents the cost of electricity at the plant's busbar. It does not include the significant system-level costs required to integrate a power source into the grid. These costs, particularly relevant for intermittent renewables like solar, include network upgrades, ancillary services, and large-scale energy storage to ensure grid stability. Depending on the penetration level, these integration costs can add 10-50% to the "true" societal cost of solar energy, a metric sometimes referred to as the Levelized Cost of System Electricity (LCOSE).

Net Present Value (NPV): NPV sums the discounted cash flows over the plant's life. A positive NPV indicates a potentially profitable investment.

NPV = Σnt=0 [ (Rₜ - Cₜ) / (1+r)⁶ ]

Internal Rate of Return (IRR): IRR is the discount rate at which the NPV of all cash flows equals zero. If the IRR is greater than the project's cost of capital, it is generally considered a worthwhile investment.

Payback Period (PBP): PBP is the time required to recoup the initial investment cost from the net cash flows. It is a simple measure of how long it takes for an investment to generate enough cash flow to cover its initial cost. A shorter payback period is generally preferred.

Payback Period for Nuclear: While the discounted payback period for solar is calculated as 10.6 years, the payback period for nuclear is expected to be significantly longer due to its high upfront capital costs and long construction time. The exact payback period for the Rooppur plant will depend on the final electricity tariff, operational efficiency, and the financing terms of the Russian loan.

Payback Period for Nuclear: While the discounted payback period for solar is calculated as 10.6 years, the payback period for nuclear is expected to be significantly longer due to its high upfront capital costs and long construction time. The exact payback period for the Rooppur plant will depend on the final electricity tariff, operational efficiency, and the financing terms of the Russian loan.

Monte Carlo Simulation

To account for the inherent uncertainty in key input parameters, a Monte Carlo simulation was conducted. This involved 10,000 iterations for each technology, performed using Python with the NumPy and SciPy libraries.

  • Each uncertain input (e.g., CAPEX, fuel cost, capacity factor) was assigned a probabilistic distribution (Triangular, Beta-PERT, or Uniform) based on literature and expert judgment.
  • The LCOE was recalculated for each iteration, resulting in a probability distribution of potential outcomes.
  • This distribution was analyzed to derive key statistical indicators: mean, median (50th percentile), and confidence intervals (5th and 95th percentiles).
Sensitivity Analysis Techniques

Local Sensitivity (One-Factor-at-a-Time): Each input parameter was varied by ±10%, ±20%, and ±30% from its baseline to observe the resulting change in LCOE. This helps identify parameters with the most significant linear influence. A normalized sensitivity index (SI) was computed as:

SI = (ΔLCOE / LCOE_base) / (ΔInput / Input_base)

Global Sensitivity (Sobol Indices): For a more comprehensive view, a variance-based global sensitivity analysis (Sobol method) was applied using the SALib library in Python. This method decomposes the variance of the model output into fractions attributable to each input variable and their interactions.

  • First-order index (Sᵢ): Measures the direct effect of an input variable on the output variance. Sᵢ = Vᵢ / V(M)
  • Total-order index (Sᴛᵢ): Measures the total effect of an input, including all its interactions with other variables. Sᴛᵢ = 1 - V_~i / V(M)

Morris Method (Screening): This technique was used to identify non-linear and interaction effects with reduced computational cost, helping to screen for the most influential factors before applying more intensive methods.

Symbol Definitionsfor Formulas
Symbol Definition
Iₜ Investment cost in year t
O&Mₜ Operation and maintenance (O&M) cost in year t
Fₜ Fuel cost in year t
Dₜ Decommissioning cost in year t
Eₜ Electricity generated in year t
r Discount rate (WACC)
T Total operational life of the plant (years)
t Time index (year) from 1 to T
Δ Change in a variable (used in sensitivity analysis)
SI Sensitivity Index

3. Multi-Criteria Decision Analysis (MCDA)

To move beyond purely financial metrics, a hybrid MCDA approach was implemented. Recognizing that national energy strategy depends on overarching policy goals, this analysis adopts a scenario-based framework rather than relying on a single set of expert opinions. This method evaluates the alternatives under three distinct strategic priorities for Bangladesh, using the Analytic Hierarchy Process (AHP) to derive criteria weights for each scenario and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the outcomes.

Step 1: Define Alternatives & Criteria. The alternatives remain Solar Power (A1) and Nuclear Power (A2). The decision criteria (C1-C5) were selected to cover economic, environmental, social, and technical dimensions.

Code Criterion Type
C1 Levelized Cost of Energy (LCOE) [$/MWh] Cost (lower = better)
C2 Lifecycle Emissions [gCO₂eq/kWh] Environmental (lower = better)
C3 Land Use [m²/MWh] Environmental (lower = better)
C4 Job Creation [jobs/GW] Social (higher = better)
C5 Reliability [% capacity factor] Technical (higher = better)

Step 2: Define Scenarios & Assign Weights. Three strategic scenarios were defined, each with a unique set of criteria weights derived via AHP to reflect the scenario's core priority.

Strategic Scenarios for MCDA
Scenario Title Core Priority & Description
1. Energy Security & Industrialization Prioritizes stable, 24/7 baseload power to support manufacturing and reduce dependence on volatile fossil fuel markets. Reliability and low land use are paramount.
2. Rapid Electrification & Cost Focus Prioritizes adding generating capacity as quickly and cheaply as possible to meet rising residential demand and expand energy access. LCOE and deployment speed are key.
3. Environmental Sustainability Prioritizes long-term environmental goals, focusing on the lowest lifecycle emissions and minimizing the physical footprint (land and resource use).
Criteria Weights Assigned per Scenario
Criterion Scenario 1 (Security) Scenario 2 (Cost) Scenario 3 (Environment)
Reliability 40% 10% 25%
LCOE 20% 40% 20%
Deployment Speed 5% 25% 5%
Low Emissions 15% 10% 25%
Low Land Use 15% 5% 20%
Low Waste Risk 5% 10% 5%

Step 3: Construct & Normalize Decision Matrix. A decision matrix is constructed with the performance values of each alternative against each criterion. This matrix is then normalized to allow for comparison across different units and scales.

Step 4: Identify Ideal Solutions (TOPSIS). TOPSIS identifies the "ideal" and "negative-ideal" solutions. The ideal solution has the best possible values for all criteria, while the negative-ideal has the worst. The distance from the ideal solution is calculated as: Si+ = √Σ(vij - vj+)² and the distance from the negative-ideal solution is calculated as: Si- = √Σ(vij - vj-)².

Step 5: Calculate Distance & Rank. The closeness coefficient (Ci) is computed to determine the final ranking. This process is repeated for each of the three defined scenarios to determine the preferred alternative under each strategic framework.

4. Data & Assumptions

The validity of this analysis depends on the quality of its input data. The following table provides a detailed justification for the key parameters used in the solar LCOE calculations, drawing from a range of reputable local and international sources.

Input Parameter Justified Value / Range Justification & Source
Capital Cost (USD/kW) 800 - 1200 Reflects current EPC cost trends in Bangladesh. IDCOL (2021) reports utility-scale plants at ~$950-1100/kW. IRENA (2023) global average in 2022 was ~$857/kW.
O&M Cost (USD/kW/year) 12 - 25 Low operating cost due to low labor costs and minimal fuel costs. IRENA (2023), GTZ (2007), and local SREDA reports confirm this range.
Project Life (years) 25 - 30 Typical PV lifespan. NREL and IEA PVPS (2018) suggest 25-30 years for crystalline silicon PV modules.
Degradation Rate (%/year) 0.5% - 1% Typical solar panel performance degradation. Sources: NREL (2022), IEA PVPS (2018), Al Mahdi et al. (2024).
Capacity Factor (%) 17% - 21% Based on solar irradiation in Bangladesh (SREDA, 2018; ESMAP, 2021). Net generation hours range from ~1500-1800 per year depending on location.
Discount Rate (%) 6% - 10% Reflects Bangladesh's weighted average cost of capital (WACC) for energy projects, including risk premiums for political/economic volatility. Sources: ADB (2021), World Bank (2020).
Inflation Rate (avg, %) 5% - 7% Historical average inflation rate in Bangladesh over the past 15 years (Bangladesh Bank, 2023). Used to estimate real vs. nominal LCOE.
Loan Interest Rate (%) 4% - 6% Based on concessional financing and public-private models. IDCOL and ADB financing usually offer lower than market rate loans (~4.5-6%).
Loan Term (years) 10 - 20 Standard loan duration for renewable energy projects in Bangladesh. Sources: IDCOL (2021), World Bank (2020).
Equity-Debt Ratio 30:70 or 20:80 Public-private partnership projects in Bangladesh commonly use 70-80% debt. Sources: ADB, IDCOL, World Bank.
Inverter Replacement Year 15 Typical inverter lifetime is 10-15 years (NREL, 2022). This cost is factored in mid-life.
Decommissioning Cost (% of Capex) 1% - 2% IRENA and GTZ estimate minimal decommissioning costs for solar, which are typically recovered by selling recyclable materials.
Salvage Value (%) 0 - 5% Generally minimal, unless salvageable PV materials are included. IRENA suggests 0-5% is standard.

Similarly, the parameters for the Rooppur Nuclear Power Plant (NPP) were derived from project-specific data, international benchmarks for VVER-1200 reactors, and financial assessments from energy agencies. The table below outlines these justifications.

Input Parameter Justified Value / Range Justification & Source
Capital Cost (USD/kW) 6,000 - 9,500 While the Rooppur contract value is ~$12.65B for 2400 MW (~$5,270/kW), this range reflects global benchmarks for Gen III+ reactors, including potential cost overruns and financing costs. Sources: IEA (2020), Lazard (2022), World Nuclear Association.
Fuel Cost (mills/kWh) 4.5 - 11.2 Represents the full fuel cycle cost (uranium, conversion, enrichment, fabrication) for VVER-1200 reactors. The range accounts for volatility in the global uranium market. Sources: Islam & Bhuiyan (2020), EIA data.
O&M Cost (Fixed & Variable) 7.45 - 15.9 mills/kWh Nuclear plants have high fixed O&M (staffing, security, insurance). This range is consistent with data from the Nuclear Energy Institute (NEI) and OECD/NEA for large light-water reactors.
Capacity Factor (%) 75% - 90% Modern VVER-1200 reactors are designed for high availability (>90%). The lower bound accounts for scheduled refueling, maintenance outages, and initial operational ramp-up. Source: Rosatom, World Nuclear Performance Report.
Discount Rate (%) 3% - 10% This wide range is critical. The low end (~3-4%) reflects the concessional Russian loan (LIBOR + 1.75%). The high end (10%) represents the rate for a project financed on commercial terms, reflecting Bangladesh's sovereign risk.
Construction Period (years) 8 - 10 Official timeline for Rooppur is ~8 years. The range includes potential for minor delays, which are common in nuclear megaprojects globally.
Plant Lifetime (years) 60 Standard design life for a VVER-1200 reactor, with potential for extension to 80 years after major refurbishment.
Decommissioning Cost (M$/GW) ~700 A standard estimate for the discounted future cost of decommissioning a large reactor. This is a regulated fund collected over the plant's life. Sources: OECD/NEA, IEA.

Policy, Risk & Impact Analysis

SWOT Analysis

Solar PV
  • Strength: Fast deployment, low cost, scalability.
  • Weakness: Intermittency, high land use.
  • Opportunity: Falling costs, job creation, hybrid storage integration.
  • Threat: Grid integration issues, policy inconsistency.
  • Threat: High costs for grid integration and stability (storage, ancillary services), policy inconsistency, and competition for land.
Nuclear Energy
  • Strength: High reliability, baseload power, low land use.
  • Weakness: High upfront cost, long construction, waste management.
  • Opportunity: Energy security, SMR development, long-term price stability.
  • Threat: Political delays, public perception, cost overruns.

PESTLE Analysis

Factor Implications
Political Government nuclear policy, solar incentives, international relations (e.g., with Russia for fuel).
Economic Financing mechanisms, inflation, currency risk, impact on local income.
Social Public perception, safety concerns, job creation patterns (short-term vs. long-term).
Technological Innovation in SMRs, grid storage solutions, panel efficiency, degradation rates.
Legal Nuclear liability regulations, IPP frameworks, land acquisition laws.
Environmental Land/water usage, lifecycle emissions, waste disposal, biodiversity impact.

Environmental and Land Use Evaluation

  • Life-Cycle CO₂ Emissions: Based on existing LCA studies, nuclear has a lower carbon footprint (12-15 gCO₂eq/kWh) compared to solar PV (40-60 gCO₂eq/kWh).
  • Land Use Analysis: Solar requires significantly more land (3-5 acres per MW) than nuclear (<1 acre per MW). In a densely populated country like Bangladesh, this is a critical factor. GIS-based land impact mapping can be applied to assess this further.
  • Cost of CO₂ Avoidance: This metric evaluates the cost-effectiveness of reducing carbon emissions. It is calculated as: Avoided Cost = (CO₂ emissions of displaced fuel - CO₂ emissions of clean source) / LCOE difference This helps compare the economic efficiency of solar vs. nuclear in displacing fossil fuels. While not explicitly calculated in this analysis, it is a crucial metric for policy decisions.

Socioeconomic Impact

Solar PV
  • Employment Creation: Promotes labor-intensive short-term roles. Estimates suggest ~10-15 jobs/MW during construction and ~0.5-1 job/MW during operation (IRENA, 2020).
  • Community Spillover: Can increase local income and provide electricity to remote areas, fostering small enterprises. However, large-scale projects can impact land use.
Nuclear Energy
  • Employment Creation: Supports high-skill, long-term employment (~500-800 permanent jobs per plant) during its 60+ year operational life (World Bank benchmarks).
  • Community Spillover: Can lead to industrial clustering and development of skilled labor. However, it also raises safety concerns and requires robust community engagement.

Scenarios & Implications

Analytical Scenarios

To assess future competitiveness, several scenarios were defined to recalculate LCOE under different policy and technology evolutions:

  • Baseline: Current market parameters as defined in the Monte Carlo simulation.
  • High Inflation: CAPEX increased by 20%, and the discount rate increased by 2% to model a challenging economic environment.
  • Technology Advancement: Solar CAPEX is reduced by 30% and its capacity factor increases by 5% (absolute), reflecting rapid innovation.
  • Carbon Tax: A $50/ton CO₂ cost is applied to fossil fuel comparators, enhancing the competitive advantage of low-carbon sources like solar and nuclear.
Interactive Scenario Simulator

Select a scenario to see its impact on the LCOE of Solar, Nuclear, and a sample Coal plant.

Strategic & Policy Implications

  • Security of Supply: Nuclear offers energy independence from fossil fuels but creates reliance on uranium suppliers and technology partners (e.g., Russia). Solar relies on imported panels but has a more diverse and less politically sensitive supply chain.
  • Land & Cost Intensity: Solar is favored for its low cost and rapid deployment but presents significant challenges with its high land intensity in a densely populated country like Bangladesh.
  • Role in Energy Transition: The analysis provides critical data for the Power System Master Plan (PSMP2041), highlighting the complementary roles of solar (rapid, distributed, peak-shaving) and nuclear (stable, centralized, baseload) in phasing out gas and coal.
  • Future Research: Further analysis should model more complex scenarios, such as a net-zero target by 2050, high fossil fuel price volatility, and the costs of grid integration and storage solutions for intermittent renewables.

Conclusion & Strategic Implications

The Final Verdict is a Choice of Priority. While solar power offers a lower plant-level LCOE with a mean of $79.27/MWh, this figure does not account for the significant system-level costs of energy storage and grid upgrades required to manage its intermittency. The holistic analysis reveals no single "best" technology. Instead, the optimal path for Bangladesh is contingent on its primary strategic goal.

Priority:

Speed, Cost & Access

When the national objective is to deploy new capacity as quickly and affordably as possible to meet immediate demand and expand energy access, the conclusion is unequivocal.

Solar Energy is the decisive winner.

Priority:

Reliability, Industry & Land Use

When the objective is to guarantee long-term energy security, power industrial growth with 24/7 baseload electricity, and conserve land, the verdict is equally clear.

Nuclear Energy is the superior strategic asset.

Final Recommendation: A Strategic Hybrid Portfolio

The most robust path forward for Bangladesh is not an "either/or" decision but a pragmatic synthesis of both technologies. The nation should pursue a dual-track strategy:

Leverage Solar for rapid, distributed deployment

+

Invest in Nuclear for stable, long-term baseload power

Abbreviations

AHP

Analytic Hierarchy Process

BDT

Bangladesh Taka

CAPEX

Capital Expenditure

CDF

Cumulative Distribution Function

CRF

Capital Recovery Factor

DCF

Discounted Cash Flow

GIS

Geographic Information System

GIZ

Deutsche Gesellschaft für Internationale Zusammenarbeit

IEA

International Energy Agency

IEEFA

Institute for Energy Economics and Financial Analysis

IPP

Independent Power Producer

IRENA

International Renewable Energy Agency

IRR

Internal Rate of Return

LCA

Life-Cycle Assessment

LCOE

Levelized Cost of Electricity

LUEC

Levelized Unit Electricity Cost

LIBOR

London Inter-bank Offered Rate

MCDA

Multi-Criteria Decision Analysis

NREL

National Renewable Energy Laboratory

NPV

Net Present Value

O&M

Operations and Maintenance

OCC

Overnight Capital Cost

PBP

Payback Period

PDF

Probability Density Function

PESTLE

Political, Economic, Social, Technological, Legal, Environmental

PSMP

Power System Master Plan

PV

Photovoltaics

ROI

Return on Investment

SMR

Small Modular Reactor

SREDA

Sustainable and Renewable Energy Development Authority

SWOT

Strengths, Weaknesses, Opportunities, and Threats

TOPSIS

Technique for Order of Preference by Similarity to Ideal Solution

UNDP

United Nations Development Programme

VVER

Water-Water Energetic Reactor

WACC

Weighted Average Cost of Capital

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