Construction of an NPP is highly capital intensive and have a long construction period. Investment costs include cost of site preparation, construction, manufacture, and commissioning of reactors. Fixing investment costs mainly depend on site characteristics, type of technology with safety features, manpower, materials, regulatory requirements, and localization of technology. It is the major percentage (70%) of the lifecycle costs of an NPP and major decision making matrices for taking a project by the policymakers. The cost of capital of an NPP is a function of the financial risk associated with the project investment (Carelli and Ingersoll 2014, Barkatullah and Ahmed 2011, Xoubi 2019, IAEA 2017).

O&M activities refer to the day-to-day operations of the plant. The assumption of O&M costs is a very important factor to estimate the NPV and IRR accurately. Early on, low O&M costs used to be considered in nuclear economics. But this assumption was proven wrong in the late 1980s and early 1990s when a small number of US NPPs were retired for the high O&M costs compared with gas power plants (EIA 1994). This happened due to the rise of uranium prices in the global market. For economic analysis, O&M costs can be assumed from the OECD/NEA (2005, 2015) or globally benchmarked data (Paks II 2015). O&M costs vary with country specific, region specific, size of a reactor, efficiency of the plant, safety features, and its major components comprising staff costs, material costs, contractor services, and taxes, etc. It owes about 15% of the lifecycle costs (IAEA 2017).

The fuel cost refers to mining, conversion, enrichment, and fabrication, which is called the front-end fuel cycle. Most of the NPPs operating countries do not have their own fuel cycle capabilities. The Aszódi report (2014) mentions fuel costs as one of the key variable costs in the formulation of the project’s LUEC and it is about 15% of the lifecycle costs (IAEA 2017). Fuel costs can be assumed from the globally benchmarked data. Although the LUEC values of NPPs are relatively insensitive to changes in fuel prices as it is almost stable in the international market compared with fossil fuels. A strategic approach needs to be developed for a fuel cycle policy. In order to have a more competitive and secured fuel supply management, an owner-operator can contract with multiple vendors and of course need to be made long term agreements.

The decommissioning costs include all costs related to the plant’s shutdown to the dismantling of nuclear and non-nuclear structures, systems, and components phase by phase. It also includes radioactive waste management and disposal including spent fuels that will arise during the operation lifetime and dismantling of the plant after its service life. According to the World Nuclear Association data, the decommissioning cost is assumed to be about 9–15% of the total capital cost of an NPP (OECD/NEA 2016). The plant owner has to accumulate this decommissioning fund during plant operation.

The LUEC/levelized cost of electricity (LCOE) is equivalent to the generation costs of electricity at the plant level that would have to be paid by the consumers to repay exactly all costs for investment, year-wise O&M costs, fuel costs, and decommissioning costs with a proper discount rate and without considering profits. It can be said in another way that LUEC is the minimum average busbar costs/selling price in which an owner-operator would precisely break-even on the project after paying all necessary expenses over its operating lifetime. This economic indicator is called a lifecycle costs of an NPP and is expressed in energy currency ($/kWh) (Mignacca and Locatelli 2020). Equation (1) can be used to calculate the LUEC without considering the cost of carbon (IAEA/NES 2018).

$\mathit{\text{LUEC}}=\frac{\sum _{t=tc}^{Lifetime}\frac{\text{(}{\mathit{\text{Investment cost}}}_t\text{+}{\mathit{\text{O\&M cost}}}_t\text{+}{\mathit{\text{Fuel cost}}}_t\text{+}{\mathit{\text{Decommissioning cost}}}_t\text{)}}{\text{(1+}\mathit{\text{r}}{\text{)}}^t}}{\sum _{t=1}^{Lifetime}\left(\frac{\mathit{\text{Annual electricity generation}}}{\text{(1+}\mathit{\text{r}}{\text{)}}^t}\right)}$ (1)

Where t; the expected lifetime of the plant (year);

t_c: the duration of construction (year);

r: annual discount rate (%);

Annual electricity generation in MWh

Here it is worthy to note that LUEC is not a complete and absolute method of assessing the economic benefits of an electricity generating source because it excludes the true reflection of market realities and network costs of a power system. In the case of nuclear power generation, the LUEC is strongly dependent on investment costs, O&M costs, and fuel costs (Lovering et al. 2016, Barkatullah 2011, Mignacca and Locatelli 2020).

The discount rate is possibly one of the most critical parameters of the economic and financial analyses of a power generating plant. It varies by country, technology, and finance specifics. LUEC is sensitive to change in the discount rate i.e. the interest rate used to calculate the present value of future cash flows. The choice of the discount rate depends on a number of factors such as, competitors, power market policy, and investor (who determine the required rate of return). In many review studies, the discount rate was arbitrarily chosen as 5% and 10% (Larsson, 2014). British economist Dimson (1989) shows in his study that the discount rate for a new NPP after tax should be 11%. In an open electricity market, building and operating an NPP is risky as cost recovery is not guaranteed. In this context, for evaluating an NPP project profitability, the energy information administration (EIA)/USDOE (1994) is proposed to take a discount rate of 10% in real terms (the 3% risk-free return plus a 7% risk premium) (IAEA/NES 2018).

Uncertainty between the plant’s idealized and realized capacity factor is a very important issue for economic and financial analyses of an NPP project (Yangbo and John, 2010). It indicates the operating performance of a plant under many O&M challenges. Usually, a plant running at a higher PCF incurs a lower unit production cost compared to a plant running at a lower PCF. The global average PCF for NPPs is about 85% (Paks II, 205). However, it took much effort to achieve such a high average PCF.

The NPV is the difference between the present value of net cash inflow(revenues) and net cash outflow (expenditures). It is used in capital budgeting to analyze the profitability of an investment or project and is expressed in [$]. For an investment project, raising the discount rate tends to reduce the NPV. This parameter is multiplication between net cash flow and discount factor (Mignacca and Locatelli, 2020). Equation (2) can be used to calculate the NPV;

$\mathrm{NPV}=\sum _{\mathrm{t}=1}^{\mathrm{T}}\frac{{\mathrm{C}}_{\mathrm{t}}}{(1+\mathrm{r}{)}^{\mathrm{t}}}+{\mathrm{C}}_{\mathrm{o}}$ (2)

Where, C_t = net cash flow during the periods ($) t, C_o = total initial investment costs ($), r = discount rate, (1+r)^t = discount factor, and t = number of time periods.

NPV is used as an indicator for viability of a project as follow;

NPV = positive value (+), Project feasible /can be accepted, higher NPV is better;

NPV = negative value (-), Project not feasible /cannot be accepted;

NPV = zero (0), neutral value/break-even (no profit or no loss).

The IRR is the discount rate at which the NPV of net cash flow (both positive or negative) from a project or investment equals to zero. It is also used to evaluate the viability of a project or investment and is expressed in dimensionless indicator [%]. When the IRR of a new project exceeds its required rate of return, the project is desirable. On the other hand, if IRR falls below the required rate of return, the project is not financially desirable (Mignacca and Locatelli, 2020). IRR can be calculated using Equation (3).

$NPV=0=C_0+\frac{C_{t1}}{(1+IRR{)}^1}+\frac{C_{t2}}{(1+IRR{)}^2}+\frac{C_{t3}}{(1+IRR{)}^3}+\cdots +\frac{C_{tn}}{(1+IRR{)}^n}$ (3)

Where C₀ = total initial investment costs ($), C_t1, ... C_tn equals the net cash flow during the periods 1, 2, 3, ... n, respectively.

Feasibility criteria of IRR gives indication as follow;

IRR > wanted discount rate (r), project feasible /accepted;

IRR < wanted discount rate (r), project not feasible /not accepted;

IRR = wanted discount rate (r), project not feasible /not accepted.

The PBP is a duration needed to return the investment cost, which is calculated from net cash flow. Net cash flow is a difference between the revenue and expenditures every year. PBP is an indicator of how many years are needed for the project to cover the total investment costs. Equation (4) can be used to calculate the PBP.

$t=PBP$ (4)

Where t = time (yr), PBP = Payback period (yr), B= benefit of profit ($), C₀ = total investment costs ($)

If the projects were constructed within the 5–6 years, the payback period would be usually within 7–9 years (Paks II 2015).

Now it is understood from the theoretical discussions that the LUEC, NPV, IRR, and PBP indicators are used to find out the competitiveness of an NPP project with other power generating sources in order to ensure the profitability. Kharitonov and Kosterin (2017) develop analytical relationships between the investment performance criteria (LUEC, NPV, IRR, discounted PBP, discounted costs) and basic engineering - economic parameters (capital costs, annual operating costs, annual revenue, construction duration, operating lifetime) of an NPP for measuring the profitability and competitiveness at the microeconomic level.

OECD/NEA (2007) predicts the LUECs and other financial risks of the Gen IV reactors with other energy sources and finds highly competitive in the international energy markets. Lucheroni and Mari (2014) suggest careful use of LCOE when someone estimates the performances of the lifecycle costs of a new NPP and compare it with other power sources as these are not homogeneous in nature. LCOE value for NPPs works as an asset to reduce the dynamics of fossil fuels and carbon prices in the volatile power markets (Mari, 2014). While calculating the LUEC, NPV, and IRR for modeling the economics of a new NPP, these indicators are found to be heavily dependent on realized input data. Winkler and Streit (2008) find the economic profitability of the three NPP projects at Beznau, Muhlenberg, and Niederamt in Switzerland. The LUEC of the Swiss operating NPPs is about 2.4 cents/kWh. IAEA/INPRO (2013) finds the economic viability of the Belarus NPP project by evaluating the LUEC, IRR, return of investment, and investment volume indicators. Paks II NPP project of Hungary is also found economically viable by evaluating LCOE, NPV, IRR, and PBP parameters (Paks II 2015).

The model for Financial Analysis of Electric Sector Expansion Plans (FINPLAN) is a world-wide recognized financial modeling tool, which is used for financial analysis of electricity generation projects (IAEA 2009). Inputs for the FINPLAN modeling tool were divided into four headings; cost related data, technical data, economic and fiscal parameters, and financial data. Cost-related data included investments, O&M costs, fuel costs, and decommissioning costs. Technical data involved plant’s power generation capacity, construction period, commercial operational year, plant lifetime, and PCF. Economic parameters referred to revenues, expenditures, inflation, exchange rates, taxes, etc. Financial parameters included credits, loans, bonds, and equity, etc. Figure 1 shows how the FINPLAN modeling tool converts from input into output parameters for each year.

The model provides outputs as cash flows, balance sheet, financial ratios, NPV, IRR, etc. Foreign currency, exchange rate, and inflation rate were considered as the important parameters in financial analysis. As such, the FINPLAN modeling tool allows options for considering one or multiple foreign currencies in the financial analysis. In the data on a product sale/purchase, the FINPLAN modeling tool needed the number of units of electrical energy to be sold per annum and the unit electricity selling price data over the plant’s economic lifetime. Nine different postulated scenarios were created for the calculation of financial and economic analysis of the Rooppur NPP project. Based on the fixed cost financial contract, plant data, general data, and some assumptions on O&M costs, fuel costs, decommissioning costs, and PCFs, etc. LUEC, NPV, IRR, and PBP were calculated for each case study.

Nine case studies were modeled based on the plant’s technical, economic, financial data, and a few assumptions for calculating the financial and economic aspects of the Rooppur NPP project. In this regard, Table 1 shows a summary of some key plant technical, financial, and economic input data. Brief descriptions of these data are given in the following sections.

4.2.1 Plant technical data

According to Table 1 and Figure 2, the Rooppur NPP project comprises the twin unit of VVER-1200 model reactors with 1200MWe electric capacity each. In this calculation, the construction time was taken as 6-year while the plant economic lifetime was considered as 60-year. The first commercial operations of both units are expected to be in 2023 and 2024 respectively.

The construction, commissioning, and commercial operation schedule of the unit-1 and unit-2 of VVER-1200MWe capacity of each reactor are shown in Figure 2. Schedule test operation of the unit-1 is going to be held in 2022 and the unit-2 in the later year. The nuclear power company of Bangladesh limited is a public limited one who is the operator of the Rooppur NPP.

4.2.2 Economic and financial data

4.2.2.1 Investment costs and its terms & conditions

According to the financial contract, Russia has agreed to provide11.38 billion USD as a State credit with an interest rate of Libor plus 1.75% and capped at 4%. This covers 90% of the total investment costs of 12.65 billion USD. This State credit is to be repaid over a period of 28 years. The government of Bangladesh provides the remaining 10% i.e. USD1.27 billion of the total investment costs (WNA 2020, Akbar 2017, Rahman 2016). The inflation rate was taken to be changed at a steady rate of 6% per year against the local currency (Bangladeshi Taka-BDT; 1USD = 80 BDT). For the USD foreign currency, the inflation rate changes at a steady rate of 2% per year. Among the four available depreciation calculation methods, linear depreciation was chosen to calculate the total depreciation over the depreciable life of the plant for simplicity. In this calculation, the depreciation time was considered over the total economic life of the plant e.g. 60-year.

4.2.2.2 Operation & Maintenance (O& M) costs

In the case of the Rooppur NPP project, it was not publicly available to get the actual data of O&M as well as fuel costs from the financial agreement between the Russian Federation and Bangladesh (Akbar 2017). Under this situation, we searched for global benchmarked data. Table 2 shows the global NPP O&M costs and fuel costs data used in different economic studies. Under different studies, the O&M costs and fuel costs data are not varied except OECD/NEA (2005). In the OECD/NEA (2005) studies, a high variation is found in both O&M and fuel costs data. In the Hungarian economic study on the Paks II NPP project, they took the global average benchmarked data (Paks II 2015).

In line with the global cost trend data, in our analysis, assumptions of O&M costs for low and high case scenarios were considered as 7.82$/MWh and 14.5$/MWh respectively. The variation of O&M costs from low to high case scenarios is about 45%. The assumed O&M costs data for the high case scenario is close to the global average data.

4.2.2.3 Fuel costs

In the case of fuel costs, the OECD/NEA’s (2005) low and high benchmarked data are 4.49$/MWh and 19$/MWh respectively while the global average data is 6.28$/MWh. In this analysis, a low and a high value of fuel costs were assumed as 4.5$/MWh and 11.2$/MWh respectively. The variation in fuel costs from low to high cases is at about 40%. The high-end fuel cost is about double to the global average data but close to the OECD/NEA average data. Russia will provide the up-to-date efficient fuels at the international market price for the entire operating lifetime of the two units of the Rooppur NPP according to the fuel supply contract (TVEL 2019). The fuel reloading cycle will recommence in every 18 months.

4.2.2.4 Decommissioning costs

In this study, a fund amounting to 1 billion USD which is equivalent to 9%, was considered for decommissioning cost in order to dismantle the two units after the end of its 60-year economic service life.

4.2.2.5 Discount rate

The discount rate was set to 10% for the nine case studies where the foreign loan interest rate is to be not more than 4%. Reasons for fixing a high discount rate for a developing economic country like Bangladesh are manifold;

(i) Quick return of investment (shorter payback period) for higher LUECs

(ii) Operational uncertainty (a high gap between demand-supply)

(iii) High inflation rate

(iv) Socio-political uncertainty and natural calamities prone country

(v) Country’s high infrastructure development cost than the neighboring countries

(vi) Possibility of high opportunity costs of 10% government fund

(vii) Possible accidents and liability.

4.2.2.6 Plant capacity factor (PCF)

In this study, four different PCFs were considered as 75, 80, 85, and 90%. However, the design PCF of the VVER-1200 is 90% and the global average PCF is 85% (Paks II 2015). It is noteworthy that the average PCF of fossil fuel-based power plants is below 50% in Bangladesh (BPDB 2018–2019). The reasons for this low PCF are due to interrupted primary fuel supply, grid instability, insufficient grid network, poor management, and less consumption of electricity during the lean period. In such a situation, Rooppur NPP may not be an exceptional one. For this, a 50% PCF was considered in a worst-case scenario to predict a high perceived risk.

The variation of NPV and IRR are plotted by varying the selling price of electricity at nine different postulated scenarios for twin units. Figures 3 and 4 depict the variation of NPV and IRR with the selling price of electricity at low O&M costs and fuel costs with four different PCFs of 75, 80, 85, and 90%. These curves are plotted by taking a gradual increment in each step of 0.125 cents/kWh (BDT: 0.1 taka/kWh). It is found in Fig. 3 that for the four case studies of 1 to 4, LUEC values stand to 4.95, 4.75, 4.60, 4.37 cents/kWh at which NPV=0. It is also seen above or below those LUEC values, NPV becomes positive or negative. At NPV=0, the total revenue (cash inflows) is equal to the total expenditures (cash outflows) of which is the break-even or minimum selling price of electricity of the project. When the selling price of electricity drops below those LUEC points, NPV becomes negative, which results in a net loss of the project. From the IRR perspective, as shown in Fig. 4, at NPV=0, the threshold IRR stands to 14.30, 17.67, 17.00, and 13.24% at four case studies of 1–4 respectively which is higher than the discount rate (10%) of the project. With the increase in the selling price of electricity as well as the PCF, a small variation of IRR is found for all the cases. However, it reaches up to 20%. On the other hand, below those LUEC values, IRR becomes less than the discount rate (10%) which is risky for the project. It can be worth mentioned here that for high investment and long operating lifetime of an NPP, a higher IRR is not expected but an attractive NPV is expected steadily over a long time of the plant. Among the four case studies, case study-4 is found better in terms of the selling price of electricity where LUEC stands to 4.37 cents/kWh at a high PCF of 90% and low O&M costs and fuel costs. However, such a high PCF matters only 12% on the LUECs. In this analysis, the relationship between NPV vs selling price of electricity and IRR vs selling price of electricity appear non-linearity as the inflation rate for both foreign and local parts of 2% and 6% respectively in which is far from the discount rate (10%).

The case studies of 5–8 (Figures 5 and 6) are drawn to see the variation of NPV and IRR with a selling price of electricity considering a high O&M cost of 14.5$/MWh and a high fuel cost of 11.2$/MWh. Graphs NPV vs. selling price of electricity (Fig. 5) and IRR vs. selling price of electricity (Fig. 6) are plotted with a gradual increment in each step of 0.127 cents/kWh (BDT: 0.1 taka/kWh).

Considering 54% increased plant O&M costs, 40% increased fuel costs, and keeping the same PCFs compared with the four low case cost studies of 1–4, the LUEC values at the four case studies of 5–8 stand to 6.37, 6.16, 5.94, 5.75 cents/kWh respectively at which NPV=0. And then, above those LUEC points, NPV becomes positive and below those LUEC points, NPV becomes negative. Even though for considering such high O&M costs and fuel costs over the 60-year lifetime of the plant, the variation of IRR over the LUECs is found insensitive which means O&M costs and fuel costs do not much affect generation costs if the discount rate, investment costs, and construction time remain fixed. With the increase of O&M costs and fuel costs, only a slight variation of the unit selling price of electricity (≅ 1cent) is found in comparison with the low O&M costs and fuel costs scenarios. And no major variation is found in the NPVs amongst all the case studies. From these findings, it can be said that levelized generation costs of an NPP do not depend much on O&M costs and fuel costs as these are contributing small portions of the lifecycle costs of the plant.

Figures 7 and 8 show the selling price of electricity considering high O&M costs, fuel costs, and very low PCF of 50%. Under this extreme situation, a high LUEC value of 8.25 is found at which NPV=0 with the threshold IRR value of 14.1%. Considering such an extremely low value of PCF, it impacts on the LUEC value but it does not impact on the IRR. This 50% low PCF can be thought of due to a shortage of electricity transmission network, grid instability, failure of major electrical equipment (generator, transformer, etc.), and inefficient fuel management during the operation lifecycle of the plant for an inexperienced and low technologically advanced countries like Bangladesh. In summary, the LUEC values are found in the range of 4.37–8.25 cents/kWh at nine postulated case studies. The case study-9 anticipates the worst possible scenario. However, the estimation of LUEC under the worst case scenario shows good agreement with the LUEC estimations of Sieed et al. (2015) and Rahman (2016). Furthermore, LUEC predicted by Bazlul and Iftekher (2017) shows disagreement with our estimations.

Figure 9 shows the comparison of LUEC values of the Rooppur NPP project with other available power generating sources in Bangladesh. According to the BPDB’s 2018–2019 annual report, LUEC values for its own plants varied from 3.13 to 54 cents/kWh. The LUEC value of 3.13 cents/kWh is the cheapest for the indigenous gas based power plant. However, future electricity costs from gas source will not be cheap as indigenous gas supply has been decreasing gradually and the shortfall will be filled in by imported LNG. Coal based power plant shows little bit high cost due to mixed mode coal supply from home and abroad. Heavy fuel oil (HFO) as well as diesel fuels which are used in power generation in both government and independent power producers (IPPs) show a high rate of electricity generation because of being costly imported oil (BPDB 2018–2019). Hence power generation from the Rooppur NPP project shows very much cost competitive with gas, coal, and imported electricity from India except oil and solar based power generating sources.

Figure 10 compares the LUECs of Bangladesh, Belarusian, and Hungarian NPP projects with the other two baseload power sources such as gas and coal. The LUEC calculated by Sieed et al. (2015) using the INPRO model shows slightly high for the Rooppur NPP project compared with coal and gas fired power plants. In our analysis, the FINPLAN model predicts a lower estimation of LUEC for an NPP. The reasons for variation in LUECs are due to considering overnight construction costs of $5000/kWe instead of lifecycle costs. In the case of the Belarusian NPP project at Ostrovets, IAEA calculation using the INPRO model shows slightly high electricity costs for the coal and gas fired power plants in comparison with nuclear (IAEA/INPRO 2013). Costs of electricity both nuclear and coal are found almost the same trend during the economic evaluation of the Hungarian Paks II NPP project. Three countries are constructing the same reactor model, electric output, similar financial terms and conditions, and same vendor country i.e. Russia. Among the three NPP projects, the costs of the Rooppur NPP project both low and high end cases show the most competitive, attractive, and risk acceptable compared with coal and gas fired power plants.

Figure 11 shows the LUECs of global NPPs. The highest lifecycle cost appears in the UK. The US and some European countries are found in similar trends in nuclear power generating costs while South Korea and China are found to be the lowest generation costs. Bangladesh stands to China and South Korea, and about half the global average unit generation costs (9.59 cents/kWh).

Figure 12 shows the project cumulative cash inflows that is loan drawn during the construction period (2017/18–2022/23) and the revenue earning from electricity sales at eight postulated case studies excluding the worst case scenario (Case-9). The breakeven point at four case studies of 1–4 is found to be in 2028 and the rest four case studies of 5–8 are found to be in 2029. The project will have cash inflows from the electricity sales in the same amount of its total investments within 7–8 years after the start of commercial operation of the two units in 2023 and 2024. Sensitivity analysis indicates that the return of investment of the project is not overly sensitive to the PCF over the operational lifetime of the plant. For a higher LUEC, quicker PBP is expected for a discount rate of 10% and a plant lifetime of 60-year.

Retained earnings (cumulative loss/profit) over the whole 60-year operation lifecycle of the plant at eight different postulated scenarios is accumulated to be 15.72 to 192.96 billion USD respectively. The revenue generated at eight cases during the operational period is anticipated to be sufficient to cover the annual cost of O&M including the funding of waste management, decommissioning, and the payment of taxes. This can be seen in Fig. 13 that the profit is adequate to enable the State to cover the cost associated with repaying the financial credit agreement and to receive investment.