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The Multi Year Tariff Order (MYTO) is the Nigerian Electricity Regulatory Commission (NERC) pricing framework for determining the Nigerian Electricity Supply Industry (NESI) pricing model. One of the objectives of the NERC’s MYTO pricing model is to ensure regulated electricity end user tariff without compromising return on investment. Achieving this objective is imperative to attract investors in the growing Nigerian electricity market. However, NESI has hitherto been faced with challenges ranging from its inability to provide sufficient power to its customers to not being viable enough to provide return on capital invested. In this paper , sensitivity analysis of power plant operation and performance parameters on the cost of electricity (CoE) generation using MYTO (power generation) pricing model were evaluated. Thermodynamic modeling and simulation of an open cycle gas turbine (OCGT) was carried out to augment scarce data on power plant performance and operation in Nigeria. Sensitivity analysis was carried out using probabilistic method based on Monte Carlo simulation (MCS) implemented in commercial software (@ Risk ® ). The result highlighted sensitivity of the model input parameters to cost of electricity generation based on technical and financial assumptions of MYTO model. Seven most influential parameters affecting generation cost were identified. These parameters and their correlation coefficients are given as: 1) foreign exchange rate, 0.76; 2) cost of fuel, 0.51; 3) thermal efficiency, -0.23; 4) variable operation and maintenance cost, 0.22; 5) fixed operating and maintenance cost, -0.03; 6) capacity factor, -0.02; and 7) average capacity degradation, 0.01. Based on the gas turbine engine and input parameter distributions statistics for this study, the generation cost lies between 9.84 to 15.45 N /kWh and the probabilities of CoE within these values were established.

Proper energy mix with adequate production is a target of every nation in achieving her energy demands and meeting stringent emission requirement. One of the major reasons Nigeria is less attracted to start a new business or expansion of existing business is unacceptable blackouts in national grid which impact customers, particularly commercial and industrial users; almost causing them to go entirely off grid [

Nigeria with low energy generation capacity and inadequate energy generation mix alike is facing multi-dimensional challenges in meeting her energy requirement of at least 10,000 MW. Hence, the Nigeria government in a bid to reposition the electric sector in 2005 came up with Electric Power Sector Reform Act (EPSRA, 2005) to boost energy generation and distribution capacities in the country. This leads to privatization of two segments of its electricity industry; generation and distribution sectors, anticipating that investors can take over and produce sufficient power to meet growing electricity needs of Nigerians. The transmission segment is still under monopoly (controlled by the government); while retail is coordinated within the distribution companies (Discos). Though the transmission and distribution infrastructures are insufficient to evacuate the proposed 10,000 MW and require attention. However, generation remains the most challenging presently. Transmission problems can be somewhat taken care of upon introduction and increased number of distributed or embedded power generation. The NESI is majorly characterized by centralized power plants, which feed into the national grid and the power is then apportioned to different distribution companies through the Nigerian bulk electricity trading (NBET) company. The current long distance transmission lines leads to technical losses, increased externalities and poor voltage profile which results to higher final tariff payable by the end user. Ensuring improved efficiency, access and reliability of the energy industry as well as establishing a competitive market in the country, requires investors in the core areas, especially in the generation segment of the industry. Thus, a visible and consistent polices to guarantee capital recovery, returns on investment for investors while maintaining affordability and quality of service to consumers must be in place [

The Section 76 (6) of the EPSRA Act, 2005 empowers Nigerian Electricity Regulatory Commission (NERC) to establish tariff methodologies for regulating electricity prices in Nigeria. NERC in 2008 introduced a pricing model, the so called ‘‘Multi Year Tariff Order (MYTO)’’ as the framework for determining the Nigerian Electricity Supply Industry (NESI) pricing structure. Close to a decade of the MYTO model introduction, the NESI has hitherto been faced with numerous challenges ranging from its inability to provide sufficient power to its customers to not being viable enough to provide return on capital invested. According to Iwayemi, 2008 [

The MYTO review is a critical step towards electricity sector viability and immediate growth [

Typically, techno-economic models like MYTO developed under several influences ranging from lack of stability of macroeconomic assumptions; unstable marketing performance; underutilization of available power plant technology; and volatile government policies will be prone to uncertainties with issue of risk allocation. Thus, the economic and technical performance of power plant in this situation carries several uncertainties arising from number of sources Sensitivity study is important for accessing the robustness and appraisal’s conclusion [

The suboptimal performance described as nonviable enough to provide return on capital invested in Nigerian electricity generating (Gencos) sector as part of challenges facing NESI is evaluated in this paper. The sensitivity analysis of the so called “Nigerian Electricity Generation Charges Multi-Year Tariff Order” financial model is evaluated against typical natural gas fired combustion turbine (CT) or open cycle gas turbine (OCGT) performance. This was done considering the performance parameters envisaged to be affected with the operational condition of the power plant experienced in Nigeria today.

There are three MYTOs; one for each of the sectors in the NESI namely: generation, transmission and distribution/retail. In preparing the MYTO pricing model, NERC had proclaimed the basic targets were: fairness; lowest possible regulated end user tariff without compromising return on investment; simple, transparent and non-excessive regulatory costs. The commission used the building block approach in determining the regulated prices for transmission, distribution and retail tariff path. The building blocks approach is based on projection of performance, believed to have a combined positive attribute of rate of return regulation and price caps. However the methodology for generation is different and employs the long run marginal cost (LRMC) method. The three standard building blocks used in the MYTO development as narrated by Ransome Owen [

The MYTO model is subject to short term minor and long term major reviews. The model since its creation in 2008 (MYTO-1) had seen two major reviews in 2012 (MYTO-2) and 2015 (MYTO-2015). However, the generation tariff order did not see a major change in the MYTO 2015. The current major review for MYTO-2015 tariffs is based on a ten (10) year tariff path (2015-2024). Section 7 of the MYTO-2 mandates the commission to carry out bi-annual minor review for generation, transmission and distribution tariffs. This stated clearly the four parameters to be considered in the review as: foreign exchange rates, gas prices, rate of inflation and generation capacity. Price variation of plus or minus five percent (± 5%) in any of these parameters will lead to the model review. whilst the MYTO major reviews is said to encompass a comprehensive review and overhaul of the overall assumptions in the model. However, this paper is concerned with the generation tariff order or generation charges multi-year tariff order. This is the regulated prices to be paid to licensed electricity generation companies (Gencos) in providing electricity to distribution and retailing companies (Discos). The generation tariff order is divided into two parts; one representing the proclamation of the order and other representing the basis of the other. The detailed information of this generation tariff order is presented in NERC document NERC/GL115 [

Based on cheap price of natural gas in Nigeria, NERC determined that the lowest-cost for new entrant generator will be an open-cycle gas turbine (OCGT) using natural gas. Cost of electricity (CoE) from this technology according to NERC is lower than combined-cycle gas turbines (CCGT) and coal-fired generation. This trend is predicted to change in near future as gas price becomes market-based; then CCGT is likely to emerge as the benchmark for a lowest-cost new entrant generator. Following the establishment of the OCGT or combustion turbine (CGT) as the new-entrant generator, benchmark for MTYO generation tariff was developed upon this technology for gas fired power plant. The initial method adopted here was to allow the NESI move to a market-based industry, where generators and electricity retailers will be free to contract with each other for supply of electricity. This employed the vesting contract envisaged to be a measure to enable transition to a competitive wholesale market. The Long Run Marginal Cost (LRMC) method which involves calculating the full life cycle cost of the lowest-efficient-cost new entrant generator was used. This was applied in two ways via:

1) benchmark costing: this creates a proxy for the market price which an efficient generator is expected to operate below and

2) individual LRMC for each generator: this sets prices for each generator according to its plant and site specific costs.

The individual (site-specific) LRMC model requires each new entrant Independent Power Producer (IPP) to apply to the NERC for approval beyond the MYTO benchmark.

The benchmark generation cost of an OCGT power plant is price required over the plant life to cover all the cost components as follows: capital cost, fuel cost, tax, transmission costs, return on capital, operating and maintenance cost both fixed and variable (with appropriate costs components escalated). These together with other factors to include capacitor factor, thermal efficiency (heat rate), plant internal energy use (auxiliary requirement) and availability form the input parameters for the LRMC financial model. The unit CoE generated is fined by calculating a price that makes the net present value of the power station equal to zero. The list of assumptions for building the LRMC financial model is depicted in

Parameters | MYTO 1 | MYTO 2 | ||
---|---|---|---|---|

New Entrant | Successor Gencos | New Entrant | Successor Gencos | |

Installed Capacity, MW | - | - | - | - |

Capacity factor, % | 70 | 63 | 80 | 65 |

Marginal Loss Factor, % | 8.05 | 8.05 | 8.05 | 8.05 |

Thermal efficiency, % | 34 | 34 | 32 | 32 |

Auxiliary requirement, % | 1 | 1 | 2 | 2 |

Construction period, years | 2 | 2 | 3 | 3 |

Plant economic life, years | 20 | 20 | 20 | 20 |

Capital cost, US$/kWh | - | - | - | - |

Fuel cost, US$/mmBtu | 1.8 | 1.8 | 1.8 | 1.8 |

Fixed O & M cost, ₦/MW/Year | - | - | - | - |

Variable O & M cost, ₦/MWh | - | - | - | - |

Availability, % | 95 | 95 | 95 | 95 |

O & M costs escalation , % | - | - | - | - |

Capital cost escalation , % | - | - | - | - |

Fuel cost escalation , % | - | - | - | - |

Corporate tax rate, % | 32 | 32 | 32 | 32 |

Gearing ratio (debt/equity) | 70:30 | 70:30 | 70:30 | 70:30 |

Risk-free rate , % | 18 | 18 | 18 | 18 |

Nominal cost of debt, % | 24 | 24 | 24 | 24 |

Nominal return on equity , % | 29 | 29 | 29 | 29 |

Nominal before tax WACC, % | 25 | 25 | 25 | 25 |

Nominal after tax WACC, % | 17 | 17 | 17 | 17 |

Real pre-tax WACC, % | 11 | 11 | 11 | 11 |

Real after tax WACC, % | 7 | 7 | 7 | 7 |

WACC = R d × D D + E + R e × E D + E (1)

In which R_{e} is the nominal cost of equity; D is the total market value of debt; E is the total market value of equity and R_{d} is the nominal cost of debt, given as:

R d = R f + D R P + I C (2)

DRP is the debt risk premium; IC is the debt issuance cost lending in Nigeria; and R_{f} is the risk free rate observed in the market. Equations (1) and (2) calculate weighted average cost of capital (WACC) without tax and return on equity respectively.

R e = R f + β e ( R m − R f ) (3)

R_{e} is the return on equity; β e is the correlation between the equity risk and overall market risk; R m is the return on the market portfolio; and ( R m − R f ) is the market risk premium. The nominal post tax WACC and real pre tax WACC are calculated with Equations (4) and (5) respectively.

NominalposttaxWACC ( w ) = R e × E V + R d × ( 1 − T c ) × D V (4)

RealpretaxWACC ( RW ) = [ 1 + ( w / ( 1 − T c ) ) / ( 1 + i ) ] − 1 (5)

Here V is the total market value of the business, i.e. debt plus equity; T c is the company tax rate; and i is the inflation rate.

As shown in

Design Point Parameter | Values |
---|---|

Fuel heating value, LHV (MJ/kg) | 49.7365 |

Ambient pressure (kPa) | 101.325 |

Pressure ratio | 18.2 |

Ambient temperature (K) | 288.15 |

Turbine entry temperature, TET (K) | 1420 |

Turbine speed [rpm] | 3000 |

Mass flow (kg) | 650 |

Ambient Relative humidity (%) | 60 |

Shaft power produced (MW) | 202.11 |

Specific fuel consumption (kg/kWh) | 0.19 |

Fuel flow (kg/s) | 10.76 |

Exhaust gas temperature (K) | 754.68 |

Exhaust gas (kg/s) | 653.03 |

Thermal efficiency (%) | 38.4 |

Description | Units | OCGT (New Entrant) |
---|---|---|

Installed capacity | MW | 250 |

Capital cost | US$/kWh | 1200 |

O & M Cost (Fixed) | ₦/MW/Yr | 2,496,000 |

O & M Cost( Variable) | ₦/MWh | 920 |

Capacity Factor | % | 80 |

Auxiliary Requirement | % | 2 |

Economic life | Year | 20 |

Construction period | Year | 3 |

Sent-out efficiency | % | 34 |

Availability | % | 95 |

Fuel costs | $/mmBtu | 1.8 |

Description | Units | OCGT (New Entrant) | Distribution Type |
---|---|---|---|

Capital cost | US$/KWh | 1033 | Triangular |

O & M Cost (Fixed) | ₦/MW/Yr | 2,496,000 | Triangular |

O & M Cost( Variable) | ₦/MWh | 920 | Triangular |

Capacity Factor | % | 80 | Triangular |

Sent-out efficiency | % | 34 | Triangular |

Availability | % | 95 | Triangular |

Fuel costs | $/mmBtu | 1.8 | Triangular |

Capacity degradation | % | 1.0 | Triangular |

Forex | $ | 168 | Triangular |

reference value and converted to 2016 value of money using Equation (6). Similarly, Equation (7) was used to convert 250 MW plant capacity cost components of

P R = P O × ( priceindexatreferenceyear priceindexduringtheoriginalprice ) (6)

P E R = P R o × ( S / S o ) f (7)

The sensitivity of input parameters (both technical and economic) to OCGT generation cost in the MYTO generation financial model is presented.

The three input parameters (thermal efficiency, fixed O & M cost, and plant capacity factor) (

Parameter | Statistics | |||||||
---|---|---|---|---|---|---|---|---|

Min. | Max. | Mean | Mode | Median | Std. Dev. | Skewness | Kurtosis | |

Capital cost | 173,544 | 300,000 | 224,180 | 198,996 | 220,085.79 | 27,305.24 | 0.474 | 2.40 |

O & M cost (Fixed) | 72,741.5 | 90,015.6 | 78,499.5 | 72,741.4 | 77,800.96 | 4071.56 | 0.57 | 2.40 |

O & M cost( Variable) | 1386.3 | 2386.3 | 1719.6 | 1386.3 | 1679.14 | 235.70 | 0.57 | 2.40 |

Sent-out efficiency | 19.0% | 32.0% | 26.3% | 28% | 26.6% | 2.72% | -0.34 | 2.40 |

Fuel costs ($/mmBtu) | 1.8 | 3.0 | 2.2 | 1.8 | 2.15 | 0.28 | 0.57 | 2.40 |

Capacity factor | 35% | 80.0% | 65.0% | 80% | 66.8% | 10.61% | -0.57 | 2.40 |

Capacity degradation | 1.0% | 3.0% | 1.7% | 1.0% | 1.6% | 0.47% | 0.57 | 2.40 |

Forex | 161.0 | 420.0 | 300 | 320 | 304.5 | 53.32 | -0.22 | 2.40 |

Depreciation | 5.0% | 8.0% | 6.0% | 5.0% | 5.9% | 0.71% | 0.57 | 2.40 |

Pre-Tax Real WACC | 11.0% | 15.0% | 12.3% | 11.0% | 12.2% | 0.94% | 0.57 | 2.40 |

Corporate tax rate | 32.0% | 40.0% | 34.7% | 32.0% | 34.3% | 1.89% | 0.57 | 2.40 |

CoE (₦/kWh) | 9.84 | 15.45 | 12.06 | 12.02 | 12.03 | 0.98 | 0.28 | 2.81 |

For instance, considering many factors that can affect thermal efficiency (part- load operations, high ambient temperature, component degradations), it means is quite sensitive to the overall CoE, since it will be almost impossible for it not to vary in the present power plant operations trend in Nigeria. Seemingly, power plant thermal efficient monitoring becomes essential for viable economic sustenance of given plant. To this end and given the present power plant operation

with protracted part-load, it is recommendable for gas turbines engines with flexible efficiency in part-load operations like the GE LMS 100 among others be considered.

Invariably part-load operations will have multiple effects as it will directly affect both efficiency and capacity factor. Impact of plant degradation is further emphasized by the average capacity degradation correlation coefficient. This highlights the important of gas turbine engine’s inlet air filtration application to limit fouling.

The probabilistic representation of CoE is useful in making decisions as it gives information on the probability of energy cost within the cost range in the probability distribution function. In

In this study, the probabilistic approach has been applied to study and discuss the sensitivity of selected input parameters to CoE generation in the MYTO generation tariff order financial model of NERC. The combined effect of protracted

part-load operation and increasing ambient temperature on the thermal efficiency of gas turbine engine was evaluated. The importance of gas turbine engines with flexible efficiency in part-load operations has been highlighted. Some selected input parameters prone to uncertainties in MYTO generation tariff order model were evaluated. The major input parameters with high uncertainty were revealed and their correlation coefficients determined. The probabilistic approach gives a better understanding of the CoE variations and to what extent it will go beyond a given expected value. The statistics of CoE in ₦/kWh is given as: minimum cost, 9.84; maximum cost, 15.45; mean cost, 12.06; mode, 12.02; median, 12.03; and standard deviation, 0.98 with cost distribution skewness and kurtosis of 0.28 and 2.81 respectively. To some extent, these results hinge on the assumption of this analysis, above all on the probabilistic distribution chosen for the MYTO model input parameters considered. This study will help investors and policy makers to take into account these variables in future development of MYTO pricing model and in achieving optimized and realistic model.

Anosike, N.B., Dara, J.E., Ngwaka, U.C. and Enemuoh, F.O. (2017) Analysis of Nigerian Electricity Generation Multi Year Tariff Order Pricing Model. Energy and Power Engineering, 9, 541-554. https://doi.org/10.4236/epe.2017.910038