Techno-Economic and Environmental Impact Analysis of Large-Scale Wind Farms Integration in Weak Transmission Grid from Mid-Career Repowering Perspective

 

Abstract

Repowering a wind farm enhances its ability to generate electricity, allowing it to better utilize areas with high mean wind speeds. Pakistan’s present energy dilemma is a serious impediment to its economic development. The usage of a diesel generator as a dependable backup power source raises the cost of energy per kWh and increases environmental emissions. To minimize environmental emissions, grid-connected wind farms enhance the percentage of wind energy in the electricity system. These wind generators’ effects, on the other hand, are augmented by the absorption of greater quantities of reactive electricity from the grid. According to respective grid codes, integration of commercial onshore Large-Scale Wind Farms (LSWF) into a national grid is fraught with technical problems and inter-farm wake effects, which primarily ensure power quality while degrading overall system operation and limiting the optimal use of attainable wind resources. The goal of this study is to examine and estimate the techno-economic influence of large-scale wind farms linked to poor transmission systems in Pakistan, contemplating the inter-farm wake effect and reactive power diminution and compensating using a range of voltage-ampere reactive (VAR) devices. This study presents a partial repowering technique to address active power deficits produced by the wake effect by raising hub height by 20 m, which contributed to recovering the active power deficit to 48% and so reduced the effects of upstream wind farms. Simulations were conducted for several scenarios on an actual test system modeled in MATLAB for comparative study using capacitor banks and different flexible alternating current transmission system (FACTS) devices. Using the SAM (System Advisor Model) and RETscreen, a complete technical, economic, and environmental study was done based on energy fed into the grid, payback time, net present value (NPV), and greenhouse gases (GHG) emission reduction. The studies suggest that the unified power flow controller (UPFC) is the optimum compensating device via comparison analysis as it improved the power handling capabilities of the power system. Our best-case scenario includes UPFC with hub height augmentation, demonstrating that it is technically, fiscally, and environmentally viable.   

1. Introduction

Energy is a very important factor in ensuring economic and social growth. Resources and the environment are becoming more significant constraints on energy generation as fossil fuels deplete and the threat of climate change intensifies. The major problems that the globe faces today are environmental security, energy resource conservation, and sustainable energy production. Because of population growth and industrialization, power consumption is continually increasing. The only solution to this catastrophe, to develop sustainable energy, is all over the world. Wind energy has grown rapidly during the previous two decades, with a global total installed wind power capacity surpassing 733 GW in 2021. Because of the unpredictable and stochastic nature of wind, the vast quantity of wind power generation poses significant problems to the steady functioning of power networks. Many hurdles face the integration of LSWF into national transmission systems in many countries, including technical, economic, and environmental considerations

2. Energy Analysis

To assess the available wind sources on the location, examining the density of wind energy helps determine how much energy is accessible in the area to transform wind energy into electricity. The formula may be used to calculate the wind energy per unit area (A) in W/m2.

P=12ρv3$$$$

The wind power can be computed using the Weibull probability density function [65].

PA=12ρ∫∞0v3f(v)dv=12ρC3Γ(1+3k)$$$$

where 

ρ$$ is the air density at sea level at 15 °C and 1.225 kg/m3 atmospheric pressure, f(v) is the probability of wind speed, v is the wind velocity, and k is the shape and scale parameters. Γ$$ is the gamma function.

The following formula is used to get the adjusted monthly air density (kg/m3):

ρ=P¯¯¯T¯¯¯Rd$$$$

where T¯¯¯$$ is the monthly average temperature of air in (K°), P¯¯¯$$ is the monthly average pressure in Pascals, and Rd$$ represents dry air gas constant. The density of the air will decrease as height and temperature increase [66].

By using (2) and (3) the wind energy density E for a given time T may be computed using (4).

E=P×T$$$$

The following formula may be used to assess the annual generation of a wind power system:

Eu=∑u=020P×P(ϰ)×8760$$$$

P(x) is the probability of a wind speed of (m/s) occurring each year, as defined by

P(ϰ)=(kc)×(ϰc)k−1×e−(ϰC)k$$$$

where k$$ denotes the form factor defined by local meteorological circumstances, and c$$ denotes the scale factor defined by wind speed.

Temperature, pressure, and other losses are considered when calculating the restructured yearly generation Es$$ (kWh).

Es=EU×P1×T0 P0×T×(1−λc)×(1−λS)×(1−λm)×(1−λd)$$$$

where P1$$ is the air pressure (kPa) of the wind turbine at the location, P0$$ is the standard atmospheric pressure (101.3 kPa), T$$ is the temperature (K) of the wind turbine at the location, and T0$$ is the standard absolute temperature (288.1 K), λc$$ represents the array loss factor (valued at 3%), λS$$ represents the airfoil loss factor (valued at 2%), λm$$ represents the miscellaneous loss factor (valued at 2%), and λd$$ represents the downtime loss factor

3. Methodology

The proposed methodology seeks to fill in the gaps in prior research on onshore LSWF grid integration studies. In recent research, a noteworthy attempt was made to handle PQ and Q compensation difficulties with capacitor banks and different (FACTS) devices for a specific WF. However, the research did not consider the LSWF’s techno-economic analysis or environmental effect. The objective of the comparative performance evaluation is to address LSWF repowering owing to wake effects as well as a cost–benefit analysis of PQ improving system stability using capacitor banks and FACTS devices such as SVC, STATCOM, SSSC, and UPFC. On a technological level, the approach is expected to give a beneficial midterm solution for expensive long-term TN reinforcements.

4. Simulations, Results, and Discussions

This scenario does not consider the wake effect. This instance is subjected to a techno-economic examination of the determination given to NEPRA to establish a baseline for comparison with the remaining possibilities in this research. FFCEL has acquired 1.5 MW Nordex S77 wind turbines with hub heights of 80 m for their project. Thirty-three of these turbines add up to a total plant capacity of 49.5 MW. The projected energy output is 143.559 GWh with a capacity factor of 33.11%. The local bank’s loan is USD 106 million with a ten-year term, and the equity from the entire cost of the project is USD 28 million with a six-year term. The O&M cost is USD 3 million/annum. To compensate for Q, the SCV device has been placed by FFCEL. The payback period for this scenario is calculated to be 5.4 years. The SPP on equity is 6.259 years, which is approximate to the proposed determination. The IRR calculated is 14.45%, and the NPV at the end of the project life is calculated as USD 209,996,976. This scenario’s total cash flow is USD 907 million