Renewable Energy Microgrids

## **Chapter 9** 100 MW Wind Turbine Power Plant

*Samuel A. Alagbada*

#### **Abstract**

Wind power production has increased by a hundredfold during the last 20 years and represents roughly 3% of the total global electricity production. In recent years, technological changes in wind turbine configurations have enabled higher capacity factors for wind turbines. The results from the studies showed that wind as a source of energy for Växjö could be explored in order to achieve the goal of energy sufficiency and as well as sustaining the greenest city status in Europe. The simulation showed that 100 MW electricity could be generated from the wind sources with respect to the available data via global wind metrological data, literature, RETScreen Expert software., LCOE and IRR analysis tools. In addition, the Internal rate of return (IRR) of 8.7% which is good enough considering the proposed energy tax, energy security and environmental benefit cost ratio as well as reduced global weighted-average levelized cost of electricity (LCOE) from wind power technology make it more attractive for investor-Växjö municipality.

**Keywords:** wind power plant, wind energy, electricity storage, RETScreen expert, LCOE

#### **1. Introduction**

The global pursuit of access to affordable, reliable, sustainable, and modern energy for all as a Sustainable Development Goal (SDG) number 7 among 193 member countries are synonymous to clean energy, carbon neutrality, efficient energy management and transition from non-renewable to a renewable source [1]. According to the long-term scenarios of the International Energy Agency identified wind and solar renewable power production as a pathway in achieving the set goals in order to avoid the worst impacts of climate change. The International Renewable Energy Agency predict that wind power will lead the way in the energy transition globally and be the prominent source as a measure to reduce carbon emissions [2].

Wind power industry has become the world's fastest growing renewable energy source [3]. More than 70 countries around the globe including Sweden, contribute to the global wind generating capacity of about 1300 TWh with an average growth rate of 25% per year globally, between the years 1994 and 2015. This correspond to roughly 3% of the total global electricity production as well as saving the planet about 1.1 billion tonnes of CO2 globally as of today [4]. In addition, in a future scenario from the International

Energy Agency (2021), wind power is forecasted to provide 18% of the total electricity globally in year 2050 with over 25 billion dollars investment as of today [4].

Wind power is placed to be one of the cornerstones of green recovery and to play an important role in accelerating the global energy transition. In the European Union, the energy generation by renewable sources has been dramatically increased to reach 740 TWh, where the energy production through wind energy is the dominant sector [5].

The Swedish Transmission System Operator (TSO) saddled with responsibility of managing the national power grid network in Sweden, envisaged the need for rapid growth of the wind power energy into the network as a bridge builder as the planned decommissioning of nuclear power plant by the Swedish government timeline of 2030 approaches [2].

Växjö municipality developed and adopted an Energy plan for Fossil -free municipality with an efficient and effective energy system. Kronoberg Energy plan 2025 (Rus -Grona Kronoberg 2025) strategy was developed at the regional level as an integral platform for the adoption of EU, Swedish national energy plan vision see Appendices 1–3 with an ambition of making the region an "Energy-plus County". The statistical data of the Växjö municipality energy balance sheet with respect to demand, supply and consumption to be 2336GWh (Renewable- 60% & Non-renewble-40%) [6, 7].

In addition, the wind power system is dominated by variable-speed among the installed wind turbines, however, technological deployment of power electronic converter, large scale energy storage regulating power, a geographical distribution of wind turbines and transmission capacity to other regions as well as configuration of the wind turbines for high wind power penetration levels are used to improve power quality [8]. The marginal production cost related to wind power are decreasing, partly driven by technology improvements and legislative treaties to achieve an overall reduction in greenhouse gases [9].

The identified factors which may influence the spot prices are electricity consumption, electricity production, electricity flow, electricity capacity, wind power production and other electricity indexes. The varying energy sport price at different region in Sweden (SE3/SE4) are synonymous to the wind production [10].

The efficiency of both land and offshore Wind power technology is synonymous to the height of the turbines tower reaching better wind conditions, the swept area generator size and rotor diameter coupled with electrical subsystems and control units. Size [9]. The literature available suggests that large-scale (WPPs) can have a significant impact on the grid [1–10].

The aim of this paper is to examine the feasibility of wind power technology in Växjö in relation, economic social and environmentally sustainable goals of being a net exporter of energy for both current and future scenario.

#### **2. Theory**

The following sections describes the energy in wind power, wind turbine, electricity storage and some economics of investments.

#### **2.1 Wind power**

Wind results from differences in air temperature, density, and pressure from uneven solar heating of the Earth's surface. Wind currents act as giant heat

*100 MW Wind Turbine Power Plant DOI: http://dx.doi.org/10.5772/intechopen.107067*

exchangers, cooling the tropics and warming the poles. The average annual wind speed, wind patterns near the ground are critical in selecting the height of the hub (center of the rotor) as well as location criteria for the installation of wind turbines. Wind shear is the change in wind speed with height, which is influenced by solar heating, atmospheric mixing, and nature of the terrain but forests and cities element tend to increase wind shear by slowing the speed of air near the surface.

#### **2.2 Wind turbine**

Wind turbine transforms kinetic energy of air currents into electrical energy. The energy is mainly extracted with the rotor, which transforms the kinetic energy into mechanical energy, and with the generator, which transforms this mechanical energy into electrical energy. It is renewable, efficient, mature and secure energy that is key to the energy transition and the decarbonisation of the economy.

A typical wind turbine consists of the following subsystems as follow in **Figure 1** [11].


Wind turbines are classified into two in relation in relation to how its spin. There are two kinds of wind turbines. Wind turbines that rotate along its vertical axis is the vertical axis wind turbines (VAWT), while the ones that spins about a horizontal axis is the horizontal axis wind turbines (HAWT).

#### *2.2.1 Wind turbine energy*

Wind energy can be described by the kinetic energy of the particles in the air. The energy content, *Ewind* in a mass of air, *mair*, and the wind speed *v*<sup>2</sup> is described as

$$E\_{wind} = 0.5 \, m\_{itr} \, . \nu^2 \tag{1}$$

*Pwind*, can be described by the energy in a volume of air that passes an area, A, and density of air (*Pair*). The density varies with the altitude and time, since it depends on the temperature and pressure.

$$P\_{wind}, \ = \text{0.5.} \space{P}\_{dir} A. v^3 \tag{2}$$

Wind power efficiency is defined as

$$
\eta = \frac{8}{27} . P\_{a\dot{r}r} . A.v^3 \tag{3}
$$

#### **Figure 1.** *A schematic diagram of a wind turbine [11].*

#### *2.2.2 Wind turbine power*

The volume of energy harvested from a turbine is a function of Wind power (*Pwind*,) and coefficient of performance (*Cp*). The coefficient of power is depending on the blade design and its configuration in relation to the blade pitch angle and the tip speed ratioð*λ*). The Optimal value of *Cp* is approximately 7 thresholds. Higher value of *Cp* above the threshold of 7 allows for better efficiency while below the threshold of 7 decrease its efficiency with increased noise, blade erosion, drag losses and increased flow around the wind turbine instead of through it.

A power curve describes the power production of a wind turbine as a function of the wind speed. Power curves could either be provided by the wind turbine manufacturer or be approximated. An approximated cubic wind power function curve, *Pcub*can be modelled for all wind speeds.

$$P\_{real}(v) = \begin{cases} 0\\ \text{0.5.} . C\_p \max\left(v\right) . P\_{air} . A. v^3 \\\ p\_r \end{cases}$$

$$\text{if } v\_{ci} > v \text{ or } v > v\_{co}$$

$$\text{if } v\_r \ge v \implies v\_{ci}$$

$$\text{if } v\_{co} \ge v \implies v\_r \tag{4}$$

where *pr* is the rated power, *v* is the rated wind speed, *vci* is the cut-in wind speed and *vco* is the cut-out wind speed usually around 3 m/s and 25 m/s respectively. A higher capacity factor and hub height are important factor for the electricity production from wind turbines.

Wind resource is a good tool for measuring the historical wind power production, wind speed at the hub height. In modelling for installation of a wind turbine, wind, a wind shear relationship is used for the data correlation at desired altitude for optimal efficiency.

$$\frac{v\_1}{v\_2} = \left(\frac{h\_2 - h\_{disp}}{h\_1 - h\_{disp}}\right)^a \tag{5}$$

where *v*<sup>1</sup> is the measured wind speed at the height *h*1, *v*<sup>2</sup> is the unknown wind speed at the hub height *h*2, *hdisp* is the displacement height, which is the height at which the wind speed is projected to be zero for modelling purposes, and *α* is the wind shear exponent, which is depending on the ground roughness.

#### **2.3 Electricity storage**

The wind power has operational risk from sudden changes in the weather that cuts supply and adversely affects grid stability. The intervention by dispatchers is alternative to compensate for the inherent unreliability of wind power. Grid storage transforms the highly variable nature of renewable power to a reliable supply under the direct control of dispatchers.

Grid storage acts as an energy buffer supplying power for current use and absorbing excess renewable power for later use. It creates value by storing wind electricity when it has little or no value and feeding it into the electricity grid when it does have value in a night and day scenario. It also acts as a grid stabiliser enhancing operating efficiency as shown in Appendix 4. The primary means today are pumped storage or gravity batteries, while the future opportunities lie with non-flow and flow batteries, with compressed air and hydrogen.

#### **2.4 Economics**

The economical potential of an investment in energy in relation to the present, and future scenarios could be analysed using economic indexes such as cash flows, the discount rate, inflation, levelized cost, risks and uncertainties of the investment. The basis for investment decisions can be made by including the variations in spot price of energy.

$$c\_{inv} = c\_{inv} \cdot \frac{r.(1+r)^{\mathcal{Y}}}{r.(1+r)^{\mathcal{Y}} - 1} \tag{6}$$

The levelized cost of electricity from a wind turbine, LCOE, expresses an equally shared production cost per unit of electricity produced by the turbine. The expression for

$$LCOE = C\_{naroO\&M} + \frac{C\_{inv} + C\_{fixO\&M}}{AEP} \tag{7}$$

where *AEP* is the annual electricity production, *CvarO*&*<sup>M</sup>* is the variable operation and maintenance (*O*&*M*) costs, and there are no fuel costs for wind power..

The yearly net profit, *Profitnet* from wind power production is defined in eq. (8). However, without taking into account regulating costs, taxes, other costs or subsidies

$$\text{Profit}\_{\text{net}} = \sum\_{k=1}^{8760} \left( \left( P\_{\text{spot}}(\mathbf{t}) - \mathbf{C}\_{\text{usrO\&M}} \right) P\_{\text{wind}}(\mathbf{t}) \right) - \mathbf{C}\_{\text{inv}} - \mathbf{C}\_{\text{fixO\&M}} \tag{8}$$

The profitability of an investment can be measured by the yearly return on investment, ROI,

$$ROI = \frac{Profit\_{net}}{C\_{inv}} \tag{9}$$

#### **3. Method**

The following chapter describes the experimental methods used to collect and analyse the data for the purpose of this paper.

This section introduces system engineering method as shown in Appendix 5 in modelling the a 100 MW capacity wind power plant for vaxjo location with geographical information shown in Appendix 6. The wind power value study is made on current power systems, electricity to be produced from wind power for the present and future in connection with a social-economic perspective.

Wind power production is estimated using wind turbine configurations, wind data from Global wind atlas [12] see Appendices 7 and 8, height of 100 M, a power curve model and other parameter values according to Eq. (1)–(5) were obtained from the Ge turbine manufacturer database [13, 14]. The Investment, Operational & maintenance costs for the wind turbines and other financial variables for the power plant is obtained from RETScreen Expert software. Post simulation analysis for LCOE and ROI according to Eqs. (7)–(9) were carried out.

The model is simulated with the RETScreen Expert software. RETScreen Expert software is a clean energy management system which intelligently enables professionals and decision-makers to rapidly identify and assess the viability of potential energy efficiency, renewable energy and cogeneration projects; and to easily measure and verify the actual and ongoing energy performance of buildings, factories and power plants around the world.

#### **3.1 Data and assumptions**

This section contains explanations of the data and the assumptions used in this case studies. The wind turbine model (50 numbers of GE Wind 1.5 s wind turbine, hub height 80 m, rotor diameter per turbine 70.5 m and swept area per turbine 3,903 *m*2. The density of air is set constant at 1*:*225 kg*=m*3. for all heights with capacity factor of 25% with 42% efficiency factor. The cut-in and cut-out speeds are set to 3m*=s* and 22*:*5m*=s*, respectively as shown Appendix 9, and the wind speeds are normalised around the average wind speed, with a standard deviation of 1m*=s* for the regional aggregation. The power curve of the turbine from the GE electric of the turbine are shown in Appendix 7.

The LCOE calculation assumptions: The Initial cost is set to \$21,000,000 and O&M cost \$7,000,000 used are those stated by the GE vendors. The economic lifetime is set to the technical lifetime of 30 years as shown in Appendix 10 [15, 16] and discount rate for the wind value study is set to 3.0% in line with other publications in this field [16]. Electrical output is assumed to be 50 Hz (European electricity grids).


**Table 1.**

*Wind power plant production target and investment.*

#### **4. Results**

In this section, the outcomes of the experiment result data were gathered for analysis. This study assesses how to optimise the revenue from wind power, in order to make profitable investment decisions through the choice of wind turbine technology. **Table 1** showed Wind power plant electricity production target, investment ROI and LCOE. The project cash flow curve, Payback period are shown in **Figure 2**.

#### **5. Discussion**

In this section, the outcomes of the experiment result data were analysis and interpreted.

#### **5.1 Choice of GE turbine**

GE Renewable Energy is one of the world's leading wind turbine suppliers, with over 49,000 units installed and generating wind electricity across the globe in providing wind energy solution that best address your challenges and priorities irrespective of the power plant location of either onshore or offshore. The high wind energy reliability as well as wind power efficiency with the use of a broad family of smart, modular turbines that are uniquely suited for a variety of wind environments has been their strength in the global player in the energy market [15]. Therefore, the choice of GE turbine for this study is appropriate in relation to Appendix 11.

#### **5.2 CO2 emissions**

The Swedish energy security scenario from 1960s up till 2018 has being implemented via an environmentally sustainable policies such as Carbon tax,

transition from non-renewable ton renewable source of energy, GHG reduction, and climate neutrality. Sweden has the highest carbon tax globally and it is charged or levied per tonne of CO2 emissions on the different fuels [17, 18]. With the proposed 100 MW wind power plant, 2.925 tons of CO2 emission would be avoided.

#### **5.3 The social cultural norms and political will**

The government and societal cultural new norms of being sustainable across board from national level to municipalities are collaborating in the pursuit of carbon neutrality goals by adopting and implementing measures, policies, legislation, and issuances of licences to potential investor in translation from non-renewable to renewable sources coupled with energy efficient system for the present and future scenario [6].

Wind power plant in Växjö would align with municipality energy plan surplus goals for export, and remain the greenest city in Europe.

#### **5.4 Technology developments**

The KTH Swedish royal institute of technology study concluded that vaxjo had a technical potential estimated of almost 9000 GWh electricity could be generated from wind power [6]. Globally, a great deal of technological progress has been made in lowering the levelized cost of electricity generated from solar and wind [19, 20] . The technical solution are evolving for the development of a super battery that can store wind electricity generated when it's not needed and act as an energy bank for dispatching when needed, also, smoothing its output as a solution to the reliability setback of wind power [21, 22]. Therefore, the SMART acronym of sustainability, reliability and affordability in the energy field could be resolved within the short- and long-term niche of technology advancement.

#### **5.5 Economics**

The Wind power plant of 100 MW with all related economical value of investment and plant production target as shown in **Table 1**. Levelized Cost of Energy (LCOE) being the minimal electricity production costs and is calculated for assessing different sources of power generation [23]. World Energy Transitions Outlook 2021 envisaged that very low-cost renewables for a speedy delivery decarbonised electricity system [24]. The global installation cost of onshore wind turbine and LCOE has being reduced by 31% and 56% respectively [19]. EIA 2020 estimates of the global Levelized cost of electricity (LCOE) and levelized cost of storage (LCOS) for onshore wind power plan has been 56\$/MWh with 10% discount rate [20]. Also, the LCOE values would be same irrespective of the size [25]. therefore, 90\$/MWh obtained from feasibility studies could be related to onshore wind power plant database from both IRENA & EIA. This study result seems are within permissible range.

#### **6. Conclusions**

The needs to be installing wind power faster over the next decade in order to stay on a net zero pathway and avoid the worst impacts of climate change is necessary.

Wind being a clean energy technology with the most decarbonization potential per MW, coupled with economies of scale in relation to the pursuit of the vision 2050 and net exporter of energy has made investment in wind power to be more attractive.

The results shows that wind power can play a major role in future power systems with zero carbon emissions.

For an optimal wind power penetration, the annual wind speed, hub height ground roughness and electricity spot price are akin to investment decision in relation to LCOE.

For the proposed 100 MW wind power plant LCOE stood at 90\$/MWh with IRR of 8.7% are feasible for an onshore wind power plant in Växjö on a social economical font so that the net exporter of electricity and energy security goal of the municipality could be accomplished and sustained.

However, a sensitivity analysis is necessary to determine how potential changes against electricity spot market prices under certain circumstances in the future scenario.

## **Acknowledgements**

My profound gratitude goes to all who has being part of this journey from inception.

## **A. Appendix 1: Swedish energy policy**

http://www.swedishenergyagency.se/.

## **B. Appendix 2: Swedish energy system 2019**

http://www.swedishenergyagency.se/.

#### **C. Appendix 3: Swedish Total energy supplied 1970–2019**

http://www.swedishenergyagency.se/.

## **D. Appendix 4: Performance of storage technologies**

See **Table A1**.


#### **Table A1.**

*Performance of storage technologies.*

#### **E. Appendix 5: System engineering model**

See **Figure A1**.

#### **Figure A1.** *Classification of energy intervention areas in the smart city.*

## **F. Appendix 6: Locational data for Vaxjo Res screen Expert**

#### See **Figure A2**.

#### **Figure A2.** *Locational data for Vaxjo Res screen expert.*

## **G. Appendix 7: Power Curve of The wind turbine GE Wind 15 s**

#### See **Figure A3**.

## **H. Appendix 8. Växjo mean power density curve (wind global atlas)**

#### See **Figure A4**.

#### **Figure A4.**

*Växjo mean power density curve (wind global atlas).*

### **I. Appendix 9. Wind speed classification for wind turbines**

#### See **Table A2**.


#### **Table A2.**

*Project table.*

#### **J. Appendix 10. Wind and solar Capacity life tine**

#### See **Table A3**.


#### **Table A3.**

*Wind and solar capacity life time [15].*

#### **K. Appendix 11. Global wind turbine manufacturer companies**

#### See **Table A4**.



#### **Table A4.**

*Global wind turbine manufacturer companies [12].*

#### **Author details**

Samuel A. Alagbada Linnaeus University, Växjö, Sweden

\*Address all correspondence to: aa224nr@student.lnu.se

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 10**
