**1. Introduction**

Although renewable energy is now a considerable element of our energy mix [1], it is predicted that it will play an even greater role in the future transition toward clean energy and sustainable power supply. Wind and solar power, two of the most important sources of the clean energy transition, suffer from intermittency and variability, which, if not addressed, could slow the pace of the climate-neutral transition and raise total energy system costs [2]. Furthermore, with increasing penetration, the variability will increase the portion of underutilized grid infrastructure, necessitating the need for grid flexibility and ancillary support. From the standpoint of market value, this may also result in lower marginal costs, which will have an impact on the project's economics [3]. During high wind periods, the marginal value of wind is expected to reduce due to the higher amount of wind energy flowing into the grid [4], and during low wind hours, the generation tends to be low to meet the demand.

In general, considering different wind regimes, wind turbines are designed to withstand a class of wind speeds as specified by International Electro-technical Commission (IEC). IEC Class I is for the high windy sites, those with an annual average wind speed of 10 m/s. Class II is designed for locations with an average wind speed of 8.5 meters per second at hub height. Class III is for even lower windy sites, with an average wind speed of no more than 7.5 m/s. Class IV was also described in older IEC Standards, which is for very low-wind sites with an average wind speed of 6 m/s, but it has been superseded by Class S. This Special class "S" turbines are with design values chosen by the designer based on site-specific conditions. It is worth noting that while the majority of class I and class II wind sites are exhausted and presently large areas suitable for class III and IV are available for development. Due to their extremely low CUF and higher LCOE, wind turbines now available in the market are uneconomical to operate in class III and IV locations. With LCOE being the prominent factor in deciding the share of the renewable mix, solar PV generation has surpassed wind generation in many countries. When these considerations are taken into account, the solution that is foreseen is the design of wind turbines with low specific power (LSP) in order to increase the deployment of wind power with reduced variability, lower LCOE, and suitability for low wind sites.

Modern-day wind turbines, especially the dominant, three-bladed, upwind turbine configuration, have undergone significant design improvements toward increased energy generation and reduced cost of energy [5]. Wind turbines have also become physically larger in several dimensions, including rotor diameter, hub height, and nameplate/rated capacity. Wind turbine design has also become tailor-made to the market environment in which the technology is going to be deployed, based on wind regimes, grid access, etc., and low specific power (LSP) turbines are considered as one of such manifestations.

As swept area increases with the square of blade length, increasing the blade length of a turbine will increase the power extraction and, with fixed generator capacity (rated power), reduce the Specific Power (SP) rating of the turbine. On the other hand, theoretically, decreasing the generator capacity while maintaining a constant swept area will also result in the desired designed specific power.

Reduced Specific Power turbines provide a number of advantages that have resulted in their widespread use in several wind markets, including India, China, and the United States, among others. Due to the larger size of the rotor, more energy can be captured when the wind moves past the blades of the wind turbine. Hence, for a given turbine generator capacity, the generator runs closer to or at its rated capacity for an increased percentage of the time duration in such reduced specific power wind turbine.

Hence, reduced specific power wind turbines naturally result in higher energy generation for their installed capacity, resulting in a higher capacity utilization factor (CUF). With the highly sophisticated control systems of modern wind turbines, this higher capacity utilization factor can often be achieved with a relatively limited impact on the overall turbine cost. In such cases, LSP wind turbines provide a direct path toward a lower levelized cost of energy (LCOE) by providing a higher generation per investment.

#### *Low Specific Power Wind Turbines for Reduced Levelized Cost of Energy DOI: http://dx.doi.org/10.5772/intechopen.103139*

Further, as reduced specific power turbines have lower-rated wind speeds, resulting in evenly distributed power production over a wide range of wind speeds, the variability of wind generation is well mitigated with such turbines. With significant penetration of RE generation into the grid, such variability management will be extremely beneficial to grid managers, and the overall energy mix can be well managed with reduced storage requirements.

Further, the energy generation profiles resulting from reduced specific power turbines have also been found to increase the wholesale market value of wind energy [6]. Turbines with reduced specific power and taller towers can be conceptually correlated with higher electricity prices in some markets by producing less during high wind hours and producing more during low wind hours [7, 8].

With such a background, this chapter analyses the LSP turbine synthesized for a target SP of close to 100 W/m<sup>2</sup> (for the study, a wind turbine with 105 W/m<sup>2</sup> is considered). Based on ground-based measurements, these LSP wind turbines are compared with other prevalent wind turbines in the Indian market with a view toward evaluating the opportunities to continue the specific power reduction in the future.

This chapter analyses the improvement in %CUF with these turbines for the IEC site classifications (i.e., high wind, medium wind, low wind, and also one of the coastal site conditions) and induces thoughts on how grid utilization will be influenced by the low-specific power turbine compared to the present-day wind turbines. In order to reduce the cost of wind turbines, the chapter further analyses the opportunity of reducing the cost of wind turbines by reducing cut-off wind speeds (varying cut-off wind speeds to 20 m/s, 18 m/s, 15 m/s, and 13 m/s) in LSP turbines as it allows the turbine blades to be lighter [9].

This chapter is expected to be useful to various stakeholders in the sector by encouraging further research in this area, as LSP wind turbines are expected to play a vital role in the wind generation fleet going forward, particularly as wind penetration increases in lower wind speed regions.

### **2. Low specific power (LSP) wind turbine**

This section explains the key properties of the LSP wind turbine, as well as the characteristics of other wind turbine types currently on the market, in greater detail. The influence of the CUF on different sites with the wind turbines under consideration is also investigated.

#### **2.1 Wind turbine characteristics**

The study covers available wind turbines with specific power (SP) ranging from 379 W/m<sup>2</sup> to 173 W/m<sup>2</sup> , which represent multi-MW scale turbine types prevalently installed in the Indian Market. These wind turbine types considered in this study, when correlated with its period of deployment, clearly show that there is a definite, reduced specific power trend in India, the United States, China, and Brazil. However, this stands in contrast to most of the European market, where the average specific power is found to be high, although it is difficult to generalize.

#### *Wind Turbines - Advances and Challenges in Design, Manufacture and Operation*


#### **Table 1.**

*Wind turbine models considered in this study.*

**Figure 1.** *Power curves considered for the study.*

**Table 1** shows the wind turbines considered in the comparison study against the LSP wind turbine model. The respective power curves (normalized) are shown in **Figure 1**. It is clearly evident from **Figure 1** that the power curve moves toward the left and is able to generate more energy at lower wind speeds, while the specific power is decreasing. To eliminate the influence of hub height variation in the comparison study, all wind turbines in the study are considered with the same hub height of 120 m.

#### **2.2 Power curve of LSP wind turbine**

The power curve of the LSP wind turbine is derived/synthesized in a unique way from one of the latest wind turbine models (SP-173) in the Indian market, not by increasing the blade length, but by reducing the generator capacity and keeping

*Low Specific Power Wind Turbines for Reduced Levelized Cost of Energy DOI: http://dx.doi.org/10.5772/intechopen.103139*

**Figure 2.**

*Comparison of SP-173 and LSP-105. (derived from SP-173 by reducing the rated capacity to 2000 kW instead of 3300 kW).*

the blade length constant. Considering the logistical and transportation-related constraints imposed by the longer blades [10], especially in complex terrain conditions, such a reverse approach seems to be justifiable. **Figure 2** shows the normalized power curves of SP-173 and LSP-105 (derived from SP-173 by reducing the rated capacity to 2000 kW from 3300 kW).

#### **2.3 Wind sites**

In order to understand the impact of Low Specific Power wind turbines on varying wind climate, four different wind sites have been chosen for the study. The sites have been chosen to represent high (High W), medium (Med W), low (Low W), and lowcoastal wind (Low coast W) regimes, considering the future wind farm development possibilities. The sites are defined by 120 m hub height wind speed data derived from one continuous year of met. Mast-based standard measurement. **Table 2** depicts the site details, wherein **Figure 3** shows the wind speed distribution of the said four sites as below:


**Table 2.** *Details of sites considered for the analysis.*

**Figure 3.**

*Wind speed distribution of the sites considered in the study. (a) Represents the histogram of high W site, wherein (b), (c), and (d) represents the histograms for med W, low W and low coast W sites.*
