**Energy, Economic, and Environmental Evaluation of a Proposed Solar-Wind Power On-grid System Using HOMER Pro**®**: A Case Study in Colombia**

**Farid Antonio Barrozo Budes 1, Guillermo Valencia Ochoa 1, Luis Guillermo Obregon 2, Adriana Arango-Manrique <sup>3</sup> and José Ricardo Núñez Álvarez 4,\***


Received: 17 March 2020; Accepted: 29 March 2020; Published: 2 April 2020

**Abstract:** The electrical sector in the Caribbean region of Colombia is currently facing problems that affect its reliability. Many thermo-electric plants are required to fill the gap and ensure energy supply. This paper thus proposes a hybrid renewable energy generation plant that could supply a percentage of the total energy demand and reduce the environmental impact of conventional energy generation. The hybrid plant works with a photovoltaic (PV) system and wind turbine systems, connected in parallel with the grid to supply a renewable fraction of the total energy demand. The investigation was conducted in three steps: the first stage determined locations where the energy system was able to take advantage of renewable sources, the second identified a location that could work more efficiently from an economic perspective, and finally, the third step estimated the number of PV solar panels and wind turbines required to guarantee optimal functioning for this location using, as a main method of calculation, the software HOMER pro® for hybrid optimization with multiple energy resources. The proposed system is expected to not only limit environmental impacts but also decrease total costs of electric grid consumption from thermoelectric plants. The simulations helped identify Puerto Bolivar, Colombia, as the location where the hybrid plant made the best use of non-conventional resources of energy. However, Rancho Grande was found to offer the system more efficiency, while generating a considerable amount of energy at the lowest possible cost. An optimal combination was also obtained—441 PV arrays and 3 wind turbines, resulting in a net present cost (NPC) of \$11.8 million and low CO2 production of 244.1 tons per year.

**Keywords:** solar energy; wind energy; energy efficiency; environmental impact; economic evaluation; on-grid system; HOMER Pro software

#### **1. Introduction**

The continuous increase in greenhouse effects in the energy field [1,2], the potential danger that represents the future of this trend, and the continuous rise in this kind of energy production boost the development of new trends of energy generation (NTEG), which lead to energy transition [3–5]. NTEG will be independent of hydrocarbons and fossil fuels because research on clean energy acquisition methods is on the rise, offering reliable solutions to the current problem [6,7]. Solar and wind energy systems are the most selected methods for clean energy production because of their viability and easy acquisition [8]. In 2006, the World Energy Outlook estimated that energy production could be duplicated in 25 years. Furthermore, the publication also expects a growth of 57% in renewable energy production [9]. The boost from new trends in renewable energy generation takes into consideration the current development of technologies that can be used to obtain energy from the movement of the sea waves, as well as ocean currents. There are other ways to obtain renewable energy, one of which is the photovoltaic (PV) system. Asia Pacific is estimated to remain the global PV leader in 2025 with the largest installed solar PV facility in the world [10]. The PV market has grown in both developed and developing countries, implying that renewable energy is a viable global resource. The world's largest PV energy production from an installed plant is located in Pavagada solar park in India. The park, which can generate up to 2 GW, was fully functional in 2019 [11]. A report in the Journal of Geophysical Research estimated that the highest reachable capacity of wind energy around the world is approximately 72 million GW, which corresponds to 500% more than the energy consumption of every kind of power [12].

Hybrid energy, which is the use of different kinds of energy, is more efficient than conventional energy generation. The availability of wind energy in Colombia, combined with biomass energy, has had a significant influence on the Caribbean region [13]. The exploitation of this source of energy can be an excellent solution to the energy problems prevalent in the region [14]. This solution lies in the design of a hybrid renewable energy plant that has the capacity to use all the renewable energy resources existing in this region [15].

However, fossil fuels are still considered the main energy source in the region, although they cause considerable damage to the environment by the high generation of greenhouse gases [6,16]. The use of fossil fuels by "fracking" increases greenhouse gases and other gases like arsenic and mercury [10]. Greenhouse gas emissions increased by at least 70% in the period between the 1970s to the beginning of the 21st century, with the energy sector being the main responsible factor [17]. An analysis by the Oak Ridge National Laboratory (Tennessee, United States) on carbon dioxide emissions states that greenhouse gasses in Colombia increased from 16 megatons to 84 megatons (80%) in the 1960s to 2014; it was compared to Portugal, Finland, Chile, Austria, Sweden, Ireland, and Hungary [18].

At present, energy potential (wind and solar energy) in the Caribbean Colombian region is going to waste, more specifically in La Guajira department; these forms of renewable energy can help mitigate greenhouse emissions and increase electricity generation. With hybrid systems, operating costs are reduced because they do not require as much maintenance as conventional energy generation methods, and owing to a learning curve that helps people understand how this technology operates [19].

The Hybrid Optimization forMultiple Energy Resources (HOMER) software provides the necessary tools to establish different simulations with multiple energy resources [20] and study behavior over time. This enables the simulation of a hybrid power plant with twice or more renewable resources. This academic tool can be used to determinate viability from economic and environmental perspectives and/or energy generation systems. Therefore, HOMER is ideal for this research work.

The main contribution of this research is to highlight the renewable energy potential in the Caribbean region of Colombia (more specifically in La Guajira), showcase the possibilities to meet the growing energy demand, and offer renewable energy as a great solution to the region's problems. The following sections of the paper are organized as follows. Section 2 provides the legal and regulatory framework for renewable energy projects, a description of the available sources in different locations of La Guajira, a scheme of the proposed system, and the planning undertaken for this research. Section 3 provides the theory and equations required to implement the study. Section 4 presents the results of the simulations with a complete analysis.

#### **2. Contextualization and Required Information**

This section highlights the Colombian policy associated with renewable integration in the electrical grid. It also gives geographical details of the different weather stations located in La Guajira, Colombia. The tables and graphs presented in this section contain relevant data obtained by the meteorological stations over 20 years. In addition, this research work's planning process is described.

#### *2.1. Political Context*

Colombia has a legal and policy framework that helps justify the development of this research work; it is shown in Figure 1 [21]. It is important to note that the legal framework is responsible for establishing limits and penalties for all future projects. An essential point of the policy is to provide economic benefits for projects that meet their guidelines and contribute to the development of new trends—in this case, the energy sector. The benefits include reduction in taxes, such as the cost of importing energy generating devices from renewable sources, and return on investment in net income coming directly from the state. In some countries, the production of clean energy in homes is promoted through incentives for those who provide this type of resource as a surplus in the community power grid.

**Figure 1.** Timeline of the legal and regulatory framework for energy management in Colombia [20,21].

#### *2.2. Energy System Scheme*

The data were obtained from different meteorological stations located in La Guajira department, Colombia, as shown in Figure 2. There are nine stations dedicated to collecting temperature, wind speed, and solar radiation data. The data of pressure, relative humidity, and temperature in Figure 2 correspond to average yearly values.

In order to provide accurate results and establish the ideal location to use solar and wind energy, it was necessary to obtain data from each of the meteorological stations and pinpoint their specific locations [22].

**Figure 2.** Geographic locations of the different meteorological stations in La Guajira, Colombia.

After studying the problem mentioned in this study, the creation of a hybrid wind and solar power generation plant was proposed to tap into the energy potential (Figure 3). This plant is proposed to have wind turbines 80 m in rotor height and with output power of 1.5 MW each, and an inverter module system like the Goldwind PMDD 1.5 MW Wind turbine [23]. A set of PV arrays (1kW per array) with 4 PV modules of 250 W each and a converter module system were proposed to work together with the turbine's wind power to take advantage of the high energy potential in the area. The map is divided into two different zones to establish areas influenced by wind speed and zones where potential renewable energy is higher and meteorological stations are located.

**Figure 3.** Proposed energy system scheme.

#### *2.3. Planning*

The investigation was carried out in three stages. The first stage involved the analysis of data collected from the weather stations over 10 years. The data concerning energy production and renewable fraction were studied without considering the costs on the system in an effort to estimate locations where renewable energy sources could be used in higher proportions.

The second stage sought to determine the most efficient location for a hybrid energy system that uses both wind and PV systems, working at the same time. This stage focuses on an economic perspective to study the results of total energy production, fraction of renewable energy, and aspects such as total net present cost (NPC) and CO2 production to determine a location with optimal behavior.

The third stage determined the most efficient arrangement of wind and PV technologies working together, that is, the ideal number of wind turbines and PV panels depending on the energy demand and characteristics of the selected location. Finally, the grid power that the plant could develop, and its optimal composition was determined.

The first and second stages were calculated using simulations made in HOMER Pro software. The third stage used an optimization process through the MATLAB optimization function called Optimtool and the TOPSIS method for Pareto optimization. The data was used in the MATLAB curve fitting complement to determine the corresponding function for two main optimization objectives (energy cost and CO2 emissions).

#### *2.4. Renewable Energy Resource Data*

Figure 4, and Tables 1 and 2 show the curves corresponding to wind speed in different locations at an altitude of 80 m with the corresponding temperature and solar radiation matrices for nine measurement points during a year, where AP means "Almirante Padilla" influence zone, and PB means "Puerto Bolivar" influence zone.

**Figure 4.** Wind speed graph for different measurement points in La Guajira [13].


**Table 1.** Temperature [◦C] matrix for the different measurement points in La Guajira [22].

**Table 2.** Solar radiation [Wh/m2day] matrix for the different measurement points in La Guajira [17].


It is important to highlight the need to consider temperature in this case study. La Guajira, being a coast, is one of the warmest places in the Colombian territory. In addition, PV panels have a deficit when the temperature over them is too high. Furthermore, the physical properties of wind see a negative change with temperature increase. This reduces the amount of energy generated from PV arrays and wind turbines. Therefore, the temperature factor in the simulations was not considered, as it could represent possible incorrect results in this investigation.

#### *2.5. Forecasting of Energy Demand*

Thermoelectric plants are essential in the Colombian energy dispatch. However, large quantities of fossil fuels are required in thermoelectric plants, which means high and continuous operating costs, in addition to the high production of polluting gases and the legal consequences [24].

For this reason, it is necessary to implement a hybrid renewable energy generation plant that can replace a high percentage of the energy produced in thermoelectric plants. It would help in reducing the use of thermoelectric plants and facilitate their operation. If optimal operation of the hybrid plant is found, it could guarantee supply security and even enable selling of the remaining energy to the electric grid.

It was necessary to determine energy demands as a function of time, as shown in Figure 5. The energy that can be produced in thermoelectric plants is then compared with that produced by wind and solar systems. The effect of temperature is taken into account. Table 3 shows the parameters used in this research.

**Figure 5.** Seasonal profile of energy demand used in this study.

**Table 3.** Different parameters requested by HOMER Pro® for the specific economic and power calculations, where O&M means Operation & Maintenance and \* indicates an estimated value.


#### **3. Methodology**

#### *3.1. Wind Speed Estimation*

The Weibull probability distribution was implemented to estimate the most probable wind speed in different locations using maximum and minimum values. A random variable *x* has a Weibull distribution if its probability density function is given as shown in Equation (1) [25].

$$f(\mathbf{x}; a, \theta) = \begin{cases} \frac{a}{\|\theta^a \cdot \mathbf{x}^{a-1}\|} \exp\left[-\left(\mathbf{x}/\theta\right)^a\right] & \mathbf{x} \ge 0\\ 0 & \mathbf{x} < 0 \end{cases} \tag{1}$$

The parameters α and θ [26] are estimated with experimental data. Depending on their values, Equation (1) can obtain the form of Equation (2), called the function of the probability density of the Rayleigh distribution:

$$f(\mathbf{x}; \sigma^2) = \frac{\mathbf{x}}{\sigma^2} \cdot \exp(-\mathbf{x}^2 / 2\sigma^2) \quad \mathbf{x} > \mathbf{0} \tag{2}$$

In this case study, the factors of the different types of distribution expressed monthly for the locations of Puerto Bolívar and Almirante Padilla are presented in Table 4. On the other hand, Figure 6 shows the different velocity distributions of wind speed.


**Table 4.** Multi-annual adjusted distributions of wind speed for Puerto Bolivar (PB) and Almirante Padilla (AP), from 2003 to 2013 [22].

**Figure 6.** Adjusted frequency distributions of wind speed for Puerto Bolivar, (**A**) January, (**B**) February; and Almirante Padilla, (**C**) January, and (**D**) February.

The data for wind velocity were taken at a height of 10 m. Hellman's exponential law was used to determine the average wind velocity at any altitude:

$$V\_{\mathbf{h}} = V\_{\mathbf{10}^\circ} (h/\mathbf{10})^\mu \tag{3}$$

where, *Vh* is wind velocity at the required altitude h, *V10* is the wind velocity at altitude of 10 m, and μ is the Hellman exponent, which varies with the roughness of the terrain [27]. These values were found to be 0.28 and 0.14 for the locations of Almirante Padilla and Puerto Bolívar airport, respectively [13].

#### *3.2. HOMER Economic Analysis*

In the principal cost analysis, total net present cost *(NPC)* and cost of energy *(COE)* are determined using Equation (4).

$$\text{NPC}(\\$) = \text{TAC} / \text{CRF} \tag{4}$$

where, *TAC* is the total annualized cost and *CFR* is the capital return factor, calculated using Equation (5).

$$\text{CRF}(\\$) = i \cdot (\mathbf{1} + i)^N / \left[ \left( \mathbf{1} + i \right)^N - \mathbf{1} \right] \tag{5}$$

where, *N* is the number of years, and *i* is the annual range of real interest [%]. The cost of energy COE is the average unit cost of energy produced [\$/kWh], and is determined using Equation (6).

$$\text{COE}(\\$/k\text{Wh}) = \text{C}\_{\text{tot.ann}}/\text{E} \tag{6}$$

where, *Ctot.ann* is the total annual cost, and *E* is the total energy consumption per year.

*3.3. HOMER Estimation of the Output Power of PV Panels*

Equation (7) is used to determine the output power of PV panels:

$$PPV = \Upsilon p \cdot f\_{PV} \cdot \left(\frac{G\_T}{G\_{T,STC}}\right) \cdot \left[1 + \alpha p \cdot \left(T\_\ell - T\_{c,STC}\right)\right] \tag{7}$$

where, *Y*PV is the nominal capacity of the panel matrix, *f* PV is the reduction factor of the panels, *GT* is the incident solar radiation in the PV matrix in the current time step, *GT,STC* is the radiation incident in standard conditions, α*<sup>P</sup>* is the power temperature coefficient, Tc is the temperature of the PV cell in the current time step, and *Tc,STC* is the temperature of the PV cell under conditions of a standard test.

#### *3.4. HOMER Estimation of the Output Power of Wind Turbines*

Equation (8) is used to determine the output power of wind turbines.

$$P\_{WTG} = \left(\frac{\rho}{\rho\_0}\right) \cdot P\_{WTG, STP} \tag{8}$$

where *PWTG* is the output power of the wind turbine, *PWTG,STP* is the output power of the wind turbine at standard conditions, ρ is the actual density of air, and ρ<sup>0</sup> is the density of air at standard conditions.

#### *3.5. Curve Fitting and Multi-Objective Optimization of the Forecast*

With tabulated data, curve fitting using regressions is necessary; thus, it is important to have the best mathematical function to make forecasts. Optimtool was thus implemented to create an optimization process using the previously found function and determine Pareto's efficiency as a way of plotting the results. Pareto's efficiency shows a set of solutions delimited by the values closest to the origin coordinate if it is a minimum optimization; or, on the contrary, if it is a maximum optimization, the solution is delimited with the farthest values. The study then proceeded to use the multiple criteria method called Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [28]. This method selects one of the values obtained in the Pareto efficiency, using the closest distance of any data from the lower vertex delimited by both ends of the graph. This technique uses the following equation:

$$\begin{aligned} d\_{\rm ix} &= \sqrt{\sum\_{j=1}^{n} \left(\mathbf{t}\_{ij} - \mathbf{t}\_{\mathbf{x}j}\right)^2}, \quad i = \mathbf{1}, \mathbf{2}, \dots, m \\\ d\_{\rm iy} &= \sqrt{\sum\_{j=1}^{n} \left(\mathbf{t}\_{ij} - \mathbf{t}\_{\mathbf{y}j}\right)^2}, \quad i = \mathbf{1}, \mathbf{2}, \dots, m \end{aligned} \tag{9}$$

where *dix* and *diy* are the distances from the selected point *tij* to the ideal positive point *txj*, and the ideal negative point *ty*j, respectively.

The relative proximity to the ideal solution (*Siy*) is determined by Equation (10):

$$S\_{iy} = \frac{d\_{iy}}{\left(d\_{iy} + d\_{ix}\right)} \qquad \mathbf{0} \le \mathbf{S}\_{iy} \le \mathbf{1} \tag{10}$$

This method allows to determine the best operating conditions for a system based on mathematical tools. However, it is also possible to achieve optimal conditions of power generation systems through advanced exergetic analysis, which are formulations based on the physical phenomena involved in each piece of equipment in the system [29,30].

#### **4. Results and Discussions**

This section presents the energy results, economic perspective, and multi-objective optimization.

#### *4.1. Energy Results*

Figure 7 shows the profile of the energy production obtained by PV systems, their nominal output power, and equivalent PV hours per year in multiple locations in La Guajira, Colombia. In this step, device replacement and capital costs are not considered, because the objective of this section is to evaluate the energy generation potential of the hybrid system.

**Figure 7.** Comparison between the rated power and energy production for the photovoltaic (PV) system.

Figure 7 shows that the most significant annual energy production is in the town of Urumita. However, this does not mean that Urumita is the best location. It is necessary to quantify the efficiency of the devices to determine an optimal location for the PV system. The fraction between the nominal PV power and the energy generated per year gives a specific number of hours for each location in one year. Therefore, it estimates how PV arrays take advantage of solar light during operation, which means that locations where PV arrays have the longer number of hours per year will take more advantage of solar radiation.

On the other hand, it is necessary to highlight that PV output power indicates the nominal power for all the set of PV arrays connected between them, which are made of 4 panels with 250 W each. On this basis, Puerto Bolívar was identified as the location where the system works with the highest number of hours, despite having less energy generation compared to the other locations. This means

that Puerto Bolívar is the best place to use our PV system. In percentage terms, the efficiency of this system is the fraction between the worked hours per year and the hours of one year, which gives an efficiency of 21.9%.

Subsequently, the optimal location for the wind system was determined. Considering that the use of wind turbines with hub height of 80 m and nominal power of 1.5 MW is fixed, a comparison was made between the number of wind turbines (given by the software for each location) and the energy production generated per year, which points towards the ideal location (Figure 8).

**Figure 8.** Comparison between the number of wind turbines and the annual energy production for each location.

Figure 8 shows six locations where the system considered five 1.5 MW turbines to reach the energy production plotted in the previous graphic for each location, while the other three only needed four turbines to reach a higher amount of energy. Thus, these three locations were analysed to find the area with the highest generation of wind power: Nazareth was identified as the location with 0.4% and 0.18% more wind power, compared to Puerto Bolívar and Rancho Grande, respectively.

After the best location for installation of the wind generation system was identified, the optimal general location was determined since the optimal sites for both types of devices are different. Therefore, it was necessary to analyse the percentage of renewable fraction present in each of the locations to identify which of these two locations generates the highest amount of renewable energy. The renewable fraction in a system is the ratio of the amount of clean energy produced and the total energy demand of our system.

Once the optimal location was determined, the parameters of the hybrid renewable energy generation plant were modified to increase power generation, resulting in a decrease in costs.

Figure 9 shows that Nazareth, Puerto Bolivar, and Rancho Grande are the locations with the highest RF percentage to produce energy by renewable resources. However, these values have a minimal difference, which means that it is necessary to verify the second step of the simulation; this refers to an economic perspective to determine the optimal location.

**Figure 9.** Comparison between the production and renewable fraction for each location.

#### *4.2. Economic Perspective*

The simulation requires capital and replacement costs for PV systems and wind turbines. A generic PV array (1 kW; 4 × 250 W) with a capital cost of USD 3000 and a replacement cost of USD 3000 was used. Generic wind turbines of 1.5 MW with a capital cost of USD 3,000,000 and a replacement cost of USD 3,000,000 were used. The effects of temperature were considered in the simulation.

Using generic energy demand based on the requirements of La Guajira department, simulation at this stage resulted in a single generic wind turbine for each location. Therefore, Figures 10 and 11 show the relationship of the number of PV panels with net present cost (NPC), total energy production, a fraction of renewable energy, and CO2 emissions for the following locations: Nazareth, Port Bolívar, and Rancho Grande.

**Figure 10.** Comparative results for Nazareth, Puerto Bolivar, and Rancho Grande: (**A**) PV units and net present cost, (**B**) comparison between PV units and total production.

**Figure 11.** (**A**) Comparison between PV units and renewable fraction for Nazareth, Puerto Bolivar, and Rancho Grande, (**B)** comparison between PV units and CO2 production for Nazareth and Rancho Grande.

Figure 10A shows that Nazareth and Rancho Grande are the locations where the NPC of the entire project, based on the number of PV panels implemented, is the lowest. Practically, the NPC for Nazareth and Rancho Grande are the same, which means that it is necessary to consider the following criteria to make the right decision about which place is optimal. In the town of Nazareth, the reason for total energy production and the number of PV panels is the lowest, compared to the localities of Rancho Grande and Puerto Bolívar. In addition, Figure 10B shows that the total energy production in Rancho Grande remains the best for at least 200 PV arrays. Another essential criterion considered was the renewable fraction. It is critical to highlight that a 1 PV device or 1 PV unit is equal to a 1 PV array for this simulation analysis.

Figure 11A shows that Nazareth and Rancho Grande have almost the same fraction values to produce energy by renewable energy, which are higher than those of Puerto Bolívar. This means that Puerto Bolívar is not the optimal place to implement a hybrid PV and wind power plant. Even if the location has good production values, its renewable fraction is the lowest, resulting in a high NPC.

The last criterion to study is the CO2 emission produced by thermoelectric power plants and the acquisition of other non-renewable energy sources. Figure 11B shows that CO2 emissions in Nazareth are lower than those of Rancho Grande when there are 110 PV arrays. For large numbers of PV units, lower CO2 production is located in Rancho Grande, making it the best possible location.

#### *4.3. Multi-Objective Optimization of Rancho Grande for Location of a Hybrid System*

Considering that Rancho Grande was identified as the optimal location to implement a hybrid wind and PV system, the next stage sought to determine, through the optimization of multiple objectives, the most efficient combination of the number of PV devices and wind turbines.

Wind turbines in the range of 1 to 10 and the number of PV arrays in the range of 50 to 500 were implemented. A matrix was then developed for current NPC and CO2 production based on PV devices and wind turbines.

A mathematical function for each criterion, NPC (Equation (11)) and CO2 (Equation (12)), was determined using the MATLAB curve fitting tool. Figures 12 and 13 show the corresponding polynomial regressions with their respective functions.

$$f\_1(\mathbf{x}, y) = \begin{array}{c} \text{9.523} + \text{8.646} \cdot \text{10}^{-6} \cdot \text{x} - \text{0.8988} \cdot y + \text{0.0006295} \cdot \text{x} \cdot y + \text{0.6652} \cdot y^2 \\ - \text{8.478} \cdot \text{10}^{-5} \cdot \text{x} \cdot y^2 - \text{0.06331} \cdot y^3 + \text{3.936} \cdot \text{10}^{-6} \cdot \text{x} \cdot y^3 + \text{0.002267} \cdot y^4 \end{array} \tag{11}$$

**Figure 12.** Polynomial regression for NPC (Rancho Grande).

**Figure 13.** Polynomial regression for CO2 emission (Rancho Grande).

These functions were useful in developing a MATLAB code for a multi-objective optimization process. Figure 12 shows the behavior of the NPC as a function of the PV devices and the wind units. It was necessary to use fourth-degree polynomial regression to obtain the minimum percentage of error, 0.0001%. Equation (11) is the mathematical function of the net present cost (NPC), where "x" is the number of PV units and "y" is the number of wind turbines.

Figure 13 shows the behaviour of CO2 production as a function of the number of wind units and PV devices. Equation (12) is the corresponding mathematical function for CO2 emissions, where "x" is the number of PV units, and "y" is the number of wind turbines. As can be seen, the effect of PV devices on CO2 production is negligible, compared to the wind unit.

The optimization was conducted considering Objective 1 (the current NPC), and Objective 2 (the production of CO2) with the two functions mentioned above. The purpose was to minimize both criteria using Pareto's efficiency. For each data of PV arrays and wind turbines, there are values of current NPC and CO2 Production, as given in Table 5 (only a range of values was placed, since it can be more extensive).

130


**Table 5.** Data collected from previous multi-objective optimizations.

f1(x,y) and f2(x,y) from Table 5, which are the values for NPC and CO2 production, respectively, were then plotted (Figure 14).

**Figure 14.** Pareto front for multi-objective optimization of NPC and CO2 emissions.

It is important to note that Table 5 shows values that are not feasible due to decimal values; for example, index 17 gives 8.58 wind turbines. Decimal values are out of range because it is impossible to install 8.58 wind turbines of 1.5 MW. When choosing the nearest integer values, a considerable error was generated. On the other hand, PV arrays do not have this problem because their units are high, making it possible to approximate decimals to the nearest integer.

For this reason, it was necessary to determine the index that has the nearest integer for wind turbine devices, with at least 0.01 difference of any integer, as shown in Table 6.


**Table 6.** Feasible values to consider as an option, obtained from Table 4.

While it is necessary to choose one of these values, it is important to note that all the previous data were optimal combinations, that is, data that is the best possible option for a specific number of selected devices. Figure 14 shows the locations of these indexes (in orange dots).

Figure 14 is a Pareto front composed of values of f1(x,y) and f2(x,y) shown in Table 5, which represents the net present cost (NPC) and CO2 emissions, respectively. In addition, the characteristic equation is presented, where "x" is the number of PV arrays, and "y" is the number of wind turbines. Orange dots are the possible values given in Table 6, which represent specific configurations for the purposed hybrid plant. The ideal point is described as the interception of the vertical axis in the lowest value of CO2 production with the horizontal axis in the lowest value of the Net Present Cost. The non-ideal point is described as the interception of the horizontal axis generated from the higher amount of CO2 production with the vertical axis generated from the higher value of the Net Present Cost. Both ideal points are represented with red dots.

The next step is to implement the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, which states that the selected point (taken in Figure 14) should have the shortest geometric length from the positive ideal solution and the most extended geometric length from the perfect negative solution. This optimization method has been widely used to improve the thermal and economic performance of energy systems [31–33]. Indexes 39 and 33 are the closest to the desired condition, as shown in Table 7.


**Table 7.** Results from TOPSIS analysis for index 33 and 39.

Equation (10) is used to choose between the two values. The index with the value of Sy closest to 1 is the right point. The index 33 is closer to 1, making it the best possible option; therefore, a total of 441 PV arrays (1764 modules of 250 W) and 3 wind turbines of 1.5 MW should be used, resulting in an estimated NPC cost of 11.8 million dollars, and estimated CO2 emission of 244.1 tons per year.

In this way, the best parameters that offer minimum energy costs to the residents, minimum emissions of pollutant gases, and maintain a reasonable energy production rate are obtained.

#### **5. Conclusions**

In this paper, an optimization process was developed to install a hybrid PV and wind power plant in La Guajira, Colombia. The study was done in three stages, with specific characteristics and region conditions. The first stage focused on an energy perspective, where it was found that three of the nine measurement locations—Nazareth, Puerto Bolivar, and Rancho Grande—were the best locations to take advantage of the available energy with 95% percentage each to produce energy using renewable energy. The renewable fraction for the other locations was close to 87%.

The second stage focused on an economic perspective, considering prices and taxes. This stage was developed only for the three best locations, finally identifying Rancho Grande as the optimal place to set up a hybrid energy plant. This location had an advantage over other areas in terms of renewable fraction, total production, and estimated CO2 reduction.

The third and final stage focused only on the town of Rancho Grande, intending to determine the optimal combination of PV panels and wind turbines. This stage identified, for the plant, an optimal combination of 441 PV units and 3 wind turbines, giving an estimated minimum NPC of \$11.8 million, and low CO2 production of 244.1 tons per year.

**Author Contributions:** Conceptualization, G.V.O., F.A.B.B., and A.A.-M.; Methodology, G.V.O. and J.R.N.Á.; Software, G.V.O., L.G.O., and A.A.-M.; Validation, G.V.O., L.G.O., and J.R.N.Á.; Formal Analysis, G.V.O., J.R.N.Á., and F.A.B.B.; Investigation, G.V.O., L.G.O., and F.A.B.B.; Resources, J.R.N.Á. and A.A.-M.; Writing—Original Draft Preparation, F.A.B.B.; Writing—Review & Editing, L.G.O., A.A.-M., and G.V.O.; Funding Acquisition, G.V.O., and J.R.N.Á. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Universidad del Atlántico, and Universidad de la Costa.

**Acknowledgments:** This research was supported by the Mechanical Engineering Program of Universidad del Atlántico. The Kai Research Group supports G. Valencia and F. Barrozo.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Sustainable Wind Power Plant Modernization**

#### **Robert Kasner, Weronika Kruszelnicka \*, Patrycja Bałdowska-Witos, Józef Flizikowski and Andrzej Tomporowski**

Department of Manufacturing Technology, Faculty of Mechanical Engineering, University of Science and Technology in Bydgoszcz, 85-796 Bydgoszcz, Poland; robert.kasner@gmail.com (R.K.);

patrycja.baldowska-witos@utp.edu.pl (P.B.-W.); fliz@utp.edu.pl (J.F.); a.tomporowski@utp.edu.pl (A.T.) **\*** Correspondence: weronika.kruszelnicka@utp.edu.pl

Received: 29 February 2020; Accepted: 18 March 2020; Published: 20 March 2020

**Abstract:** The production of energy in wind power plants is regarded as ecologically clean because there being no direct emissions of harmful substances during the conversion of wind energy into electricity. The production and operation of wind power plant components make use of the significant potential of materials such as steel, plastics, concrete, oils, and greases. Energy is also used, which is a source of potential negative environmental impacts. Servicing a wind farm power plant during its operational years, which lasts most often 25 years, followed by its disassembly, involves energy expenditures as well as the recovery of post-construction material potential. There is little research in the world literature on models and methodologies addressing analyses of the environmental and energy aspects of wind turbine modernization, whether in reference to turbines within their respective lifecycles or to those which have already completed them. The paper presents an attempt to solve the problems of wind turbine modernization in terms of balancing energy and material potentials. The aim of sustainable modernization is to overhaul: assemblies, components, and elements of wind power plants to extend selected phases as well as the lifecycle thereof while maintaining a high quality of power and energy; high energy, environmental, and economic efficiency; and low harmfulness to operators, operational functions, the environment, and other technical systems. The aim of the study is to develop a methodology to assess the efficiency of energy and environmental costs incurred during the 25-year lifecycle of a 2 MW wind power plant and of the very same power plant undergoing sustainable modernization to extend its lifecycle to 50 years. The analytical and research procedure conducted is a new model and methodological approach, one which is a valuable source of data for the sustainable lifecycle management of wind power plants in an economy focused on process efficiency and the sustainability of energy and material resources.

**Keywords:** wind power plant; efficiency; sustainable development; modernization; recycling

#### **1. Introduction**

The production of energy in wind power plants is regarded as ecologically clean because there being no direct emission of harmful substances during the conversion of wind energy into electricity [1,2]. Analyses of the environmental impact of wind power plants have mainly concerned their impact on birds, vibration emissions, noise—both audible and of the infrasound type—as well the impact on the surrounding landscape [3–5]. In a wider context, analyses have been conducted on the impact of wind farm lifecycles with the aid of the Life Cycle Assessment (LCA) method, including areas of potential impact on human health, the quality of the natural environment, and natural resources [6–9]. Analyses have also concerned the impact of the lifecycle of wind power plants on water and soil environment as well as their emissions into the atmosphere [10–12]. Many papers have also dealt with the aspects of the impact of particular elements and structural assemblies of wind power plants during their lifecycle, elements which include rotors, blades, towers, and various types of foundations,

both offshore and onshore, with the result being that foundations and towers are the greatest source of potential negative impacts [1,13–17].

Less attention, however, has been given to the relationship between the benefits, costs, and efficiency in a given lifespan of a wind power plant [18]. Kasner [19] introduced the concept of the universal wind power plant integrated efficiency indicator which allows one to assess the efficiency of the use of inputs (costs) on the generation, use, and post-use management of the materials and components of a wind power plant in three areas: potential emissions, energy consumption, and financial costs to obtain benefits in the form of electricity production. Piasecka et al. [20], however, has proposed a method of assessing the destructiveness of costs appearing in the wind power plant lifecycle in four categories: de-ergonomicity, defunctionality, environmental performance breakdown, and biosphere conservation breakdown, showing that the greatest negative impact of both onshore and offshore wind power plants occur in the area of de-ergonomicity. Much research has examined the benefits resulting from the operation of wind power plants, i.e., electricity production, or the ways to both predict or increase wind farm productivity [21–26]. Economic matters relating to the operation of wind power plants have also been addressed as one important aspect thereof [27–30].

The significant potential of construction materials such as steel, plastics, and concrete are used to produce wind power plant components. Energy costs must also be incurred in both the production and operation thereof [1]. In the process of decommissioning wind power plant units, both their material and energy potential must be utilized by means of landfilling or recycling. With regard to the material stages of the lifecycle, production, operation with servicing and power supply, and the post-use utilization of the wind power plant are significant in a complete environmental impact assessment [31–33].

Europe has launched international efforts to protect the environment in the adopted provisions of, inter alia, the Kyoto Protocol on climate change and the focus of European Union (EU) policy on sustainable development and low-carbon economies [34]. The concept of sustainable economic development requires that one take into account the environmental, energy, economic, and social aspects of the lifecycles of technical facilities as well as changes in the management of their lifecycles [35–39], e.g., by modernizing wind power plants and extending their lifecycles [40–42] (Figure 1).

**Figure 1.** Spaces in sustainable lifecycle management, WT—wind turbine.

The lifespan of wind power plants is accepted to be 25 years and, in most cases, they are disassembled after 25 years [20,43–46]. Many components, including structural elements, can continue to operate for the next 25 years, while the wind power plant itself can operate much longer after the replacement of some components and elements with new ones, which will further reduce the harmful environmental impact of energy production in wind power plants [40,46]. There are basically three distinct strategies for dealing with the wind turbines at the end of their lifecycles: decommissioning, lifetime-extension, and repowering. The aim of decommissioning is to disassemble the wind power plant after it has reached the end of its lifecycle and to then recycle them [41,42,47,48]. Lifetime-extension, in turn, includes measures to extend the wind power plant's lifecycle by replacing worn out elements with new ones. This therefore includes the modernization of mechanical parts and control systems (Table S1 in Supplementary Materials) [40]. The idea behind repowering is to erect a new wind power plant with greater power production capacity at the site of the old one which has ended its lifecycle [40].

The modernization of wind power plants, the replacement of their parts with new ones, and the extension of their lifecycle all fall in line with the aim of sustainable development, and in particular, with the implementation of the slogan: "A resource-efficient Europe." Changes in wind power plant lifecycle management are primarily meant to be harmless, efficient, and of high-quality. Efforts to undertake sustainable modernization make it possible to:


In the world literature one usually finds analyses concerning mainly the assessment of wind power plant profitability and productivity after lifetime-extension and repowering [41,42,47,48], yet what is missing is a comprehensive assessment of the benefits as well as the ecological, energy, and economic costs arising from modernizations in wind power plant lifecycles. Ziegler et al. [41] performed analysis of the technical, economic, and legal possibilities relating to lifetime-extension showing that the profitability of these processes depend on energy market prices and thus differ depending on the country. Piel et al. [42] proposed a decision-support system for wind power plant operators on how to end the facility's lifecycle, taking into account land topography, wind resources, turbine type, and financial data in the profitability assessment of the processes of decommissioning, repowering, and lifetime extension. A methodology was also developed to assess the possibilities of extending the lifetime of wind turbine towers with special consideration given to wind and stress variables occurring in this structural element [49]. Martinez et al. [50] have shown that the repowering process causes an increase in the wind turbine's production of electricity at the same site with a smaller environmental impact. Studies concerning repowering have shown that such efforts give rise to an increase in both power and productivity [48]. There are no studies on the impact that standard replacements and modernizing replacements have (serving to extend wind power plant lifetime) on integrated efficiency. Therefore, it is worth considering the hypothesis as to whether the benefits and costs of operating a wind power plant after modernization, e.g., in a 50-year operation period, are as effective as in a 25-year lifecycle.

In light of the above, the aim of this paper is to develop a methodology to assess the efficiency of energy and environmental costs incurred in the lifecycle processes of a 2 MW wind power plant in a 25-year use-period and to assess the very same power plant after it has been subject to sustainable modernization to extend its lifecycle to 50 years. The analytical and research procedure conducted is a new model and methodological approach, one which is a valuable source of data for the sustainable lifecycle management of wind power plants in an economy focused on process efficiency and the sustainability of energy and material resources.

#### **2. Materials and Methods**

#### *2.1. The Integrated E*ffi*ciency of Sustainable Modernization*

Efficiency of operation is a goal, a state of energetics that makes it possible to estimate, optimize, modernize, and innovate ideas, constructions, and processes by comparing benefits, costs, and their relation in a given lifecycle. It means effectively using incurred costs to obtain benefits or expected results [19,51,52].

Efficiency in systems engineering is treated as a system feature, one that is measurable, useful for comparing systems of a given class, which expresses different aspects of performance in different time intervals and can be expressed differently depending on the class of the systems, their purpose, and conditions [19,51,53].

The assessment of modernization efficiency herein was based on mathematical models which allow for the analyses and assessments consisting of a comparison of benefits and costs in a lifecycle.

To assess the operation of a wind power plant's lifecycle, an integrated efficiency indicator was defined [19], which can describe efficiency in the environmental, economic, and energy spheres depending on the reference point that is established [19]

$$E(t) = \frac{\mathcal{U}(t)}{\mathcal{N}(t)}\tag{1}$$

where:

*E(t)*—the integrated efficiency indicator in the lifecycle,

*U(t)*—the benefits in the lifecycle (environmental, energy, and economic),

*N(t)*—costs in the lifecycle (environmental, energetic, economic),

*t*—time of use.

The size of *U(t)* and *N(t)* indicate the values of the benefits obtained (the effects) and the costs incurred up to time t from the beginning of operation (t = 0).

Benefits from the operation of a wind power plant *U(t)* include, inter alia, a product in the form of energy, financial revenue, improvement in quality, reductions of emissions into the environment, the diversification of energy sources, the development and activation of the surroundings, as well as other potential benefits which as of today cannot be defined.

In a given period, the function *U(t)* can take the form of both positive and negative values [19],

$$u(t) = \frac{d\mathcal{U}(t)}{dt} \tag{2}$$

while the value of function *U(t)*, on the basis of the elementary values *u(t),* depending on whether they are continuous functions or discrete ones, can be determined from the following dependencies [19]

*<sup>U</sup>*(τ) <sup>=</sup> <sup>τ</sup> 0 *u*(*t*)*dt U*(τ) = *l i ui* · Δ*ti* (3)

Operation costs *N(t)* are to be understood as: negative impacts on the environment, energy use in the entire lifecycle, financial costs incurred, negative societal costs, and potential costs which are currently unknown.

The function *N(t)* in the time interval (0, *t*) is a non-diminishing time function, that is, in each of the elementary intervals *(*Δ*t* or dt/) the elementary value of costs is not less than zero [19]

$$m(t) = \frac{dN(t)}{dt} \tag{4}$$

while the value of function *N(t)*, depending on whether the function *n(t)* is continuous or discrete, can be determined from the dependency [19]

$$N(\tau) = \int\_0^{\tau} n(t)dt\tag{5}$$

$$N(\tau) = \sum\_{i}^{T} n\_{i} \cdot \Delta t\_{i} \tag{6}$$

The cost can be expressed with various elements in various units.

For a wind turbine undergoing modernization during its use, the integrated efficiency indicator is defined by a system of equations (Figure 2)

$$E(t) = \begin{cases} \frac{lL\_{l'}t}{N\_W^1 + N\_{r'}t + N\_Z^1} & \text{dla} \quad 0 \le t \le t\_{L\subset 1} \\ \frac{lL\_{l'}}{(N\_W^1 + N\_W^2) + N\_{r'}t + (N\_Z^1 + N\_Z^2)} & \text{dla} \quad t\_{L\subset 1} < t \le t\_{L\subset 2} \\ \frac{(N\_W^1 + N\_W^2 + N\_W^3) + N\_{r'}t + (N\_Z^1 + N\_Z^2 + N\_W^3)}{(N\_W^1 + N\_W^2) + N\_{r'}t + (N\_Z^1 + N\_Z^2 + N\_W^3)} & \text{dla} \quad t\_{L\subset 2} < t \le t\_{L\subset 3} \\ \vdots & \vdots \\ \frac{lL\_{r'}t}{N\_W^1 + N\_{r'}t + \sum\_{i=1}^{n+1} N\_Z^i} & \text{dla} \quad t\_{L\subset (n)} < t \le t\_{L\subset (n+1)} \end{cases} \tag{7}$$

where: *Ur* average annual productivity, *t* – time counted from the beginning of the use-stage, *n –* number of use-stages throughout the lifecycle, *tLCn* – the end of the subsequent use-stage, *N*<sup>1</sup> *<sup>W</sup>*, *<sup>N</sup>*<sup>2</sup> *W*, *N*3 *<sup>W</sup>*, *<sup>N</sup><sup>n</sup> <sup>W</sup>* – costs at the production-stage of elements in the first, second, third *n*- nth stages, respectively, *Nr –* average annual costs at each stage of use, *N*<sup>1</sup> *<sup>Z</sup>*, *<sup>N</sup>*<sup>2</sup> *<sup>Z</sup>*, *<sup>N</sup>*<sup>3</sup> *<sup>Z</sup>*, *<sup>N</sup><sup>n</sup> <sup>Z</sup>* – costs at the post-use management stage of elements utilized in the first, second, third, *n*-nth use-stages, respectively.

**Figure 2.** A graphical interpretation of the integrated efficiency indicator in the lifecycle (dependency (7)), including the sustainable modernization indicator (dependence (20)) and the time of the return on costs for modernization (dependence (18)).

The model proposed herein can be applied to assess the efficiency of existing wind power plants as well as those being designed to compare the effects of their operation after the modernization and extension of their lifecycle.

**Sustainable modernization**, which in essence refers to the process of replacing structural components with new or modernized ones, is a process that encompasses multiple aspects, all of which serve the goal of erecting a wind power plant with the use-properties of a new one. This process includes the use of wind power plant materials and resources, and additionally, its executive and energy resources after completion of the use-stage.

#### *2.2. Payback Time for Moderinzation Costs*

The first indicator of the assessment of sustainable modernization is time *TMn* after which there is a return to the pre-modernization efficiency of the use of costs (Figure 2). The payback time for modernization costs is defined as

$$T\_{Mn} = t\_n - t\_{L\mathbb{C}n} \tag{8}$$

where:

*tn* – the time after which post-modernization efficiency is equal to pre-modernization efficiency, counted from the beginning of use,

*tLCn* – the end of a subsequent use-stage

Time *TMn*, after which pre-modernization efficiency is achieved, is essentially unknown, but determinable. If one condition is met (Figure 2)

$$E(t\_n) = E(t\_{\rm LCrn}), \quad \text{gdzie} \quad t\_n \neq t\_{\rm LCr} \tag{9}$$

then after modernization at time *tn* one achieves the efficiency of the source of energy which is the moment modernization work was begun at time *tLCn*. The result is the equation

$$\frac{\mathbf{U}\_{\mathbf{r}} \cdot \mathbf{t}\_n}{\sum\_{i=1}^{n+1} \mathbf{N}\_W^i + \mathbf{N}\_r \cdot \mathbf{t}\_n + \sum\_{i=1}^{n+1} \mathbf{N}\_Z^i} = \frac{\mathbf{U}\_{\mathbf{r}} \cdot \mathbf{t}\_{\mathrm{LCn}}}{\sum\_{i=1}^n \mathbf{N}\_W^i + \mathbf{N}\_r \cdot \mathbf{t}\_{\mathrm{LCn}} + \sum\_{i=1}^n \mathbf{N}\_Z^i} \tag{10}$$

After transformations one obtains

$$t\_n \left(\sum\_{i=1}^n N\_W^i + N\_r \cdot t\_{\rm LCn} + \sum\_{i=1}^n N\_Z^i \right) = t\_{\rm LCn} \left(\sum\_{i=1}^{n+1} N\_W^i + N\_r \cdot t\_n + \sum\_{i=1}^{n+1} N\_Z^i \right) \tag{11}$$

$$t\_n \cdot \sum\_{i=1}^n N\_W^i + t\_n \cdot t\_{\rm LCn} \cdot N\_r + t\_n \cdot \sum\_{i=1}^n N\_Z^i - t\_{\rm LCn} \cdot \sum\_{i=1}^{n+1} N\_W^i - t\_n \cdot t\_{\rm LCn} \cdot N\_r - t\_{\rm LCn} \cdot \sum\_{i=1}^{n+1} N\_Z^i = 0 \tag{12}$$

$$t\_n \cdot \sum\_{i=1}^n N\_W^i + t\_n \cdot \sum\_{i=1}^n N\_Z^i - t\_{\text{LCn}} \cdot \left( N\_W^{n+1} + \sum\_{i=1}^n N\_W^i \right) - t\_{\text{LCn}} \cdot \left( N\_Z^{n+1} + \sum\_{i=1}^n N\_Z^i \right) = 0 \tag{13}$$

$$t\_n \cdot \sum\_{i=1}^n N\_W^i + t\_n \cdot \sum\_{i=1}^n N\_Z^i - t\_{\rm LCn} \cdot N\_W^{n+1} - t\_{\rm LCn} \cdot \sum\_{i=1}^n N\_W^i - t\_{\rm LCn} \cdot N\_Z^{n+1} - t\_{\rm LCn} \cdot \sum\_{i=1}^n N\_Z^i = 0 \tag{14}$$

$$\left(\left(t\_{\mathrm{n}} - t\_{\mathrm{LCn}}\right) \cdot \sum\_{i=1}^{n} \mathrm{N}\_{\mathrm{W}}^{i} + \left(t\_{\mathrm{n}} - t\_{\mathrm{LCn}}\right) \cdot \sum\_{i=1}^{n} \mathrm{N}\_{\mathrm{Z}}^{i} = t\_{\mathrm{LCn}} \cdot \mathrm{N}\_{\mathrm{W}}^{n+1} + t\_{\mathrm{LCn}} \cdot \mathrm{N}\_{\mathrm{Z}}^{n+1} \tag{15}$$

$$t\_{\rm tr}(t\_{\rm tr} - t\_{\rm LCn}) \cdot \left(\sum\_{i=1}^{n} N\_{\rm W}^{i} + \sum\_{i=1}^{n} N\_{\rm Z}^{i}\right) = t\_{\rm LCn} \left(N\_{\rm W}^{n+1} + N\_{\rm Z}^{n+1}\right) \tag{16}$$

$$t\_n - t\_{\rm LCn} = \frac{t\_{\rm LCn} \left(\mathcal{N}\_W^{n+1} + \mathcal{N}\_Z^{n+1}\right)}{\sum\_{i=1}^n \mathcal{N}\_W^i + \sum\_{i=1}^n \mathcal{N}\_Z^i} = \frac{t\_{\rm LCn} \left(\mathcal{N}\_W^{n+1} + \mathcal{N}\_Z^{n+1}\right)}{\sum\_{i=1}^n \left(\mathcal{N}\_W^i + \mathcal{N}\_Z^i\right)}\tag{17}$$

Because *TMn* = *tn* – *tLCn,* the dependency of the payback time on pre-modernization efficiency is expressed as follows:

$$T\_{Mn} = \frac{t\_{LCn} \left( N\_W^{n+1} + N\_Z^{n+1} \right)}{\sum\_{i=1}^n \left( N\_W^i + N\_Z^i \right)} \tag{18}$$

#### *2.3. The Sustainable Modernization Indicator*

The second indicator of sustainable modernization assessment is the indicator of sustainable modernization *EM*, which expresses the relation of the integrated efficiency indicator after the completion of the use-stage after modernization *E*(*tLC(n*+1)) is performed to the integrated efficiency indicator before the beginning of modernization *E(tLCn)* (Figure 2)

$$E\_{\rm Mn} = \frac{E(t\_{\rm LC(n+1)})}{E(t\_{\rm LCn})} \tag{19}$$

$$E\_{M\text{tr}} = \frac{\frac{t\_{LC(n+1)} \cdot t\_r}{\sum\_{i=1}^{n+1} N\_W^i + \sum\_{i=1}^{n+1} N\_Z^i + t\_{LC(n+1)} \cdot N\_r}}{\frac{t\_{LCn} \cdot t\_r}{\sum\_{i=1}^n N\_W^i + \sum\_{i=1}^n N\_Z^i + t\_{LCn} \cdot N\_r}} = \frac{t\_{LC(n+1)} \left[ \sum\_{i=1}^n \left( N\_W^i + N\_Z^i \right) - t\_{LCn} \cdot N\_r \right]}{t\_{LCn} \left[ \sum\_{i=1}^{n+1} \left( N\_W^i + N\_Z^i \right) - t\_{LC(n+1)} \cdot N\_r \right]} \tag{20}$$

where *EMn* is the efficiency of subsequent modernizations

Figure 2 presents the dependence of integrated efficiency as a function of the time of use. Integrated efficiency as a function of time is a non-decreasing function. Its increase at the use-stage results from the benefits that grow in time that are generated at this stage. In the wind power plant's lifecycle, an increase in the benefits of its operation is observed (an increase in the electricity produced), with a constant value of both the costs to produce elements and costs relating to post-use management, and a slight increase in costs at the use-stage (maintenance, repairs, energy consumption). The increase in costs at the use-stage is much smaller than the increase in benefits at the same time. Considering the fact that the efficiency indicator is a ratio of benefits to costs, with the assumptions above, said indicator will be an increasing function. At the time of modernization (*tLCn*), a reduction in integrated efficiency results from the calculation of the costs to manufacture the replaced elements and to undertake their post-use management during modernization, which increases the value of costs throughout the lifecycle.

#### *2.4. Methodology for Determining the Benefits and Costs in a Wind Power Plant's Lifecycle*

A Vestas V90/105 m wind power plant with a nominal electrical capacity of 2 MW located in central Poland was analyzed in detail. Table 1 presents the basic data of the wind power plant analyzed with the LCA method.


**Table 1.** Basic data of the Vestas V90 wind turbine.

The Vestas V90 three-blade rotor with a 90 m diameter and the nacelle housing the main shaft, generator, transformer, gearboxes, and brakes are located atop a 105 meter tower. The Vesta V90 wind turbine is an upwind turbine with an adjustable blade pitch system featuring active directionality. The turbine is equipped with a 2.0 MW power-rated generator. It uses a OptiTip®microprocessing system for blade pitch control as well as the OptiSpeedTM function (for speed regulation). Owing to these functions, the wind turbine rotor can work at variable rotational speeds, helping to keep power output at or close to the rated power (data from the producer) [54].

The V90 turbine is equipped with a rotor possessing a diameter of 90 m which consists of three blades and a hub. The 44 m blades, laminated with the use of a "prepreg" type material (PP), are constructed from carbon fiber and fiberglass. The blades consist of two panels attached to a support beam (data from the producer) [54].

Wind energy transferred to the turbine is corrected by adjusting the blade pitch, depending on the control strategy that has been adopted. The blades connect to the hub by means of double row, four-point contact ball bearings. The hub is used to seat the three blades and transmit the forces of action to the main bearing. The hub design supports the blade bearings and the cylinder that regulates the blade angle. The main gearbox transmits torque from the rotor to the generator. It consists of a planetary gearbox connected to a two-stage parallel gearbox, reaction rods, and vibration dampers. The torque is transferred from the high-speed shaft to the generator via a composite clutch behind the disc brake. The generator bearings are lubricated and the grease is fed continuously by an automatic lubricator. The grease flow is approximately 2400 cm3 per year. The high-speed shaft coupling transmits torque from the high-speed output shaft of the gearbox to the input shaft of the generator. It consists of two composite discs, an intermediate sleeve with two aluminum flanges and a fiberglass sleeve. The nacelle housing is made of glass-fiber-reinforced polymers. The nacelle base plate consists of two parts: the front one, made of cast iron, and the back one in the form of a girder construction. A three-phase asynchronous generator with a coiled rotor is connected to the Vestas Converter System (VCS, Vestas Converter System) via a slip ring system (data from the producer) [54].

The Vestas V90 tubular towers have internal flange connections. The tower is erected on a reinforced concrete foundation with an embedded starter ring. The tower is connected to the ring by means of bilateral flanges (internal and external). The list of materials that form the Vestas V90 2 MW wind power plant is presented in Table S2 (in Supplementary Materials).

A comparative analysis of the efficiency of the aforementioned wind power plant in a 25-year operation period [43–46] was performed and the same was done for the same turbine modernized in a 50-year lifecycle. The analysis is primarily intended to describe the existing reality, but also to help model future changes to define recommendations to develop more pro-environmental solutions.

The wind power plant examined herein is a technically mature construction. As part of a service package, the producer guarantees its constant time-availability and productivity throughout the entire 25-year use-period. The periods of reduced reliability at the beginning and end of the use-period of this wind power plant as well as the decreasing operational efficiency throughout its use-period [55,56] are negligible and do not affect the efficiency assessments of the costs incurred.

The energy benefits *U(t)* are the sum of the average annual values of energy production in the wind power plant *U*<sup>r</sup> over an assumed lifetime *tLC1* of 25 years [43–46]. The annual environmental and energy benefits *Ur* are defined as the average annual long-term productivity determined on the basis of data from three years of production (2013–2015) and long-term data that takes into account the 25-year reference period. These benefits came to 5325 MWh/year for the power plant tested [19].

The costs for the 25-year period of use were determined as the sum of costs at individual stages of the wind power plant lifecycle, i.e., costs to generate *N*<sup>1</sup> *<sup>W</sup>*, the average annual costs for operation *Nr*, and the post-use management *N*<sup>1</sup> *<sup>Z</sup>* in the form of landfilling and recycling. For costs on post-use management, it was assumed that 90% of materials are recycled and 10% are landfilled, which results from the current possibilities of material processing and available reports on wind power plant recycling [57].

The study took into consideration the management of post-use materials and elements of the wind power plant, which included 90% recycling and 10% landfilling of the waste for materials that could not be reused or processed. As a complex mechanical structure, a wind power plant is built of many different materials, including steel, polymer materials, carbon fibers, fiberglass, copper, iron, and rubber materials, which are subject to various recycling methods to varying degrees, including mechanical recycling and reuse, material recovery, incineration with energy recovery, and pyrolysis [58]. The data presented by the wind power plant manufacturers [59–63] indicate that it is technically feasible to recycle 90% of its materials and to transfer 10% of its materials to landfills. This data correspond to the actual conditions. Some of the materials of the blades, nacelle and rotor housing require a specialized method of energy recycling (incineration with energy recovery, pyrolysis), during which waste intended for the landfill is produced. The analysis in this paper covered the entire lifecycle of the wind power plant, including post-use management; therefore, to calculate the result of the wind power plant's total environmental impact, it is important to take into account the management methods, as—depending on the level of recycling and landfilling—results will vary. This paper took into account data concerning the recyclability of components corresponding to the actual technical possibilities thereof.

#### 2.4.1. Determining Costs with the Aid of the LCA Method

The LCA method was used to determine energy and environmental costs in the lifecycle, a method which is a technique from process management that allows for the assessment of potential environmental threats triggered by proesses arising in the lifecycle of a given technical object. Analysis was conducted in accordance with ISO norm 14044 [64].

#### *Aim and scope of the analysis*

The purpose of analyzing environmental impacts is to identify potential negative environmental impacts occurring at particular material stages of the lifecycle, assuming a 25-year wind power plant lifecycle and a 50-year lifecycle for the modernized power plant where the nacelle and rotor have been replaced to determine the values of the integrated efficiency indicators in the context of environmental and energy costs, as well as the time of return on modernization and the sustainable modernization indicator. Environmental impact was determined using the Eco-indicator 99 model using SimaPro software. The Eco-indicator 99 method belongs to a group of methods modelling environmental impact at the level of endpoints of an environmental mechanism. The characterization process takes place for eleven impact categories, falling within three larger groups defined as impact areas or damage categories [65–67]. The following impact areas have been distinguished: human health, ecosystem quality, and raw material resources [68]. The results of the third stage of environmental analysis, i.e., grouping and weighing, were used to determine the environmental costs. Environmental coefficients are the result of this stage, and they are expressed in Pt (environmental points), which are aggregated units that make it possible to compare the results of the ecobalances herein [69,70]. One thousand environmental points are equal to the environmental impact of an average European during a year [65,71]. The cut-off point was equal to 0.05%. The analysis included eleven impact categories, yet in order to determine environmental costs, only the total impact of the wind power plant's lifecycle at particular stages of the lifecycle, i.e., production, use, and post-use development, was taken into account. The analysis also took into account the values of emissions of substances causing acidification SO2eq (in kg) and emissions of substances causing eutrophication PO4eq (in kg), which were treated as environmental costs in the determination of sustainable modernization assessment models. The procedure of determining environmental impacts consisted of four stages: defining the aim and scope of the analysis, analysis of the collection of inputs and outputs, LCAI analysis, and interpretation of the results [72,73].

The cumulative energy demand method (CED) makes it possible to define the cumulative energy demand (expressed in GJ) for particular stages of the lifecycle [74,75]. The reults of the CED analysis were the input value for determining the value of the integrated efficiency index from energy costs, from the time of the return on energy costs for modernization, and from the modernization indicator in relation to energy costs.

The Intergovernmental Panel on Climate Change, Global Warming Potential method (IPCC) was used to determine the amount of greenhouse gas emissions in certain stages of the material lifecycle of the wind power plant [76,77]. This gave the value of CO2eq emissions (in kilograms), which was used to determine the value of integrated efficiency (Equation 7).

#### *System limit and the functional unit*

The borders of Poland were assumed as a territorial limitation. The function of the object was the production of electricity. The productivity of the wind power plant in a 25-year period of use was taken as the functional unit.

The analysis assumed a 25-year wind power plant lifecycle [43–46] and a 50-year lifecycle of the same wind power plant which was subject to modernization. The scope of the modernization included the replacement of the nacelle and rotor along with the blades after a 25-year period of use. Three material stages of the lifecycle were examined: production, use, and post-use management in the form of recycling (90%) and landfilling (10%) [57]. Excluded from the system were the stages of transportation, of sale, of technical tests and storage because of a lack of proper data and the impact differences in the means of transport, which to a large degree are dependent on the location of the object. In the Figure 3 the scope of LCA analysis is presented.

**Figure 3.** Scope of the LCA analysis of the turbine in a 25-year lifecycle and the same turbine after modernization.

#### *Analysis of the collection of inputs and outputs, data aggregation and validation*

Data were collected and divided into particular processes and unit elements with the identification of their inputs and outputs. Process inputs are primarily comprised of materials, natural resources, and energy while outputs are comprised of waste and emissions. The data correspond to one wind power plant. The input data concerned materials and elements of the wind power plant acquired from materials of the producer and from the authors' own research. Data concerning productivity come from a 3 years period (2013 – 2015) of the wind power plant's operation. Less essential data concerning materials and processes were taken from SimaPro databases. In order to create an inventory table, individual environmental impacts of the same type were summed up for all unit processes.

Table S2 (in Supplementary Materials) displays the material and elements that formed the basis of the wind power plant's lifecycle analysis for a 25-year period of use. The total weight of the its materials and elements came to approximately two tons, 76% of which belonged to concrete. Besides concrete, the most important materials used in the wind power plant's lifecycle are steel (approx. 19%), cast iron (approx 3%), polymer materials (approx. 1%) as well as aluminum, copper, and oils (in total approx. 1%). It was assumed that in a 25-year period of use there will be two replacements of the main gearbox [78] and an oil change every 5 years in both the hydraulic system and the main gearbox, with a total weight of 2560 kg.

Table 2 lists the materials and elements of the nacelle and rotor that are subject to replacement after 25 years of use for the scenario in which a 50-year lifecycle was assumed along with the modernization of the test object, while Table 2 lists the weight of the materials replaced. Approximately 48% of the materials and components of the nacelle and over 53% of the materials and components of the rotor undergo replacement. The following elements are replaced with completely ones: the generator and its accompanying cooler, the gearbox, cooler, hydraulic system, and blades (Table 2). This constitutes a small percentage of the total mass of the power plant undergoing modernization. The components in the nacelle that are replaced constitute less than 2% of the total mass, while the rotor components account for slightly over 1% (Table 2). Most of the materials listed are polymeric materials (1.15%), which are replaced in their entirety (Table 3). The material with the second highest share in replacement is cast iron (1.02%), and the third is steel (0.60%).

#### 2.4.2. Determining the Efficiency Indicator from Environmental and Energy Costs

Efficiency of environmental costs is understood as the relation between environmental benefits in the lifecycle and the environmental costs incurred in the form of: the total impact of one thousand Europeans in the course of one year (efficiency of environmental costs), greenhouse gas emissions (environmental efficiency of CO2 emissions), emissions of substances causing acidification (environmental efficiency of SO2 emissions), and emissions of substances causing eutrophication (environmental efficiency of PO4 emissions).

Energy efficiency is understood as the relation between energy benefits in the lifecycle and the energy costs incurred.

To analyze the efficiency of the wind power plant in a 25-year period of use, dependency (7), expanding upon formula (1), was used

$$E(t\_{LC1}) = \frac{\mathcal{U}(t\_{LC1})}{\mathcal{N}(t\_{LC1})} = \frac{\sum\_{i=1}^{t} \mathcal{U}\_{i}}{\mathcal{N}\_{W}^{1} + \sum\_{i=1}^{t} \mathcal{N}\_{i} + \mathcal{N}\_{Z}^{1}} = \frac{\mathcal{U}\_{\mathcal{I}} \cdot t\_{LC1}}{\mathcal{N}\_{W}^{1} + \mathcal{N}\_{\mathcal{I}} \cdot t\_{LC1} + \mathcal{N}\_{Z}^{1}} \tag{21}$$

where: *E(tLC1)* – the integrated efficiency indicator during *t* years of use, *Ui* – benefits during the *i*-nth year of use, *N*<sup>1</sup> *<sup>W</sup>* – costs at the production stage of the 25-year lifecycle, *Ni* – costs in the *i*-nth year of use *N*1 *<sup>Z</sup>* – costs during post-use management during the 25-year lifecycle, *tLC1* – time of use, *Ur* – average annual benefits, *Nr* – average annual use-related costs.

For a wind power plant that has been modernized during a 50-year period of use, the efficiency indicator is determined by a system of equations

$$\begin{cases} E(t) = \frac{\mathcal{U}\_{\tilde{r}} \cdot t}{N\_W^1 + N\_{\tilde{r}} \cdot t + N\_{\tilde{Z}}^1} & \text{dla} \quad 0 \le t \le 25\\ E(t) = \frac{\mathcal{U}\_{\tilde{r}} \cdot t}{(N\_W^1 + N\_{\tilde{W}}^2) + N\_{\tilde{r}} \cdot t + (N\_{\tilde{Z}}^1 + N\_{\tilde{Z}}^2)} & \text{dla} \quad 25 < t \le 50 \end{cases} \tag{22}$$

where: *N*<sup>2</sup> *<sup>W</sup> –* costs at the stage of producing new elements used in the modernization process, *<sup>N</sup>*<sup>2</sup> *Z* – costs at the stage of post-use management of the replaced elements of the wind power plant.


**Table 2.** List of the materials and elements replaced during wind power plant modernization.

**Table 3.** Mass of materials replaced during wind power plant (WPP) modernization.


In accordance with dependencies (21) and (22), integrated efficiency indicators were determined:


#### 2.4.3. Determining the Payback Time for Modernization

For the wind power plant that has undergone modernization analyzed herein, the payback time for modernization (18) is defined by

$$T\_{M1} = \frac{t\_{LC1} \left(N\_W^2 + N\_Z^2\right)}{N\_W^1 + N\_Z^1} \tag{23}$$

Payback times for modernization were determined for ecological costs in the form of greenhouse gas emissions, substances causing acidification, substances causing eutrophication, and energy costs for the wind power plant in a 25-year and 50-year lifecycle.

#### 2.4.4. Determining the Sustainable Modernization Indicator

For the wind power plant that has undergone modernization analyzed herein, the sustainable modernization indicator is defined by:

$$E\_{M1} = \frac{E(t\_{LC2})}{E(t\_{LC1})} = \frac{E(t\_{LC2} = 50)}{E(t\_{LC1} = 25)} = \frac{\frac{50 \text{J}\_r}{(N\_W^1 + N\_W^2) + 5 \text{N}\_r + (N\_Z^1 + N\_Z^2)}}{\frac{25 \text{J}\_r}{N\_W^1 + 25 \text{N}\_r + N\_Z^1}} = \frac{2\left(N\_W^1 + N\_Z^1 + 25 \text{N}\_r\right)}{\left(N\_W^1 + N\_W^2 + N\_Z^1 + N\_Z^2 + 50 \text{N}\_r\right)}\tag{24}$$

The value of the of the sustainable modernization indicator was determined for the efficiency of ecological costs, of greenhouse gas emissions, of substances causing acidification, of substances causing eutrophication, and of energy costs for the wind power plant in a 25-year and 50-year lifecycle.

The two dependencies above (23), (24) make it possible to assess the modernization process. They may be applied to assess other machines and devices than the one analyzed in this paper.

#### **3. Results and Discussion**

#### *3.1. Costs in the Lifecycle of a Wind Power Plant in a 25-year Lifecycle and in One Subjected to Modernization*

Table 4 presents the results of LCA analysis with the use of Eco-indicator 99 modelling (total value of the eco-indicator, acidification, eutrophication) and IPCC modelling (greenhouse gas emissions) at particular stages of the wind power plant's lifecycle, which are the ecological costs in the model of the integrated ecological efficiency indicator.


**Table 4.** Ecological costs determined with the aid of the LCA method in the material stages of a 25 years and 50-year lifecycle of a wind power plant (the Eco-indicator 99 method).

In each of the lifecycle stages considered, higher environmental costs in the form of a negative impact on the ecosystem and human health were recorded for the 50-year lifecycle of a wind power plant. However, if the values under consideration were compared not to one 25-year lifecycle of a wind power plant, but to the sum of two lifecycles (with disassembly and recycling after 25 years or landfilling the installation plus the installation of a new one along with 25 years of operation), it is evident that the use of the power plant over a 50-year period with modernization being performed after 25 years of operation will result in lower values of the eco-indicator and greenhouse gas emissions by approx. 40–50% and lower emissions of substances causing acidification or eutrophication by approx. 40% (depending on the stage of the lifecycle) compared to the use of two wind power plants during this period. The highest costs in both scenarios occurred at the stage of production of wind power plant components, while the lowest were at the stage of post-use management, where the value of costs is generally reduced because of the possibility of recovering materials and energy from elements ending their lifecycles. In both cases throughout the entire lifecycle there were costs in the form CO2 emissions, while the lowest costs were in the form of emissions causing eutrophication.

Table 5 displays the results of energy consumption analysis at different lifecycle stages which constitute energy costs in the energy efficiency indicator model. In each of the lifecycle stages that were assessed, the greatest energy costs were recorded for the 50-year wind power plant lifecycle. However, again, if these values were compared not to one 25-year wind power plant lifecycle, but to the sum of two lifecycles, it is evident that the use of the wind power plant over a 50-year period results in a reduction of energy costs from approx. 30 to 50% (depending on the lifecycle stage), where most important thing is to reduce energy consumption at the production stage of wind power plant elements.


**Table 5.** Energy costs determined with the use of the LCA method in the material stages of a 25 years and 50 years wind power plant lifecycle (cumulative energy demand (CED) method).

#### *3.2. E*ffi*cieny Indicators from Ecological Costs and Energy Costs and Payback Time for Modernization*

The specified benefits and costs, after substituting them into dependencies (21) and (22), made it possible to determine the values of the integrated ecological efficiency indicator from ecological costs, from greenhouse gas emissions, from emissions of substances causing acidification, from emissions of substances causing eutrophication. It was also possible to determine the values of the integrated energy efficiency indicator.

In the case of a modernized wind power plant, for every 1 Pt of environmental impact approximately 2.32 GJ of electricity is produced, for every 1 t of CO2eq emissions—246 GJ of electricity, for every 1 t of

SO2eq emissions—5247 GJ of electricity, for every 1 t of PO4eq emissions—854,766 GJ of electricity. The energy benefits of a wind power plant after modernization are almost 16 times higher than the costs incurred. What is noticeable is that, in the case of a modernized wind power plant, the values of the efficiency indicator from ecological costs and energy costs increase from 1.4 to 1.6 times in relation to the wind power plant with a 25-year lifecycle (Figures 4–8). These values reflect the better use of costs in the wind power plant's lifecycle to produce benefits when the wind power plant is modernized and its lifecycle is extended by another 25 years.

**Figure 4.** A graphical interpretation of the integrated efficiency indicator from ecological costs for a wind power plant undergoing modernization during a 50-year period of use.

**Figure 5.** A graphical interpretation of the integrated ecological efficiency indicator from CO2 emissions for a wind power plant undergoing modernization during a 50-year period of use.

**Figure 6.** A graphical interpretation of the integrated ecological efficiency indicator from SO2 emissions for a wind power plant undergoing modernization during a 50-year period of use.

**Figure 7.** A graphical interpretation of the integrated ecological efficiency indicator from PO4 emissions for a wind power plant undergoing modernization during a 50-year period of use.

**Figure 8.** A graphical interpretation of the integrated efficiency indicator from energy costs for a wind power plant undergoing modernization during a 50-year period of use.

Figures 4–8 present a graphical interpretation of the efficiency indicator for a wind power plant undergoing modernization during a 50-year period of use.

Figure 4 presents the dependence of the integrated efficiency from ecological costs for a 50-year period of use with modernization performed after 25 years. For the integrated efficiency from ecological costs, energy production at the use-stage was defined as a benefit, and environmental points resulting from LCA analysis were used as costs. Figure 4 shows that the payback time for environmental costs incurred for wind power plant modernization is 5.04 years, and the integrated ecological efficiency after 50 years of use is higher than after 25 years. Unfortunately, at the time WPP elements are replaced, there is an increase in costs, resulting in a decrease in efficiency below the level for the WPP operating for 25 years.

Figure 5 presents the dependence of the integrated efficiency from CO2 emissions for a 50-year period of use with modernization performer after 25 years. For the integrated efficiency from the emissions of CO2 equivalent, energy production at the use-stage was defined as a benefit, and the emissions of CO2 equivalent resulting from LCA analysis were used as costs. Figure 5 shows that the payback time for costs in the form CO2 emissions incurred for wind power plant modernization is 6.36 years, and the integrated efficiency from CO2 emissions after 50 years of use is higher than after 25 years. Unfortunately, at the time WPP elements are replaced, there is an increase in costs, resulting in a decrease in efficiency below the level for the WPP operating for 25 years. In this case, payback with regard to CO2 emissions occurs more slowly than in the case of total ecological costs. This is primarily the result of a significant increase in costs in the form of CO2 emissions during the production and use-stage (Table 4).

In the case of integrated efficiency from the emissions of substances causing acidification (SO2), energy production at the use-stage was defined as a benefit (Figure 6). Costs were constituted by the total emissions of SO2 equivalent at individual stages of the WPP lifecycle. As in the case of the integrated efficiency indicator from CO2 emissions, the payback time for modernization was more than 6 years, which may be affected primarily by the increase in the emissions of substances causing acidification (SO2eq) at the production stage (Table 4). Figure 6 shows that the integrated efficiency from SO2 emissions after 50 years of use is higher than after 25 years.

In accordance with Figure 7, the integrated efficiency indicator from the emissions of substances causing eutrophication (PO4) is higher after 50 years of use than after 25. The WPP returns to pre-modernization efficiency after 6.14 years, which is a value close to the payback time from the emissions of substances causing acidification (SO2). The payback time is mainly affected by the increase in costs in the form of PO4 emissions at the stage at which the replaced WPP elements are produced and by the emissions generated at the use-stage which are connected to maintenance, to oil changes in particular.

In the case of the integrated efficiency from energy costs, energy production at the use-stage was again defined as a benefit (Figure 8). Costs were constituted by total energy consumption at particular stages of the WPP lifecycle. In this impact area, the payback time for modernization was more than 6 years, which is tied primarily to the increase in energy costs necessary to produce the replaced elements and to the energy consumption at the use-stage relating to the energy consumption for maintenance and for the power plant's own needs.

Values of payback time for modernization were determined on the basis of dependence (23). The wind power plant, after modernization, returns to the efficiency it achieved in the last year before modernization after about 6 years, depending on the efficiency area which is being considered (Figures 4–8). The quickest return to pre-modernization efficiency is in the case of environmental efficiency from ecological costs (approx. 5 years) and the longest such return is in the case of efficiency from greenhouse gas emissions (over 6 years) (Figures 4–8).

By modernizing a wind power plant, one achieves an extension, its time of use, as well as an increase in benefits from its functioning in the form of electricity production. Owing to modernization, there was an increase both in the integrated ecological efficiency indicator and in the integrated energy efficiency indicator throughout the wind power plant's lifecycle, despite the fact that, during the years directly proceeding modernization, there was a drop in these values. Every subsequent modernization and replacement of elements will cause a temporary drop in the integrated efficiency indicator, which is related to an increase in ecological costs and energy costs the moment new elements are added.

The modernization considered in this paper included the replacement of the nacelle, rotor, and blades with new ones of the same power. A constant average annual productivity was assumed, one which was the same for the 25-year and 50-year cycles. Replacing the elements with one of greater power would cause a change both in benefits (such as an increase in annual average productivity) and in costs. Costs would potentially be higher than in the case of a rotor of the same power because of an increase in the mass of the elements, and, as previous research has shown [10], the environmental impact of materials used to produce wind power plants is strongly related to their mass (greater mass = higher eco-indicator values). However, considering technological advances and developments in the construction of wind power plants, it is difficult to predict how, over the next 25 years, constructions, production methods, and the materials used to produce such objects will change, which means it is not possible to clearly determine how ecological costs and energy costs will change, the same being true for the values of the integrated efficiency indicators from ecological costs and energy costs.

Of crucial importance is the fact that, as a result of extending the lifecycle of a wind power plant, the use of natural resources is thereby limited, as is the energy used to produce its components, and, not least of all, the post-use management of a part of its elements is postponed.

#### *3.3. Sustainable Modernization Indicators*

Table 6 presents the values of the sustainable modernization indicator in relation to environmental costs and energy costs for wind power plant modernization. Values were determined on the basis of dependence (24). The higher the value of the sustainable modernization indicator, the greater was the increase in the efficiency of the wind power plant because of modernization. The highest value of the sustainable modernization indicator was obtained with respect to the wind power plant's ecological efficiency from ecological costs, and the lowest with respect to the efficiency indicator from the emissions of substances causing eutrophication (Table 6). In the case of all the areas under consideration, an increase was recorded in the efficiency of the use of costs by about 1.44–1.6 times, which is primarily the result of an increase in benefits (total electricity production) over a 50-year lifecycle.


**Table 6.** Values of the sustainable modernization indicator.

#### **4. Summary and Conclusions**

The aim of the paper was achieved by developing a methodology to assess devices subjected to sustainable modernization, i.e., restoration of an object such that it has the properties of a new device. An assessment of the ecological efficiency and energy efficiency of a modernized wind power plant was carried out with the use of an integrated efficiency indicator. Two indicators were proposed to assess modernization efficiency: the payback time of costs for modernization (23) and the sustainable modernization indicator (24).

In the case of the wind power plant analyzed herein, the payback time for costs incurred for modernization (23) came to 6.36 years from greenhouse gas emissions, 6.22 years from energy costs, and, from the remaining costs analyzed, from 5.04 years to 6.15 years.

In the case of the wind power plant analyzed herein, the sustainable modernization indicator from greenhouse gas emissions came to 1.42, and from energy costs—1.45 (Table 6). This means an over 40% increase in the ecological efficiency of the use of energy costs from greenhouse gases and in the ecological efficiency of the use of energy costs after the completion of the lifecycle of the wind power plant subjected to sustainable modernization.

Analysis of the ecological costs throughout the lifecycle of a modernized wind power plant showed that higher environmental costs in the form of negative impacts on the ecosystem and on human health were recorded in the case of the 50-year lifecycle of a wind power plant. If the values under consideration were compared not to one 25-year lifecycle of a wind power plant, but to the sum of two lifecycles (with disassembly and recycling after 25 years or landfilling the installation plus the installation of a new one along with 25 years of operation), it is evident that the use of the power plant over a 50-year period with modernization being performed after 25 years of operation will result in lower values of the eco-indicator and greenhouse gas emissions by approx. 40–50% and lower emissions of substances causing acidification or eutrophication by approx. 40% (depending on the stage of the lifecycle) compared to the use of two wind power plants during this period.

The indicators of modernization assessment proposed herein fit into a higher assessment of the closed-loop economy, that is, an economic system in which the consumption of raw materials, energy, emissions, and waste volume is minimized by creating a closed process loop. Of crucial importance is the fact that, as a result of extending the lifecycle of a wind power plant, the use of natural resources is thereby limited, as is the energy used to produce its components, and, not least of all, the post-use management of a part of its elements is postponed.

Sustainable modernization also fits into a strategy of sustainable development where an increase in electricity production essential for development results in a reduction of ecological and energy costs per unit of energy produced.

The authors call for the creation of incentive programs via legal changes for wind power plant investors and producers which relate to the implementation of sustainable modernization that results in a reduction of the environmental burdens accompanying energy production.

The assessment methodology proposed herein as well as the models developed to assess the effects of modernization are universal and can be applied to other technical facilities as both the benefits and the costs can be expressed in various units adapter to the specific nature of any technical object's operation. The indicators presented herein constitute one of the elements to assess the effects of modernization and are a form of support for operators and those who manage the lifecycles of technical objects.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1996-1073/13/6/1461/s1. Table S1: Lifetime-extension methods and operations (the authors' own work based on [34]). Table S2: Materials and elements used to build a wind power plant.

**Author Contributions:** Conceptualization, R.K. and J.F.; methodology, R.K. and W.K.; software, P.B.-W. and R.K.; validation, P.B.-W., R.K., and A.T.; formal analysis, J.F. and A.T.; investigation, R.K., W.K., and P.B.-W.; resources, R.K.; data curation, R.K., P.B.-W., and W.K.; writing—original draft preparation, R.K and W.K.; writing—review and editing, R.K., W.K., J.F., and A.T.; visualization, W.K. and P.B.-W.; supervision, R.K., J.F., A.T.; project administration, R.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding. The APC was paid by University of Science and Technology in Bydgoszcz

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **The E**ff**ect of CO2 Partial Pressure on CH4 Recovery in CH4-CO2 Swap with Simulated IGCC Syngas**

**Ya-Long Ding 1,2, Hua-Qin Wang 2, Chun-Gang Xu 1,3,4,5 and Xiao-Sen Li 1,3,4,5,\***


Received: 16 January 2020; Accepted: 24 February 2020; Published: 25 February 2020

**Abstract:** To investigate the influence of CO2 partial pressure on efficiency of CH4-CO2 swap from natural gas hydrates (NGHs), the replacement of CH4 from natural gas hydrate (NGH) is carried out with simulated Integrated Gasification Combined Cycle (IGCC) syngas under different pressures, and the gas chromatography (GC), in-situ Raman, and powder X-ray diffraction (PXRD) are employed to analyze the hydrate compositions and hydrate structures. The results show that with the P-T (pressure and temperature) condition shifting from that above the hydrate equilibrium curve of IGCC syngas to that below the hydrate equilibrium curve of IGCC syngas, the rate of CH4 recovery drastically rises from 32% to 71%. The presence of water can be clearly observed when P-T condition is above the hydrate equilibrium curve of IGCC syngas; however the presence of water only occurs at the interface between gas phase and hydrate phase. No H2 is found to present in the final hydrate phase at the end of process of CH4-CO2 swap with IGCC syngas.

**Keywords:** CH4 hydrate; replacement; IGCC syngas; in-situ Raman

#### **1. Introduction**

Natural gas hydrates (NGHs) are ice-like compounds formed by water molecules and gas molecules under low temperature or/and high pressure. Water molecules form cavities with different morphology and dimension by hydrogen bonding and gas molecules fill the cavities by van de Walls force [1]. Gas hydrates in sea floor or permafrost usually exist in three structures such as structure I (sI), structure II (sII) and structure H (sH). According to literature reports, most NGH deposits are sI gas hydrates [2,3] which are composed of six 51262 cages and two 512 cages [1]. The NGHs, attracting much attention for their abundant reserves in the seabed and permafrost regions, have the potential to be an alternative energy resource in the future because it is estimated that the total amount of carbon resources contained in the NGHs is about twice as much as that in the proven fossil fuel reserves [4–9]. To date, many studies have been carried out to exploit CH4 from NGHs deposits and several field trails have been conducted in last 40 years. The exploitation methods mainly include thermal stimulation, depressurization and chemical inhibitor injection, but the disadvantages such as enormous energy consumption, risks of the probably catastrophic landslide and serious environmental pollution are the main issues we are facing [10]. Notably, methane is an about 20 times more efficient greenhouse gas than CO2 [11]. Therefore, effective and environmental friendly production technologies are expected, including CH4-CO2 swap, in-situ combustion and in-situ catalytic oxidation, etc.

The concept of CH4-CO2 swap in natural gas hydrates was firstly proposed by Ohgaki et al., [12–14] and this attracted worldwide attention for its ability to simultaneously produce CH4 from NGHs and sequestrate CO2 in sea sediments. Firstly, the researchers studied the thermodynamic feasibility of CH4-CO2 swap in gas hydrates, [15–18] and the results of Sivaraman's work showed that CO2 hydrate could be formed under more moderate conditions of temperature and pressure than CH4 hydrate and the heat of CO2 hydrate formation (−57.98 kJ/mol) is larger than that of CH4 hydrate dissociation (54.49 kJ/mol) [19]. Ors and Sinayuc [20] conducted an experimental study on the CH4-CO2 swap between gaseous CO2 and CH4 hydrate in porous media at 3.7 MPa and 277.15 K, and their results revealed that the CH4-CO2 swap process mostly took place at the gas-solid surface and the injection of gaseous CO2 caused the dissociation of methane hydrate. Ota and Inomata et al. [21,22] studied the replacement of CH4 in the hydrate by use of liquid CO2 and the CH4 recovery rate of 35% was obtained in 307 h, they also found that the CH4 hydrate decomposed during the replacement process and the decomposition of the large cage (51262) in the CH4 hydrate proceed faster than that of the small cage (512). They also suggested that CH4 hydrate decomposition was probably dominated by rearrangement of water molecules in the hydrate whereas CO2 hydrate formation seemed to be dominated by CO2 diffusion in the hydrate phase. Lee and Ripmeester [23] used solid-state NMR methods to investigate the limiting equilibrium compositions and the distribution of guest molecules over different cages of the mixed hydrate formed from different CO2 concentration gas mixture of CH4 and CO2, they suggested that the ratio of CH4 in large cages to CH4 in small cages of sI hydrate declined steadily to a value with increasing CO2 concentration in the gas mixture. Schicks et al. [24] investigated the conversion of the primary CH4 hydrate into a CO2-rich hydrate using in situ microscopy, confocal Raman spectroscopy and powder XRD, and they suggested that the conversion process was induced by the gradient of the chemical potential between the hydrate phase and the environmental gas phase, and the conversion process could be described as a decomposition and reformation process. The conversion rate depended on the surface area of the hydrate phase and the concentration gradient of one component between the hydrate phase and the gas phase.

With further research, many researchers studied the feasibility of explore CH4 from natural gas hydrates with gas mixture of CO2 and N2 which was the main component of flue gas. Kvamme [25] studied the feasibility of simultaneous CO2 storage and CH4 production from natural gas hydrate using mixtures of CO2 and N2, and suggested that the fast exchange between CH4 and CO2/N2 mixtures was achieved through a new hydrate formation and the adding of N2 into CO2 is advantageous to gas permeability in the hydrates. Also, they suggested there were two primary mechanisms for the conversion of CH4 hydrate into CO2 hydrate, one was the direct solid state conversion [23] and the other was that new CO2 hydrate formed from injected CO2 and free water in the pores [26,27]. Lee et al. [28] explored the swap phenomenon occurred in sI and sII hydrates, and CH4 recovery rates of 64% and 85% were obtained with CO2 and CO2/N2, respectively, and the results showed that the sII hydrate was transformed into sI when sII hydrates were exposed to CO2 and CO2/N2. Beyond that, they also investigated the recovery of CH4 from gas hydrates intercalated within natural sediments using CO2 and CO2/N2 gas mixtures, and they found that the recovery efficiency was nearly identical [2].

In addition to the above laboratory studies, there also were some field tests to produce CH4 from CH4 hydrate reservoirs in permafrost or subsea sediments [29–33]. In 2002 and 2008, the production tests were carried out in the permafrost reservoir of Mallik in northern Canada by injection of hot water and depressurization, and limited amounts of CH4 was successfully exploited in a few days. After that, in 2012, the method of injection of 23% CO2 and 77% N2 was tested in the Alaska North Slope. The gas production were conducted above and near the P–T condition of CH4 hydrate equilibrium respectively, and the total volume of produced gas mixture approached 30,000 m<sup>3</sup> over the whole test period though the mole fraction of CH4 in the gas mixture were different at the different test period [31,34,35]. Then the depressurization technique was firstly used in offshore hydrate reservoirs at the eastern Nankai Trough in 2013, and the total gas production was around 120,000 m3 in six days. Eventually the test was suspended due to the bad weather conditions and sand control problems. Therefore, the commercial NGH exploitation is both highly complex and technologically challenging because the complex geologic structure, harsh engineering conditions and the risks of collapse of marine and potential serious damage to the marine ecosystem.

The changes of temperature and pressure have great influence on CH4 recovery from NGHs via CH4-CO2 replacement. In order to clarify the influence of temperature and pressure and the possible influence of different added gas in CO2, in this work, the changes of gas phase and hydrate phase during the replacement process under different pressure condition, using simulated IGCC syngas as displacement gas, are determined by in situ Raman spectroscopy, powder XRD and gas chromatography.

#### **2. Experimental Section**

#### *2.1. Apparatus*

As shown in Figure 1, the experimental apparatus consists of a gas supply system, a high-pressure vessel used as the hydrate formation or dissociation reactor, a cooling system and detection equipment. The stainless-steel reactor with an inner volume of 100 mL is embedded in the notch which is linked with the cooling system, and two quartz windows are mounted on front and back sides of the reactor for viewing the swap process and applying Raman measurement. In the cooling system, the ethylene glycol solution with the volume ratio of 1:3 are used as coolant, and the temperature can be controlled in the range of 253.15–303.15 K, and a Pt100 thermocouple (JM6081) supplied by Jiangsu Hongbo machinery manufacturing co. LTD (Nantong, Jiangsu, China) with uncertainties of ±0.1 K is employed to measure the temperature.

**Figure 1.** Schematic of experimental apparatus.

The gas component is determined by an Agilent 7890A gas chromatograph (GC, Agilent Technologies Inc., Palo Alto, CA, USA). The experimental CH4 gas with a purity of 99.9% and 39.9% CO2 +60.1% H2 gas mixture were supplied by Foshan Huate Gas Co., Ltd. (Foshan, China). The deionized water with the resistivity of 18.25 mΩ cm−<sup>1</sup> is produced with an ultra-pure water machine from Nanjing Ultrapure Water Technology Co., Ltd. (Nanjing, China).

The Raman spectra are obtained from on a LabRam Raman spectrometer (Jobin Yvon, Paris, France) with a 50 times tele lens and a single monochromator of 1800 grooves/mm grating and a multichannel air-cooled charged-coupled device (CCD) detector. In addition to this, the Raman spectrometer uses an Ar-ion laser source, which emits a 532 nm line with a power of 100 mW. The silicon (Si) crystal standard of 520.7 cm−<sup>1</sup> is employed to calibrate the subtractive spectrograph.

The XRD patterns are recorded at 193 K on a D/MAX-2500 device (Rigaku, Tokyo, Japan) using graphite-monochromatized Cu Kα1 radiation (λ = 1.5406 Å) in the θ/2θ scan mode. The XRD experiments are carried out in step mode with a fixed time of 3 s and a step size of 0.03◦ for 2θ = 10–60◦ for each hydrate sample.

The gas component in the collected gas samples and the final dissociated gas was analyzed on an Agilent 7890A GC (Agilent Technologies Inc., Palo Alto, CA, USA), equipped with a flame ionization detector (FID) and thermal conductivity detector (TCD). Besides that, the gas samples are detected with the method of uniform heating from 298.15–523.15 K, and H2 (30 mL/min) is used as combustion gas, air (400 mL/min) is used as combustion-supporting gas and helium (25 mL/min) is used as make-up gas.

#### *2.2. Procedure*

Deionized water (60 mL) is injected into the reactor, followed by cooling down the system to 274.15 K and removing the air in the gas phase by injecting CH4 gas continuously into the water from the bottom of reactor at a system pressure of 1.0 MPa, and after that the system is pressurized to 4.5 MPa. During the CH4 hydrate sample preparation period, the intake of CH4 gas and the agitation of the deionized water are maintained so that the injected CH4 gas could dissolve well in the water and the experimental CH4 hydrate sample forms evenly in the water phase. About five days later, once there is no water in the reactor as determined by Raman analysis, the CH4 hydrate formation reaction is deemed finished. Hereafter, the simulated IGCC syngas of 39.9% CO2 and 60.1% H2 gas mixture is injected into the reactor from bottom of reactor, and the CH4 gas above the hydrate is discharged at the same time under the stable pressure of 4.5 MPa. Once the CH4 in gas phase is lower than 2%, the outlet valve is shut off and then the gas mixture of CO2 and H2 gradually displaces CH4 from the hydrate. In the process of the replacement, the composition of the gas phase, gas-hydrate interface and the hydrate phase are determined through Raman spectroscopy every 24 h, and the component of the gas phase is also determined through GC analysis. At the end of replacement process, the hydrate is dealt with liquid nitrogen and the gas phase is removed, then the reactor is placed at room temperature for dissociation of the hydrate. Meanwhile, one same replacement reaction is conducted in another same reactor and at the end of the replacement process the hydrate is took out for powder XRD detection. These two experiments are labeled as experiment 1.

In addition to that, another two experiments, which are labeled as experiment 2, were conducted under the same conditions as above method, except for the fact the hydrate formation and the replacement processes are carried out at 6.0 MPa.

#### **3. Results and Discussion**

For monitoring the changes of the hydrate phase and gas phase during the replacement process, three fixed points are selected for Raman detection as point "A" in the hydrate phase, point "B" at the interface of gas phase and hydrate phase and point "C" in the gas phase.

After the formation of pure CH4 hydrate, the Raman spectra and powder XRD pattern are obtained and shown in Figure 2. As shown in Figure 2a, all the hydrate samples have the representative Raman peaks of sI hydrate. The strong peak at 2905 cm−<sup>1</sup> and relatively weak peak at 2915 cm−<sup>1</sup> with the intensity ratio of 3:1 are attributed to the C-H symmetric stretching vibration of CH4 molecules in large cages and small cages of sI hydrate, respectively. The peak at 2917 cm−<sup>1</sup> which is detected in the gas phase is attributed to C-H symmetric stretching vibration of CH4 gas. In addition, the powder XRD pattern of the hydrate samples are shown in Figure 2b, it can be seen that the hydrate samples show all peaks of the sI hydrate and little ice Ih. It can be concluded that all the formed hydrates are sI hydrates.

**Figure 2.** The Raman spectra for point "A", "B" and "C" (**a**) and powder XRD pattern for experiment 1 and 2 (**b**).

Once the water is fully converted into hydrate, the gas is quickly replaced by CO2/H2 under the same temperature and pressure conditions. During the process of replacement, the component changes in hydrate phase, gas-hydrate interface are determined by in situ Raman every 24 h until the intensities of Raman peaks obtained from point "A" show no changes.

Figure 3 shows the changes of Raman spectra of CO2 hydrate at point "A" during the replacement process for both experiments, and the picture a and b represent the low pressure (4.5 MPa) and high pressure (6.0 MPa) experiments, respectively. The Raman peaks at 1277 and 1380 cm−<sup>1</sup> correspond to the C = O stretching vibration and bend vibration, respectively. Because of Fermi splitting, we cannot judge which cages are occupied by CO2. It can be seen that the Raman peak intensity corresponding to CO2 hydrate increases gradually for both low and high pressure experiments, but the growth rate for low pressure experiment is larger than that for high pressure experiment. The possible reason is that the partial pressure of CO2 in the gas mixture of the low pressure experiment (1.8 MPa) is lower than that of high pressure experiment (2.4 MPa), bringing about the smaller driving force of CO2 molecule in low pressure experiment, resulting in the retard of CO2 hydrate formation. On the contrary, the driving force of the CO2 molecules in the high pressure experiment is large enough to form CO2 hydrate, and the formed CO2 hydrate covers the CH4 hydrate and impedes the gas mixture from contacting CH4 hydrate.

**Figure 3.** The Raman spectroscopy change of CO2 hydrate at point "A" for experiment 1 (**a**) and experiment 2 (**b**), where the 1d, 2d, 8d represent the first, the second, the eighth day of the experiment process.

For the same smaller driving force reason more and more water results from the decomposition of the hydrate phase and is carried by the gas mixture up to the interface of the gas phase and hydrate phase in the low pressure experiment, and the more CO2 hydrate is formed at the interface, as shown in Figure 4. The Raman peak of CO2 hydrate for low pressure experiment increases faster than that for high pressure experiment, and finally, a stronger intensity of peak for low pressure experiment is obtained. Furthermore, it can be observed that there is significant water phase at the interface between gas phase and hydrate phase. However, a similar phenomenon has not been observed for the high pressure experiment in which the presence or not of the water from decomposition of hydrate can only be determined by in situ Raman, as shown in Figure 5, where the pictures a and b show the photo of vessel at the first and sixth day of replacement reaction of low pressure experiment, and the pictures c and d show the Raman peak change between 3100 and 3500 cm−<sup>1</sup> which is attributed to O-H symmetric stretching vibration of H2O molecules during the replacement reaction of high pressure experiment [36].

**Figure 4.** The Raman spectroscopy change of CO2 hydrate at point "B" for experiment 1 (**a**) and experiment 2 (**b**), where the 1d, 2d, 8d represent the first, the second, the eighth day of the experiment process.

**Figure 5.** The picture of vessel at the first (**a**) and sixth (**b**) day of replacement reaction for experiment 1, and the Raman peak change between 3100 and 3500 cm−<sup>1</sup> during experiment 2 (**c**,**d**).

It can be seen that the Raman peak shows sharp appearance at about 3160 cm-1 and short appearance at about 3400 cm−<sup>1</sup> which are attributed to O-H symmetric stretching vibration of H2O molecules in sI hydrate, then along with the replacement progress the peak appearance changes to low peak at 3200 cm−<sup>1</sup> which is attributed to Fermi resonance between O-H stretch and bending mode of H2O molecules in water phase, and high peak at 3400 cm−<sup>1</sup> which is attributed to the O-H symmetric stretching vibration of H2O molecules in water phase. This observation strongly verifies the decomposition of hydrate during the replacement process, though the degree of decomposition is different between low pressure experiment and high-pressure experiment. The presence of water from decomposition of hydrate phase may improve the permeability of hydrate phase and gives one reason for the higher CH4 recovery yield in the low-pressure experiment.

During the replacement process, when the CH4 hydrate is attacked by the CO2/H2 gas mixture, the hydrate lattice is decomposed and then the water from the decomposition of CH4 hydrate forms CO2 hydrate with the CO2 gas molecules, so the amount of CH4 hydrate at monitoring point "A" gradually decreases to a certain extent, as shown in Figure 6, which represents the changes of Raman spectra of CH4 hydrate at point "A" during replacement process for both low and high pressure experiments. As can be seen that the Raman peaks of CH4 hydrate gradually decrease during the replacement process, and the drop rate in the low pressure experiment is higher than that in the high pressure experiment, and the result is consistent with the rule of the increase of CO2 hydrate in the Figure 3, where the increment of Raman peaks for CO2 hydrate in low pressure experiment is higher than that in high pressure experiment. Apart from this, another result that can be seen from the picture *b* in Figure 6 is the changes of ratio of Raman peak intensity at 2905 cm−<sup>1</sup> to that at 2915 cm−<sup>1</sup> in the high pressure experiment. As known that the peak at 2905 cm−<sup>1</sup> represents the CH4 molecules in large cages of sI hydrate and the peak at 2915 cm−<sup>1</sup> stands for the CH4 molecules in small cages. During the high pressure replacement process, the intensity ratio of peak at 2905 cm−<sup>1</sup> and peak at 2915 cm−<sup>1</sup> changes gradually from premier 3:1 to final 2:1 and this decreasing ratio demonstrates that the replacement reaction only conducts in the large cage of the sI hydrate. The reason for this result may be the fast formation of CO2 hydrate at the interface between gas phase and hydrate phase limits the injected gas mixture further contacting CH4 hydrates.

**Figure 6.** The Raman spectroscopy change of CH4 hydrate at point "A" for experiment 1 (**a**) and experiment 2 (**b**), where the 1d, 2d, 8d represent the first, the second, the eighth day of the experiment process.

For preventing the released CH4 from forming mixed hydrate with CO2 gas, the gas injection and simultaneous discharge of gas mixture is conducted every 24 h, following the Raman detection, to decrease the CH4 component concentration to less than 2% again, and the gas samples are collected at the beginning and end of the ventilation process and analyzed by gas chromatography. The results are shown in Table 1 and the data in Table 1 is plotted in Figure 7. It can be seen that in the low pressure experiment the CH4 component concentration increases from less than 2% to 18.60, 17.18, and 7.48 mol% respectively in the first 3 days and tends to remain unchanged in the next 5 days

till stabilizes at 1.0 mol% in the last 2 days. However, in the high-pressure experiment, the CH4 component concentration only increases from less than 2% to 9.71 and 7.67 mol% in the first 2 days and the increment tends to remain unchanged in the following days till stabilizes at 0.5 mol% in the last 2 days. What should be noted is that the CO2 percent decrement is a little larger than the of CH4 percent increment in both experiments. This result could be attributed to the decomposition of CH4 hydrate and the new formation of CO2 hydrate, as well as the low-pressure experiment shows a greater extent of decomposition of CH4 hydrate than the high pressure experiment.

**Table 1.** The increment of CH4 fraction and the decrement of CO2 fraction in the collected gas samples in experiment 1 and experiment 2.


**Figure 7.** The increment of CH4 fraction and the decrement of CO2 fraction in the collected gas samples in experiment 1 and experiment 2.

When the Raman peak intensity of CH4 hydrate and CO2 hydrate at the point "A" and "B" show no changes and the CH4 percent increment in the collected gas samples is less than 0.5, the replacement process is considered finished. Thereafter, the vessel is cooled with liquid nitrogen and the gas phase is withdrawn quickly by a vacuum pump, then the vessel is placed in the atmosphere for dissociation of the hydrate phase and the gas samples of decomposed hydrate phase are detected by gas chromatography. The gas chromatography results of the gas from decomposition of the final hydrate are shown in Table 2. As seen, the CH4 residues in the hydrate phase after replacement process are 29.29% and 67.76%, indicating that the CH4 recovery rates are 70.21% and 32.24% for low pressure experiment and high-pressure experiment, respectively. Also, the same low-pressure experiment and high pressure experiment are conducted, the final hydrate phases are removed from vessel and

detected by powder XRD to determine the structure of the hydrates. The powder XRD patterns of the hydrates are shown in Figure 8, the peaks with blue arrows are attributed to the cubic crystal system which represents the sI hydrate. It can be concluded that the hydrate structure has no change during the replacement. In both low and high pressure experiments, there is no presence of H2 in hydrate phase on the basis of Raman results, thus it can be concluded that the H2 molecules does not occupy cages of the hydrate, they may only play a promotion role during the replacement process. Comparing with the results obtained previously, where the replacement process of CH4 hydrate is conducted with gaseous CO2, the obtained CH4 recovery efficiency was 50%. Due to the adding of H2, the CH4 recovery efficiency was increased from 50% to 70.29%, it could be referred that the stability of the cages is broken and the CH4 molecules in the cages could spilled over when the H2 molecules contact with the hydrate lattices. However, the lattices could form again once CO2 molecules step into the cages. The higher CH4 yield for the low pressure experiment is due to the lower driving force of CO2 in the gas mixture, and thus the disturbed hydrate lattices resulting from the connection of hydrate with H2 molecules could not occupied by CO2 molecules in good time, which causes the decomposition of hydrate. However, during the high-pressure replacement process, the CO2 molecules can occupy the disturbed hydrate cages for the high CO2 partial pressure, and the hydrate lattice stabilizes again.

**Table 2.** Gas compositions in the dissociated gases after the completion of replacements for experiment 1 and experiment 2.

**Figure 8.** The PXRD pattern of gas hydrate phase after replacement process of experiment 1 and experiment 2.

#### **4. Conclusions**

The replacement of CH4 from CH4 hydrate with a gas mixture of CO2/H2 at the low pressure of 4.5 MPa and the high pressure of 6.0 MPa are investigated. The experimental results show that the CH4 yield for the low-pressure experiment is 71% and the value for the high pressure experiment is 32% and H2 molecules are not present in the final hydrate phase after the two replacement processes. Water appears obviously during the low-pressure experiment and the water from decomposed hydrate phase is not obvious during the high pressure experiment.

**Author Contributions:** Conceptualization, X.-S.L.; Investigation, Y.-L.D. and H.-Q.W.; Methodology, C.-G.X.; Supervision, X.-S.L.; Writing—original draft, Y.-L.D.; Writing—review & editing, C.-G.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** We are grateful for the support of the National Natural Science Fund of Guangdong Province, China (2019A1515011490), the Key Program of National Natural Science Foundation of China (51736009), the Special project for marine economy development of Guangdong Province (GDME-2018D002), Key Research Program of Frontier Sciences, CAS (ZDBS-LY-SLH041), the CAS Science and Technology Apparatus Development Program (YZ201619), Frontier Sciences Key Research Program of the Chinese Academy of Sciences (QYZDJ-SSW-JSC033).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


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