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Article

Supply Chain Model for Mini Wind Power Systems in Urban Areas

by
Isvia Zazueta
1,
Edgar Valenzuela
1,*,
Alejandro Lambert
1,
José R. Ayala
2 and
Rodny Garcia
3
1
Faculty of Engineering, Autonomous University of Baja California, Boulevard Benito Juárez S/N, Mexicali 21900, Mexico
2
Institute of Engineering, Autonomous University of Baja California, Boulevard Benito Juárez S/N, Mexicali 21900, Mexico
3
Chint Power of Mexico, Blvd. Lázaro Cárdenas 119, Mexicali 21600, Mexico
*
Author to whom correspondence should be addressed.
Resources 2025, 14(3), 38; https://doi.org/10.3390/resources14030038
Submission received: 9 December 2024 / Revised: 9 February 2025 / Accepted: 19 February 2025 / Published: 26 February 2025
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Abstract

:
The pursuit of energy security has become one of the most important challenges facing modern societies worldwide. The increase in energy consumption and the need to promote sustainability puts pressure on power generation systems. In this context, renewable energy sources have become a favorable option to improve both energy security and sustainability while promoting the use of domestic energy sources. The supply chain is an optimized methodology that includes all necessary activities to bring a product to the final consumer. Traditionally applied in the manufacturing industry, recent evidence shows its successful implementation in various renewable energy sectors. In this work, a novel methodology based on a supply chain was designed to evaluate the feasibility of mini wind power systems in urban areas in an integrated and measurable manner. The main contribution lies in the integration of several different approaches, currently recognized as the most relevant factors for determining the viability of wind energy projects. A five-link supply chain model was proposed, which includes an evaluation of wind potential, supplier network, project technical assessment, customer distribution, and equipment final disposal. Specific metric indicators for each link were developed to evaluate technical, legislative, and social considerations. The methodology was applied in a case study in the city of Mexicali, Mexico. The findings show that although wind as a resource remains the most important factor, local government policies that promote the use of renewable energy, the supplier’s availability, qualified human resources, and spare parts are also of equivalent significance for the successful implementation of mini wind power systems.

1. Introduction

1.1. Wind Power

The depletion of fossil fuels, coupled with environmental concerns and climate change mitigation strategies, has shifted global attention toward the feasibility and development of clean, reliable energy resources. The growing demand for energy, driven by technological advancements and the growing global population, along with the quest for sustainability, is placing significant pressure on power generation systems. In this context, domestic renewable energy sources have emerged as a favorable option to promote both energy security and sustainability. Among these, wind power stands out as one of the most sustainable energy sources [1].
Factors such as its widespread distribution and availability have driven wind energy to grow worldwide at an annual rate of nearly 20% over the past decade. During this period, countries like China, Germany, and the United States have doubled their installed capacity [2]. Among renewable sources, wind power boasts one of the highest energy densities, even surpassing photovoltaic systems [3]. Additionally, wind energy offers significant environmental benefits regarding land use compared to hydropower and exhibits excellent circularity at the end of its equipment’s lifecycle. Wind power is a mature technology that can be effectively utilized in both large and small-scale generation systems, such as wind farms or domestic installations. When combined with other energy sources, wind power enhances energy security and is a favorable option for countries committed to diversifying their energy generation with renewable sources.
Despite its advantages, wind power faces significant challenges. Wind energy generation schemes are generally complex, multi-component projects that encompass technical, economic, and political elements. While the location of a wind power facility is primarily determined by on-site wind properties, socio-political considerations also play a crucial role in the operation and success of wind power plants. Numerous methodologies have been developed to evaluate the feasibility of wind power projects, all of which include the forecasting of meteorological aspects, such as wind characteristics, speed, and seasonality [4,5,6,7,8].
Some models prioritize economic aspects, considering the initial investment, electricity prices, and the uncertainty in wind resource availability as key determinants of the economic success of power projects [9,10,11,12,13]. Other models emphasize the importance of incorporating social and political implications for the successful implementation of wind power generation projects [14,15,16,17,18]. In urban areas, there is a growing interest in evaluating the potential for wind power generation as a measure to promote energy security and sustainability. Favorable estimates that help reduce uncertainty have been obtained through models that incorporate Weibull distribution and on-site wind measurements and analysis [19,20,21].
In general, existing methodologies have focused on three main areas: (i) the evaluation of local wind potential, (ii) financial analysis to determine the economic viability of the project, and (iii) medium and long-term political certainty. Nevertheless, other factors, such as local policy frameworks, the availability of qualified manpower, and consistency with energy billing schemes, have demonstrated a strong direct relationship with the success of renewable energy generation investments.

1.2. Supply Chain as a Framework for Urban Wind Power Projects Analysis

The supply chain is a methodology used for the design, planning, and operation of companies, primarily focusing on production processes to achieve financial and operational profitability [22]. In recent decades, adaptations of the supply chain have been proposed in various areas and with different approaches, such as optimizing urban transport systems [23], enhancing logistics and traceability for manufacturing industries [24], and improving agricultural production processes [25], among others. Generally, after its implementation, the supply chain methodology has proven capable of improving sustainability, reducing waste, and increasing profitability [26].
In the field of renewable energy, previous research has highlighted the integration of the supply chain as a multi-criteria analysis to promote sustainable business practices in terms of economic, social, and environmental factors [27,28,29]. Poulsen and Lema (2017) address the role of the supply chain in the diffusion process of renewable energy, using the case of the offshore wind energy sector in Europe and China to assess the supply chain’s contribution to regional green transformation. Their study identified opportunities for improvement in equipment transportation, qualified manpower availability, and wind energy legislation. Similarly, companies involved in energy project development have leveraged the multi-component structure of the supply chain methodology not only for evaluating wind potential but also for addressing financial requirements, infrastructure development, regional regulations, policies, and environmental and socioeconomic aspects. However, the incorporation of the supply chain methodology has primarily focused on large-scale power plants. There is a research gap regarding mini-scale wind energy projects, which have considerable potential to contribute to distributed generation and penetrate the renewable energy market [14,30].
Despite the positive impact and applicability of the supply chain in the renewable energy sector, some authors have identified areas that require improvement. Previous research emphasizes the necessity of quantitative analysis in supply chain evaluations to enhance the transparency and reliability of performance indicators [31,32]. In particular, Ahi and Searcy (2015) highlighted the need to develop clear metrics that use standardized language when addressing the same core issues, thereby promoting greater comparability in the evaluation of different supply chains [15,33]. Similarly, when analyzing the role of the supply chain in developing countries, deficiencies have frequently been found in equipment marketing routes, making it a priority to evaluate the availability of equipment suppliers and qualified manpower [34].
In this work, a supply chain model was designed to evaluate distributed generation projects for mini wind energy in urban areas. The main contribution lies in the integration of several different approaches, currently recognized as the most relevant factors for determining the viability of wind energy projects. Unlike other approaches, this proposed methodology is based on quantitative analysis and clear objectives, highlighting both their potentialities and the barriers that could limit their implementation.

2. Methodology

2.1. Case Study

The case study was conducted in Mexicali, Mexico, located in the extreme northwest of the country on the border with the United States, at co-ordinates 32°39′48″ N latitude and 114°42′ to 115°56′ W longitude. Mexicali has a predominantly hot desert climate (BWh), with summer temperatures reaching up to 52 °C and winter temperatures ranging between 7 and 23 °C. The case study focuses on the domestic sector, which, according to the weather conditions, is classified under the 1F rate. This rate is divided into four categories, with a maximum limit of 30,000 kWh/year, after which the High Consumption Rate (HCR, or DAC in Spanish) scheme applies to all consumption above this threshold. A total of 364,046 1F users and 60 HCR clients were registered for the study. Table 1 describes the 1F rates in detail, with four categories that increase energy pricing according to consumption levels.
Under these criteria, the impact of various wind power generators was assessed by assigning a grade to each machine according to its contribution to the total energy consumption. The average annual consumption for 1F and HCR users is presented in Figure 1.
To calculate the equivalent percentage of the rate, the consumption of each segment was added cumulatively. The basic category, which goes up to 2250 kWh/yr, represents the first 7.5% of the 1F rate. The low intermediate and high intermediate categories account for the next 3900 and 2400 kWh/yr segments, respectively. When added to basic consumption, these categories comprise 20.5% and 28.5% of the total, respectively. The surplus category applies to all consumptions ranging between 8550 kWh/yr (the sum of the previous levels) and 30,000 kWh/yr. Finally, the HCR rate is applied to all users who exceed the limit of 30,000 kWh/yr and imposes a fixed price for all their consumption, which can be up to eight times the price per kWh compared to the basic 1F rate.
According to Figure 1a, the average historical consumption for 1F users has ranged between 6000 and 6500 kWh/yr, while HCR consumers have an average consumption of 35,000 kWh/yr, showing a decline in the past few years. This trend can be attributed to the local desert climate, where air conditioning systems are the predominant domestic load. Under these conditions, the consumption trend depicted in Figure 1b is typically related to the continuous improvement in the energy efficiency of these systems [35,36,37].
A five-link model based on the supply chain framework was designed to facilitate a comprehensive evaluation of projects for developing mini wind power systems in urban areas. Six mini wind turbines from three different brands (AEOLOS, ENAIR, and COLIBRI) with power generation capacities ranging from 200 W to 10 kW were evaluated. As with most wind energy projects, the first step was to analyze the availability and characteristics of the on-site wind resource. In the same way, a close relationship has been observed between the availability of qualified manpower and the existence of a marketing network for equipment and spare parts, which is crucial for the successful development of renewable energy projects [38,39,40]. Therefore, the existence and quality of commercial suppliers, as well as specialized manpower, were analyzed. Subsequently, the generation impact on the billing for electrical consumption was determined for local domestic users [41,42]. Finally, the potential for a circular lifecycle or life extension of mini wind turbines was estimated [43,44].

2.2. Supply Chain Proposed Model

The proposed methodology incorporates five elements: (1) evaluation of wind potential, (2) supplier network, (3) project technical assessment, (4) customer distribution, and (5) final disposal of equipment. The model is summarized in Figure 2.
In accordance with the reviewed literature, the evaluation of each link incorporates parameters designed to address the key barriers identified for the development of the wind energy sector [45,46].
After each link was analyzed, the results were indicated using a “Traffic Light” color code. Red represents the lowest scores (zero and one point), yellow corresponds to two points, and green indicates the highest score (three). Consequently, the highest scores represent the most favorable conditions for the successful development of the project.

2.3. Supply Chain Links Evaluation Criteria

The evaluation of the first link in the supply chain, evaluation of wind potential, involves analyzing on-site wind conditions using three indicators: (i) the wind turbine start-up speed in meters per second (m/s), (ii) the capacity factor in percentage (%), and (iii) the criteria used to estimate the significance of energy generation contribution relative to user consumption. This calculation aligns with both domestic and international renewable energy regulations, including the energy contribution required to meet renewable generation objectives [47,48,49].
The second link in the supply chain is supplier network; this was structured under quality, delivery time, technology capacity, cost-price, customer support, and supply network. These criteria were originally proposed by Taherdoost and Brard (2019) to standardize the evaluation and selection of suppliers [50].
Project technical assessment was the third link in the supply chain. The instrument included 12 statements classified into four sections that describe the technical requirements necessary for the interconnection of mini wind power systems in accordance with established local legislation. The sections are (i) distributed generation, (ii) interconnection of distributed generation power plants, (iii) contract models, and (iv) compliance and surveillance.
The fourth link, customer distribution, evaluates the characteristics of the clients or market targeted by the project, such as the average consumption range (kWh/yr) and tariff specifications. This link provides an estimated impact of the project based on the client’s energy requirements and determines the minimum necessary contribution of renewable energy to access more economical electricity rates [51,52,53].
Finally, the equipment final disposal link was evaluated by analyzing the main component materials in mini wind power turbines and their applicable reverse logistics processes. This analysis identified materials with greater potential for life extension and processes with low resource consumption. The component materials were obtained directly from the technical datasheets provided by the manufacturers; the applicable processes were established according to ISO 14040 [54] and are in agreement with the International Electrotechnical Commission (IEC). Each link and its methodological approaches are summarized in Table 2 [55].
After assigning a value to each of the five links in the supply chain, the scores are summed and expressed as a percentage of the maximum possible, as illustrated in Figure 3. A critical evaluation (marked in red) is indicated when the score is between 0 and 60%. In this case, the supply chain model does not recommend proceeding with the development of the project. A regular evaluation (marked in yellow) is given if the overall score is between 60 and 80%, indicating acceptable conditions for the project’s development. However, it is recommended to address the points identified as risks increasing the project’s chances of success. Finally, a good evaluation (marked in green) is assigned when the score is between 80 and 100%, suggesting that the critical parameters of the project, both technical and legislative, are favorable for development, with a high likelihood of success (Figure 3).

2.4. Evaluation of Wind Potential

Six small wind turbines from the brands AEOLOS, ENAIR, and COLIBRI, with power generation capacities ranging from 200 W to 10 kW, were evaluated based on the datasheets provided by the manufacturers. Ten-minute wind speed data were collected at a sampling rate of 10 Hz for a full year (2017) using a Campbell Scientific CSAT3B sonic anemometer positioned at a height of 20 m in an area free of buildings or obstacles. The scoring criteria for link 1 were determined by considering that typical start-up wind speeds range between 1 and 6 m/s for horizontal mini wind turbines with capacities from 40 W to 10 kW [56,57,58]. The indicators and the scores for link 1 are summarized in Table 3 [59,60,61,62].
Consequently, in the start-up wind speed scoring criteria, a score of 0 was assigned to machines with a start-up speed of 4 m/s and higher, 1 for devices that start at 3 m/s, a score of 2 for those that start between 1 and 2 m/s, and a score of 3 for those under 1 m/s. For the capacity factor, four operational intervals were selected: 0–5% scored a 0, 6–10% scored a 1, 11–15% scored a 2, and a capacity factor of 16–20% scored a 3 [59,60,61]. Additionally, an indicator for generated energy was included to positively evaluate machines that contribute enough renewable energy to meet local government policy objectives. A wind turbine receives a maximum score of 3 if it can contribute at least 5% to consumption, as specified under RES/142/2017 from the Energy Regulatory Commission [62]. Table 3 summarizes the proposed scoring criteria for link 1.

2.5. Supplier Network Link

Two different instruments were developed to evaluate the supplier network link. The survey was structured around six criteria originally proposed by Taherdoost and Brard (2019) [50]. Four descriptions are added to each criterion to quantify them using a Likert scale. The values range from 1 to 5, where 1: totally disagree, 2: disagree, 3: neither agree nor disagree, 4: agree, 5: totally agree. The survey instrument is shown in Table 4.

2.6. Project Technical Assessment Link

The project technical assessment was conducted using a checklist of 12 statements classified into four sections, as depicted in Table 5. This instrument describes the technical requirements necessary for the interconnection of mini wind power systems. The first section covers distributed generation, the second addresses the interconnection of distributed generation power plants, the third deals with contract models, and the final section analyzes compliance and surveillance. These statements were proposed in accordance with applicable local regulations, specifically articles 694 on mini wind power systems and 705 on interconnected power generation sources of NOM-001-SEDE-2012 [64].

2.7. Customer Distribution

To evaluate the customer distribution link, the energy generated by the wind turbines was compared with the domestic sector average consumption to estimate the impact of the energy produced in terms of improvements in the tariff scheme. Local domestic electricity pricing is divided into two main categories: the 1F tariff and the HCR tariff. Users fall into one category or the other depending on their average consumption over the past 12 months. If the monthly average is lower than 2500 kWh, the user is classified under the 1F tariff. Otherwise, the user falls into the HCR scheme.

2.8. Equipment Final Disposal

The final disposal scoring criteria were proposed based on the reverse logistics processes for each component and material described in the wind turbine datasheet. Four scenarios were reviewed and categorized according to their environmental suitability: (i) reuse, (ii) remanufacturing, (iii) recycling, and (iv) disposal. If multiple processes are applicable to the same component or material, their scores are summed. Therefore, the more reverse logistics processes that apply to the components and their materials, the higher the final rating, which is then expressed as a percentage. Among the four applicable processes, reuse is weighted as the one with the lowest resource consumption and receives 4 points. The rating decreases for the other processes, down to 1 point assigned to the disposal process. Table 6 depicts the score of each component of the wind turbines.
According to Table 6, the materials with the highest life extension include steel, neodymium magnets, zinc, inverter electronics, and wind turbine on/off controllers. Aluminum and copper are also in this category, excluding those materials located in the blades, terminals, wiring, and connections. In contrast, polymeric materials received a lower score compared to metallic-based materials due to their remanufacturing capabilities.

3. Results and Discussion

3.1. Evaluation of Wind Potential Link

The Weibull distribution has been used to determine wind speed distribution, and energy generation was calculated based on the individual parameters of each wind turbine. The results for link 1 are shown in Table 7; the scores were obtained by contrasting with Table 3.
The AEOLOS 200 received the highest score due to its low start-up speed and highest capacity factor, as per the criteria in Table 3. However, its energy generation was among the two lowest, with only 222 kWh/yr, just above the AEOLOS 400, which generated 207 kWh/yr. On the other hand, the largest generator, the COLIBRI 10000, was rated with 2 points due to its energy generation and capacity factor, but it had the drawback of a high start-up wind speed. Medium-sized generators like the ENAIR 5000 also received 2 points, penalized by their low-capacity factor, while the ENAIR 3000 and COLIBRI 5000 were the worst evaluated, with only 1 point each. Similar analyses in urban areas were conducted by Vallejo Díaz, Ricci, and Mitkov, using Computational Fluid Dynamic simulations to estimate generated energy. They emphasize the importance of in situ wind measurements to avoid potential overestimations [65,66,67].

3.2. Supplier Network and Project Technical Assessment Links

The combined results for both the supplier network link and the project technical assessment link are shown in Table 8.
In the analysis of the supplier network link, suppliers X and Y obtained the best scores, considering their remarkable delivery time and process operation based on an established quality system. Supplier Z received a slightly lower grade because it failed in the technology and capacity criteria. The analysis showed that organization Z conducts limited research on advances in the field of wind turbine technology and lacks information on the status of its largest competitors. However, in the project technical assessment link, supplier Z was the top-ranked, owing to the high technical capacity of its human resources, which demonstrated the potential to provide valuable advice and support to clients at every stage of the process.

3.3. Customer Distribution Link

The average consumption for 1F and HCR users is illustrated in Figure 4. Additionally, the energy generated by each wind power turbine is shown to visualize each generator’s contribution to average user consumption and its potential impact on changing the user’s tariff scheme. For 1F users with an average energy consumption of 6595 kWh/yr, the Aeolos 200 and Aeolos 400 only made contributions of 222 and 207 kWh/yr, respectively. This falls within the first 7.5% of the 1F rate and is considered basic consumption. Medium-sized turbines, such as the ENAIR 3 kW and 5 kW, showed only small improvements, contributing 301 and 644 kWh/yr, respectively. In contrast, under the same conditions, the COLIBRI 5 kW performed better, generating 1250 kWh/yr and potentially producing around 80% of total consumption for 1F users.
In the same figure, when analyzing the wind power contribution to HCR users, only the COLIBRI 10 kW was capable of producing enough energy to make a shift in the tariff scheme from HCR to 1F possible. In contrast, although the turbines of the ENAIR 3 kW and 5 kW allow energy savings, their generation was not enough to produce a change in the tariff scheme. The AEOLOS 200 W and 400 W turbines contributed only marginally in terms of energy savings.
A more detailed analysis of the AEOLOS 200 W and 400 W turbines reveals that even when operating 24/7 under optimal wind conditions, their power generation is limited to 1728 and 3456 kWh/yr, respectively. Both values fall below the 5000 kWh/yr required to shift the tariff scheme from HCR to the 1F pricing rate. When considering these results, wind turbines smaller than 5 kW should not be recommended under this tariff scheme. Table 9 shows the scores given to each device according to its energy contribution.

3.4. Equipment Final Disposal Link

Three national companies specialized in the sale of mini wind turbines were sampled, identified as supplier X, supplier Y, and supplier Z. Figure 5 shows the results for the final disposal evaluation by the supplier, with scores concentrated by component and material type. It was observed that the steel of the braced tower (70%, 70%, and 100%), the steel of the inverter (60%, 60%, and 100%), the aluminum of the wiring (100%, 100%, and 29%), and the high-density polyurethane (100%, 14%, and 100%) of the mini wind turbine body are the best-evaluated materials in this category, with the highest potential for reuse and the lowest environmental impact.
Companies X and Y obtained 46% and 35%, respectively, both being rated as critical. In contrast, company Z obtained 79%, classifying it as regular. The results of extrapolating the percentages obtained for the decision criteria in the proposed model are shown in Table 10. This indicates that suppliers X and Y were the worst evaluated, each with a score of 1, while supplier Z was only slightly superior with a score of 2 points.

3.5. Supply Chain Model Score

The evaluation results are shown in Table 11. The case study considered a total of 21 possible options, including the performance of 6 different models of mini wind turbines along with the on-site wind potential evaluation, 3 different distributors in the suppliers’ network, 3 companies for project technical assessment, and 3 more for equipment final disposal.
To determine project feasibility, selecting the best-rated options in each link was required. The final score was calculated, and it should be addressed to the decision-making criteria, as shown in Table 12. The project outcome can be categorized among three possible scenarios: (i) good: All items in the supply chain were evaluated high enough to consider that the project has high possibilities of success, and its implementation is recommended. (ii) Regular: In this case, the model identifies shortcomings that have the potential to be improved. The model recommends the execution of the project only after the identified obstacles are corrected. (iii) Critical: Finally, this rate is assigned when the model finds major drawbacks that not only block the execution of the project but create a situation where no potential solutions are identified either.
The findings presented in Table 10 highlight the highest-rated options for each link of the proposed model to analyze a mini wind power project feasibility in Mexicali, Mexico. According to the proposed model, the final evaluation for the project is 87%, rated as good. This suggests its potential implementation, with a high probability of success.
The proposed methodology considers that links 2, 3, and 5 are independent; therefore, different companies can be selected for each link. However, a different situation is observed for links 1 and 4, where it is mandatory to select the same wind turbine for both links. Although the AEOLOS 200 obtained the best score in link 1, its poor performance in the customer distribution link resulted in a final project evaluation of 73%. Therefore, it is important to consider that, only in the case of wind turbine evaluation, additional inspection must be performed to determine the combination that yields the most favorable result.

4. Conclusions

In this case study, a model for evaluating the supply chain of mini wind energy in urban areas was designed and implemented. The proposed methodology enables the generation of quantitative results for each of the five links, allowing for a comprehensive assessment of project feasibility through standardized and comparable scoring. This approach facilitates decision-making in wind energy generation projects. Quantitative ratings were obtained for each of the model’s five links, resulting in an overall score representing the links’ interaction. The final score provides a basis for decision-making based on multifactorial criteria, increasing project success probability. Additionally, the supply chain model makes it possible to visualize the risks associated with each step of the process to assess potential project improvements.
For the wind potential evaluation, three important indicators were integrated: wind turbine start-up speed, capacity factor, and generated energy. These indicators are usually evaluated separately, but in this case, they were evaluated simultaneously, creating a cause-effect relationship between the results achieved and the performance drivers. When start-up wind speed and capacity factors are considered, small wind turbines rated below 1 kW typically score better compared to medium 2 kW and large 10 kW wind generators. However, the model also assigned significant weight to the energy generated and its impact relative to the average user’s consumption. For the final selection criteria of the wind power turbine, the model recommends considering all these factors based on the evaluated wind turbines’ performance.
Therefore, it is suggested that a study using statistical tools with different wind turbines and wind conditions be performed to determine capacity factors that maximize energy generation in mini wind power turbines. This is crucial, as there is limited information in the literature on the optimal capacity factor ranges for mini wind power turbines.
When evaluating suppliers, item quality was the highest rated for all organizations. This demonstrated the importance of having a quality management system in place. Additionally, equipment compliance with applicable regulations and the clear written presentation of the wind turbine technical specifications were found to be equally relevant. Another finding indicates that the best-evaluated companies included a design department within their organization, operating in conjunction with the production and distribution areas. In contrast, the worst-rated suppliers only offered commercialization services.
The amount of energy generated by the wind turbines could have three possible implications that vary in relevance based on local energy billing schemes and policies. One implication is directly related to economic savings. The second is associated with government requirements that mandate users to contribute a percentage of their energy consumption through renewable energy. Finally, the generated energy may offer the possibility of modifying the electric tariff scheme to a more favorable one.
Regarding the final disposal of equipment, it was observed that the braced tower and inverter steel, the wiring aluminum, and the turbine body high-density polyurethane were the best-evaluated materials due to their highest potential for reuse; hence, they had the lowest environmental impact. Conversely, the inverter high-density polyurethane, the fiberglass body, and the rudder high-density polyurethane obtained the lowest scores and were classified as the elements with the highest environmental impact.
Additionally, the proposed model structure can be applied to any geographic site by adjusting each of the links to the local conditions. This involves integrating the energy estimation based on the site wind speeds for the first link, applying evaluations to new wind turbine suppliers for the second link, and adapting the technical evaluation checklist for the study site in the third link, which varies according to the country and the applicable regulations regarding the classification of energy generation. For the fourth link, a characteristics analysis of the electricity tariff system applicable to the new study and the specific clients’ demand data must be performed to strengthen the general model. Finally, for the last link, as new wind turbines are integrated into the model, it will be necessary to analyze components and materials to propose the appropriate final disposal.
No public information was found regarding suppliers specializing in renewable energies despite the legislation in Baja, California, requiring it. It is suggested that a collaborative network of products and services be created to promote the penetration of renewable energies in Mexico and globally. It is highly recommended that an equipment catalog for services and suppliers (national and international) related to energy generation projects with renewable sources be created to strengthen the model design for future users. Additionally, the checklist used for technical evaluation must be adjusted to include the rest of the current tariff categories: domestic, commercial, industrial, and agricultural services. Clean Energy Certificates and tax benefits should be considered as a benefit to the customer distribution link. Regarding the final disposal link, an opportunity was identified to develop a business model for the remanufacturing and recycling of wind turbine components.
Previous models have focused on the technological, meteorological, and economic aspects of wind power development. However, the methodology proposed here seeks to address a broader range of parameters that are recognized as key barriers to wind power development, including the availability of skilled labor and spare parts. It also incorporates a life extension reverse logistics analysis through a detailed examination of the primary components and materials of mini wind power turbines. Moreover, the model can be improved if other factors are included, such as (i) a study of public acceptance of the incorporation of small wind power projects in urban areas, (ii) increasing the number of points for the study of wind due to the complex topography in the cities, and (iii) considering the impact of environmental pollution on the components of wind turbines, which could be a significant parameter.
Although this study was conducted in a specific region, its structure and key findings provide a valuable foundation for the scientific community, as well as technicians and practitioners in other areas seeking to apply a comprehensive analysis for assessing the feasibility of renewable energy projects in urban settings. For instance, the model can be adapted to additional renewable energy areas such as high-power wind, solar photovoltaics, and others. To achieve this adaptation, the specific needs of each renewable source must be studied and compared with the current model parameters, integrating new elements to address the differences. For example, solar photovoltaics require a method different from wind power for the first link to estimate energy generation. In this case, the adapted model would have a first link that is focused on evaluating solar power potential instead of wind power potential. The same five links of the model could work with adjustments for different renewable sources.

Author Contributions

Conceptualization, I.Z., E.V. and A.L.; Methodology, I.Z., E.V. and A.L.; formal analysis, I.Z., E.V. and A.L.; original draft preparation, I.Z., J.R.A. and R.G.; reviewing and editing I.Z., J.R.A. and R.G.; project administration, I.Z. and E.V.; funding acquisition, I.Z. and R.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) through a Master’s degree, grant number 714982.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

Author Rodny García was employed by the company Chint Power of Mexico. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Historical consumption: (a) 1F; (b) HCR.
Figure 1. Historical consumption: (a) 1F; (b) HCR.
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Figure 2. Main links in the supply chain for the proposed model.
Figure 2. Main links in the supply chain for the proposed model.
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Figure 3. Model design. Proposed supply chain model, supported by a color code that includes red, yellow, and green, simulating a traffic light.
Figure 3. Model design. Proposed supply chain model, supported by a color code that includes red, yellow, and green, simulating a traffic light.
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Figure 4. Contribution of generated energy for each tariff.
Figure 4. Contribution of generated energy for each tariff.
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Figure 5. Final disposal evaluation by supplier.
Figure 5. Final disposal evaluation by supplier.
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Table 1. Evaluation criteria for link customer distribution.
Table 1. Evaluation criteria for link customer distribution.
Value ScaleAccumulated Portion of 1F (%)kWh/yrConsumption Categories
07.52250Basic
120.53900Winter SummerLow intermediate
228.52400SummerHigh intermediate
310030,000Surplus
Table 2. The proposed five links based on the supply chain framework (input/output of the methods used for its analysis).
Table 2. The proposed five links based on the supply chain framework (input/output of the methods used for its analysis).
OutputInputLink
  • Small wind turbine start-up speed.
  • Wind speed.
  • Capacity factor.
  • Estimation of generated energy.
  • On-site wind speed measurements.
  • Analysis of wind turbine technical datasheets.
  • Evaluation of wind potential
  • Evaluation of brand performance in aspects such as system quality, logistics, delivery times, spare parts availability, warranties, customer service, etc.
  • Likert scale survey.
2.
Supplier network
  • Evaluation of the technical capacity for the execution of mini wind power project.
  • Supplier survey based on the regulations and technical requirements for the execution of a mini wind energy project in urban areas (NOM-001-SEDE-2012, Art. 694 and 705).
3.
Project technical assessment
  • Consumption of users in the domestic sector tariff 1F (kWh/year).
  • Number of registered 1F tariff users (potential market).
  • Wind power energy results regarding the tariff scheme.
  • Analysis of the information available from the Federal Electricity Commission (Mexican state sole supplier), where the total consumption by tariff and the number of users is obtained.
4.
Customer distribution
  • Classification of wind turbine components and materials according to their percentage of recovery, reuse, and/or remanufacturing.
  • Survey of suppliers to learn about locally available sites for recycling, recovery, and/or disposal processes.
  • Analysis of the components and materials that constitute a wind turbine.
  • Analysis of the lifecycle of components and materials to determine the relevant reverse logistics processes.
5.
Equipment final disposal
Table 3. Indicators for the evaluation of wind potential.
Table 3. Indicators for the evaluation of wind potential.
IndicatorsUnitsCriteria
0123
Start-up speedm/s431–20–1
Capacity factor%0–56–1011–1516–20
Generated energy (5%)kWh/yr0%1–2%3–4%5–6%
Table 4. Criteria for the supplier network link.
Table 4. Criteria for the supplier network link.
DescriptionCriterion
The company has a system that guarantees the quality of its products (Quality Manual Procedures).Quality
The company has a documentation control procedure to guarantee the manufacture of its products and processes.
The product is manufactured according to the IEC 61400 standard [63]
They have written technical specifications for the products they manufacture.
The company delivers the product within the time established in the purchase order.Delivery time
The delivery time of the product is considerably competitive.
There are defined routes for the delivery of the products at the point of use.
The logistics for the delivery of the products are simple.
The company develops new technology in the area of Wind Turbines for Small Wind Power.Technology and capacity
The company knows what its competition is doing in terms of R&D.
There are well-identified national suppliers that can increase national consumption.
The company sells spare parts for wind turbines.
The price of the product is competitive.Cost-Price
The company provides a clear breakdown in its quotes.
The company considers price savings with respect to purchase volume.
Product warranties are included in the wind turbine’s sale price.
The response time when requesting a quote does not exceed 24 h.Customer
Support
Several communication channels are provided so as to be in contact with the client (social networks, messaging apps, email, telephone, etc.).
The advertised information is up-to-date (prices, locations, promotions, etc.).
The company has different offices in the country.
The company has several routes defined for the delivery of products at the point of use (do not depend on a single route).Supply network
The time between placing the order and the product being delivered is adequate.
The company has a system for product tracking along the delivery route to the point of use.
The special normative requirements for the transport of the product are considered.
Table 5. Checklist for project technical assessment.
Table 5. Checklist for project technical assessment.
StatementSection
1. Interconnection schemes: There is an interconnection scheme for LV (low voltage) power plants for the installation and interconnection of the distributed generation power plant, with the general technical specifications approved by the Energy Regulatory Commission (CRE).Distributed generation
2. Metering system: The fiscal meter (MF) is installed at the points that must be metered according to the interconnection scheme used.
3. Telemetry Equipment: The distributed generation power plants that include information and communication technology for sending information and data comply with the interoperability and information security requirements indicated in the Network Code and in the applicable regulation.
4. Disconnection devices: The switches or protection and disconnection devices (I1 and I2) used in distributed generation power plants are designed to disconnect in the event of failures in the power plant itself or in the General Distribution Networks. These devices were selected based on the characteristics of the type of power plant installed and the type of current at the installation point (direct current or alternating current), in accordance with NOM-001-SEDE-2012 “Electrical Installations (Use)” standards
5. Operational technical requirements: The interconnection of distributed generation power plants does not cause imbalances in distribution circuits, nor does it generate electrical disturbances for the circuit or other users. This guarantees the conditions of efficiency, quality, reliability, continuity, security, and sustainability of the National Electric System, thereby allowing the integration of a greater number of distributed generation power plants into the General Distribution Networks.
6. Inspection: When the construction of the power plant is completed, and it is interconnected at low voltage, it is exempt from requiring an inspection unit. However, the Applicant may request the opinion of an inspection unit if deemed necessary. Was an inspection unit requested?
1. Interconnection Request: The administrative procedure for the interconnection of distributed generation power plants is carried out in accordance with the guidelines outlined in the Interconnection Manual.Interconnection of distributed generation power plants
2. Interconnection Works Requirements: The work required to physically interconnect the distributed generation power plant to the General Distribution Networks complies with general technical specifications and, where appropriate, the specific infrastructure characteristics required.
3. Reinforcement Works Requirements: The necessity of reinforcement work is evaluated through an analysis of the General Distribution Networks and, if applicable, through an interconnection study. Does it meet the reinforcement work requirements?
1. Interconnection contract: An interconnection contract establishes the rights and obligations of the Applicant and the Distributor when interconnecting a distributed generation power plant, Distributed Clean Generation, or any power plant with a capacity of less than 0.5 MW, using typical interconnection schemes to the General Distribution Networks.Contract
models
2. Compensation contract: A compensation contract establishes the rights and obligations of the Basic Services Supplier and the Exempt Generator concerning the compensation associated with the interconnection of a power plant with a capacity of less than 0.5 MW regarding the electrical energy generated and delivered to the General Distribution Networks.
1. Surveillance: The compliance surveillance for these provisions will be subject to the regulatory bases issued by the CRE, authorizing inspection units specialized in distributed generation power plants. These units will establish indicators, metrics, and other mechanisms for evaluating the behavior of the National Electric System.Compliance and
surveillance
Table 6. Main components of mini wind turbines.
Table 6. Main components of mini wind turbines.
ComponentMaterialReverse Logistics ProcessesReverse Logistic Metrics (%)
Reuse (4)Remanufacturing (3)Recycling (2)Disposal (1)
BladesAluminum alloy✓✕✓✓70%
Fiberglass✓✕✓✓70%
Shaft|ArrowSteel✓✓✓✓100%
DC Generator
Permanent magnets		Copper✓✓✓✓100%
Neodymium N50✓✓✓✓100%
Orientation rudderhigh density polyurethane✓✕✓✓70%
Fiberglass✓✕✓✓70%
Bodyhigh density polyurethane✓✕✓✓70%
Fiberglass✓✕✓✓70%
ControllerElectronic components✓✓✓✓100%
InverterElectronic components✓✓✓✓100%
Steel✓✓✓✓100%
Aluminum✓✓✓✓100%
high density polyurethane✓✓✓✓100%
Wiring (Connections)Copper✓✕✓✓70%
Aluminum✓✕✓✓70%
Braced TowerSteel✓✓✓✓100%
(Galvanized)Zinc✓✓✓✓100%
The symbol ✕ means that the reverse logistics process of that column does not apply to those components/materials, on the other hand the symbol ✓ means that it does apply.
Table 7. Results of the first link in the supply chain: evaluation of wind potential.
Table 7. Results of the first link in the supply chain: evaluation of wind potential.
ResultsScoreAt Least 5%
Contribution
1F (6407
Kwh/a)		At Least 5%
Contribution
DAC
(30,000 Kwh/a)		Generated
Energy
(kWh/yr)		ScoreCapacity
Factor (%)		ScoreStart-Up Speed
(m/s)		Small Wind
Turbine
32NoNo222314%31AELOS 200
21NoNo20716%31AEOLOS 400
10NoNo30101%22ENAIR 3000
23YesNo64403%22ENAIR 5000
13YesNo125003%13COLIBRÍ 5000
23YesYes545527%13COLIBRÍ 10,000
Table 8. Supplier network and project technical assessment results.
Table 8. Supplier network and project technical assessment results.
SupplierResults (%)Link Evaluation Criteria Results (%)Link Evaluation Criteria
Supplier NetworkProject Technical Assessment
X963581
Y933752
Z762833
Table 9. Customer distribution results.
Table 9. Customer distribution results.
Link Evaluation CriteriaResults (%)Small Wind Turbine
04%AELOS 200
04%AEOLOS 400
05%ENAIR 3000
111%ENAIR 5000
222%COLIBRÍ 5000
394%COLIBRÍ 10,000
Table 10. Equipment final disposal results.
Table 10. Equipment final disposal results.
Link Evaluation CriteriaResults (%)Supplier
146%X
135%Y
279%Z
Table 11. Best-scored alternatives for each link.
Table 11. Best-scored alternatives for each link.
Proposed ModelSupplierScore
1EVALUATION OF WIND POTENTIALCOLIBRÍ 10,0002
2SUPPLIER NETWORKSUPPLIER X3
3PROJECT TECHNICAL ASSESSMENTSUPPLIER Z3
4CUSTOMER DISTRIBUTIONCOLIBRÍ 10,0003
5EQUIPMENT FINAL DISPOSALSUPPLIER Z2
Final evaluationGOOD87%
Table 12. Decision criteria for supply chain evaluation.
Table 12. Decision criteria for supply chain evaluation.
DecisionCriteriaRangeScore
Supply chain management suggests a feasible outcome; the project has a high probability of being successful.GOOD81–100%
17–21 Points		3 points
Supply chain management suggests an acceptable outcome; a revision of the project is proposed to increase the likelihood of success.REGULAR61–80%
13–16 Points		2 points
Supply chain management suggests a non-feasible outcome; it is proposed that the project is not implemented.CRITICAL0–60%
0–12 Points		0–1 points
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Zazueta, I.; Valenzuela, E.; Lambert, A.; Ayala, J.R.; Garcia, R. Supply Chain Model for Mini Wind Power Systems in Urban Areas. Resources 2025, 14, 38. https://doi.org/10.3390/resources14030038

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Zazueta I, Valenzuela E, Lambert A, Ayala JR, Garcia R. Supply Chain Model for Mini Wind Power Systems in Urban Areas. Resources. 2025; 14(3):38. https://doi.org/10.3390/resources14030038

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Zazueta, Isvia, Edgar Valenzuela, Alejandro Lambert, José R. Ayala, and Rodny Garcia. 2025. "Supply Chain Model for Mini Wind Power Systems in Urban Areas" Resources 14, no. 3: 38. https://doi.org/10.3390/resources14030038

APA Style

Zazueta, I., Valenzuela, E., Lambert, A., Ayala, J. R., & Garcia, R. (2025). Supply Chain Model for Mini Wind Power Systems in Urban Areas. Resources, 14(3), 38. https://doi.org/10.3390/resources14030038

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