Techno-economic evaluation of a smart power management system based on wind turbine hybrid model using Homer tool: a case study – Nature

Executive Summary

This report details a techno-economic feasibility study of wind energy resources, aligning with key Sustainable Development Goals (SDGs). The primary objective was to investigate optimized hybrid wind energy systems for hilly terrains, contributing directly to SDG 7 (Affordable and Clean Energy), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Using the Homer Pro Tool, twelve operational scenarios were simulated by incorporating three small Wind Turbine Generators (WTGs) into four distinct system models. The analysis was grounded in real-time load data and precise wind resource data from a Weather Monitoring Station (WMS) in the study area. Key findings indicate that the optimal hybrid wind turbine systems can curtail utility grid electricity consumption by approximately 88.4%, significantly enhancing energy independence and resilience. The Levelized Cost of Electricity (LCOE) for these systems ranges from ₹3.14 to ₹5.78/kWh, presenting an economically viable solution to meet the local net demand of 165.44 kWh/day. Furthermore, a sensitivity analysis varying turbine hub height was conducted to forecast future power generation and cost parameters, ensuring the long-term sustainability and scalability of the proposed infrastructure.

Introduction: Aligning Wind Energy with Sustainable Development Goals

The global energy sector is transitioning towards a sustainable future, targeting net-zero greenhouse gas emissions by 2050. This transition is fundamental to achieving SDG 13 (Climate Action). Wind energy, despite its intermittency, is a rapidly growing Renewable Energy Source (RES) that supports this goal. This study addresses the challenge of wind energy’s unpredictability by evaluating hybrid systems that integrate WTGs with batteries, generators, and the grid. Such systems enhance reliability and ensure optimal resource utilization, which is crucial for building resilient infrastructure as outlined in SDG 9 (Industry, Innovation, and Infrastructure). By proposing a single-RES solution focused on wind power, this research explores a novel approach to providing reliable and clean energy for remote communities, directly supporting SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities). The study is conducted in the Namthang block of Sikkim, India, a region where traditional hydroelectric power is threatened by climate change-induced glacier recession, making the exploration of alternative RES a critical priority.

Methodology for Sustainable Energy Assessment

The methodological framework was designed to ensure a comprehensive techno-economic evaluation, with sustainability as a core principle. The process began with data collection and proceeded to simulation and optimization using the Homer Pro Tool to identify the most cost-effective and environmentally sound solutions.

Study Area and Load Profile

The study focuses on a cluster of 21 villages in the Namthang Block, Namchi district, Sikkim, India. This area is characterized by high wind power potential but suffers from frequent power outages. An electrical load survey was conducted using time-series data from the State Electricity Board to create a detailed profile of the community’s energy needs, ensuring the proposed solution is tailored to local demand and supports the goal of sustainable community development (SDG 11).

Wind Resource Assessment and Turbine Selection

Real-time wind resource data was acquired from a WMS device installed at the study site. This site-specific data is crucial for accurate modeling and contributes to innovative and resilient infrastructure planning (SDG 9). Based on this data, a comprehensive evaluation was performed:

1. Six empaneled small WTGs were evaluated based on technical specifications and manufacturer data.
2. The capacity factor (C_f), a key indicator of real-world performance, was calculated for each turbine.
3. Three WTGs with the highest C_f values were selected for the simulation:
   • Excel-10
   • Montana-3310
   • SD6-701

This rigorous selection process ensures the chosen technology is best suited to the local wind regime, maximizing clean energy generation (SDG 7).

System Design and Optimization for SDG 7

To identify the most effective power management strategy, three selected WTGs were integrated into four different hybrid system architectures, creating twelve unique combinations for simulation. The models were designed to operate in both isolated and grid-connected modes to ensure a continuous and reliable power supply.

• Model I: WTG + Converter + Battery (Isolated)
• Model II: WTG + Converter + Battery + Grid (Grid-Connected)
• Model III: WTG + Converter + Battery + Gen set (Isolated)
• Model IV: WTG + Converter + Battery + Gen set + Grid (Grid-Connected)

The Homer Pro Tool was used to simulate these architectures and identify the winning models based on their ability to provide reliable and affordable energy, the core tenets of SDG 7.

Techno-Economic and Socio-Environmental Evaluation

The performance of each system was evaluated against a set of metrics that reflect economic viability, environmental impact, and social benefits, aligning with multiple SDGs.

• Economic Metrics (SDG 7 & 8): Net Present Cost (NPC) and Levelized Cost of Electricity (LCOE) were the primary criteria for determining financial feasibility and affordability. The Simple Payback (SPB) period was also assessed.
• Environmental Metrics (SDG 13): Greenhouse Gas (GHG) emissions were calculated to quantify the system’s contribution to climate change mitigation. The reduction in CO₂ emissions compared to conventional diesel generator use was a key performance indicator.
• Socio-Economic Metrics (SDG 8): The potential for employment generation was estimated to assess the project’s contribution to local economic growth and decent work.

Results: Techno-Economic and Environmental Performance

The simulation and optimization process identified four winning system architectures, one for each model type, based on the lowest NPC and LCOE values. The results demonstrate a strong potential for sustainable development in the region.

Optimal System Configurations and Economic Viability

The analysis revealed that the SD6-701 wind turbine was the most economically viable choice for Models I, II, and III, while the Excel-10 turbine was optimal for Model IV. The cost factors for the winning models to meet the local net demand of 165.44 kWh/day were as follows:

• Net Present Cost (NPC): Ranged from ₹2.1 Crore to ₹3.5 Crore.
• Levelized Cost of Electricity (LCOE): Ranged from ₹3.14/kWh to ₹5.78/kWh.

These LCOE values are below the tiered rates established by the state electricity board, confirming the affordability of the proposed clean energy solution and its alignment with SDG 7.

Contribution to Climate Action (SDG 13) and Energy Security (SDG 7)

The hybrid systems demonstrated significant environmental and energy security benefits. The winning grid-connected models (Model II and Model IV) achieved an impressive 88.4% curtailment of electricity from the utility grid. This drastic reduction in reliance on conventional power sources directly contributes to SDG 13 (Climate Action) by lowering the carbon footprint. Furthermore, by harnessing local wind resources, the systems enhance energy independence and provide a reliable power supply for the community, a key target of SDG 7.

Socio-Economic Impact (SDG 8)

The establishment of wind energy systems creates employment opportunities across their life cycle. The study assessed the job creation potential for each winning model, highlighting the project’s capacity to stimulate local economic growth and provide decent work, thereby contributing to SDG 8.

Sensitivity Analysis for Future-Proofing Sustainable Infrastructure (SDG 9)

To ensure the long-term viability and resilience of the proposed energy systems, a comprehensive sensitivity analysis was conducted. This analysis evaluated the impact of variations in the net energy demand and the wind turbine’s hub height on the system’s economic performance. This forward-looking approach is essential for developing sustainable and adaptable infrastructure, as promoted by SDG 9. The analysis explored three cases:

1. Increased energy demand with constant hub height.
2. Constant energy demand with varied hub height.
3. Increased energy demand with increased hub height.

The results showed that adjusting the hub height can effectively compensate for future increases in load demand while maintaining control over NPC and LCOE. This confirms that the system can be scaled and adapted to meet the community’s future energy needs sustainably.

Conclusion: A Blueprint for Sustainable Energy in Hilly Terrains

This study presents a strategic and adoptable power management framework that leverages wind energy to meet the needs of remote, hilly communities. The findings confirm that a small wind turbine hybrid system is a technically feasible, economically viable, and environmentally sound solution that aligns strongly with the United Nations Sustainable Development Goals.

• By significantly reducing dependence on the utility grid (by 88.4%), the system promotes SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).
• The economic analysis confirms the affordability of the generated electricity, while the assessment of job creation potential highlights contributions to SDG 8 (Decent Work and Economic Growth).
• The innovative system design and forward-looking sensitivity analysis provide a model for developing resilient and sustainable infrastructure (SDG 9) that enhances the quality of life in rural communities (SDG 11).

This research serves as a pilot study and a blueprint for implementing similar sustainable energy projects in regions with comparable topography and climatic conditions, paving the way for a cleaner and more equitable energy future.

Analysis of Sustainable Development Goals (SDGs) in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on the techno-economic evaluation of a wind turbine hybrid model addresses several Sustainable Development Goals (SDGs) through its focus on renewable energy, economic viability, environmental impact, and community development. The primary SDGs connected to the issues are:

• SDG 7: Affordable and Clean Energy: The core of the study is to investigate and implement wind energy, a clean and renewable source, to provide reliable and affordable electricity to a community facing power outages.
• SDG 8: Decent Work and Economic Growth: The article evaluates the economic feasibility of the project through metrics like NPC and LCOE and explicitly analyzes the “job creation potentials” of establishing wind energy systems.
• SDG 9: Industry, Innovation, and Infrastructure: The research focuses on developing a sustainable and resilient energy infrastructure (wind turbines, battery storage, grid integration) in the hilly terrains of Sikkim, India, using innovative models and optimization tools (Homer Pro).
• SDG 11: Sustainable Cities and Communities: By aiming to provide a reliable power supply to a cluster of 21 villages and reducing reliance on a potentially unstable grid, the study contributes to making rural settlements more resilient and sustainable.
• SDG 13: Climate Action: A key objective mentioned is to promote a “sustainable approach to reduce global carbon emissions.” The article quantifies the reduction of Greenhouse Gas (GHG) emissions by replacing or supplementing conventional power sources, including diesel generators.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s content, the following specific SDG targets can be identified:

1. SDG 7: Affordable and Clean Energy

   • Target 7.1: Ensure universal access to affordable, reliable and modern energy services. The study directly addresses this by designing a system to “mitigate energy challenges in hilly terrains” and solve the problem of “frequent power outages” for a community in Sikkim.
   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix. The entire project is centered on implementing wind energy. The article states that the proposed system “significantly reduces dependence on the utility grid by approximately 88.4%,” which demonstrates a substantial increase in the share of renewable energy for the target area.
   • Target 7.a: Enhance international cooperation to facilitate access to clean energy research and technology… and promote investment in energy infrastructure and clean energy technology. The study utilizes advanced optimization tools like “Homer Pro Tool” and evaluates various wind turbine technologies to promote investment in the most efficient and cost-effective clean energy infrastructure.

2. SDG 8: Decent Work and Economic Growth

   • Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation. The article explicitly assesses the “socio-economic aspects” of the project, including its “employment generation potential,” which is a direct contribution to this target.

3. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure… with a focus on affordable and equitable access for all. The study’s objective is to create a “reliable, secure, and efficient power” supply system for end-users in a remote, hilly region, thereby developing sustainable energy infrastructure.
   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies. The project proposes a hybrid system integrating wind turbines as a clean technology to reduce reliance on the conventional utility grid and diesel generators.

4. SDG 13: Climate Action

   • Target 13.2: Integrate climate change measures into national policies, strategies and planning. The study’s primary goal is to “reduce global carbon emissions.” It quantifies GHG emissions from diesel generators and highlights that the proposed “winning system models” have “notably lower” emissions, demonstrating the integration of climate action at a project planning level.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions and implies several quantitative and qualitative indicators that can be used to measure progress towards the identified targets:

1. Indicators for SDG 7 (Affordable and Clean Energy)

   • Share of renewable energy (Indicator 7.2.1): The article calculates that the proposed systems lead to an “88.4% electricity curtailment from the utility grid,” directly measuring the increased contribution of wind energy. The annual energy production figures (e.g., “84,802 kWh/yr” for Model I) also serve as a direct indicator.
   • Affordability of energy: The Levelized Cost of Electricity (LCOE) is used as a key economic metric. The article states that the LCOE ranges from “₹3.14 to ₹5.78/kWh,” which is compared against the grid purchase cost of “₹5.58/kWh.” This provides a clear measure of affordability.
   • Reliability of energy supply: The study aims to address “frequent power interruptions.” The system’s design to meet the “local net demand of 165.44 kWh/day” in both isolated and grid-connected modes serves as an indicator of its reliability.

2. Indicators for SDG 8 (Decent Work and Economic Growth)

   • Job Creation: The article provides a specific formula for measuring job creation, stating it “predicts the job potential of 10⁻⁷ jobs per kilowatt-hour per year using Homer Pro Tool” and that “wind energy anticipates 0.27549 employment potentials per kWh.” This is a direct, quantifiable indicator.

3. Indicators for SDG 9 (Industry, Innovation, and Infrastructure)

   • Investment in sustainable infrastructure: The Net Present Cost (NPC) ranging from “₹2.1 to ₹3.5 Crore” is an indicator of the investment required to build this sustainable infrastructure. The Simple Payback (SPB) period is another financial indicator of the project’s viability.

4. Indicators for SDG 13 (Climate Action)

   • Greenhouse Gas (GHG) Emissions (related to Indicator 9.4.1 – CO2 emission per unit of value added): The article explicitly discusses and quantifies GHG emissions. It calculates that a diesel generator would produce “34485.20 kg/yr of CO₂” and notes that the proposed winning models result in significantly lower GHG emissions, providing a direct measure of climate action.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com