Addressing Nigeria’s Energy Challenge: Applying Geospatial AI for Strategic Solar Microgrid Deployment in Northern Nigeria

Introduction

Power supply from Nigeria’s national grid is intermittent nationwide, with many locations facing limited access to electricity, particularly in rural and underserved areas. Thankfully, the SBT (Service-Based Tariff) policy has helped to improve the stability of the grid by ensuring a more coordinated and fair distribution in some areas within the country. However, the supply remains unpredictable in these areas due to issues with infrastructure and ongoing challenges in the power sector.

The main source of electricity to the grid is thermal gas (natural gas), and while it remains a significant option for generating power, Nigeria, like many other countries, is facing increasing pressure to diversify its energy mix and meet Net Zero goals. Yet, harnessing the full potential of natural gas remains a challenge due to factors such as inefficient gas infrastructure, high production costs, and environmental concerns, resulting in low access to electricity.

In northern Nigeria, the situation is even more critical due to the concentration of most power plants in the central and southern regions, leaving northern areas at the far end of the grid.

ResearchGate. (n.d.). An overview of the Nigerian power sector, the challenges of its national grid and off-grid development as a proposed solution – Scientific figure on ResearchGate. Retrieved February 23, 2025, from https://www.researchgate.net/figure/Location-of-the-25-on-grid-power-plants-and-330-kV-transmission-line-concentration-in_fig2_328764424

This region, which has a large population and a vast landmass, consists of both structured and scattered settlements, making it difficult to develop and maintain efficient power infrastructure. These challenges highlight the need for decentralized power solutions, such as microgrids, to provide reliable electricity to northern Nigeria.

Open Infrastructure Map. (n.d.). Night time light visualization from OpenStreetMap data. Retrieved February 23, 2025, from https://openinframap.org

Although projects like the Zawaciki Minigrid and Kasuwan Magani Solar Hybrid Mini Grid are making progress, the scale of solar energy deployment in northern Nigeria remains insufficient. Key challenges include securing adequate funding, developing necessary infrastructure, building a skilled workforce, and making strategic decisions.

To accelerate progress toward Nigeria’s 2050 Net Zero Vision and energy transition goals, it is crucial to implement solar solutions quickly, especially in northern Nigeria. Identifying optimal locations for solar-powered microgrids and accurately predicting solar power potential with the help of Geospatial AI is key to making informed, strategic decisions. Geospatial AI allows for the analysis of spatial data to optimize the placement and performance of solar microgrids, accounting for factors such as solar irradiance, terrain, population density, and power infrastructure.

This article will explore how Geospatial AI can assist in identifying the best locations for solar microgrids in northern Nigeria, reducing blackouts, and supporting Nigeria’s broader energy transition plan. Additionally, it will examine two relevant global use cases—Google’s Project Sunroof and Australia’s Virtual Power Plants (VPPs)—to show how Geospatial AI is already making an impact in other parts of the world, and how similar approaches could benefit Nigeria’s energy sector.

The Role of Geospatial AI

Geospatial AI plays a crucial role in identifying optimal locations for solar microgrids by integrating artificial intelligence with geographic data analysis. This approach enables the assessment of key factors such as solar irradiance, land use, topography, population distribution, and proximity to existing power infrastructure. By leveraging these insights, decision-makers can strategically deploy solar microgrids in areas where they will have the greatest impact.

Geospatial AI processes large datasets and applies machine learning algorithms to optimize site selection for solar microgrids. This technology enhances decision-making by:

• Identifying High-Potential Solar Sites – AI models analyse historical and real-time solar irradiance data to pinpoint areas with the highest energy generation potential.

• Optimizing Land Use and Environmental Impact – By evaluating land cover and terrain features (elevation, slope, aspect), AI can help ensure that solar farms are placed on suitable land while minimizing environmental disruption.

• Improving Energy Access and Demand Matching – Population density and distribution data help determine where solar microgrids can provide the most benefit, ensuring alignment with electricity demand.

• Enhancing Grid Integration and Resilience – Proximity to national grid infrastructure is considered to determine where microgrids can complement the existing grid or function as standalone systems.

• Reducing Costs and Risks – Data-driven insights reduce uncertainties in project planning, leading to cost savings in land acquisition, infrastructure development, and long-term maintenance.

By applying these AI-driven techniques, energy planners and policy makers can make informed decisions that align with Nigeria’s energy transition goals and the 2050 Net Zero Vision.

Global Applications of Geospatial AI in Solar Energy

The use of Geospatial AI in solar microgrid deployment is not just a theoretical concept—it has been successfully implemented in real-world projects to optimize energy access, enhance grid stability, and drive cost-effective renewable energy solutions. Two notable examples are Google’s Project Sunroof and Australia’s Virtual Power Plants (VPPs), both of which showcase how AI-driven geospatial analysis can transform energy planning and decision-making.

Case Study 1: Google's Solar API

Google’s Solar API is an advanced geospatial AI-powered tool designed to analyse rooftop solar potential using high-resolution mapping, satellite imagery, and machine learning models. The API provides insights into solar energy generation capacity by assessin g factors such as solar irradiance, shading from nearby structures, and roof orientation. It helps solar energy developers, utilities, and policymakers make data-driven decisions about where to deploy solar systems efficiently.

• The Solar API consists of three main endpoints:

  • Building Insights: Provides detailed information about a building’s size, structure, and estimated solar potential.

  • Data Layers: Offers access to solar datasets, including sun exposure and shading patterns for specified areas.

  • GeoTIFF Solar Data: Supplies raw raster data with encoded solar energy insights, such as digital surface models and annual solar flux maps.

By leveraging AI-driven analysis, Google’s Solar API enables accurate predictions of energy generation potential, improving cost estimation and financial planning for solar projects. However, its current limitation is its geographic coverage, which is primarily restricted to the USA, Europe, and some parts of Asia. This means that direct implementation in Nigeria is not feasible without additional data sources and adaptations.

Despite these limitations, the core methodology behind Google’s Solar API—geospatial AI-powered solar mapping—can serve as a model for developing similar tools tailored for Nigeria. By integrating local solar irradiance data, climate records, and geospatial datasets, a similar system could be built to assess the feasibility of large-scale solar microgrids in northern Nigeria. This approach would help energy planners optimize site selection, predict energy output, and design cost-effective solar solutions suited to local needs.

Case Study 2: South Australia VPP

A notable example is the South Australia VPP, which creates a decentralized energy system by networking small-scale solar and battery storage units by connecting rooftop solar panels and battery storage across thousands of households, allowing stored solar energy to be shared across the network. This reduces peak electricity demand and stabilizes supply. Geospatial AI plays a crucial role in optimizing VPP operations by analyzing spatial and temporal energy patterns, predicting demand, and managing distributed solar generation. AI-driven tools leverage real-time data to forecast electricity consumption based on historical trends, weather conditions, and household behavior, ensuring efficient grid integration. This enhances grid resilience by balancing energy loads, preventing power outages, and reducing strain on centralized power stations. The impact of VPPs in Australia includes lower electricity costs, better utilization of surplus solar energy, and reduced reliance on fossil fuels. For Northern Nigeria, where power supply is unreliable, a VPP-inspired model could support the development of interconnected solar microgrids that provide local electricity while also feeding excess power into the national grid. AI-driven energy management could optimize solar generation and distribution, improving energy access in underserved areas. By balancing energy loads dynamically, this approach would enhance grid reliability, promote economic and environmental benefits, and support Nigeria’s Net Zero goals by increasing solar adoption and reducing fossil fuel dependence.

The Role of Geospatial AI in Nigeria’s 2050 Net Zero Vision and Energy Transition

Nigeria’s 2050 Net Zero Vision aims to achieve a carbon-neutral economy by reducing dependence on fossil fuels and accelerating the transition to clean and renewable energy sources. However, the country faces major challenges, including an unreliable power grid, inadequate renewable energy infrastructure, slow policy implementation, and limited access to electricity. Embracing Geospatial AI is crucial in overcoming these challenges and ensuring a data-driven, efficient, and sustainable energy transition.

1. Optimising Renewable Energy Deployment

To meet Net Zero targets, Nigeria must rapidly scale up solar, wind, and hydropower while phasing out fossil fuels. Geospatial AI plays a vital role in identifying the best locations for renewable energy projects by:

• Analyzing solar irradiance, wind speeds, and hydrological data to determine the most productive and cost-effective sites.

• Evaluating land use, environmental impact, and grid accessibility to reduce risks and ensure sustainability.

• Supporting smart grid planning, allowing for better integration of renewable energy into the national grid.

By removing guesswork from site selection and ensuring strategic investments, Geospatial AI can significantly accelerate renewable energy adoption in line with Nigeria’s Net Zero goals.

2. Enhancing Grid Stability and Reducing Blackouts

Nigeria’s national grid is fragile, prone to frequent collapses and blackouts due to aging infrastructure and supply-demand imbalances. To transition smoothly to a renewable energy-driven grid, Geospatial AI can:

• Monitor grid performance in real-time, identifying weak points and predicting failures before they occur.

• Balance energy supply and demand by forecasting peak load times and optimizing energy distribution.

• Guide the deployment of energy storage solutions (batteries) to store excess solar power for nighttime use, improving reliability.

By leveraging predictive analytics, Nigeria can move toward a more resilient and stable energy system, reducing disruptions and increasing the efficiency of renewable energy integration.

3. Expanding Energy Access in Underserved Regions

Many areas in northern Nigeria suffer from low electricity access due to limited grid expansion and challenging terrain. Geospatial AI enables a targeted, data-driven approach to rural electrification by:

• Identifying remote communities that would benefit most from solar microgrids and standalone renewable energy systems.

• Mapping population density and energy demand to ensure cost-effective electrification projects.

• Helping off-grid solar developers strategically plan energy networks that match local needs.

By prioritizing data-backed solutions, Nigeria can close the energy access gap and reduce reliance on fossil fuel-based energy sources, moving closer to its Net Zero targets.

4. Reducing Carbon Emissions and Environmental Impact

Nigeria’s energy sector is a major contributor to greenhouse gas emissions, with heavy reliance on diesel generators and gas-fired power plants. Geospatial AI aids in reducing emissions by:

• Identifying carbon-intensive regions and guiding the transition to cleaner energy alternatives.

• Optimizing land use planning to prevent environmental degradation from large-scale solar or wind projects.

• Supporting climate risk assessments to ensure power infrastructure is climate-resilient and sustainable.

By integrating environmental sustainability into energy planning, Geospatial AI ensures that renewable energy expansion aligns with climate action and ecological preservation.

5. Attracting Investments and Driving Policy Innovation

Nigeria’s energy transition requires significant investment in renewable infrastructure, smart grids, and battery storage. However, investors often face uncertainty due to lack of reliable data. Geospatial AI helps:

• Provide real-time energy market insights, making investment decisions more transparent and predictable.

• Support data-driven policy recommendations that create a stable regulatory environment for clean energy investments.

• Enable the monitoring and evaluation of renewable projects, ensuring funds are allocated efficiently.

By reducing investment risks and guiding policymakers, Geospatial AI can help attract more funding for Nigeria’s renewable energy sector, accelerating the transition to Net Zero by 2050.

Conclusion

Meeting Nigeria’s 2050 Net Zero Vision requires urgent action in renewable energy adoption, grid modernization, and electrification of underserved areas. Geospatial AI provides the necessary tools to ensure that energy projects are efficient, cost-effective, and sustainable. By leveraging this innovative technology, Nigeria can accelerate its energy transition, reduce carbon emissions, and build a resilient and inclusive energy system for the future.