Fundraising Fundraising Tracker Name Type Priority Status Owner Remarks Arinda Family  Recoverable Grant High 1k Contributed Kimbowa Family Recoverable Grant High Offered 1k USD, not yet contributed Tushabe Family Recoverable Grant High 10k USD Contributed on Dec 1st 2024 Added an addition ~1800 USD in October 2025 Mercy Corp Ventures Equity  medium Reviewed the deck in May 2025.  While the team liked the community angle, the key concerns were The scalability of the model and Traction (it's still early) Requested to be added to investor newsletter Aaron Tushabe We are no longer pursuing equity funders because as a co-op, we don't have equity to offer.  Energy Sector Management Assistance Program - ESMAP (World bank) Grant medium Echoing Green Fellowship Recoverable Grant high Submitted on October 7th 2025 Aaron Tushabe Waiting for feedback in March 2025 EPP Africa (Nordic Development Fund) Grant high Closed for 2025, planning to apply for 2026. Not sure when it opens again Aaron Tushabe Africa Climate Change Fund Grant African Green Banks (AFBD) Grant + Debt Alliance for Green Infrastructure (AFDB) Grants + Debt high Send an email to info@africa50.com   on August 23rd.  Aaron Tushabe Beyond the Grid No open applications yet    https://beyondthegrid.africa/funding-rounds/ US Department of Energy Grants Nithio Debt Needs at least 2 years of business to pass loan application Aaron Tushabe Mission 300 (World bank) low Focusing on power for remote and off grid communities so no aligned with our early stage objectives Factor E Equity We are no longer pursuing equity funders because as a co-op, we don't have equity to offer.  Kiva.org Debt Check back on August 15th Digital Africa ? Applied on June 17th 2025. Waiting for Feedback Aaron Tushabe Aquarious Foundation Grant or Debt Applied on June 17th 2025. Waiting feedback Aaron Tushabe Start-coop Grant Applied on June 18th.    We did not qualify because we are not (yet) based in North America Aaron Tushabe Expect $5k to $50k LabStart Grant Submitted application on July 7th.  Not Accepted Aaron Tushabe CataCap Grant / DAF medium Aaron Tushabe  Mission300 Grant?  Aaron Tushabe Energy IoT Open Source Grant Pledged 3k by Arila Barnes (founder and CEO) Aaron Tushabe gone cold Project Spark EU Grant high Submitted on September 15th 2025 with EnAccess and The Gym Rwanda Aaron Tushabe Got feedback on Nov 21st 2025 that we did not meet the eligibility criteria but they didn't elaborate on which criteria AMAP medium Praxis High Submitted on September 20th 2025.  Aaron Tushabe We did not get in. They thought we were among the top applicants and recommended we apply again next year. UGEFA medium Aaron Tushabe / Dansturn Requires 2+ years of operations so we would need to apply via green volta Green Volta Recoverable Grant high Waiting on Budget from NFE to confirm how much they can contribute Dansturn Samuel Tondo Recoverable Grant high Waiting on Budget from NFE to confirm how much they can contribute Digital Energy Challenge Grant high Waiting on Call for Applications to open up in March 2026 Aaron Tushabe D-Prize Grant high Opens October 13th Aaron Tushabe Submitted on October 28th 2025, expecting to hear back in 4 to 6 months MSISV Grant and Equity  high Email sent on Oct 16th 2025 to find out when applications will be open again Aaron Tushabe Sunbird AI  Fellowship They are offering a research program that could help us develop V1 of our autonomous microgrid Aaron Tushabe  Sustainable Energy Fund for Africa (SEFA) Grant low No links to getting involved Aaron Tushabe Energy IoT Open Source Donations medium EIoT can service as a fiscal sponsor to receive tax deductible US donations on our behalf.  Need to register a US entity for this  Funding Hope Crowd funding medium Aaron Tushabe Develop Ventures Grant matching  medium Uganda not eligible right now Aaron Tushabe  Closes Dec 31 2025 Carbon Credits low Initial meeting with Ashaba told us that this is going to be a long lead time option to explore.. at least 7 years before any credits are verified and bankable Aaron Tushabe DRK Foundation high Focusses on post pilot, pre-scale. I think we are not yet post pilot Aaron Tushabe Recoverable Grant Term Sheet - 50k Example 1. Grantor (Funder): Green Future Foundation (GFF) 2. Grantee (Recipient): Nearly Free Energy Co-op (NFE) 3. Purpose of Grant: To fund the capital expenditure and initial setup of a 20 kW solar microgrid serving 50 households in [Community Name], Uganda. 4. Grant Amount: $50,000 USD 5. Disbursement Schedule: 40% upfront upon signing agreement 30% upon installation of core infrastructure 30% upon commissioning and first billing cycle ⚖️ 6. Recoverability Clause (Repayment Terms): Trigger Repayment Terms If the microgrid achieves ≥ $1,000/month in net revenue for 6 consecutive months within 3 years Grantee repays the full grant amount over 4 years, at 0% interest If the project is not commercially viable by year 3 (e.g., < $1,000/month in net revenue) No repayment is required If the grantee secures follow-on grant investment of > $100,000 Grantee repays full grant or 10% of the investment value, whichever is lower 7. Use of Funds: Solar panels, batteries, smart meters, inverters Site preparation and installation labor Community training and billing setup Product development for Microgrid OS 8. Reporting Requirements: Quarterly reports for 3 years on: Energy generated and distributed Financial performance Number of customers served Maintenance issues and resolutions 9. Intellectual Property (IP): All software or monitoring systems developed under this project must remain open-source and licensed under AGPL or GPL or any other Free Software license 10. Dispute Resolution: Mediation first, then arbitration in Uganda under the Uganda Centre for Arbitration and Dispute Resolution (CADER). Google for Startups Accelerator –Application 1. Applicant Contact Information What is your full name (first and surname)? Your Business/Company Email Address What is your role? Preferred Contact Number Contact Number Type 4. Additional Information Why are you interested in joining this program? How can Google help? Response: Nearly Free Energy builds community-owned microgrids to deliver affordable, reliable electricity in underserved communities. Our mission is to lower energy costs while improving resilience through decentralized systems. As we scale beyond pilots, we are increasingly using software and AI for distribution, forecasting, billing, and reliability. Google can help us accelerate this by enabling scalable microgrid OS development on GCP with advanced analytics and AI. How did you hear about this program? Response: LinkedIn startup and AI founder community. If selected, are you interested in participating in possible interviews with local press outlets, as requested? Response: Yes. Please list any past accelerator or startup program participation. Response: Please list links to any press coverage, awards or nominations. n/a https://enaccess.org/open-source-energy-access-community-showcase-with-aaron-tushabe/ Do you want to receive updates or communication from the Accelerator program about other programs within Google? Response: Yes. 5. AI & Technology How is your company primarily leveraging AI? Response: We are building AI as an intelligent grid operator for our microgrids—optimizing distribution, forecasting demand, managing battery dispatch, and detecting anomalies. We are also developing agents to automate support, billing, and outage notifications. What are the key 1–3 challenges that your company is facing in adopting AI? Response: (1) Limited high-quality real-time energy data; (2) Integrating AI with physical infrastructure and edge devices; (3) Cost-efficient deployment in low-resource environments. What kind of data does your AI model use? Response: Energy consumption data; meter readings; system performance data; environmental and load patterns. What data do you use as part of your AI solution? Response: Smart meter data, usage logs, battery and solar performance data, and system telemetry. How would you categorize your product? Response: AI-enabled energy infrastructure platform. Do you have a dedicated AI team? Response: Cross-functional team with growing AI specialization. In 2 sentences, explain what problem you are solving with AI. Response: Energy systems lack real-time intelligence to manage distributed supply, storage, and demand, leading to outages and inefficiency. We use AI as a grid operator to optimize distribution, forecast demand, manage storage, and automate customer interactions. AI maturity Response: Early production-stage AI with live data pipelines and initial models in deployed microgrids, transitioning toward autonomous operations. What kind of AI does your product use? Response: Currently AI-assisted development; roadmap includes predictive analytics, time-series forecasting, and optimization models for demand, dispatch, and anomaly detection. Cloud platform Response: Google Cloud Platform (GCP). Which Google products are you using? Response: GCP (Compute Engine, Cloud Run, Cloud Storage) and BigQuery. Do you use AI/ML today? Response: Yes—AI-assisted development and early data-driven monitoring, with planned rollout of forecasting, optimization, and automated operations. System architecture Response: Edge devices (smart meters via RS-485/Modbus) feed data to local controllers, then to GCP (Cloud Run, Storage). BigQuery supports analytics. We are adding AI for forecasting, anomaly detection, and optimization within a modular microgrid OS. Accelerator goal Response: Build an AI-driven microgrid OS that autonomously balances supply/demand, forecasts load, optimizes battery use, and manages operations. This includes agent workflows for forecasting, anomaly response, billing, support, and outage alerts, targeting improvements in uptime, cost/kWh, and customer experience. 6. Traction & Financials Investors Response: Founder, friends, and family. MRR Response: ~$120–$150 MRR from a live pilot (~UGX 450,000–550,000/month), with expansion pipeline. Revenue source Response: Electricity sales (NFE-owned) plus upcoming SaaS fees and revenue share from partner-owned microgrids. 7. Product & Market Stage Response: Live pilot with revenue (10 customers) and active expansion pipeline. Customer Response: Primary: developers, landlords, and community operators. End users: residents benefiting from reliable power, automated billing, and improved service. Customers now Response: 10 paying customers on a live pilot microgrid, with expansion pipeline. Business model Response: (1) NFE-owned: revenue from electricity sales; (2) Partner-owned: SaaS fee per connection + % of energy sales; plus maintenance and value-added services. Industry Response: Energy / Climate Tech Verticals Response: Climate tech; energy infrastructure; smart grids. Company description Response: Nearly Free Energy advances energy resilience, reliability, and abundance through community-owned solar and battery-backed smart microgrids. Grid-connected systems improve stability by managing peak load. Problem Response: Electricity is unreliable, costly, and lacks real-time intelligence. Peak demand strains grids, while communities lack local control. Solution Response: Grid-connected, community-owned microgrids with an AI-driven OS that optimizes distribution, manages peak load, automates operations, and improves reliability and cost. 8. Team Full-time founders Response: 1 Founder Response: Aaron Tushabe – Co Founder Hillary Arinda - Co founder  Dansturn Kimbowa - Co Founder  Employees Response: 2 9. Uploads Pitch deck Response: [] NFE Pitch - Funders.pdf Links Response: N/A Photos PXL_20251217_100220589~2.jpg [PENDING] Logo Response: NFE site logo.png 10. Consent Submit Communications Google.org Impact Challenge: AI for Government Innovation Nearly Free Energy (NFE) – Draft Application I. Organization and Submitter Info 1. Organization Name Nearly Free Energy 2. Country Uganda (with operations expanding to East Africa and pilot work in the United States) 3. Classification Social Enterprise 4.a Founded 2024 4.b Annual Budget (USD) ~$50,000 (early-stage, pilot operations) 4.c Full-time Employees 2 5.a Website https://nearlyfreeenergy.com (or placeholder) 6. Google.org funding before No 7. Discovery Social Media (LinkedIn) 8. Primary Contact Aaron Tushabe – Co-Founder II. Impact 11. Project Name AI-Powered Distributed Grid Intelligence for Public Infrastructure 12. Topics Resilience; Economy (public infrastructure and affordability) 13. Geographic Scope County / Municipal; National (scalable) 14. Regions EMEA; North America 15. Stage Prototype (live pilot with paying users) 16. Problem Statement 16.a Public electricity systems in rapidly growing urban and peri-urban areas are increasingly unreliable, expensive, and unable to manage real-time demand fluctuations. Utilities lack visibility into distributed consumption and have limited tools to optimize load, leading to frequent outages, inefficient infrastructure investment, and constrained economic activity. 16.b This project directly affects grid load balancing, demand forecasting, outage response, and infrastructure planning workflows within utilities and regulatory bodies. It introduces real-time decision support and automation into how public electricity systems are monitored, managed, and optimized. 16.c The challenge is significant and growing at a global scale. In 2013, approximately 1 billion people lacked access to electricity, with another 1 billion connected to unreliable grids. By 2023, global electrification efforts reduced those without access to ~600 million, but the number of people connected to unreliable grids has surged to an estimated 3 billion. In South Africa, recurring load shedding disrupts economic activity and essential services, while in Lagos, Nigeria, widespread reliance on 24/7 diesel generators drives high costs and severe air pollution. These trends highlight a critical gap: expanding access alone is not enough—there is an urgent need to improve reliability through intelligent, flexible grid systems. 17. Proposed Solution 17.a We are building an AI-powered distributed grid intelligence platform that serves as a real-time control layer for national electricity systems—turning community-scale microgrids into coordinated assets that improve reliability, reduce peak demand, and expand affordable access. 17.b The platform integrates smart meter and telemetry data (RS-485/Modbus), edge controllers, and GCP (Cloud Run, BigQuery) with AI models for time-series forecasting, demand optimization, anomaly detection, and agentic workflows. These AI agents continuously analyze grid conditions and autonomously trigger actions such as battery dispatch, demand response, outage alerts, and customer support, enabling dynamic, real-time system optimization. 17.c We have demonstrated feasibility through a live pilot microgrid with paying users, where we collect real-time energy data and operate a working system that improves uptime, visibility, and load balancing. Early results show smoother demand curves and reduced dependence on unstable grid supply. 17.d To ensure adoption, we are designing the platform as a government-integrated system with dashboards, APIs, and reporting aligned to utility workflows such as grid planning, outage management, and demand response. We are engaging regulators and utilities to enable distributed energy resources to function as coordinated grid assets within national systems. 18. End Beneficiaries 18.a Urban and peri-urban households, small businesses, utilities, and regulators. 18.b We incorporate feedback through pilot deployments, user billing data, and direct engagement with communities and operators. 18.c Initial reach: hundreds of users, scaling to tens of thousands across multiple deployments over 36 months. 19. Expected Outcomes 19.a Improved electricity reliability, reduced outages, and more efficient grid utilization. 19.b Metrics: uptime, cost per kWh, peak load reduction, customer satisfaction. 19.c Failure signals: no measurable improvement in reliability, low adoption by utilities, or inability to integrate with existing workflows. 19.d Expected improvements include 20–40% reduction in peak load stress and significant improvements in uptime. 19. Expected Outcomes 19.a This solution will improve public electricity services by increasing reliability, reducing dependence on diesel generation, and enabling governments and utilities to manage distributed energy resources as coordinated grid assets. It will expand access to clean, affordable, and reliable electricity while improving planning and operational efficiency. 19.b Key metrics include: number of people with improved reliable electricity access; MWh of distributed energy storage deployed; reduction in peak grid load (%); uptime improvements (%); reduction in diesel generator usage; and number of utilities/regulators actively using platform data for planning. 19.c Failure indicators include: inability to scale deployments; low adoption by utilities or regulators; no measurable improvement in reliability or peak load reduction; or lack of engagement from DER operators and ecosystem partners. 19.d Within 12 months, we aim to enable deployment of at least 1 MWh of distributed energy storage across Africa through direct deployments and partner-led adoption. Within 36 months, we target improving access to reliable, clean electricity for at least 1 million people by scaling autonomous microgrids and supporting an open ecosystem of DER operators using our platform. III. Innovative Use of Technology 21. Why is your proposed solution necessary to address the problem versus currently available alternatives? Current approaches either expand centralized grid capacity (slow, capital-intensive) or deploy isolated off-grid systems (limited coordination, no grid support). Existing tools lack real-time, system-wide intelligence and cannot integrate distributed energy resources (DERs) into utility operations. Our solution introduces an AI-driven control layer that coordinates microgrids as grid assets—enabling forecasting, automated dispatch, and demand response. This uniquely improves reliability at scale, reduces peak stress without new generation, and provides governments with actionable, real-time planning data. 20. Technologies GCP (Cloud Run, BigQuery), smart meters, edge computing, time-series AI models, optimization algorithms. 21. Why needed Existing systems lack real-time intelligence and integration of distributed energy resources. 22. Dataset Yes 23. Data access Through smart meters, utility collaboration, and anonymized operational data. 24. Ethics We use anonymized data, ensure transparency, and align with responsible AI principles. 25. Open source Yes 26. How might you leverage Google's pro bono technical support and expertise to accelerate project outcomes? We will leverage Google’s AI and cloud expertise to build and scale our distributed grid intelligence platform. Specifically, we seek support in developing robust time-series forecasting and optimization models, designing agentic AI workflows for autonomous grid operations, and optimizing our architecture on GCP (BigQuery, Cloud Run) for real-time data processing at scale. We also aim to learn from Google’s experience operating highly reliable, large-scale infrastructure (e.g., data centers) to inform how we design resilient, fault-tolerant energy systems. Additionally, we would benefit from guidance on responsible AI deployment, model evaluation, and integration with public sector data workflows to ensure reliability, scalability, and government adoption. We will leverage Google’s AI and cloud expertise to build and scale our distributed grid intelligence platform. Specifically, we seek support in developing robust time-series forecasting and optimization models, designing agentic AI workflows for autonomous grid operations, and optimizing our architecture on GCP (BigQuery, Cloud Run) for real-time data processing at scale. We would also benefit from guidance on responsible AI deployment, model evaluation, and integrating our system with public sector data workflows to ensure reliability, scalability, and government adoption. AI model optimization, scaling infrastructure, and system architecture design. IV. Feasibility 27. Why is your organization uniquely positioned to lead this project? Nearly Free Energy uniquely combines hands-on microgrid deployment with AI-driven software development in emerging markets. We operate a live pilot with real users and data, giving us practical insight into grid constraints, customer behavior, and operational challenges. Our team spans energy systems, embedded hardware, and cloud/AI engineering, enabling end-to-end execution from meters to models. We are also actively engaging regulators and utilities on DER policy and integration, positioning us at the intersection of infrastructure, data, and government adoption—where this problem must be solved. 29. Describe the work you have done to demonstrate the technical feasibility of your approach. We have deployed a live pilot microgrid with ~10 paying customers, instrumented with smart meters (RS-485/Modbus) and cloud ingestion to GCP (Cloud Run, BigQuery). We collect continuous telemetry (kWh, voltage, load profiles) and run initial analytics for load visibility and anomaly detection. We have tested controlled battery dispatch to smooth peak demand and validated end-to-end data pipelines (edge → cloud → dashboards). Success metrics include sustained data uptime (>95%), accurate load measurement, improved peak smoothing on pilot circuits, and reliable billing/alert workflows. These results demonstrate feasibility of real-time data-driven operations and AI-assisted optimization. 30. Key technical risks, dependencies, maintenance, and mitigation strategies Key risks include hardware integration variability, intermittent connectivity, data quality gaps, and model performance in low-data environments. Adoption depends on utility/regulatory alignment and integration with existing workflows. Ongoing needs include device maintenance, data pipeline reliability, and model monitoring. Mitigations: modular, standards-based design (Modbus/REST); offline-first edge control with local fallbacks; redundancy and buffering for connectivity; continuous data validation and monitoring; phased model rollout with human-in-the-loop; training and SLAs with partners; and close coordination with regulators/utilities to ensure smooth integration and sustained operations. 31. Policy, administrative, privacy, and logistical risks and mitigation Policy risk: delays or uncertainty in DER interconnection and PPA frameworks. Mitigation: early engagement with ERA/utility stakeholders, alignment to existing codes, and pilot-based regulatory sandboxes. Administrative risk: slow procurement/adoption within public entities. Mitigation: lightweight pilots, clear ROI metrics, and integration with existing workflows and reporting. Privacy/data risk: handling consumer energy data. Mitigation: data minimization, anonymization, role-based access, encryption in transit/at rest, and compliance with local data protection laws. Logistical risk: installation/maintenance at scale. Mitigation: standardized hardware kits, local partner installers, remote monitoring, and SLA-driven support. 32. How will public servants be trained, supported, and incentivized to adopt and use this solution as part of their regular workflows? Government entities are not primary users of the platform; their role is to create enabling regulatory frameworks that build trust in DER operators. We will support regulators through targeted briefings, data-sharing dashboards, and policy workshops that translate system insights into actionable regulation. By providing clear visibility into demand patterns, grid impact, and reliability improvements, we enable regulators to confidently design and enforce DER policies. Incentives for adoption come from improved oversight, better planning data, and the ability to expand reliable electricity access without additional public infrastructure investment. 33. Provide additional detail about 3-5 key project team members, especially those in technical roles. Microgrid/Energy Systems Engineer – designs and operates DER systems, battery dispatch, and grid integration. Backend/Cloud Engineer – builds scalable data pipelines and APIs on GCP (Cloud Run, BigQuery). Data Scientist/ML Engineer – develops forecasting, optimization, and anomaly detection models. Embedded/IoT Engineer – integrates smart meters (Modbus/RS-485) and edge controllers. Operations/Deployment Lead – manages field installation, partner coordination, and system reliability at scale. Policy & Partnerships Lead – drives regulatory engagement (e.g., ERA), supports DER policy development, secures government buy-in, and enables partner operators in new markets to navigate regulation and scale deployments. VI. Scalability 36. Based on your previous selection, detail how you’d replicate success beyond your initial proposal. We will scale through a dual approach: direct deployments and an open ecosystem model. We will standardize our microgrid architecture (hardware + AI software) into repeatable deployment kits and publish the platform as open source, enabling local DER operators to adopt and deploy in their markets. We will partner with utilities and regulators to integrate these systems into national grids, creating a coordinated network of distributed assets. As we scale, we will expand our team across engineering, partnerships, and operations to support multi-country deployments and ecosystem growth. 37.a Financial sustainability Sustained via electricity sales (10–30% gross margins) from NFE-owned microgrids, plus SaaS fees and revenue share from partner-operated systems. As deployments scale, recurring revenues from energy and platform services cover operations, maintenance, and continued expansion without grant funding. 37.b Technical sustainability Cloud-native architecture on GCP with automated monitoring and CI/CD; offline-capable edge controllers ensure resilience. Open-source platform enables contributions from partners/operators. Standardized hardware kits and SLAs support maintenance, while local partners handle installation and support at scale. 38. What key learnings, datasets, models, codebases, or other artifacts will your project generate, and how will you share them with other organizations to help advance the field? We will generate real-world energy datasets (anonymized usage, demand response, grid performance), AI models for forecasting and optimization, and an open-source microgrid OS. Our company culture is to work in the open—we already publish our work via our website and public wiki (nearlyfreeenergy.com; bookstack.nearlyfreeenergy.com). We will continue sharing code, documentation, and operator playbooks to enable other DER operators and governments to replicate and build on our approach globally. VII. Project Budget and Timeline 41. Funding Request $2,500,000 42. Personnel & Staffing — $600,000 Covers salaries for core team: AI/ML engineers, backend/cloud engineers, energy systems engineers, and operations staff. Includes hiring additional technical talent to build forecasting, optimization, and agentic AI systems, as well as project management and field deployment coordination. 43. Technology Development — $300,000 Development of AI models (forecasting, optimization), software platform (microgrid OS), APIs, dashboards, and data pipelines. Includes costs for software engineering, model training, testing, and integration with edge devices and utility systems. 44. Infrastructure & Deployment — $1,900,000 Procurement and deployment of smart meters, edge controllers, and battery-integrated systems for pilot and scale-up sites. Battery storage is a primary cost driver, with an estimated requirement of ~1.5 kWh per household at approximately $450 per kWh deployed. This category covers battery capacity, installation materials, field operations, connectivity, and cloud infrastructure (GCP compute, storage, and data processing). 45. Partnerships & Ecosystem Growth — $150,000 Government engagement, regulatory workshops, and partnership development with utilities and DER operators. Includes ecosystem-building activities, training sessions, and support for onboarding partners to deploy and operate microgrids using the platform. 46. Monitoring, Evaluation & Overhead — $50,000 Measurement of impact metrics (uptime, peak reduction, access), reporting, and program evaluation. Includes minimal indirect costs such as administration, coordination, and compliance (kept under 5%).