By Sunil Mani, Ashwitha Tunga, Sanskriti Gupta, Aravind Edamanakunnel Soman

Decentralized renewable energy (DRE) projects often launch with great fanfare, but the real challenge isn’t turning the lights on—it’s keeping them on. To scale what works, we need a smarter approach: predictive maintenance, real-time data, and digital innovation.

Snapshot

DRE has long promised to light up underserved communities, fuel local development, and close the energy access gap across the Global South. However, while many pilot projects have delivered short-term success, too many have faltered in the long run—not because of poor technology, but because of a critical gap in operations and maintenance. Traditional models have failed to meet the complex realities of rural and remote systems. 

This piece breaks down why so many DRE systems stall, and what a truly scalable solution looks like: predictive maintenance, digital integration, and a shift from reactive fixes to intelligent, connected systems that keep the power on.

What’s Holding DRE Back 

DRE’s promise has always been more than just clean electricity—it has represented hope. A hope to light up the remotest corners of the world, to enable children to study after sunset, to power irrigation pumps and health care facilities where the grid never quite made it, and to drive local economies through reliable, decentralized energy access.

In 2014, Dharnai in Bihar became a celebrated example as a “model solar village,” powering homes, schools, and livelihoods through a dedicated 100kW solar microgrid.

While the microgrid significantly improved energy access in its initial phase, it started facing significant operational challenges over time. 

Studies documented performance variability in power generation, influenced by practical issues like inconsistent maintenance practices, shading, and improper panel orientation. The absence of integrated real-time monitoring systems and data analytics compounded these problems, making it difficult to proactively identify and address maintenance and performance-related issues. 

In 2013, Lakshmipura-Jharla, Rajasthan, a solar microgrid was established to provide dedicated energy services to rural households. While the microgrid improved energy access, studies revealed performance variability in power generation influenced by factors such as shading, panel orientation, and maintenance practices. The absence of continuous performance monitoring and data analytics made it challenging to identify and address these issues proactively, emphasizing the importance of integrating real-time monitoring systems to maintain consistent energy output.

Similarly, in 2015, Barapitha became Odisha’s first fully solar-powered village, and its success made headlines. In Maligaon, deep in Odisha’s Kalahandi district, and Lehni II in Uttar Pradesh, solar microgrids not only electrified homes but also introduced community governance mechanisms like village development committees to manage and maintain the systems. These were not just infrastructure projects. They were symbols of decentralized empowerment.

But then, one by one, these systems started failing. These are not isolated incidents—they are quite common, particularly in rural areas.

Across the Global South, hundreds of DRE systems tell the same tale: excellent at the beginning, but lacking the operational resilience needed for long-term sustainability. A 2018 study by the International Renewable Energy Agency emphasized that without strong post-installation support and system upkeep, even the most promising DRE interventions risk falling into disrepair. The core issue? Operations and maintenance.

This has long been the Achilles’ heel of DRE systems, especially in the rural context, where road access is poor, skilled labour is scarce, and costs must be kept low. Traditional models, whether reactive (fix it when it breaks) or preventive (check it on a schedule regardless of need), have failed to serve the dynamic and remote nature of rural DRE. 

Further, the challenge of DRE today extends beyond just keeping systems running. It is also about scaling them up and integrating them meaningfully into the broader electricity ecosystem. As DRE transitions from pilot projects to mainstream infrastructure, we are faced with a new layer of complexity: once these thousands of weather-dependent DRE sources get connected to the grid, how do we forecast their generation? How do we coordinate that with energy demand and supply contracts (such as long-term power purchase agreements)? And how do we ensure the grid remains stable while doing so?

Scaling DRE demands more than duplication. It also requires orchestration. That means employing predictive models and digital tools to simulate generation variability, optimize system configurations, and make real-time decisions about when to draw power from the grid and when to feed into it. Without this layer of intelligence, large-scale DRE risks becoming a patchwork of uncoordinated systems, straining both local operations and national grid reliability.

This is where the future of DRE must pivot.

It is no longer sufficient to build and hand over the systems. Instead, we need innovative, technology-driven solutions that anticipate failures before they happen, enable remote diagnostics, simulate system behaviour, and support communities with real-time data and insights. This is where digital technologies like Internet of Things (IoT)-based remote monitoring, artificial intelligence (AI)-driven predictive maintenance, and digital twins not only help keep the lights on but also ensure that DRE becomes a flexible and resilient pillar of future energy systems.

The Digital Shift: From feasibility to frontier

The research and implementation of DRE have increasingly leveraged technology to optimize system performance and enhance energy access solutions.

In the 1980s and 1990s, pilot projects focused on proving that DRE systems could work and analyzing their socio-economic viability for rural electrification. Over time, this evolved into using software to model and simulate renewable energy systems. Tools like HOMER Energy allowed planners to design optimal system configurations using local resource and demand data, simulating thousands of scenarios to identify the most cost-effective mix. These tools marked the beginning of digitalization in DRE.

But that was just the start. The field is now entering a new phase, where we don’t just model feasibility but use digital tools to manage real-time operations, predict faults, and support integration with broader electricity markets.

Bridging the Gap Between Innovation and Adoption

While the potential of integrating technology into DRE systems is immense, there could be various potential challenges as well. Some of them are highlighted below:

  • The high costs of technology adoption could deter small-scale developers. Even if the developer tries to pass on the cost to the end consumers, the resultant tariff of such systems will become high, making it lose its cost competitiveness against subsidized electricity rates, as is the case in most developing countries.
  • Connectivity issues in remote areas may hinder real-time data transmission and monitoring.
  • Limited digital literacy among the operators and technicians responsible for managing/maintaining these systems could further complicate the operations, and in case of an emergency, might add to the layer of existing challenges.
  • Data silos and lack of knowledge sharing remain an issue. The DRE pilot projects are often limited to specific locations. However, sometimes due to concerns over data privacy and a lack of open collaboration, insights and data from such interventions are inaccessible for broader use. This limitation hampers their full potential, as the valuable learnings from such projects could inform and enhance future interventions.
  • Other factors include the following: These digital innovations also face market risks, as limited demand for emerging, nascent products, due to a small market footprint, lack of awareness among developers, or the absence of supportive policies, can lead to their decline.

These roadblocks make a compelling case for broader policy support and capacity building.

Enabling the Future: Policy, finance, and capacity building

To unlock the full potential of digital DRE systems, we need a multi-pronged strategy:

  • Commercial viability of technology integration must be built into every stage, from planning to operations. Innovative business models, blended finance, and public–private partnerships can help make digitized DRE both impactful and investable.
  • Governments should scale up digital infrastructure in rural areas. Governments should continue to incentivize the expansion of rural digital infrastructure, including last-mile Internet connectivity through complementary technologies, such as satellite broadband and local manufacturing of IoT components.
  • Training and capacity building at the local level, through digital literacy programs and technician training, can ensure long-term community ownership.
  • Developing regulatory frameworks to break silos between different DRE interventions would enable interoperability between such digital solutions, and also ensure that the data from all these interventions gets effectively utilized for improvements in the sector.
  • Policy incentives and risk guarantees can reduce entry barriers for developers and communities to adopt intelligent DRE systems.

Frontier Technologies 

Emerging technologies like blockchain-enabled peer-to-peer trading and smart IoT-based demand response tools have been constantly pushing the boundaries of possibilities. Pilot projects in Karnataka and Uttar Pradesh are exploring blockchain platforms for community energy exchange, demonstrating a glimpse into decentralized, participatory electricity markets. In fact, Uttar Pradesh became the first state in India to launch a pilot project for peer-to-peer trading of rooftop solar power.

However, technology itself is not the silver bullet to scale the uptake of DRE.

The Road Ahead

Previous DRE interventions have shown that the challenge is not in installing DRE systems. The challenge lies in keeping the lights on and scaling these systems smartly, year after year, storm after storm, user after user. And that will only happen when DRE is not just distributed, but predictive, integrated, and intelligent. 

This blog has been accessed from the official website of International Institute for Sustainable Development (IISD) and can be accessed here