Smart grids go digital: AI, blockchain and IoT fuel sustainable energy goals

Smart grids support three key Sustainable Development Goals (SDGs) outlined in the study: SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). With the EU’s "Fit for 55" initiative aiming to cut greenhouse gas emissions by 55% by 2030, smart grid deployment is accelerating. According to the report, renewable energy surpassed fossil fuels in Europe for the first time in 2024, driven by increased investments in solar and wind infrastructure and supported by intelligent grid management systems.

Smart grids are emerging as the digital backbone of the global energy transition, combining artificial intelligence, the Internet of Things, and advanced cybersecurity frameworks to optimize power generation and consumption. A new study titled “Smart Grids and Sustainability: The Impact of Digital Technologies on the Energy Transition”, published in the journal Energies, provides a detailed, data-driven analysis of the current research landscape surrounding smart grids and the pivotal role of digital innovations in reshaping Europe's energy systems.

By conducting a systematic literature review of 179 peer-reviewed papers indexed in the Scopus database and using bibliometric mapping software VOSviewer, the authors identify key themes and technologies at the forefront of the smart energy movement. From AI-powered forecasting and peer-to-peer blockchain trading to real-time monitoring through IoT, the paper confirms that digital transformation is not just enhancing energy efficiency - it’s becoming essential for climate targets, infrastructure resilience, and the stability of modern power systems.

How are digital technologies reshaping smart grid operations?

The study underscores that digital technologies are indispensable in building agile, efficient, and low-emission energy systems. AI and IoT play a foundational role in managing dynamic grid demands, predictive maintenance, and decentralized energy flows. Machine learning algorithms are now embedded in demand forecasting tools that help grid operators manage variability, especially with intermittent renewable sources like wind and solar. Reinforcement learning has even been applied to stabilize grid-following converters, a complex task in fluctuating voltage conditions.

Edge computing is also gaining prominence. By decentralizing data processing closer to the point of generation, it reduces latency and enhances real-time decision-making. These tools work in tandem with blockchain networks that manage secure, transparent peer-to-peer energy exchanges, reducing reliance on centralized authorities and increasing the flexibility of microgrids. In one cited case, smart contracts enabled energy trading within IoT-equipped homes while preserving user privacy.

The study also highlights the role of digital twins and simulation tools in grid optimization. These models allow utilities to test demand response scenarios, assess network vulnerabilities, and plan infrastructure upgrades with precision. The shift from reactive to predictive and even prescriptive analytics represents a transformative leap in energy systems engineering.

What role do smart grids play in achieving sustainability targets?

Smart grids support three key Sustainable Development Goals (SDGs) outlined in the study: SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). With the EU’s "Fit for 55" initiative aiming to cut greenhouse gas emissions by 55% by 2030, smart grid deployment is accelerating. According to the report, renewable energy surpassed fossil fuels in Europe for the first time in 2024, driven by increased investments in solar and wind infrastructure and supported by intelligent grid management systems.

Smart grids contribute to these objectives by reducing transmission losses, enhancing energy efficiency, and enabling the integration of decentralized renewable resources. Technologies like real-time sensors and distributed control systems allow for smoother load balancing, more reliable distribution, and even automatic reconfiguration of power networks in response to outages or demand spikes. Notably, AI-based forecasting helps mitigate the unpredictability of solar and wind output, improving grid reliability and planning.

In 2023, the EU saw an 18% drop in emissions related to energy production - a figure the authors partially attribute to smart energy management systems. These digital tools help reduce energy waste, optimize resource allocation, and streamline grid operations across national and regional systems. The study also notes that nuclear and wind remain the top contributors to EU electricity consumption, followed by natural gas and increasingly significant contributions from solar and hydroelectric sources.

What are the critical challenges and future research directions?

Despite the promise, several barriers persist. Chief among them is cybersecurity. As energy systems become more connected, the risk of cyberattacks increases. The study emphasizes the need for advanced, AI-enhanced security measures capable of detecting and responding to emerging threats in real time. Intrusion detection systems, encryption protocols, and autonomous recovery mechanisms are becoming non-negotiable in modern grid design.

Another concern is interoperability. The integration of diverse digital tools, from AI models to blockchain platforms, requires standardized communication protocols and regulatory alignment. Without this, grid components from different vendors may struggle to operate harmoniously, limiting scalability. Economic constraints also remain a factor; while digital upgrades improve efficiency, their upfront costs can deter wide-scale adoption, especially in underfunded municipalities or developing regions.

The authors call for stronger policy frameworks and public-private partnerships to accelerate deployment. They also recommend expanding research into hybrid systems that combine AI with traditional engineering models, as well as deeper exploration into socio-technical factors like user behavior, governance, and market incentives. A notable gap exists in longitudinal studies assessing the long-term performance and ROI of smart grid investments.

In addition, the paper identifies the growing importance of weather forecasting models tailored to renewable energy systems. Hybrid deep learning frameworks, such as CNN-LSTM architectures, have shown potential in improving the accuracy of solar and wind generation predictions. These tools will be vital for dynamic energy planning and for responding to climate-related volatility.