The solar energy sector is at the forefront of the energy transition, tasked with ensuring a stable, reliable power supply while advancing ambitious sustainability goals. As demand for clean energy surges, the industry must address challenges such as grid integration, storage, and fluctuating weather – all while maintaining efficiency and adaptability. The question is clear: how can solar providers meet these demands while setting new standards for operational resilience?
A global shift is underway toward renewable energy grids, advanced storage, and cutting-edge technologies. Tools such as the Industrial Internet of Things (IIoT), artificial intelligence (AI), and cloud technologies enable real-time monitoring, predictive analytics, and seamless integration with traditional systems. These innovations help energy companies optimise load balancing, protect equipment, and improve reliability by analysing electricity demand, consumption trends, and weather patterns.
The untapped potential of data
Data is pivotal in this transformation. Energy companies managing vast, distributed power grids depend on structured, reliable information. Digital twins – virtual replicas of physical assets or systems – are gaining traction in the sector. They combine data on geometry, physical properties, and environmental factors from multiple sources, enabling real-time monitoring and predictive analysis.
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A hybrid or cloud-based digital twin fosters collaboration by giving all stakeholders access to a unified, accurate data source. This enhances transparency and coordination across teams and partners in the value chain. For energy providers, this level of insight enables precise, data-driven adjustments that boost operational flexibility and decision-making.
The need for real-time insights
Despite the availability of advanced tools, a gap remains in accessing real-time data. According to AVEVA’s Industrial Intelligence Index Report, 55% of energy executives say they rarely or never have real-time data when making critical decisions.
Aveva
To leverage these benefits, all energy providers should look to pioneer projects in their field and learn from one another – regardless of energy source. Swedish energy giant Vattenfall, for example, addressed this challenge by modernising its hydropower plants. With 11,475 megawatts of capacity and an annual output of 40 terawatt-hours, Vattenfall’s outdated maintenance systems had relied on static data, requiring reactive measures. To shift to a proactive approach, the company adopted the AVEVA PI (Plant Information) system, integrating historical and real-time data for advanced monitoring and analysis.
An example in modernisation
Vattenfall’s implementation of AVEVA PI enabled condition-based monitoring using real-time data and automated alerts. This shift reduced unplanned maintenance, cut downtime, and improved efficiency. Within a year, maintenance costs fell by 1.5%, operational reliability increased, and the groundwork was laid for a future hydro information portal offering real-time KPIs and analytics.
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Magnus Holmbom, Development Engineer for Maintenance at Vattenfall, highlighted the platform's scalability and its role in ensuring planned rather than reactive maintenance. The pilot project showcased how digital transformation can reduce costs, enhance sustainability, and boost operational efficiency.
The role of AI in maintenance
Building on systems like Vattenfall’s, artificial intelligence is advancing predictive maintenance. Generative AI and machine learning models are now used to estimate the remaining useful life of equipment. Unlike traditional monitoring systems, which rely on static intervals, AI-driven approaches analyse sensor and operational data for subtle performance changes, enabling earlier fault detection and process stabilisation.
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Condition-based maintenance, supported by AI, provides a proactive way to improve plant reliability and efficiency. By identifying anomalies early, energy providers can prevent costly disruptions and maximise asset lifespan.
Technology as the catalyst for change
The energy sector is navigating complex challenges, including strict climate targets, operational efficiency, and volatile markets. Data-driven technologies are becoming indispensable tools for addressing these issues. By leveraging real-time insights, predictive analytics and AI, energy companies can enhance sustainability, resilience and adaptability.
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As these advancements reshape the industry, energy providers are not just managing change—they are leading the way toward a sustainable, efficient and reliable energy future. (Sue Quense/hcn)
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