Daniel Maldonado Quinto

Integrating renewable energy is critical for maintaining grid stability while striving towards global climate goals. Concentrating solar power tower plants offer a promising solution but their competitiveness is currently hindered by high operational costs, limited data availability and slow adoption of emerging technologies. To address these barriers, here we introduce PAINT, a FAIR (findable, accessible, interoperable and reusable) open-access database for operational solar tower plant data. PAINT provides 849 GB of high-resolution data collected over multiple years from the Jülich solar tower plant, including heliostat properties, calibration and deflectometry measurements and fine-grained weather data. The database is organized using the SpatioTemporal Asset Catalog metadata specification and supports the development of digital twins, artificial intelligence-based calibration methods, predictive maintenance and improved solar flux prediction. We also introduce standardized benchmarks to promote reproducibility and fair comparisons. Providing access to high-quality data, PAINT enables broader participation in solar research, accelerates innovation and facilitates data-driven solutions in solar tower power plant research.

Concentrated solar thermal (CST) plants can provide dispatchable renewable energy but remain complex and costly to operate due to tightly coupled subsystems, dynamic environmental conditions, and high safety requirements. This work presents the Solar Energy Autopilot (SOLEA), a holistic four-layer architecture that transforms a traditional solar power tower plant into an autonomous cyber-physical system (CPS). The architecture integrates plant hardware and control systems, advanced optical and meteorological measurement systems, a CPS middleware platform with a standardized digital twin, and a multi-agent automation layer for operation planning and control. SOLEA is implemented and evaluated at an operational research facility, where intelligent agents autonomously plan operating days, start and shut down the plant, and regulate the receiver flux and outlet temperature under varying environmental conditions. A dual-sided evaluation quantifies benefits in terms of efficiency, robustness, and cost-related indicators and assesses the system’s autonomy using a multi-dimensional maturity model. The results show substantial reductions in operational and investment costs, highly robust operation despite challenging environmental conditions, and efficiency gains in plant operation enabled by an increased autonomy level of the facility. Overall, the study demonstrates that the layered modular SOLEA architecture can effectively bridge the gap between isolated automation solutions and plant-wide autonomous CST operation and provides a reusable blueprint for advancing autonomy in future CST plants and comparable industrial environments.

With the advancement of digitization in the era of Industry 4.0 (I4.0), highly automated, semi-autonomous, and fully autonomous systems are emerging. Within this context, multi-agent systems (MAS) offer a promising approach for automating tasks and processes based on autonomous agents that work together in an overall system to increase the degree of system autonomy stepwise in a modular and flexible way. A critical research challenge is determining how these agents can collaboratively engage with both other agents and human operators to facilitate the gradual transition from automated to fully autonomous industrial systems. To close transparency and connectivity gaps, this study contributes with a framework for the collaboration of agents and humans in increasingly autonomous MAS based on a Digital Twin (DT). The framework specifies a standards-based data model for MAS representation and proposes to introduce a DT infrastructure as a service layer for system coordination, supervision, and interaction. To demonstrate the feasibility and assess the quality of the framework, it is implemented and evaluated in a case study in a real-world industrial scenario. Although additional long-term evaluations across different contexts are needed, the assessment of functional completeness and selected quality attributes show that the proposed framework provides a solid technical foundation that facilitates a transparent and seamless collaboration between agents and humans within increasingly autonomous industrial MAS.

Alongside the vision of autonomous systems, similar system concepts are being discussed in the research fields of highly automated, intelligent, adaptive, autonomic, and organic systems. Although these types of system are studied in scattered research fields that consider them as distinct system classes, they share similar characteristics and are interrelated to some extent. Experts in various fields present a very heterogeneous view on the intersection of autonomous and comparable system concepts, for example, as interchangeable, distinct, or complementary research approaches. Therefore, this study performs a systematic literature review based on more than 300 articles to investigate the intersection of the system classes, emphasizing their similarities, differences, and relationships from the current state-of-the-art.

With the advancement of digitization in the era of Industry 4.0 (I4.0), highly automated, semi-autonomous, and fully autonomous systems are emerging. Within this context, multi-agent systems (MAS) offer a promising approach for automating tasks and processes based on autonomous agents that work together in an overall system to increase the degree of system autonomy stepwise in a modular and flexible way. A critical research challenge is determining how these agents can collaboratively engage with both other agents and human operators to facilitate the gradual transition from automated to fully autonomous industrial systems. To close transparency and connectivity gaps, this study contributes with a framework for the collaboration of agents and humans in increasingly autonomous MAS based on a Digital Twin (DT). The framework specifies a standard-based data model for MAS representation and proposes to introduce a DT infrastructure as a service layer for system coordination, supervision, and interaction. To demonstrate the feasibility and assess the quality of the framework, it is implemented and evaluated in a case study in a real-world industrial scenario. As a result of the study, we infer that the DT framework offers significant benefits in facilitating transparent and seamless cooperation between agents and humans within increasingly autonomous industrial MAS.

Concentrating solar power tower plants hold significant potential for sustainable energy generation but face challenges related to high operational costs, limited data availability, and slow adoption of novel technologies. To address these barriers, we introduce PAINT, the first FAIR open-access database for operational solar tower plant data. PAINT provides 849 GB of high-resolution operational data collected over multiple years from the Jülich solar tower plant, including heliostat properties, calibration and deflectometry measurements, and fine-grained weather data. Organized using the SpatioTemporal Asset Catalog metadata specification, PAINT supports the development of digital twins, AI-based calibration methods, predictive maintenance and fault detection, and improved solar flux prediction. It also introduces standardized benchmarks to promote reproducibility and fair comparisons. By democratizing access to high-quality concentrating solar power data, PAINT enables broader participation in solar research, accelerates innovation, and lays a foundation for data-driven improvements in the performance and reliability of solar tower power plants.