Event Programme 2026

As the global space sector undergoes a radical transformation, the demand for innovative, high-performance power solutions has never been more critical. This lecture class considers the evolution of space photovoltaic systems and how emerging applications are driving innovation.

This lecture class covers:

• Unique features of the space environment requiring custom PV solutions: AM0 spectrum, plasma and atomic oxygen, thermal cycling and particle radiation.
• Radiation induced degradation: Damage mechanisms, simulation methods and correlation with ground-based testing, along with the role of shielding.
• Key design features of current space photovoltaic solutions, powering payloads delivering critical space-based services today and their qualification standards.
• Emerging applications and evolving design criteria. Next generation satellite networks; high power space infrastructure; space exploration and science in space placing new demands on the PV power systems including: lower cost, high specific power, on-orbit assembly compatibility and extended duration survival in hostile space environments.
• Novel space PV concepts: potential advantages and challenges.

This lecture class would be useful for researchers from all career stages looking to understand specific challenges associated with photovoltaics in space environments, tool kits and methodologies for modelling these effects and correlating with prospective mission profiles, current state-of-the-art space power solutions and how they are qualified, and how the demands of emerging applications are driving innovation in this field. The course has been developed as an academic/industrial collaboration and would suit attendees interested in the challenges of translating novel device designs and alternative material platforms to scalable space photovoltaics technology solutions.

Artificial intelligence is rapidly becoming relevant to photovoltaics, but its most valuable uses are often application-specific and require careful integration with domain expertise. This 2.5-hour tutorial will provide a practical introduction to how AI can support the PV innovation and deployment lifecycle, from research and development to manufacturing, field operations, and grid integration. The emphasis will be on actionable workflows rather than general AI theory, with examples chosen to reflect real challenges in PV research and industry.

The tutorial will cover concrete use cases including experimental design, defect and failure analysis, manufacturing-variance reduction, performance monitoring, production forecasting, and congestion management. These examples will illustrate where AI can accelerate learning, reveal patterns in complex data, and support decision-making under uncertainty. They will also highlight important limitations, including data quality, low-data regimes, model validation, interpretability, and the risks of over-reliance on automated outputs.

Participants will learn practical workflows for building bespoke AI tools with frontier models, including how to structure data, design prompts, check outputs, integrate expert review, and keep humans in the loop. The session will conclude with a hands-on activity in which participants apply these concepts to a representative PV problem. Attendees will leave with a clearer understanding of where AI is useful in PV today, where caution is required, and how to design AI workflows that improve learning speed & quality in PV research & industrial practice.

The economics of renewables are shifting. The first wave of support schemes drove PV deployment at scale — but rising penetration brings its own structural challenges. Grids must manage greater variability and increasingly rely on enhanced ancillary services for stability, while midday market prices fall as each additional kilowatt-hour of unmanaged solar erodes its own value: the cannibalisation effect. Batteries are the natural complement: they move energy across time, deliver services to the grid, and turn price volatility from a threat into a source of revenue. Yet deciding how much storage to build, how to operate it, and whether the resulting business case is bankable are all, fundamentally, modeling questions. This tutorial gives newcomers a structured, end-to-end view of how PV+BESS systems are modeled — standalone and as combined systems — combining a techno-economic perspective with the forecasting and optimization methods used to run these assets in practice.
We start with the building blocks: how rising PV penetration reshapes the economics of the grid, the configuration options for storage — co-located versus standalone — as a structural response, and the technical parameters that drive every downstream decision: power rating, energy capacity, duration, round-trip efficiency, cycling and degradation. The grid connection enters as a hard constraint from the outset. We then introduce the revenue stack — the set of markets and contracts a single asset can serve simultaneously: wholesale energy arbitrage on day-ahead and intraday markets, ancillary and balancing services, capacity remuneration, and renewable support schemes. We map how the current European and Italian regulatory frameworks — including long-term tolling mechanisms and Contracts for Difference — shape what is worth modeling.

The second half turns to operation. Because revenues depend on uncertain prices, we look at how electricity prices form and how they are forecast with statistical and machine-learning methods, and how those forecasts feed a dispatch-optimization problem that sets charge and discharge schedules. One or two optional deep-dives make this concrete — a compact linear-programming formulation for arbitrage, and a forecast-to-dispatch pipeline — while the main thread stays conceptual. We discuss degradation-aware and multi-market co-optimised dispatch, and the role of algorithmic trading in automating these decisions.

Throughout, modeling choices are tied back to investability: revenue uncertainty, and how hedging structures change the optimal design. Figures from real utility-scale PV+BESS portfolios illustrate how the pieces fit together. By the end, participants will understand how technical design, market rules, grid constraints and forecasting jointly determine the economic and system value of storage — and will be equipped to read, and to question, the models behind real projects.