The NetZero-Opt project builds on a systematic exploration of time series aggregation (TSA) for optimization models, addressing large-scale, complex power systems. Check out the project website:
https://www.tugraz.at/en/institutes/iee/research/current-projects/netzero-opt(öffnet in neuem Fenster)Proof of concept: a posteriori structure-aware TSA for linear programs
The project originates from Wogrin (2023), which shows for linear programs that explicitly accounting for model structure (Simplex basis) enables substantial data aggregation without loss of solution quality. This work established the theoretical possibility of exact aggregation in the objective function and outputs, providing the conceptual foundation for NetZero-Opt.
Extension to network and ramping constraints
Building on this foundation, Cardona-Vasquez et al. (2024) extended TSA to models with network and ramping constraints, common in electricity and gas systems. The study demonstrated that even with time-linking constraints—such as thermal unit ramping—perfect aggregation is possible. These results, requiring full model solutions, served as proofs of concept and established the framework for structure-aware TSA.
Machine learning for practical TSA without full model solutions
A subsequent step addressed the need for full outputs. In Cardona-Vasquez et al. (2025), machine learning was used to predict the activation of time-linking constraints. Identifying inactive constraints allowed partitioning the system matrix into independent subproblems without output errors and enabled parallelization, achieving 300× speed-ups on the Austrian grid.
Introducing performance bounds for aggregated models
Another key result is the development of bounded TSA methods for optimization models, including MILP formulations such as Virtual Power Plant (VPP) investment planning. Santosuosso et al. (2025) formally proved how to construct aggregated models that provide both lower and upper bounds on the full model solution, ensuring performance guarantees and feasibility throughout iterative procedures—beyond the heuristic nature of existing TSA approaches.
Towards exact aggregation with energy storage constraints
In subsequent work, Klatzer et al. (2025) and Santosuosso et al. (2025b) achieved exact TSA for models with energy storage time-coupling constraints. By exploiting active constraint sets, dual information, and machine learning for period classification and clustering, the methodology enables accurate aggregation in co-scheduling problems with variable renewable energy and storage.
Extension to spatial aggregation and model predictive control
To address challenges from high spatial resolution, NetZero-Opt extended its framework to spatial aggregation. Stöckl et al. (2025) developed aggregation methods based on a Network Congestion Price metric that preserve the impact of nodal injections on line congestion, outperforming standard Locational Marginal Price–based approaches. The project also released the open-source Python Network Partitioning & Aggregation Package (NPAP) to support practical application. Moreover, in an initial publication (Santosuosso et al 2025c) we show that structure-aware aggregation can be extended to other fields such as model predictive control.
Summary of achievements
NetZero-Opt established a comprehensive framework for time series and spatial aggregation of large-scale energy system optimization models, including:
1. Structure-aware TSA proofs of concept
2. Extensions to network and ramping constraints
3. Practical TSA via machine learning without full model solutions
4. Performance-bounded aggregation for LP and MILP models
5. Exact TSA for storage-coupled systems
6. Novel spatial aggregation methods and an open-source software package (NPAP)
7. Extension to model predictive control
These results advance the state of the art by combining theoretical rigor, computational efficiency, and practical applicability for scalable energy system optimization.
