Periodic Reporting for period 1 - NETOPT (Modeling and forecasting supply networks using functional time series and mathematical programming)
Periodo di rendicontazione: 2022-09-01 al 2024-08-31
As the global energy landscape evolves in response to rising demands and geopolitical uncertainties, the need for reliable forecasting models becomes increasingly critical. In Europe, where natural gas is a cornerstone of the energy supply, accurately predicting supply and demand dynamics across an extensive pipeline network is essential for economic prosperity and environmental sustainability. This project aims to develop a large-scale functional network time series model that incorporates balance constraints, contributing to a more efficient and environmentally friendly energy distribution system aligned with the European Strategic Energy Technology Plan (ESETP).
The motivation behind this initiative stems from real-life energy forecasting challenges that require enhanced efficiency and sustainability. Transmission system operators (TSOs) face the pressing need to effectively plan for energy supply and demand balances. However, current forecasting methods often struggle to capture the complexities of dynamic energy networks. Additionally, classical continuous optimization methods can be computationally expensive, complicating the task of estimating to optimal solutions. To tackle these issues, we build a large-scale functional network time series model with balance constraints that employs a mathematical programming approach, enhancing the connection between statistical learning and operational research techniques. This dynamic model enables TSOs to interpret the underlying dynamics of the network while effectively maintaining the balance between supply and demand. This approach allows for better resource allocation and risk management, enabling TSOs to respond proactively to potential disruptions in the energy network.
The methodologies developed here can also be adapted for other large-scale networks, including water and renewable energy systems, further promoting sustainability and efficiency. Ultimately, this initiative promises to enhance the stability of the European energy market, benefiting individual countries and the broader energy community. By fostering a more stable and secure energy future, the project aligns with strategic goals of energy security and sustainability at both national and regional levels.
The motivation behind this initiative stems from real-life energy forecasting challenges that require enhanced efficiency and sustainability. Transmission system operators (TSOs) face the pressing need to effectively plan for energy supply and demand balances. However, current forecasting methods often struggle to capture the complexities of dynamic energy networks. Additionally, classical continuous optimization methods can be computationally expensive, complicating the task of estimating to optimal solutions. To tackle these issues, we build a large-scale functional network time series model with balance constraints that employs a mathematical programming approach, enhancing the connection between statistical learning and operational research techniques. This dynamic model enables TSOs to interpret the underlying dynamics of the network while effectively maintaining the balance between supply and demand. This approach allows for better resource allocation and risk management, enabling TSOs to respond proactively to potential disruptions in the energy network.
The methodologies developed here can also be adapted for other large-scale networks, including water and renewable energy systems, further promoting sustainability and efficiency. Ultimately, this initiative promises to enhance the stability of the European energy market, benefiting individual countries and the broader energy community. By fostering a more stable and secure energy future, the project aligns with strategic goals of energy security and sustainability at both national and regional levels.
We developed a novel functional network time series model with a balance constraint, tailored to accurately capture the dynamics of complex systems. The model was estimated using Mixed Integer Programming (MIP), ensuring precise optimisation of parameters. Through simulations, we confirmed the model’s robustness and accuracy. When applied to real data, the model demonstrated strong forecasting performance, providing reliable predictions for natural gas distribution. Moreover, the results provide valuable insights into natural gas supply and demand patterns. The interpretation of the network offer a comprehensive understanding of the underlying relationships within the natural gas system, paving the way for improved decision-making and operational efficiency.
The model’s accurate forecasting of hourly gas flows over several days provides valuable insights for Transmission System Operators (TSOs), enabling them to make informed operational decisions, optimize resource allocation, and maintain system balance. Identifying influential network nodes offers a strategic advantage by pinpointing critical areas where intervention may be necessary to avoid disruptions. The use of a consistent network adjacency matrix enhances the understanding of natural gas flow dynamics, allowing for better management of the network and more efficient distribution.
This model has the potential to significantly improve operational efficiency, enhance supply-demand balance, and support decision-making processes in energy management systems.
Key needs for further research:
1. Operational Robustness: Further research could focus on enhancing the model’s ability to handle more complex constraints or real-time data integration, improving its responsiveness to sudden changes in gas flows.
2. Scalability and Adaptability: Investigating the scalability of the model for larger and more complex networks, including a cross-country or cross-continental scale, would be beneficial. Incorporating data from different European countries or regions, as well as cross-border interconnectivity, could enhance the model’s relevance. Additionally, adapting the model for other energy sectors, such as oil or water, or integrated multi-energy systems, would expand its utility across various domains.
3. Nonlinear Dependence: Future research will aim to expand the current model to account for nonlinear dependencies within the network. This will allow for a more accurate representation of complex interactions and improve the model’s forecasting capabilities, especially in dynamic and non-linear systems.
This model has the potential to significantly improve operational efficiency, enhance supply-demand balance, and support decision-making processes in energy management systems.
Key needs for further research:
1. Operational Robustness: Further research could focus on enhancing the model’s ability to handle more complex constraints or real-time data integration, improving its responsiveness to sudden changes in gas flows.
2. Scalability and Adaptability: Investigating the scalability of the model for larger and more complex networks, including a cross-country or cross-continental scale, would be beneficial. Incorporating data from different European countries or regions, as well as cross-border interconnectivity, could enhance the model’s relevance. Additionally, adapting the model for other energy sectors, such as oil or water, or integrated multi-energy systems, would expand its utility across various domains.
3. Nonlinear Dependence: Future research will aim to expand the current model to account for nonlinear dependencies within the network. This will allow for a more accurate representation of complex interactions and improve the model’s forecasting capabilities, especially in dynamic and non-linear systems.