A two-step cascade modelling between EnergyScope Pathway-BO and PyPSA-BO for energy transition planning. Part B: Geo-spatial characterization and effects

Fernandez Vazquez, Carlos A.A.;Jimenez Zabalaga, Pablo;Balderrama, Sergio;Quoilin, Sylvain
(2024) The 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems — Location: Rhodes, Greece (30.June.2024)

Files

ECOS2024_CarlosFernandezVazquez_PabloJimenezZabalaga.pdf
  • Open Access
  • Adobe PDF
  • 1.05 MB
Download

Details

Authors
  • Fernandez Vazquez, Carlos A.A.ULiege
    Author
  • Jimenez Zabalaga, Pabloorcid-logoUCLouvain
    Author
  • Balderrama, Sergio
    Author
  • Quoilin, Sylvain
    Author
Abstract
As the transition of energy systems becomes more relevant and urgent worldwide, countries and communities are searching for pathways toward sustainable energy systems. This involves developing long-term energy plans and deciding on the key resources and technologies required to meet their future energy needs. In this context, Energy System Optimization Models (ESOMs) are commonly used to analyze scenarios that assess the development of the energy system over extended periods, typically spanning multiple years or decades. Nevertheless, the technical, temporal, and spatial detail levels vary as systems and problems become more complex. In this sense, models balance their accuracy to simulate the analyzed systems and the computational resources required to make the model practical. As a result, various modelling tools are currently available, each tackling issues using distinct approaches or addressing specific aspects of the system's behaviour. Thus, considering a common objective and structure, redundancies among ESOMs could be used to check consistency across models and provide an additional validation layer. Alternatively, models can be enhanced by including inputs or supplementary information from other tools, considering the aspects each tool focuses on. This article is one of a two-part study that focuses on this cross-modelling effect, exemplifying the synergies and complementarities of running two alternative models to tackle the issue of planning the development of a country in the long term, aiming for a sustainable energy transition. The models selected (PyPSA-Earth and EnergyScope) were considered given that both of them have similar optimization objectives (minimizing costs of investment and operation), have complementary approaches (one by representing energy consumption at end-use level and the other by representing in more detail the power system) and have been adapted to run the same case study (the Bolivian energy system). An assessment of their structures and results shows that each model can provide relevant insights when running them as stand-alone tools. Nevertheless, by considering their particularities, their use in tandem can improve their results with minimum changes or updates in their structure. As a first step (Part A), the electrical demand characterization for the future is extracted from simulations run in EnergyScope-Bolivia. These are later introduced to the projections database in PyPSA-BO to develop a more detailed representation of the electrical system in the future, considering the geo-spatial component (Part B). This section of the study (Part B) aims to define the optimal geo-spatial representation of the system, while minimizing the computational time and resources required to solve the problem and the discrepancies between results with alternative clustering aggregations. For this, the PyPSA-BO model has been used, where the k-means clustering method is considered for the aggregation process of the network's components and demand projections for the year 2050 are introduced based on projections from EnergyScope-Bolivia (Part A). Results analyzed for comparing the deviations between 30 alternative clustering options and their impacts were: total installed capacities by technology, total generation by technology, operational costs associated with the simulated year, transmission capacities, and processing time required to find the optimal solution. Results show that the effects of aggregation in the system are most relevant for the cases with fewer nodes and that after a certain number, deviations become minor for each additional node considered in the process. In addition to these referential values, by taking into account the optimization times associated with each run between these limits, an optimal aggregation configuration for this system is found according to a Pareto analysis. This node configuration represents an essential input for future modelling endeavours by providing a baseline that should be considered when analyzing alternative scenarios or when integrating network characteristics into complementary models. This is a critical aspect, especially for developing countries, given that scattered populations would be highly constrained by the transmission system and its capability for exchanging energy.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Fernandez Vazquez, C. A. A., Jimenez Zabalaga, P., Balderrama, S., & Quoilin, S. (2024). A two-step cascade modelling between EnergyScope Pathway-BO and PyPSA-BO for energy transition planning. Part B: Geo-spatial characterization and effects. Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energ, 37(37), 1339-1351. https://hdl.handle.net/2078.5/241031 (Original work published 2024)