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Exploring spatial and temporal resolution in energy systems modelling

a model-based analysis focused on the developing electricity systems

Time: Wed 2023-11-15 10.00

Location: Kollegiesalen, Brinellvägen 8, Stockholm

Language: English

Subject area: Energy Technology

Doctoral student: Nandi Moksnes , Energisystem

Opponent: Professor Hannah Daly, University College Cork

Supervisor: Professor Viktoria Martin, Energisystem; Professor Mark I. Howells, ; Universitets lektor William Usher, Energisystem; Dr Holger Rogner,

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The energy system is undergoing a transition in many parts of the world with this transition being driven by several factors such as climate change, and economic and social development. Agenda 2030, with its 17 Sustainable Development Goals (SDGs), has set the direction on where development should be focussed. There are still around 675 million people who lack access to electricity (SDG7), mainly in Sub-Saharan Africa. The energy system is also responsible for emitting most greenhouse gas (GHG) emissions and is closely connected to SDG 13, climate action.

Energy models can provide insight into the implications of different interventions in the system. However, the transition also poses new challenges for energy modelling. New spatiotemporal questions arise with 1) the penetration of renewable technologies to mitigate GHG emissions, with location-specific intermittent supply options such as wind and solar PV panels, and 2) the low share of the population living near the existing electricity network in many Sub-Saharan countries and the decreasing cost of off-grid and mini-grid supply options.

This change increases the number of technologies and details needed in the system which in turn increases complexity in the models. Complexity can be defined in terms of four aspects: spatial, temporal, mathematical and, system scope. However, more detail, both parametrical and structural, can introduce more potential errors and uncertainty into the model. Therefore, energy models should be as simple as possible and as complex as necessary.

This thesis aims to give quantifiable and qualitative insights into the mathematical, spatial, and temporal aspects of energy systems modelling for both national and regional system scopes, along with their policy implications. The thesis explores the trade-offs between which mathematical method is applied when modelling electricity access, and the global sensitivity of parametrical and structural parameters in ESOMs.

The method for achieving the aim of the thesis uses a four-step approach. First, the geospatial electrification problem is explored by developing two different models, a linear programming model, using the model generator GEOSeMOSYS, and a heuristic method, soft-linking the open-source tools OnSSET and OSeMOSYS. Second, these two models are compared in order to understand the differences between them with respect to computational effort, results, insight, and effectiveness in modelling electricity access in a developing country. Third, the linear programming model developed for this thesis is then explored using the method of Morris global sensitivity analysis to understand the importance of spatial and temporal resolution compared to other parameters such as demand, discount rate, and capital cost. Fourth and finally, the global sensitivity analysis method of factor mapping, using scenario discovery, is explored to understand parameters that determine cost and low carbon futures in the regional multi-country energy systems optimisation model ‘South America Model Base’ (SAMBA).

The results show that the two methods for optimising electrification show similar trends when the demand is changed, with low demand predominantly resulting in PV panels and batteries to serve the formerly unelectrified population, while higher demand results in more grid-connected households. The demand level and profile for newly electrified households result in different service levels and possibilities for adding more appliances over time. The competitiveness of PV panels with batteries decreases significantly when the demand profile increases during the night. The two methods in this thesis have different solution times with the linear programming method having a much longer solution time, furthermore, the mathematical approaches to solve the transmission network are different, and both methods have trade-offs in their results. These trade-offs are in the mathematical approach where OnSSET uses a one-at-the-time optimisation leading to a suboptimal overall network, and GEOSeMOSYS rely on the assumption of linearity, which leads to very small incremental installations of transmission lines.

The global sensitivity analysis of GEOSeMOSYS for electricity access showed that the structural parameters, spatial and temporal resolution, influence the result parameters and cannot be simplified without changing the results. The temporal resolution had a greater influence on the assessed results parameters than the spatial resolution, indicating that it is more significant. For the South American system, the parameters that determine low carbon emission pathways are low/medium demand and low/medium discount rates.

This thesis has therefore shown that, even though models should be as simple as possible, the spatial and temporal resolution cannot be simplified to a one-node analysis or low temporal resolution without this affecting the results. The mathematical choice for selecting the method of electricity access was analysed and trade-offs were highlighted. The main trade-off was in the network expansion where both methods use approximations that can lead to over/underestimating the investment need. The soft-linked method is a good option to understand a higher level to explore electricity access. If the question is more complex (e.g., adding transportation, heating and cooling), then GEOSeMOSYS provides more readily available options for expanding the analysis, but at a coarse spatial resolution. Demand is a critical parameter in energy models, as is shown in this thesis, and determines both the cost and the potential for achieving low carbon futures. Therefore, including more demand functionalities (such as demand side management and price elasticity) in energy models could help to further detail future demands, and this is identified as future work.