The Role of Spatial Scale in Electricity System Optimisation Models

To investigate possible pathways to reduce greenhouse gas emissions in the electricity sector, researchers build optimisation models that typically minimise the total system costs such that all technical and physical constraints are met. For systems based on renewable energy, whose greatest expansion potentials are found for wind and solar generation, the chief challenge is dealing with their variability. To tackle this challenge, the optimisation models typically include large transmission networks to smooth renewable feed-in in space or storage technologies to smooth the variability in time. However, all aspects of the energy system at all levels of detail cannot currently be contained in a single model because of computational constraints. Instead, one must make simplifications and compromises that affect the optimality of the result from the point of view of the complete system. While reductions on the temporal scale and linearisation approaches of the model formulation have been previously analysed, in this thesis we focus on the quantification of the impact of the spatial scale. This is important because it is scientific practice to simplify models spatially while only little is known on the error made by the aggregation. The contents of this dissertations spatial scale analysis are three-fold and build upon one another: (i) A novel clustering methodology enables us to disentangle and quantify the error that is made by spatially aggregating generation sites where renewable electricity can be sourced versus the error made by aggregating transmission lines and, thus, electricity interactions between spatially distributed substations. By clustering the network on both features in tandem, we can verify the results and learn which of these two effects dominates the optimisation. (ii) Insights from (i) are used to improve existing spatial aggregation methods and to develop novel similarity measures to be applied for clustering electricity system models such that the spatially simplified model can better approximate the original, highly-resolved model with respect to renewable generation sites and the transmission grid. (iii) The prevailing best clustering method is applied on optimisation models with high shares of renewable generation to investigate if the spatially clustered low-resolved solutions are feasible with regard to the full, spatially highly-resolved model. To this end we propose novel inverse methods to spatially disaggregate the coarse optimisation solution in terms of the resulting, aggregated variables across the highly dimensioned model

The Role of Spatial Scale in Electricity System Optimisations

To investigate possible pathways to reduce greenhouse gas emissions in the electricity sector, researchers build optimisation models that typically minimise the total system costs such that all technical and physical constraints are met. For systems based on renewable energy, whose greatest expansion potentials are found for wind and solar generation, the chief challenge is dealing with their variability. To tackle this challenge, the optimisation models typically include large transmission networks to smooth renewable feed-in in space or storage technologies to smooth the variability in time. However, all aspects of the energy system at all levels of detail cannot currently be contained in a single model because of computational constraints. Instead, one must make simplifications and compromises that affect the optimality of the result from the point of view of the complete system. While reductions on the temporal scale and linearisation approaches of the model formulation have been previously analysed, in this thesis we focus on the quantification of the impact of the spatial scale. This is important because it is scientific practice to simplify models spatially while only little is known on the error made by the aggregation. The contents of this dissertations spatial scale analysis are three-fold and build upon one another: (i) A novel clustering methodology enables us to disentangle and quantify the error that is made by spatially aggregating generation sites where renewable electricity can be sourced versus the error made by aggregating transmission lines and, thus, electricity interactions between spatially distributed substations. By clustering the network on both features in tandem, we can verify the results and learn which of these two effects dominates the optimisation. (ii) Insights from (i) are used to improve existing spatial aggregation methods and to develop novel similarity measures to be applied for clustering electricity system models such that the spatially simplified model can better approximate the original, highly-resolved model with respect to renewable generation sites and the transmission grid. (iii) The prevailing best clustering method is applied on optimisation models with high shares of renewable generation to investigate if the spatially clustered low-resolved solutions are feasible with regard to the full, spatially highly-resolved model. To this end we propose novel inverse methods to spatially disaggregate the coarse optimisation solution in terms of the resulting, aggregated variables across the highly dimensioned model

Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?

Spatially highly-resolved capacity expansion models are often simplified to a lower spatial resolution because they are computationally intensive. The simplification mixes sites with different renewable features while ignoring transmission lines that can cause congestion. As a consequence, the results may represent an infeasible system when the capacities are fed back at higher spatial detail. Thus far there has been no detailed investigation of how to disaggregate results and whether the spatially highly-resolved disaggregated model is feasible. This is challenging since there is no unique way to invert the clustering. This article is split into two parts to tackle these challenges. First, methods to disaggregate spatially low-resolved results are presented: (a) an uniform distribution of regional results across its original highly-resolved regions, (b) a re-optimisation for each region separately, (c) an approach that minimises the “excess electricity”. Second, the resulting highly-resolved models’ feasibility is investigated by running an operational dispatch. While re-optimising yields the best results, the third inverse method provides comparable results for less computational effort. Feasibility-wise, the study design strengthens that modelling countries by single regions is insufficient. State-of-the-art reduced models with 100–200 regions for Europe still yield 3%–7% of load-shedding, depending on model resolution and inverse method

A comparison of clustering methods for the spatial reduction of renewable electricity optimisation models of Europe

Modeling the optimal design of the future European energy system involves large data volumes and many mathematical constraints, typically resulting in a significant computational burden. As a result, modelers often apply reductions to their model that can have a significant effect on the accuracy of their results. This study investigates methods for spatially clustering electricity system models at transmission level to overcome the computational constraints. Spatial reduction has a strong effect both on flows in the electricity transmission network and on the way wind and solar generators are aggregated. Clustering methods applied in the literature are typically oriented either towards preserving network flows or towards preserving the properties of renewables, but both are important for future energy systems. In this work we adapt clustering algorithms to accurately represent both networks and renewables. To this end we focus on hierarchical clustering, since it preserves the topology of the transmission system. We test improvements to the similarity metrics used in the clustering by evaluating the resulting regions with measures on renewable feed-in and electrical distance between nodes. Then, the models are optimised under a brownfield capacity expansion for the European electricity system for varying spatial resolutions and renewable penetration. Results are compared to each other and to existing clustering approaches in the literature and evaluated on the preciseness of siting renewable capacity and the estimation of power flows. We find that any of the considered methods perform better than the commonly used approach of clustering by country boundaries and that any of the hierarchical methods yield better estimates than the established method of clustering with k-means on the coordinates of the network with respect to the studied parameters

The strong effect of network resolution on electricity system models with high shares of wind and solar

Energy system modellers typically choose a low spatial resolution for their models based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for wind and solar generation. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different wind and solar capacity factors in the same resource class tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe's power system with a high share of renewables, taking advantage of newly-available high-resolution datasets as well as computational advances. We vary the number of nodes, interpolating between a 37-node model based on country and synchronous zone boundaries, and a 512-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10.5% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 19% as generation has to be sourced more locally at sites with worse capacity factors. These effects are most pronounced in scenarios where grid expansion is limited, for example, by low local acceptance. We show that allowing grid expansion mitigates some of the effects of the low grid resolution, and lowers overall costs by around 15%.Comment: 15 pages, 16 figures, 7 tables, preprint submitted to Elsevier updated version: figures with log-scale, scenario expansion for all simulations to be conducted at the same resolution (1024 nodes), minor changes to the text to account for the expansion of simulation

The strong effect of network resolution on electricity system models with high shares of wind and solar

Energy system modellers typically choose a low spatial resolution for their models based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for wind and solar generation. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different wind and solar capacity factors in the same resource class tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s power system with a high share of renewables, taking advantage of newly-available high-resolution datasets as well as computational advances. We vary the number of nodes, interpolating between a 37-node model based on country and synchronous zone boundaries, and a 1024-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 23% as generation has to be sourced more locally at sites with worse capacity factors. These effects are most pronounced in scenarios where grid expansion is limited, for example, by low local acceptance. We show that allowing grid expansion mitigates some of the effects of the low grid resolution, and lowers overall costs by around 16%

PyPSA-Earth. A New Global Open Energy System Optimization Model Demonstrated in Africa

Macro-energy system modelling is used by decision-makers to steer the global energy transition toward an affordable, sustainable and reliable future. Closed-source models are the current standard for most policy and industry decisions. However, open models have proven to be competitive alternatives that promote science, robust technical analysis, collaboration and transparent policy decision-making. Yet, two issues slow the adoption: open models are often designed with limited geographic scope, hindering synergies from collaboration, or are based on low spatially resolved data, limiting their use. Here we introduce PyPSA-Earth, the first open-source global energy system model with data in high spatial and temporal resolution. It enables large-scale collaboration by providing a tool that can model the world energy system or any subset of it. This work is derived from the European PyPSA-Eur model using new data and functions. It is suitable for operational as well as combined generation, storage and transmission expansion studies. The model provides two main features: (1) customizable data extraction and preparation scripts with global coverage and (2) a PyPSA energy modelling framework integration. The data includes electricity demand, generation and medium to high-voltage networks from open sources, yet additional data can be further integrated. A broad range of clustering and grid meshing strategies help adapt the model to computational and practical needs. A data validation for the entire African continent is performed and the optimization features are tested with a 2060 net-zero planning study for Nigeria. The demonstration shows that the presented developments can build a highly detailed energy system model for energy planning studies to support policy and technical decision-making. We welcome joining forces to address the challenges of the energy transition together.Comment: 36 pages, 14 figures, 3 table