Integration of spatial planning and energy system modeling at the national leve

The transition to a climate-neutral energy system poses spatial planning challenges due to the growing dependence on decentralized renewable energy and the uneven distribution of supply, demand, and infrastructure. While Energy system models (ESM) can be instrumental in identifying energy transition pathways and they are able to assess the effects of energy and climate policies, they usually lack the adequate incorporation of spatial elements, e.g., conflicts among different land claims. The integration of ESM with spatial models can help in identifying the impact of spatial elements on the energy transition pathways by considering, e.g., socioeconomic dynamics, land use conflicts, and infrastructure constraints. This study aims to develop a modeling framework to explore interactions between spatial planning and the design of a climate-neutral energy system. For this purpose, we improve the spatial resolution of an ESM and design a spatial model that incorporates spatial planning scenarios and ESM requirements. Moreover, we elaborated the parameter exchange between these two models.  

For enhancing the ESM resolution, we develop a nested approach to increase spatial granularity in five steps. First, we provide the spatial input data such as solar and wind potential in high resolution, e.g., in 20 km². Second, an initial clustering is performed to generate the desired number of nodes for the country (e.g., 30 nodes), which we refer to as the full-resolution ESM. Then,  the country is divided into macro regions (e.g., 5 macro regions) through a second clustering to cost-optimize the ESM at lower resolution. The energy system optimization is then performed individually for each macro region at full resolution. Finally, all optimized macro-regions are then combined to achieve a national-scale ESM at full resolution.

For integrating spatial planning, we use national spatial planning scenarios to guide land use allocation within high-resolution spatial grids. We designed a spatial optimization model to allocate  required energy system components and other land use demands, ensuring the energy system is spatially feasible at minimum cost. This methodology employs a recursive platform to exchange feedback between the spatial model and ESM that enable the iterative improvement to obtain more reliable results. For example, the ESM may initially determine the placement of wind farms based on land availability and suitability criteria for wind energy at each node. However, if the spatial model cannot accommodate the required wind farm area in a specific region due to competing land use claims, it provides feedback to modify the ESM in subsequent iterations. Each individual component of this framework has been tested in separate studies, and  this comprehensive framework is part of our future work for the case of the Netherlands.