Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models.
An efficient, well-balanced North Sea Offshore Grid (NSOG) requires an area-based approach for large-scale OWF deployment. However, the essential coordination of environmental, spatial and energy planning at a basin scale is lacking. This study offers a systematic approach for unidirectional coupling of spatially explicit offshore development scenarios potentials(km2), with an integrated energy system model, IESA-NS. Under the NSOG concept, we calculate spatial potentials for 8 predefined energy hubs(clusters). By combining the potential spatial availability, deployment and energy system costs(IESA-NS) and the risk management options (OWFs/fisheries/marine protected areas-MPA), we unfold trade-offs emerging in the planning of the future NSOG. Hence, a lower-cost NSOG, in reaching the North Sea 2050 energy targets, depends on integrated, collaborative space management, fast deployment of fixed-bottom OWFs by 2030(3.5 times the current capacity) and multi-use with static gear fisheries (Cluster 3) and MPAs (Cluster 7). Alternatively, a higher-cost NSOG with lower impacts on the MPAs and fisheries, is highly dependent on floating OWFs (32.6GWs by 2030), from 2 British NSOG clusters. In both cases, floating OWFs are essential, the effective use of cluster space requires basin-scale collaboration (Cluster 7-Dogger Bank), and the untapped potential of Cluster 8(floating OWFs) can lower the pressure on other NSOG clusters.
The importance of spatial resolution for energy modelling has increased in the last years. Incorporating more spatial resolution in energy models presents wide benefits, but it is not straightforward, as it might compromise their computational performance. This paper aims to provide a comprehensive review of spatial resolution in energy models, including benefits, challenges and future research avenues. The paper is divided in four parts: first, it reviews and analyses the applications of geographic information systems (GIS) for energy modelling in the literature. GIS analyses are found to be relevant to analyse how meteorology affects renewable production, to assess infrastructure needs, design and routing, and to analyse resource allocation, among others. Second, it analyses a selection of large scale energy modelling tools, in terms of how they can include spatial data, which resolution they have and to what extent this resolution can be modified. Out of the 34 energy models reviewed, 16 permit to include regional coverage, while 13 of them permit to include a tailor-made spatial resolution, showing that current available modelling tools permit regional analysis in large scale frameworks. The third part presents a collection of practices used in the literature to include spatial resolution in energy models, ranging from aggregated methods where the spatial granularity is non-existent to sophisticated clustering methods. Out of the spatial data clustering methods available in the literature, k-means and max-p have been successfully used in energy related applications showing promising results. K-means permits to cluster large amounts of spatial data at a low computational cost, while max-p ensures contiguity and homogeneity in the resulting clusters. The fourth part aims to apply the findings and lessons learned throughout the paper to the North Sea region. This region combines large amounts of planned deployment of variable renewable energy sources with multiple spatial claims and geographical constraints, and therefore it is ideal as a case study. We propose a complete modelling framework for the region in order to fill two knowledge gaps identified in the literature: the lack of offshore integrated system modelling, and the lack of spatial analysis while defining the offshore regions of the modelling framework.
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