Regionalized integrated energy system models considering stakeholder inputs are uncommon in the literature. This study tested and validated an existing quantitative optimization-based OPERA regional modeling framework. Stakeholder responses to surveys resulted in multiple future scenarios and sensitivities, applied to the Dutch province of Groningen energy transition. Stakeholder reflections in a workshop confirmed the potential of the model as a strategic decision-supporting tool. The tool successfully analyzed trade-offs, compromises, and complementarities regarding the different choices of stakeholders. The study reflected on the modest role of solar photovoltaics, which supplied 6.6–17.5 % of the primary energy, in comparison to policies and stakeholder assumptions. Biomass energy, at 18.2–28.5 %, was more prominent than expected. Similarly, choosing a scenario close to the current policy implied a strong dependency on imports, with net imports constituting 50 % of the energy supply. On the other hand, regional self-sufficiency implied spatial implications beyond stakeholder expectations. For example, land use associated with onshore wind energy was ∼13 % of the provincial land. The stakeholder interaction process highlighted capacity investments via other harmonized model linkages and the importance of the science-policy interfaces. Compared with contemporary models, the major advancements are spatial interfacing and the inclusion of land-use planning and policy constraints.@en

Regional level energy system analyses and corresponding integrated modeling is necessary to analyze the impact of national energy policies on a regional level, while considering regional constraints related to energy infrastructure, energy supply potentials, sectoral energy demands, and their interactions. Nevertheless, current literature on energy system analysis largely overlooks the regional level. In response, this study provided a systematic approach to refining and improving the spatial resolution of an existing regional energy system modeling framework. The methodology involved creating regions and nodes within the modeling framework under categories corresponding to land use (cities and other regions), energy supply, and energy infrastructure. We established a unidirectional soft linking with geographical information system-based modeling results allocating spatially sensitive elements, such as renewable resources or heat demand. We provided a detailed breakdown of sectoral energy demand, supply options, and energy infrastructure for electricity and heat, including district heating (DH). This framework explicated regional differences in terms of demand–supply mismatch, supply options, and energy infrastructure. Our case study of the Dutch province of Groningen demonstrated clear differences compared to the previous crude regional model, with, e.g., an increased role of biomass (+460 % change) and decreased role of solar (−59 %), while cities with high heat demand densities and/or compact structures exhibited serious DH penetration, ranging from 11 to 21 %. The systematic steps allow for the replication of the model in other regional analyses. Our framework is complementary for energy system analysis at the national and pan-European levels and can assist regional policymakers in decision-making.@en

Spatially sensitive regional renewables’ potentials are greatly influenced by existing land-use claims and related spatial and environmental policies. Similarly, heat particularly related to low-temperature demand applications in the built environment (BE) is highly spatially explicit. This study developed an analytical approach for a detailed spatial analysis of future solar PV, onshore wind, biomass, and geothermal and industrial waste heat potentials at a regional level and applied in the Dutch Province of Groningen. We included spatial policies, various spatial claims, and other land-use constraints in developing renewable scenarios for 2030 and 2050. We simultaneously considered major spatial claims and multiple renewable energy sources. Claims considered are the BE, agriculture, forest, nature, and network and energy infrastructure, with each connected to social, ecological, environmental, technical, economic, and policy-related constraints. Heat demand was further analyzed by creating highly granular demand density maps, comparing them with regional heat supply potential, and identifying the economic feasibility of heat networks. We analyzed the possibilities of combining multiple renewables on the same land. The 2050 renewable scenarios results ranged 2–66 PJ for solar PV and 0–48 PJ for onshore wind and biomass ranged 3.5–25 PJ for both 2030 and 2050. These large ranges of potentials show the significant impact of spatial constraints and underline the need for understanding how they shape future energy policies. The heat demand density map shows that future heat networks are feasible in large population centers. Our approach is pragmatic and replicable in other regions, subject to data availability.@en

Integrated energy system modeling tools predominantly focus on the (inter)national or local scales. The intermediate level is important from the perspective of regional policy making, particularly for identifying the potentials and constraints of various renewable resources. Additionally, distribution variations of economic and social sectors, such as housing, agriculture, industries, and energy infrastructure, foster regional energy demand differences. We used an existing optimization-based national integrated energy system model, Options Portfolio for Emission Reduction Assessment or OPERA, for our analysis. The modeling framework was subdivided into four major blocks: the economic structure, the built environment and industries, renewable energy potentials, and energy infrastructure, including district heating. Our scenario emphasized extensive use of intermittent renewables to achieve low greenhouse gas emissions. Our multi-node, regionalized model revealed the significant impacts of spatial parameters on the outputs of different technology options. Our case study was the northern region of the Netherlands. The region generated a significant amount of hydrogen (H₂) from offshore wind, i.e. 620 Peta Joule (PJ), and transmitted a substantial volume of H₂ (390 PJ) to the rest of the Netherlands. Additionally, the total renewable share in the primary energy mix of almost every northern region is ∼90% or more compared to ∼70% for the rest of the Netherlands. The results confirm the added value of regionalized modeling from the perspective of regional policy making as opposed to relying solely on national energy system models. Furthermore, we suggest that the regionalization of national models is an appropriate method to analyze regional energy systems.

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