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Variable Renewable Energy Sources (VRES) are characterized by intensive land-use and variable production. In existing optimization models that minimize the total cost of the energy system, location-specific VRES production profiles are often used to estimate VRES potential, but land-use and land cover aspects have been largely ignored. In this study, we therefore connect the literature in land cover assessment, VRES potential estimation and energy system optimization modelling by proposing a spatially explicit planning approach. This approach was applied to a case of the Netherlands to showcase its applicability and strength and to give results towards various RES targets. A baseline land-use scenario, a scenario with stricter constraints on land-use that reflects social resistance and spatial policy on wind energy and, thirdly, a scenario assuming unlimited land availability were analyzed. The baseline scenario results show the optimal geographical distribution of the generation capacities over the Netherlands. Wind energy dominates the generation mix and storage is only present at the 100% RES target. Under the strict constraints on land-use, 92% of the suitable land in the country will be deployed to place wind turbines in order to reach 100% RES share compared to 37% in the baseline case. However, the cost of electricity only increases by no more than 5 €/MWh. The unlimited land scenario highlights that the regional optimized capacities are infeasible. Apart from the useful results from the case study, the proposed approach is a first-of-a-kind contribution to the literature and provides a data-driven way to operationalize the location-specific land-use of VRES such that the role of the constraints on the land-use of VRES can be revealed and that policy-relevant results can be obtained.
Original languageEnglish
Article number114233
JournalApplied Energy
Volume260
DOIs
Publication statusPublished - 2020

    Research areas

  • Variable Renewable Energy Sources (VRES), Generation mix, Land-use, VRES potential, Optimization modelling, Spatial planning

ID: 67473742