Integration of disamenity costs and equality considerations regarding onshore wind power expansion and distribution into energy system optimization models

Abstract Background Social acceptance of energy infrastructure projects affects public support for the energy transition and is essential for the transition’s sustainability and success. Despite extensive research focusing on the social acceptance of renewable energy, and on the acceptance of onshore wind power in particular, energy system models have largely prioritized techno-economic aspects. This focus has created a gap between model results and decision-makers’ needs. In this study, we offer recommendations for integrating disamenity costs and equality considerations—two critical social aspects related to onshore wind power—into energy system optimization. To achieve this, we use a spatially distributed model from a climate-neutral Germany and explore various implementations and trade-offs of these two social aspects. Results We identified effective linear formulations for both disamenity costs and equality considerations as model extensions, notably outperforming quadratic alternatives, which exhibit longer solution times (+ 50–115%). Our findings reveal that the endogenous consideration of disamenity costs in the optimization approach can significantly reduce the human population’s exposure to wind turbines, decreasing the average disamenity per turbine by 53%. Drawing on notions of welfare economics, we employ two different approaches for integrating equality into the optimization process, permitting the modulation of the degree of equality within spatial distributions in energy system models. The trade-offs of the two social aspects compared to the cost-optimal reference are moderate, resulting in a 2–3% increase in system costs. Conclusions Disamenity costs emerge as a predominant factor in the distribution of onshore wind power in energy system optimization models. However, existing plans for onshore wind power distribution in Germany underscore equality as the driving factor. The inclusion of social aspects in energy system models facilitates the identification of socially superior wind turbine locations. Neglecting disamenity costs and equality considerations leads to an overestimation of the practical solution space for decision-makers and, consequently, the resulting energy system designs.