High sensitivity to methodological choices when integrating social acceptance data in electricity system modeling

Energy scenarios that are used for policymaking and investment decisions mostly stem from techno-economic optimization models, while methods to integrate social factors in such models are at a nascent stage. Here, we model socially acceptable Swiss electricity supply scenarios for 2035 with spatially-explicit electricity system model EXPANSE using spatial acceptance data from a large population survey (N = 6′203). Specifically, to further evaluate the characteristics and implications of integration methods, we investigate the sensitivity of scenario results to methodological choices, where we combine (i) three different methods that modify the technology potential constraints, costs, and objective function in the model, (ii) five methods for spatially aggregating survey data, (iii) two methods to quantify acceptance or resistance scores, and (iv) three function shapes to translate survey data into model parameters. Our results highlight the enormous sensitivity of scenario results to the methodological choices, especially for a couple of key technologies, such as solar PV, onshore wind power, nuclear power, and interconnection in Switzerland. The search for robustness points to the merit of the method that modifies technology costs. Despite such high sensitivity to methodological choices, all methods lead to scenarios with higher shares of renewable technologies, especially solar PV and sometimes wind power, when acceptance data is integrated in a cost-optimization model.