From national to local scale: The optimal renewable integration strategy in Switzerland

With the phase-out of nuclear power in Switzerland by 2035, planning the nation's energy system towards a predominantly renewable future is both a critical and complex challenge. Optimization models offer valuable insights by enabling the exploration of temporal dynamics and spatial configurations of renewable energy deployments, in particular also for the spatially distributed sources wind and solar. Addressing the uncertainties of the evolving renewable landscape driven by many exogenous parameters requires models that can integrate diverse climate data sources, emerging technology features and adapt to modifications of existing ones.

We introduce OREES (Optimized Renewable Energy by Evolution Strategy), a generic optimization framework for spatial wind turbine and solar panel allocation based on a genetic algorithm and able to support economic, social and environmental objectives. OREES integrates diverse inputs, including radiation, wind and hydrological data, load demand, grid characteristics, existing infrastructure, and a detailed hydro-power system modeling, to optimize the spatial configuration of renewable energy projects while minimizing imports, costs, and/or biodiversity impacts. Its flexibility enables seamless incorporation of emerging and improving technologies, and contextual scalability, from national systems to urban or alpine scenarios. This model's scalability enables the validation of urban-scale projects within the broader context of national-level modeling.

OREES exemplifies a methodology for dynamically evaluating renewable energy deployment strategies, bridging diverse spatial and temporal dimensions, and supporting stakeholders in navigating the complexities of energy transitions. Using the example of Switzerland, with its strong climate heterogeneity, we show how an optimal placement of wind and solar installations allows for a stable supply with minimal import. We further show that the system is able to accommodate further constraints such as a preference for one or the other technology.