Participatory modelling to generate alternatives to support decision-makers with near-optimal decarbonisation options

Institutions of higher education promote and lead sustainability and climate action by example. Their campuses often have significant energy demands and emissions, potential for sector-coupling, and are centrally organised so that real data and key decision-makers can be involved. Energy system models are a key tool for providing guidance in such complex situations, however, we identify three typical shortcomings: (i) model uncertainty, (ii) simplification of real-world dynamics and pure cost-minimisation, and (iii) lack of interaction with decision-makers despite claiming to provide decision support. Concerning (i) and (ii), we developed a highly flexible implementation of modelling to generate alternatives (MGA) to explore diverse, near-cost-optimal decarbonisation paths for the energy supply of a large university campus. Concerning (iii), we apply a participatory approach with stakeholders to define the modelling targets and develop the energy system model. We use Ruhr University Bochum as a case study, representative of a large comprehensive university campus, and base the model on real data for heating, cooling and electricity. We find that the cost-optimum is extremely flat with significant room for choices in heating and cooling technologies only 1 % above the least cost. At 10 % extra cost, there are no must-have heating technologies. Bio-methane or pellet boilers can each provide up to 100 % of the heating demand, while high- and low-temperature air-water heat pumps can provide up to 86 % and 47 %, respectively, and a deep geothermal plant up to 38 %. Due to area availability, electricity supply always relies on 90 % procurement from the grid. Within this room for choices, we highlight alternatives that could be particularly desired due to risk diversification, realisation and maintenance efforts or synergies with research and teaching activities. The implementation of MGA reduces structural and parametric model uncertainty while the participatory modelling approach supports the internal decision-making process. Together with stakeholders, we develop guidelines to transfer our approach of early, holistic and near-optimal planning to other similarly scaled and organised systems such as neighbourhoods, municipalities, large companies and institutions or urban energy systems more generally and lead the energy transition by example.