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Implementing a highly adaptable method for the multi-objective optimisation of energy systems

Finke, Jonas and Bertsch, Valentin (2022): Implementing a highly adaptable method for the multi-objective optimisation of energy systems.

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In order to mitigate climate change, the energy sector undergoes a transformation towards a climate-neutral future based on renewable energy sources. Energy system models generate insights and support decision making for this transformation. In the face of, e.g., growingly complex and important environmental assessments and stakeholder structures, considering multiple objectives in these models becomes essential to realistically reflect existing interests. However, there is a lack of highly adaptable energy system models incorporating multiple objectives. We present an implementation of the augmented epsilon-constraint method with the highly adaptable energy system optimisation framework Backbone. It enables the simultaneous optimisation of multiple objectives, such as the minimisation of costs, CO2 emissions or self-sufficiency for a broad range of energy systems including different sectors and scales. For this purpose, new objective functions and constraints are implemented in Backbone. They are used by an external algorithm in a sequence of parallelised optimisations to cope with the complexity of real-world applications. The method is adaptable to further objectives and scalable to large and complex systems. Applications to the Western and Southern European power sector in 2050 and a sector-coupled mixed- integer household-level model demonstrate its benefits and adaptability. Pareto fronts, technology use and trade-offs are analysed and quantified. In the European power sector, emission reductions of up to 90 % can be achieved at marginal CO2 abatement costs of below 100 EUR/(t CO2). For the household, energy imports from the public grids can be reduced by 70 % at 20 % higher cost and average cost of self-sufficiency of 2.6 ct/kWh. We expect that the presented methods and models reveal new valuable insights to modellers and decision makers.

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