Modelling to generate near-Pareto-optimal alternatives (MGPA) for the municipal energy transition

Jonas Finke, Febin Kachirayil, Russell McKenna and Valentin Bertsch

Applied Energy, 2024, vol. 376, issue PA, No S0306261924015095

Abstract: Energy system models are frequently used to support energy planning. Especially at the local level, however, decision-making is difficult because of conflicting interests of diverse stakeholders. This study proposes a new method to overcome such policy dilemmas and demonstrates it for municipal heat and power planning. Modelling to generate near-Pareto-optimal alternatives (MGPA) is a novel combination of the augmented epsilon-constraint method (AUGMECON) and modelling to generate alternatives (MGA). This approach tackles explicit, easy-to-formulate objectives first before exploring a spectrum of alternatives within a region of interest in a second step. MGPA is implemented in a highly adaptable energy system optimisation framework (Backbone) and applied to two municipalities with heterogeneous demands and renewable potentials. By first generating a Pareto front between cost and CO2 emissions, marginal CO2 abatement costs and their corresponding decarbonisation potentials are identified. Subsequently, near-Pareto-optimal alternatives are generated and technological trade-offs as well as must-haves and must-avoids are discussed. Depending on the local renewable energy potential available, decarbonisation costs at the municipal level can differ by a factor of five. The diversification resulting from the use of MGPA reveals a broad range of viable solutions, for example without large-scale renewable infrastructure or with up to 95% local power autonomy, but switching to heat pumps remains a must-have in both municipalities. We recommend that energy system modellers adopt a combination of such different multi-objective optimisation methods to improve decision support for the energy transition.

Keywords: Multi-objective optimisation; Modelling to generate alternatives; Energy system model; Energy planning; Decision-making; Urban energy system (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.124126

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