Abstract

Traditionally, modelling tools for Multi-energy systems planning and management focus on the minimization of a single monetary objective. Multiple objectives are usually merged via monetization rates. The work presented herein aims to develop modelling frameworks to explore of configurations of Multi-energy systems according to non-comparable objectives and extract trade-off solutions through optimization algorithms. Three different methodologies are presented, integrating the single-objective configuration model CALLIOPE with multi-objective algorithms for exploring the decision space. These are tested on a synthetic case study and evaluated for their input data requirements, computational demands and ability to thoroughly map the solution space. Results show each approach returns optimal system configurations, with considerably different technology mixes depending on objective priorities. The methodologies highlighted here represent a significant step forward in the search for multi-objective models for Multi-energy systems planning and management, to support the search for truly integrated, efficient, and sustainable solutions.