MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems

This study presents MANGO (Multi-stAge eNerGy Optimization), a novel optimization model that incorporates a multi-year planning horizon, along with flexible, multi-stage investment strategies for the effective, longterm design of decentralized multi-energy systems (D-MES). By considering the dynamic surrounding energy and techno-economic landscape that evolves over time, MANGO harnesses the strategic value of investment flexibility and can optimally phase D-MES investments in order to benefit, for instance, from projected future reduced technology costs and technical improvements. To achieve this, the model considers the most relevant dynamic aspects, such as year-to-year variations in energy demands, changing energy carrier and technology prices, technical improvements and equipment degradation. MANGO is also capable of optimizing the design of complex configurations composed of multiple, interconnected D-MES installed at different locations. Finally, the model?s formulation also addresses end-of-horizon effects that can distort solutions in multi-stage energy system models. Besides presenting the key aspects and the mathematical formulation of MANGO, this study also uses the model to develop a six-stage energy design plan, along a 30-year project horizon, for an urban district composed of 3 sites in Zurich, Switzerland. One candidate D-MES is considered per site and different scenarios are examined regarding building retrofitting and D-MES interconnections. Results overall show that retrofitting leads to lower emission levels, but significantly higher costs. On the other hand, D-MES interconnections improve both the economic and the environmental system performance. Finally, regarding optimal D-MES configurations, a variety of technologies is used, with combinations of air-source heat pumps and natural gas boilers leading to better economic performance and combinations of ground-source heat pumps and biomass boilers to more environmentally-friendly designs. Overall, MANGO facilitates D-MES decision-making at the strategic level by delivering flexible multi-stage investment strategies, at the economic level by providing detailed information about the systems? economic performance during each project year and, finally, at the technical level by specifying the optimal technical configurations of each D-MES and their optimal operating schedules. With its long-term perspective, MANGO can offer insights that closely match the dynamic class of real-world energy system design projects led by energy developers.

Automated summary

MANGO is an optimization model that integrates a multi-year planning horizon and flexible, multi-stage investment strategies for designing decentralized multi-energy systems. The model considers dynamic factors such as year-to-year variations in energy demands, changing energy prices, and technology improvements, while optimizing complex configurations of multiple D-MES installations at different locations.