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Optimization modelling is popular method for evaluating the design of energy systems in a given urban area. This is largely because the design of urban energy systems requires one to make complex decisions about the choice of technologies, their location, and the fuels they use. This study presents an approach for modelling and optimizing decisions for retrofitting urban energy systems, with a focus on the optimal configuration and operation of supply side and demand side technologies required to satisfy the energy requirements. A MINLP model is formulated in GAMS and solved using Lindo optimizer. A case study in urban China is presented to verify the model and to identify opportunities for systems integration. Three scenarios are analysed, namely baseline, high energy price and low carbon, and the results show that a potential reduction in space heating and CO 2 emissions of up to 57.7% and 50% are possible by retrofitting building envelopes with PV, GSHP and natural gas CHP systems. Sensitivity analysis and multi-objection optimization further indicate that CO 2 emission plays the most important role in decision-making. This approach enables us to identify design tradeoffs of complex urban energy systems so as to evaluate the potential of alternative technology mix.

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