Co-optimization of urban morphology and distributed energy systems is key to curb energy consumption and optimally exploit renewable energy in cities. Currently available optimization techniques focus on either buildings or energy systems, mostly neglecting the impact of their interactions, which limits the renewable energy integration and robustness of the energy infrastructure; particularly in extreme weather conditions. To move beyond the current state-of-the-art, this study proposes a novel methodology to optimize urban energy systems as interconnected urban infrastructures affected by urban morphology. A set of urban morphologies representing twenty distinct neighborhoods is generated based on fifteen influencing parameters. The energy performance of each urban morphology is assessed and optimized for typical and extreme warm and cold weather datasets in three time periods from 2010 to 2039, 2040 to 2069, and 2070 to 2099 for Athens, Greece. Pareto optimization is conducted to generate an optimal energy system and urban morphology. The results show that a thus optimized urban morphology can reduce the levelized cost for energy infrastructure by up to 30%. The study reveals further that the current building form and urban density of the modelled neighborhoods will lead to an increase in the energy demand by 10% and 27% respectively. Furthermore, extreme climate conditions will increase energy demand by 20%, which will lead to an increment in the levelized cost of energy infrastructure by 40%. Finally, it is shown that co-optimization of both urban morphology and energy system will guarantee climate resilience of urban energy systems with a minimum investment.

共同优化城市形态和分布式能源系统是遏制能源消耗和优化利用城市可再生能源的关键。当前可用的优化技术专注于建筑物或能源系统，但大多忽略了它们之间相互作用的影响，这限制了可再生能源的整合和能源基础设施的坚固性；特别是在极端天气条件下。为了超越当前的最新技术水平，本研究提出了一种新颖的方法，可以优化城市能源系统，使其成为受城市形态影响的相互连接的城市基础设施。基于15个影响参数，生成了代表20个不同邻域的一组城市形态。针对希腊雅典从2010年至2039年，2040年至2069年和2070年至2099年的三个时间段内的典型和极端温暖和寒冷天气数据集，评估并优化了每种城市形态的能源性能。进行帕累托优化，以生成最佳的能源系统和城市形态。结果表明，这样优化的城市形态可以将能源基础设施的平均成本降低多达30％。该研究进一步揭示出，模拟社区的当前建筑形式和城市密度将分别导致能源需求增加10％和27％。此外，极端气候条件将使能源需求增加20％，这将导致能源基础设施的平均成本增加40％。最后，