This study proposes a multi-objective optimization methodology for planning multi-energy complementary distributed energy systems considering process synergy and thermal integration. The process integration tech-nique is integrated into the Energy Hub model to deal with the multi-process synergy and temporal source-load matching. The system design and dispatch strategy are optimized by an augmented epsilon-constraint method with three objectives (economics, carbon emission, and fossil fuel consumption), and then the optimal tradeoff so-lution is identified by the Technique for Order Preference by Similarity to an Ideal Solution. Moreover, a novel multi-energy complementary distributed energy system is developed, which includes comprehensive utilization of solar energy (photovoltaic, photothermal, and thermochemical) and middle-low temperature heat utilization technologies, as well as hybrid energy storage technologies. Finally, a case study located in Beijing is selected as an illustrated example. The obtained single-objective optimization solutions and Pareto optimal solutions are further analyzed and compared in terms of system configuration, hourly/yearly energy balance, and thermal integration condition. The results show that the multi-energy complementary distributed energy system presents an economic benefit (reducing 25% of the annual total cost) compared to a gas turbine-based integrated energy system. Considering thermal integration contributes to 5.13% of the cost reduction. The configuration of the energy storage devices will reduce 18% energy supply cost, 9% fossil fuel consumption, and 42% carbon emission with the storage devices' boundary increase from 2 MWh to 60 MWh. Moreover, the optimal design of the system provides a reference for decision-making and a basis for flexible operation. The annual total cost, carbon emission, and fossil fuel consumption of the optimal solution in the Pareto frontier are 8.19 million CNY, 2.91 kt CO2-eq./year, and 18.4 GWh, respectively.