Multi-objective optimization of capacity configuration for district heating and cooling system based on life cycle cost and annual carbon dioxide emissions

District heating and cooling (DHC) systems with energy storage are highly promising due to their high economic viability and energy-saving potential. However, inadequate consideration of time-of-use (TOU) pricing, annual load demand variations and control strategies often leads to excessive capacities and low energy efficiency. A multi-objective optimization (MOO) model is established by combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique Order Preference by Similarity to an Ideal Solution (TOPSIS) method, aiming to minimize life cycle cost (LCC) and annual CO 2 emissions. Taking an office park in Beijing as an example, a comprehensive optimal capacity configuration is obtained from both economic benefits and energy efficiency perspectives. Results indicate that the optimal capacities for solar collectors, gas boiler, electric heating boiler, electric chiller, heat energy storage tank, and cold energy storage tank are 2564 m 2 , 1260 kW, 1350 kW, 1400 kW, 344386 kW, and 7234 kW, respectively. Compared to configurations individually considering LCC and CO 2 emissions, the optimal scheme reduces CO 2 emissions by 10.75% and LCC by 1.12%. Compared to the traditional DHC system without energy storage, the initial investment for the DHC system with energy storage increases by 29.48%, but the LCC decreases by 52.33%, annual electricity expenses fall by 57.53%, and annual CO 2 emissions drop by 49.63%. This study provides valuable insights for designing the DHC system with energy storage that achieve both high economic efficiency and low CO 2 emissions. • An optimization model of district heating and cooling systems is established. • The objective functions are minimizing life cycle cost and carbon dioxide emission. • A comprehensive optimal capacity configuration for each equipment is obtained. • The time-of-use pricing and annual load demand dynamic variations are considered. • The optimized system has good economic and environmental benefits.