With the rapid increase of renewable energy penetration in China

the regulation capacity of urban multi-energy networks has been continuously weakened

which restricts the processing capacity and operational efficiency of large-scale energy-consuming and energy-supplying waste treatment facilities.To address these challenges

this paper proposes a coordinated optimization method for multi-energy systems oriented towards“zero-waste city” development.Firstly

multi-temporal scale models for waste treatment facilities and multi-source energy regulation systems are established.Subsequently

a topological structure model for the zero-waste city multi-energy system (ZWC-MES) is proposed.Building upon this foundation

a multi-objective coordinated optimization model for ZWC-MES is developed and solved using the deep Q-Network algorithm from deep reinforcement learning.The proposed optimization method enables coordinated dispatch of high-penetration renewable energy and diversified urban loads

satisfying comprehensive requirements including economic efficiency

waste reduction

and environmental quality improvement in ZWC-MES

while effectively describing the complex coordination process between waste treatment and multi-energy system optimization.Finally

simulation analysis was conducted using actual multi energy operation data from a city in northeast China.The results showed that the proposed waste free city multi energy system can effectively improve the energy backbone network regulation capability and waste treatment efficiency

reduce urban pollution

and achieve rapid reduction and resource utilization of large-scale urban waste.