The path to a low-carbon economy is an essential route for cities to become smart. This paper first analyzes the relevant applications of smart transportation, smart energy, and smart waste management systems within the low-carbon economy. Addressing the challenge of low energy efficiency utilization in cities, this paper proposes a dual-layer ADN scenario planning model to promote efficient energy use. It first establishes a fundamental framework for ADN planning within a low-carbon economic system. Recognizing the nonlinear dual-layer mixed-integer programming characteristics of the ADN planning model, it employs the Cuckoo Search algorithm—known for its strong global search capability—and the fast, efficient primal-dual interior point method to solve the upper and lower sub-layers of the model, respectively. Experimental results demonstrate that compared to models like PWL-MILP and SO-WGA, the ADN planning model reduces emission costs by 40.44% in both active distribution network operation and DHN independent operation scenarios, yielding resource allocation schemes with the lowest total configuration costs. In the development of wind power, photovoltaics, and energy storage in Province X, the AND planning model can rapidly increase photovoltaic penetration rates and power generation capacity while reducing photovoltaic costs, providing a feasible green innovation approach for regional low-carbon energy transition.
