Multi-Scenario Coordinated Planning of Distributed PV, Energy Storage, and EV Charging Infrastructure Considering Differentiated Supply-Area Requirements

China’s carbon-peaking and carbon-neutrality targets are accelerating the integration of distributed photovoltaic (PV) systems, energy storage, and electric vehicle (EV) charging infrastructure, requiring distribution-network planning that reflects local resource, demand, and reliability characteristics. This paper proposes a multi-scenario coordinated planning framework that maps the characteristics of six representative supply-area types into area-specific planning parameters, capacity limits, and reliability requirements. Each supply-area type is evaluated as a separate planning case with three probability-weighted PV weather scenarios, shared investment decisions, and fixed base-load and EV charging profiles across weather scenarios. The model jointly determines the deployment and sizing of PV systems, stationary and mobile energy storage, and EV charging infrastructure. It minimizes the equivalent daily life-cycle cost augmented by reliability and operational-performance terms, capturing area-specific economic-reliability trade-offs. A dynamic arithmetic optimization algorithm (DAOA) incorporating an iteration-dependent step-size mechanism is developed to solve the resulting model. Case studies on the IEEE 33-bus system, additional representative distribution networks, and a real feeder topology yield differentiated resource configurations consistent with supply-area characteristics. On the IEEE 33-bus system, planned PV capacity ranges from 64.71 kW in the high-reliability area to 419.43 kW in the urban-village area, while stationary battery energy storage capacity ranges from 750 to 2000 kWh across the six area types. In repeated-run comparisons, DAOA achieves the lowest mean final fitness among five algorithms under identical test settings. These results illustrate how supply-area parameterization translates heterogeneous planning requirements into differentiated resource deployment decisions.