Economic Energy Consumption Strategy Considering Multimodal Energy Under the Base Station Cluster of Multi-Device Communication Private Networks

The large-scale deployment of electric power wireless private networks (EPWPNs) has significantly increased the number of base stations in substations, transmission corridors, and distribution terminals, leading to rapidly rising electricity expenditure for continuous wireless coverage and power-grid monitoring services. However, the increasing number of base stations deployed across substations and distribution networks has led to rising electricity expenditure, making cost-effective energy supply a critical challenge. To reduce the operating costs of base station clusters and enhance the economic efficiency of power supply, this paper proposes a multimodal power consumption optimization method that coordinates wind energy, solar energy, and energy storage based on user interaction behavior. First, considering user interaction characteristics and the complementarity of multiple energy sources, a dual-layer cellular network architecture consisting of macro- and micro-base stations is constructed. This architecture incorporates grid power purchases, wind power generation, and photovoltaic energy. An optimization model is then developed, which includes both equipment operation constraints and energy interaction constraints. Second, the key factors influencing energy consumption are analyzed using operational research methods. The existence of an optimal solution for the energy consumption function is demonstrated based on the Weierstrass optimization theorem. An energy-saving strategy for base stations under user group access is then derived using Karush–Kuhn–Tucker (KKT) conditions. Through spatio-temporal (ST) dynamic analysis, the coupling relationships among wind power, solar energy, energy storage, and grid electricity purchases are quantified. Based on this analysis, a multimodal cost optimization scheme utilizing dynamic bandwidth allocation is proposed. Simulation results demonstrate that, compared with traditional single-source power supply models and representative existing optimization schemes, the proposed multimodal energy scheduling framework can significantly reduce the operating cost of base station clusters while maintaining communication performance.