## 1 Introduction
Incorporating metaheuristic techniques into optimal strategies' training process allows a balance between solution accuracy and computational efficiency and speed without increasing problem-specific constraints [18; 19]. In [20; 21], it proposes an intelligent Ant Lion Optimizer (ALO) algorithm, which can effectively solve the multi-objective reconfiguration optimization problem of distribution networks. However, the ALO algorithm has a relatively slow convergence speed. The computational speed and accuracy of the Seagull Optimization Algorithm (SOA) are enhanced through chaotic mapping initialization and Levy flight strategies [22], effectively improving the stability and economy of the reconfigured active distribution network. However, the SOA algorithm's performance is susceptible to the initial population and search strategy, resulting in unstable adaptability to the problem and variable solution quality. An Improved Fireworks Algorithm (IFA) has been proposed, tailored to distribution networks' topological structure and operational technical requirements. This IFA is designed to optimize the established multi-objective model. By employing various explosion modes, the IFA effectively overcomes the shortcomings of the SOA and better coordinates the accommodation of new energy with reconfiguration costs [23]. Nevertheless, integrating many novel load resources, such as 5G base stations and IDCs, introduces new constraints to the optimization and reconfiguration problem of UDNs incorporating VPPs, significantly increasing the complexity of the optimization problem. This can degrade the performance of the aforementioned conventional metaheuristic algorithms, resulting in inaccurate solutions. To summarize, in order to enhance the voltage security of UDNs with large-scale integration of novel electrical load resources, such as 5G base stations and IDCs, and to address the insufficient performance of existing metaheuristic algorithms in solving these problems, this paper proposes an efficient proactive voltage control strategy for UDNs incorporating VPPs and the Improved Termite Life Cycle Optimizer (ITLCO) algorithm. The contributions of this paper are as follows:  
* A classification model for novel electrical load resources, such as 5G base stations and IDCs in UDNs is established, and their regulation potential when acting as internal resources of VPPs is derived, thereby improving the control capability of the efficient proactive voltage control strategy. * The TLCO algorithm is improved by employing a chaotic map and an incremental pheromone update model, which enables it to explore the solution space more effectively and avoid falling into local optima, thus enhancing its global optimization capability. * The effectiveness of the proposed efficient proactive voltage control strategy for UDNs is verified through simulations on the modified IEEE 33-bus system. The structure of this paper is as follows: Section 1 introduces the architecture of UDNs incorporating VPPs. Section 2 presents the modeling of novel electrical load resources in UDNs incorporating VPPs. Section 3 establishes the optimization and reconfiguration model of UDNs incorporating VPPs as the control strategy for efficient proactive voltage security. Section 4 proposes the ITLCO algorithm and outlines the solution process for UDNs' efficient proactive voltage control strategy. Section 5 provides case studies. Section 6 concludes the paper and offers future work directions.