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• A voltage regulation optimization problem is formulated considering source-load-storage collaboration. • An attention-based multi-agent deep reinforcement learning approach is designed for voltage control. • A multi-stage parallel training framework and an evolutionary selection mechanism is adopted to improve the scalability of voltage control. With the increasing penetration of renewable energy in active distribution networks (ADNs), voltage regulation problem is becoming more and more challenging. In this article, we focus on providing a scalable data-driven approach to ensure the voltage security of ADNs with high penetration of PVs. To this end, we first formulate an optimization problem for real-time voltage control considering source-load-storage collaboration while minimizing the total active power curtailment of PVs. Then, we reformulate the above problem as a Markov game and propose a novel voltage regulation algorithm based on evolutionary curriculum-based multi-agent deep reinforcement learning (EC-MADRL) to solve it. The key idea of the proposed algorithm is to adopt a multi-stage parallel training framework based on attention multi-agent deep deterministic policy gradient algorithm (Attention-MADDPG) and use an evolutionary mechanism to select voltage regulation models with high fitnesses in the previous stage, which means that good agents with best adaptability could be utilized for learning in the environment with a larger number of PVs. Simulation results show the effectiveness and scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]