Article: Optimal scheduling energy for 'wind-solar-load-storage' AC-DC hybrid distribution network system based on multi-agent algorithm Journal: International Journal of Global Energy Issues (IJGEI) 2026 Vol.48 No.8 pp.24 - 42 Abstract: Aiming at the real-time optimisation problem of AC/DC hybrid distribution network with high proportion of new energy access, a 'wind-solar-load-storage' collaborative scheduling framework based on multi-agent reinforcement learning (MARL) is proposed. Firstly, the Markov game model is constructed, and wind power, photovoltaic (PV), energy storage and flexible load are modelled as heterogeneous agents, and a mixed action space integrating DQN (Deep Q-Network) and Actor-Critic is designed, and the federated-edge collaborative mechanism is introduced to realise the privacy protection training of 'data-fixed model moving'. The single step decision-making time is less than 70 ms, and the voltage fluctuation is strictly controlled within ±5%. It achieves the coordinated optimisation of economy, safety, and privacy, providing a new paradigm for real-time scheduling of high proportion new energy distribution networks. Inderscience Publishers - linking academia, business and industry through research

Title: Optimal scheduling energy for 'wind-solar-load-storage' AC-DC hybrid distribution network system based on multi-agent algorithm

Authors: Bo Wei; Chunxiang Yang; Kequan Liu; Wen Tang; Xuanrong Zhang

Addresses: State Grid Gansu Electric Power Company, Electric Power Dispatching Center, Lanzhou 730050, China ' State Grid Gansu Electric Power Company, Electric Power Dispatching Center, Lanzhou 730050, China ' State Grid Gansu Electric Power Company, Electric Power Dispatching Center, Lanzhou 730050, China ' State Grid Gansu Electric Power Company, Electric Power Dispatching Center, Lanzhou 730050, China ' State Grid Gansu Electric Power Company, Electric Power Dispatching Center, Lanzhou 730050, China

Abstract: Aiming at the real-time optimisation problem of AC/DC hybrid distribution network with high proportion of new energy access, a 'wind-solar-load-storage' collaborative scheduling framework based on multi-agent reinforcement learning (MARL) is proposed. Firstly, the Markov game model is constructed, and wind power, photovoltaic (PV), energy storage and flexible load are modelled as heterogeneous agents, and a mixed action space integrating DQN (Deep Q-Network) and Actor-Critic is designed, and the federated-edge collaborative mechanism is introduced to realise the privacy protection training of 'data-fixed model moving'. The single step decision-making time is less than 70 ms, and the voltage fluctuation is strictly controlled within ±5%. It achieves the coordinated optimisation of economy, safety, and privacy, providing a new paradigm for real-time scheduling of high proportion new energy distribution networks.

Keywords: AC-DC hybrid distribution network; multi-agent algorithm; optimal scheduling; wind-solar-load-storage; multi-agent reinforcement learning; Markov game; energy.

DOI: 10.1504/IJGEI.2026.153242

International Journal of Global Energy Issues, 2026 Vol.48 No.8, pp.24 - 42

Received: 30 Dec 2025
Accepted: 17 Feb 2026
Published online: 29 Apr 2026 *

Title: Optimal scheduling energy for 'wind-solar-load-storage' AC-DC hybrid distribution network system based on multi-agent algorithm

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