Mid-long Term Production Simulation of High-proportion Renewable Energy Power System Based on Probabilistic Sequences and Approximate Dynamic Programming
|更新时间:2026-03-13
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Mid-long Term Production Simulation of High-proportion Renewable Energy Power System Based on Probabilistic Sequences and Approximate Dynamic Programming
YANG Xusong, 1, LUO Xianjue, et al. Mid-long Term Production Simulation of High-proportion Renewable Energy Power System Based on Probabilistic Sequences and Approximate Dynamic Programming[J]. 2025, 49(12): 5156-5165.
DOI:
YANG Xusong, 1, LUO Xianjue, et al. Mid-long Term Production Simulation of High-proportion Renewable Energy Power System Based on Probabilistic Sequences and Approximate Dynamic Programming[J]. 2025, 49(12): 5156-5165. DOI: 10.13335/j.1000-3673.pst.2024.1092.
Mid-long Term Production Simulation of High-proportion Renewable Energy Power System Based on Probabilistic Sequences and Approximate Dynamic Programming
With the rapid increase in the penetration rate of renewable energy
the randomness of power supply and load of the power system have superimposed on each other
increasing the difficulty of mid-long term power consumption balance and causing power shortage and renewable energy power abandonment to occur intertwined. The above issue poses a great challenge to the security and reliability of the power system. Therefore
this paper proposes a new mid-long-term production simulation method based on probabilistic sequences and approximate dynamic programming (ADP). First
this paper divides the entire year into multiple periods
either monthly or weekly. Before the start of each period
decisions regarding the power system operation mode need to be made. Considering source-load uncertainty
probabilistic sequence theory is used to calculate the system operating state for the next period
and a Markov decision process (MDP) is created for the power grid operation. Third
ADP is introduced into MDP to search for the optimal decision at different periods
and grid operation mode can be obtained. The new scheme avoids excessive reliance on heuristic principles and scenario selection in traditional methods
and it can significantly reduce the total cost while ensuring power supply safety and renewable energy consumption in extreme scenarios. Finally
the effectiveness and progressiveness of the proposed method are verified in the testing system.