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<p>Long-term renewable hydroelectric energy scheduling is a complex, nonlinear optimization problem involving several constraints that are critical for power system operations. This paper discusses the use of Reinforcement Learning (<i>RL</i>) to address several shortcomings of traditional techniques, such as Dynamic Programming (<i>DP</i>), particularly those related to the curse of dimensionality. The long-term planning problem’s objective is to establish the optimal policy for electricity generation while minimizing total operating expenses over the planning period and considering all generation constraints. This system comprises hydroelectric plants and thermal energy as a supplementary energy source in the event of a renewable energy deficit. The Deep Deterministic Policy Gradient (<i>DDPG</i>) in <i>RL</i> has been applied to multi-reservoir systems to derive optimal operational strategies, demonstrating superior performance compared to (<i>DP</i>) methods. The results show that <i>RL</i> consistently achieves lower costs by allocating hydro resources more efficiently during high-demand periods.</p>

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Long-Term Reservoir Management Using Reinforcement Learning

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • Tareg Sukah,
  • Mamane Dodo Amadou,
  • Maarouf Saad,
  • Imad Mougharbel

摘要

Long-term renewable hydroelectric energy scheduling is a complex, nonlinear optimization problem involving several constraints that are critical for power system operations. This paper discusses the use of Reinforcement Learning (RL) to address several shortcomings of traditional techniques, such as Dynamic Programming (DP), particularly those related to the curse of dimensionality. The long-term planning problem’s objective is to establish the optimal policy for electricity generation while minimizing total operating expenses over the planning period and considering all generation constraints. This system comprises hydroelectric plants and thermal energy as a supplementary energy source in the event of a renewable energy deficit. The Deep Deterministic Policy Gradient (DDPG) in RL has been applied to multi-reservoir systems to derive optimal operational strategies, demonstrating superior performance compared to (DP) methods. The results show that RL consistently achieves lower costs by allocating hydro resources more efficiently during high-demand periods.