| 杨莹,张鑫,丁浩洋,赵为光,苏勋文,安佰杰,孟祥萌.矿井水耦合废弃矿洞复合储能的矿区综合能源系统DQN优化策略[J].电力需求侧管理,2026,28(2):37-43 |
| 矿井水耦合废弃矿洞复合储能的矿区综合能源系统DQN优化策略 |
| DQN optimisation strategy for mine integrated energy system with composite energy storage in mine water coupled to abandoned mine caverns |
| 投稿时间：2025-11-23 修订日期：2026-01-20 |
| DOI：10.3969/j.issn.1009-1831.2026.02.006 |
| 中文关键词: 废弃矿洞 压缩空气储能 抽水蓄能 深度Q学习网络 综合能源系统 |
| 英文关键词: abandoned mine compressed air energy storage pumped-storage power deep Q-learning network integrated energy systems |
| 基金项目:国家自然科学基金资助面上项目(51677057) |
|
| 摘要点击次数: 0 |
| 全文下载次数: 0 |
| 中文摘要: |
| 针对矿区废弃资源浪费和规模化储能的迫切需求问题,充分利用矿区废弃地下矿洞空间资源,结合矿井涌水巨大位势落差的储蓄能力及其所蕴含的低焓值地热能量,采用多层级能量回收方式减小资源浪费,将抽水蓄能(pumped-storage power, PSP)、压缩空气储能(compressed air energy storage,CAES)和水源热泵(water source heat pump,WSHP)技术耦合,提出复合储能型矿区综合能源系统架构。为克服传统优化建模方法的局限,将综合能源系统运行优化问题转化为马尔可夫决策过程(markov decision process,MDP),以系统运行净利润、风电消纳和低碳排放为目标,基于深度Q学习网络(deep Q-learning network,DQN)强化学习算法设计矿区综合能源系统优化调度策略。最后利用不同场景进行算例仿真分析,验证了DQN优化策略能够较好解决系统非线性和风电、负荷的不确定性问题,并保障了调度策略的实时响应能力;且提出的系统模型能够有效节约能量,提高储能密度,获得较好的经济和环境效益。 |
| 英文摘要: |
| In response to the pressing issues of wasted resources in mining areas and the urgent demand for large-scale energy storage, a novel integrated energy system framework for mining areas with composite energy storage is proposed. The framework utilizes abandoned underground mine caverns as spatial resources and harnesses the storage capacity from the huge potential difference of mine water inflow along with its contained low-enthalpy geothermal energy. A multi-level energy recovery approach is adopted to minimize resource waste by coupling pumped hydro storage, compressed air energy storage, and water-source heat pump technologies. To overcome the limitations of traditional optimization modeling methods, the operational optimization problem of the integrated energy system is transformed into a markov decision process. An optimal scheduling strategy is then developed based on the deep Q-learning network reinforcement learning algorithm, aiming to maximize net operational profit, enhance wind power consumption, and reduce carbon emissions. Finally, case studies under different scenarios are conducted through simulation analysis. The results verify that the proposed DQN-based optimization strategy can effectively address system nonlinearities and uncertainties from wind power and load demand, while ensuring real-time response capability for scheduling. Moreover, the proposed system model is demonstrated to achieve significant energy savings, improved energy storage density, and considerable economic and environmental benefits. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|