基于统计机器学习的园区能源互联网随机规划技术探析

Exploration and analysis of park energy Internet stochastic planning technology based on statistical machine learning

中文关键词: 能源互联网  统计机器学习  场景模拟  人工智能规划求解

英文关键词: energy Internet  statistical machine learning  scenario simulation  artificial intelligence programming solution

中文摘要:

      随着新能源发电和空调负荷广泛接入园区能源互联网，其源、荷侧的不确定性导致运行场景复杂多变，对园区节能运行的影响不可忽视。如何实现园区能源互联网高效经济规划是当下亟待研究的重点问题。因此，首先对现阶段随机规划理论进行了归纳总结，并分析国内外研究现状；其次，基于统计机器学习对以下3个关键问题进行技术探析：一是新能源出力和需求响应的不确定性导致的能源互联网复杂运行场景模拟技术；二是典型场景生成技术；三是安全稳定的人工智能规划求解技术。最后，提出发展展望，统计机器学习理论将是园区能源互联网随机规划未来发展的重要方向之一。

英文摘要:

      When the widespread access of new energy power generation and air conditioning to the park energy Internet, the uncertainty of the source-load side leads to the complex and changeable operation scenarios.The impact on the energy-saving operation of the park cannot be ignored. How to realize the efficient economic planning of the park energy Internet is an urgent problem to be studied at present. Therefore, the current stochastic programming theory is firstly summarized, and the research status at home and abroad is analyzed. Based on statistical machine learning, the following three key problems are analyzed. Firstly, the complex operation scenario simulation technology of energy Internet caused by the uncertainty of new energy power generation and demand response. The second is the typical scenario generation technology；The third is the safe and stable artificial intelligence programming solution technology. Finally, the development prospect is put forward. Statistical machine learning theory will be one of the important directions of the future development of the energy network in the park.