湖南大学电气与信息工程学院
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陈远扬, 谭益, 李勇. 融合深度神经网络的电力系统经济-环保随机调度方法[J]. 电网技术, 2025,49(5):1993-2003.
陈远扬, 谭益, 李勇. 融合深度神经网络的电力系统经济-环保随机调度方法[J]. 2025, 49(5): 1993-2003.
陈远扬, 谭益, 李勇. 融合深度神经网络的电力系统经济-环保随机调度方法[J]. 电网技术, 2025,49(5):1993-2003. DOI: 10.13335/j.1000-3673.pst.2023.1424.
陈远扬, 谭益, 李勇. 融合深度神经网络的电力系统经济-环保随机调度方法[J]. 2025, 49(5): 1993-2003. DOI: 10.13335/j.1000-3673.pst.2023.1424.
通过优化调度改善电网有功潮流分布、减小火电大气污染物与二氧化碳排放,是实现电力系统环保、经济、安全运行的重要途径。针对含碳捕集电厂、风力发电、常规火电等多种电源的电力系统,该文综合考虑二氧化碳与大气污染物排放、风电出力随机性、N-1故障等多类型因素,建立了面向环保、安全、经济运行的电力系统有功随机调度模型。在该模型中,目标函数考虑了火电的环保与燃料成本、风电成本、N-1故障后校正控制成本等因素,约束条件包括正常运行约束、N-1故障后计及校正控制的电网安全约束等。针对所提有功随机调度模型的特点,该文提出了融合全连接型深度神经网络的快速高效求解方法。该方法通过全连接型深度神经网络构建用于优化软件寻优搜索的初始点,进而加速所提模型的求解过程。最后,该文通过3个修改后的IEEE测试系统验证了所提模型与方法的有效性。
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