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2026 年 第 59 卷
优化解法,具有较强的工程适用性和政策对接价 YU Lei, YAO Junwei, YANG Jinlong. Probabilistic evaluation
值。未来可进一步拓展至多能源系统协同优化、 method for renewable energy integration capability for wind-
极端扰动下的调控鲁棒性分析以及碳交易机制的 photovoltaic-storage coupling system with small sample[J]. Smart
动态反馈建模等方向。 Power, 2024, 52(10): 9–15.
本文提出的计及生产特性的工业园区低碳调 [6] DOS SANTOS S A B, SOARES J M, BARROSO G C, et al.
度方法及改进鲸鱼优化算法具有较强的创新性和 Demand response application in industrial scenarios: a systematic
实际价值,但仍存在以下不足:模型在应对极端 mapping of practical implementation[J]. Expert Systems with
工况及多种随机扰动时的鲁棒性有待提升;算法 Applications, 2023, 215: 119393.
在大规模工业园区场景中的计算效率需要进一步 [7] 韩刚, 黎雄, 徐箭, 等. 计及需求响应下典型工业负荷排放特性的环
优化。未来研究可考虑引入更灵活的调控模型, 境经济调度 [J]. 电力系统自动化, 2023, 47(8): 109–119.
如动态多目标优化,结合人工智能技术提高算法 HAN Gang, LI Xiong, XU Jian, et al. Environmental economic
自适应能力,并进一步加强在多能源协同和碳交 dispatch considering emission characteristics of typical industrial
易市场中的应用探索。 loads under demand response[J]. Automation of Electric Power
Systems, 2023, 47(8): 109–119.
参考文献: [8] ZHOU X F, CAI C Y, LI Y J, et al. A robust optimization model for
demand response management with source-grid-load collaboration to
[1] 孔祥玉, 刘超, 陈宋宋, 等. 考虑动态过程的可调资源集群多时间节 consume wind-power[J]. Global Energy Interconnection, 2023, 6(6):
点响应潜力评估方法 [J]. 电力系统自动化, 2022, 46(18): 55–64. 738–750.
KONG Xiangyu, LIU Chao, CHEN Songsong, et al. Assessment [9] 甘磊, 杨天禹, 陈星莺, 等. 基于低碳工艺与流程控制的钢铁工业园
method for multi-time-node response potential of adjustable resource 区 综 合 能 源 系 统 低 碳 调 度 方 法 [J]. 电 网 技 术 , 2023, 47(8):
cluster considering dynamic process[J]. Automation of Electric 3099–3113.
Power Systems, 2022, 46(18): 55–64. GAN Lei, YANG Tianyu, CHEN Xingying, et al. Low-carbon
[2] 林宇豪, 杨军, 王弘利, 等. 考虑决策依赖不确定性的光储充一体化 scheduling of integrated energy system in iron & steel industrial park
电站优化运行策略 [J]. 浙江电力, 2025, 44(10): 91–101. considering low-carbon techniques and process control[J]. Power
LIN Yuhao, YANG Jun, WANG Hongli, et al. An optimal operation System Technology, 2023, 47(8): 3099–3113.
strategy for photovoltaic-storage-charging integrated stations [10] 吴林林, 陈璨, 胡俊杰, 等. 支撑新能源电力系统灵活性需求的用户
considering decision-dependent uncertainty[J]. Zhejiang Electric 侧资源应用与关键技术 [J]. 电网技术, 2024, 48(4): 1435–1450.
Power, 2025, 44(10): 91–101. WU Linlin, CHEN Can, HU Junjie, et al. User side resource
[3] 赵航, 孙改平, 陈耿, 等. 计及源-荷联合出力特性的配电网多目标 application and key technologies for flexibility demand of renewable
优化调度 [J]. 浙江电力, 2025, 44(11): 83–92. energy power system[J]. Power System Technology, 2024, 48(4):
ZHAO Hang, SUN Gaiping, CHEN Geng, et al. Multi-objective 1435–1450.
optimal scheduling of distribution networks considering joint source- [11] KWAC J, KIM J I, RAJAGOPAL R. Efficient customer selection
load output characteristics[J]. Zhejiang Electric Power, 2025, 44(11): process for various DR objectives[J]. IEEE Transactions on Smart
83–92. Grid, 2019, 10(2): 1501–1508.
[4] 王仕龙, 张汉雄, 卢嘉琛, 等. 基于梯级水电调节的风—光—水联合 [12] CHEN X, NIE Y T, LI N. Online residential demand response via
跨区消纳优化调度 [J]. 智慧电力, 2025, 53(7): 28–35. contextual multi-armed bandits[J]. IEEE Control Systems Letters,
WANG Shilong, ZHANG Hanxiong, LU Jiachen, et al. Optimal 2021, 5(2): 433–438.
dispatch for cross-regional integration of wind: PV: hydropower [13] LI Y Y, HU Q R, LI N. Learning and selecting the right customers
hybrid systems based on cascade hydropower regulation[J]. Smart for reliability: a multi-armed bandit approach[C]//2018 IEEE
Power, 2025, 53(7): 28–35. Conference on Decision and Control (CDC). Miami, FL, USA. IEEE,
[5] 于雷, 姚俊伟, 杨金龙. 小样本下风光储耦合系统的新能源消纳能 2019: 4869-4874.
力概率评估方法 [J]. 智慧电力, 2024, 52(10): 9–15. [14] HEYDARIAN-FORUSHANI E, GOLSHAN M E H, MOGHADDAM
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