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Bidding strategy of cascade hydropower plants based on day-ahead electricity price forecasting |
LI Huaqu, HE Peishan, WU Dianning, ZHOU Na |
Kunming Power Exchange Center Co., Ltd, Kunming 650011 |
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Abstract Aiming at the high proportion hydropower power market, establishing a reasonable and effective day-ahead quotation strategy of hydropower plants is of great significance to ensure the effective participation of hydropower in the market and promote the consumption of hydropower. Firstly, considering the uncertainty of the clearing price of the system on the operation day, based on the identification of historical similar days, the probability prediction method of day-ahead clearing price is established by using Gaussian process regression. Then, aiming at maximizing the power sales revenue of hydropower plants, the analytical calculation method of the day-ahead subsection capacity quotation of cascade upstream hydropower stations is constructed, and the output coupling model between cascade upstream and downstream hydropower stations is established. Therefore, the biding strategy of downstream power station is obtained based on the output coupling relationship. Finally, the simulation analysis of the day-ahead segmented capacity declaration of the actual cascade hydropower plants verifies the feasibility and effectiveness of the proposed bidding strategy. The simulation results show that the proposed method can provide a reasonable auxiliary decision-making reference for hydropower plants to participate in the day-ahead bidding in the high proportion hydropower power market.
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Received: 10 May 2022
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Cite this article: |
LI Huaqu,HE Peishan,WU Dianning等. Bidding strategy of cascade hydropower plants based on day-ahead electricity price forecasting[J]. Electrical Engineering, 2022, 23(9): 40-47.
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URL: |
http://dqjs.cesmedia.cn/EN/Y2022/V23/I9/40
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[1] 李泽宏, 曾杨超, 周畅游, 等. 基于电力现货市场出清模拟的节点电价影响因素分析[J]. 电气技术, 2020, 21(5): 41-47. [2] 韩晓言, 丁理杰, 陈刚, 等. 梯级水光蓄互补联合发电关键技术与研究展望[J]. 电工技术学报, 2020, 35(13): 2711-2722. [3] LIU Shuangquan, YANG Qiang, CAI Huaxiang, et al.Market reform of Yunnan electricity in southwestern China: practice, challenges and implications[J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109265. [4] 张宏图, 熊志杰, 朱燕梅, 等. 基于“虚拟水库”的梯级水电现货报价单元组建方法[J]. 电工技术学报, 2022, 37(3): 719-728. [5] 王京景, 吴旭, 王正风, 等. 基于多目标模糊优化的抽蓄水火电联合调峰方法[J]. 电气技术, 2019, 20(11): 33-38, 45. [6] MOISEEVA E, HESAMZADEH M R.Strategic bidding of a hydropower producer under uncertainty: modified benders approach[J]. IEEE Transactions on Power Systems, 2018, 33(1): 861-873. [7] 王力, 杜济园. 基于电力市场环境的机组组合优化运行研究[J]. 电气技术, 2019, 20(8): 38-44. [8] FAN Lei, WANG Jianhui, JIANG Ruiwei, et al.Min-max regret bidding strategy for thermal generator considering price uncertainty[J]. IEEE Transactions on Power Systems, 2014, 29(5): 2169-2179. [9] 郭通, 李永刚, 徐姗姗, 等. 考虑多主体博弈的火电机组灵活性改造规划[J]. 电工技术学报, 2020, 35(11): 2448-2459. [10] 路轶, 胡晓静, 孙毅, 等. 适应高水电占比的电力现货市场机制设计与实践[J]. 电力系统自动化, 2021, 45(9): 162-170. [11] 邓玉敏, 石峰, 夏清, 等. 跨省区水电参与现货市场的机制和交易策略[J]. 电网技术, 2021, 45(8): 3190-3202. [12] YUAN Xiaohui, WANG Yunyan, XIE Jun, et al.Optimal self-scheduling of hydro producer in the elec-tricity market[J]. Energy Conversion and Management, 2010, 51(12): 2523-2530. [13] 于旭光, 李刚, 李亚鹏, 等. 计及电价风险和差价合同的梯级水电站日前市场竞价模型[J]. 电力系统自动化, 2022, 46(5): 62-72. [14] AASGÅRD E K, NAVERSEN C Ø, FODSTAD M, et al. Optimizing day-ahead bid curves in hydropower production[J]. Energy Systems, 2018, 9(2): 257-275. [15] 伍永刚, 高英. 一种基于预测电价的水电站报价方法研究[J]. 电工技术学报, 2004, 19(9): 90-94, 89. [16] HE Li, CHEN Dong, LI Hui.A simple piecewise bidding strategy for hydropower plant in liberalised electricity market[C]//2010 9th IEEE/IAS International Conference on Industry Applications-INDUSCON, Sao Paulo, Brazil, 2010: 1-5. [17] 杨颖, 杨少华, 张燕, 等. 基于相似日的短期电价区间预测[J]. 智慧电力, 2018, 46(12): 23-29. [18] 刘升伟, 王星华, 鲁迪, 等. 基于改进高斯过程回归的短期负荷概率区间预测方法[J]. 电力系统保护与控制, 2020, 48(1): 18-25. [19] 张粒子, 刘方, 许通, 等. 多运营主体梯级水电站参与的日前市场出清模型[J]. 电力系统自动化, 2018, 42(16): 104-110. [20] 四川省经济和信息化厅. 四川电力现货市场建设试点方案及相关实施细则(模拟试运行征求意见稿)[Z]. 成都, 2019. |
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