
浏览全部资源
扫码关注微信
1.电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市 200240
2.上海非碳基能源转换与利用研究院,上海市 200240
3.中国电力科学研究院有限公司(南京),江苏省南京市 210003
4.国家电网有限公司华东分部,上海市 200120
许多(2000—),女,硕士研究生,主要研究方向:光伏功率预测。E-mail:xdd0816@sjtu.edu.cn
徐潇源(1989—),男,通信作者,副教授,主要研究方向:电力系统不确定性分析、电力系统优化运行。E-mail:xuxiaoyuan@sjtu.edu.cn
秦放(1991—),女,博士,主要研究方向:新能源资源评估及卫星遥感应用。E-mail:qinfang@epri.sgcc.com.cn
收稿:2024-05-29,
修回:2024-09-27,
网络出版:2024-11-22,
纸质出版:2025-02-25
移动端阅览
许多,徐潇源,秦放等.面向光储电站运行收益提升的光伏功率价值导向预测方法[J].电力系统自动化,2025,49(4):152-164.
XU Duo,XU Xiaoyuan,QIN Fang,et al.Value-oriented Photovoltaic Power Forecasting Method for Operation Revenue Improvement of Photovoltaic-Energy Storage Plants[J].Automation of Electric Power Systems,2025,49(4):152-164.
许多,徐潇源,秦放等.面向光储电站运行收益提升的光伏功率价值导向预测方法[J].电力系统自动化,2025,49(4):152-164. DOI: 10.7500/AEPS20240529001.
XU Duo,XU Xiaoyuan,QIN Fang,et al.Value-oriented Photovoltaic Power Forecasting Method for Operation Revenue Improvement of Photovoltaic-Energy Storage Plants[J].Automation of Electric Power Systems,2025,49(4):152-164. DOI: 10.7500/AEPS20240529001.
光储电站运行包含光伏功率预测与资源优化调度两部分,通常首先以预测精度最高为目标进行光伏功率预测,再基于预测曲线进行光储电站优化调度。然而,优化调度问题的目标函数值与光伏功率预测误差具有非线性、非对称关系,在相同的预测误差水平下,以精度最高为目标的光伏功率预测结果并不一定使得光储电站运行收益最大。对此,提出了面向光储电站运行收益提升的光伏功率价值导向预测方法。首先,构建包含光伏功率预测与电站运行的双层优化问题,上层为光伏功率日前预测模型训练问题,下层为给定光伏功率预测模型下的光储电站日前投标与日内运行两阶段优化调度问题。然后,将上层预测问题变换为组合预测形式,预测模型参数设置为权重系数,设计了基于迭代优化的预测模型参数求解方法。最后,采用实际光伏电站数据以及电价数据进行算例分析,并与以预测精度最高为目标的光伏功率预测方法进行对比,验证所提出方法在提升电站运行收益方面的有效性。
The operation of photovoltaic-energy storage plants includes two parts: photovoltaic power forecasting and resource optimal scheduling. The photovoltaic power forecasting is usually carried out first with the goal of the highest forecasting accuracy
and then the optimal scheduling of the photovoltaic-energy storage plant is carried out based on the forecasting curve. However
there is a nonlinear and asymmetric relationship between the objective function value of optimal scheduling problem and the photovoltaic power forecasting error. At the approximate forecasting error level
the photovoltaic power forecasting result aiming at the highest accuracy does not necessarily lead to the maximum operation revenue of the photovoltaic-energy storage plant. In this regard
a value-oriented photovoltaic power forecasting method for improving the operation revenue of photovoltaic-energy storage plants is proposed. Firstly
a bi-level optimization problem is constructed
which includes photovoltaic power forecasting and power plant operation. The upper layer is the training problem of the day-ahead photovoltaic power forecasting model
and the lower layer is the two-stage optimal scheduling problem of day-ahead bidding and intra-day operation for the photovoltaic-energy storage plant under the conditions of given photovoltaic power forecasting model. Then
the upper forecasting problem is transformed into a combined forecasting form
and the forecasting model parameters are set as weight coefficients. A parameter solving method for the forecasting model based on iterative optimization is designed. Finally
using actual photovoltaic power plant data and electricity price data for case analysis
and compared with the photovoltaic power forecasting method that aims to achieve the highest forecasting accuracy
the effectiveness of the proposed method in improving the operation income of power plants is verified.
朱敏 . 我国新能源消纳困难原因及其对策 [J]. 中国发展观察 , 2018 ( 21 ): 35 - 37 .
ZHU Min . Causes and countermeasures of difficulties in the consumption of new energy in China [J]. China Development Observation , 2018 ( 21 ): 35 - 37 .
王强强 , 姚良忠 , 盛万兴 , 等 . 基于机会约束规划的配电网分布式光伏承载力提升方法 [J]. 电力系统自动化 , 2023 , 47 ( 18 ): 132 - 141 .
WANG Qiangqiang , YAO Liangzhong , SHENG Wanxing , et al . Enhancement method for distributed photovoltaic hosting capacity of distribution network based on chance-constrained programmings [J]. Automation of Electric Power Systems , 2023 , 47 ( 18 ): 132 - 141 .
武昭原 , 刘婧宇 , 周明 , 等 . 分散决策下分布式光伏储能系统外部性价值量化评估 [J]. 电力系统自动化 , 2024 , 48 ( 5 ): 38 - 47 .
WU Zhaoyuan , LIU Jingyu , ZHOU Ming , et al . Quantitative evaluation of externality value for distributed photovoltaic energy storage system under decentralized decision-making [J]. Automation of Electric Power Systems , 2024 , 48 ( 5 ): 38 - 47 .
龚莺飞 , 鲁宗相 , 乔颖 , 等 . 光伏功率预测技术 [J]. 电力系统自动化 , 2016 , 40 ( 4 ): 140 - 151 .
GONG Yingfei , LU Zongxiang , QIAO Ying , et al . An overview of photovoltaic energy system output forecasting technology [J]. Automation of Electric Power Systems , 2016 , 40 ( 4 ): 140 - 151 .
LIU Y Q , SUN Y , INFIELD D , et al . A hybrid forecasting method for wind power ramp based on orthogonal test and support vector machine(OT-SVM) [J]. IEEE Transactions on Sustainable Energy , 2017 , 8 ( 2 ): 451 - 457 .
吴春华 , 董阿龙 , 李智华 , 等 . 基于图相似日和PSO-XGBoost的光伏功率预测 [J]. 高电压技术 , 2022 , 48 ( 8 ): 3250 - 3259 .
WU Chunhua , DONG Along , LI Zhihua , et al . Photovoltaic power prediction based on graph similarity day and PSO-XGBoost [J]. High Voltage Engineering , 2022 , 48 ( 8 ): 3250 - 3259 .
魏联滨 , 王彬 , 王莹 , 等 . 基于气象相似日选取与提升回归树的光伏发电短期功率预测 [J]. 电子器件 , 2022 , 45 ( 1 ): 183 - 188 .
WEI Lianbin , WANG Bin , WANG Ying , et al . Short-term power forecast of photovoltaic power generation based on weather similarity day and boosting regression tree [J]. Chinese Journal of Electron Devices , 2022 , 45 ( 1 ): 183 - 188 .
LIU D , SUN K . Random forest solar power forecast based on classification optimization [J]. Energy , 2019 , 187 : 115940 .
王育飞 , 付玉超 , 薛花 . 计及太阳辐射和混沌特征提取的光伏发电功率DMCS-WNN预测法 [J]. 中国电机工程学报 , 2019 , 39 ( 增刊1 ): 63 - 71 .
WANG Yufei , FU Yuchao , XUE Hua . DMCS-WNN prediction method of photovoltaic power generation by considering solar radiation and chaotic feature extraction [J]. Proceedings of the CSEE , 2019 , 39 ( S1 ): 63 - 71 .
KIM H , PARK S , PARK H J , et al . Solar radiation forecasting based on the hybrid CNN-CatBoost model [J]. IEEE Access , 2023 , 11 : 13492 - 13500 .
ZHANG Y , WEN H , WU Q . A contextual bandit approach for value-oriented forecasting interval forecasting [J]. IEEE Transactions on Smart Grid , 2024 , 15 ( 2 ): 2271 - 2281 .
AMOS B , KOLTER J Z . OptNet: differentiable optimization as a layer in neural networks [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/1703.00443v5 https://arxiv.org/abs/1703.00443v5 .
SUN H X , SHI Y , WANG J Y , et al . Alternating differentiation for optimization layers [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/2210.01802v2 https://arxiv.org/abs/2210.01802v2 .
AGRAWAL A , BARRATT S , BOYD S , et al . Differentiating through a cone program [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/1904.09043v4 https://arxiv.org/abs/1904.09043v4 .
ZHANG Y F , WEN H L , BIAN Y X , et al . Deriving loss function for value-oriented renewable energy forecasting [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/2310.00571v1 https://arxiv.org/abs/2310.00571v1 .
ZHANG Y F , JIA M S , WEN H L , et al . Toward value-oriented renewable energy forecasting: an iterative learning approach [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/2309.00803v2 https://arxiv.org/abs/2309.00803v2 .
MANDI J , KOTARY J , BERDEN S , et al . Decision-focused learning: foundations, state of the art, benchmark and future opportunities [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/2307.13565v4 https://arxiv.org/abs/2307.13565v4 .
ELMACHTOUB A N , GRIGAS P . Smart “predict, then optimize ”[J]. Management Science , 2022 , 68 ( 1 ): 9 - 26 .
MULAMBA M , MANDI J , DILIGENTI M , et al . Contrastive losses and solution caching for predict-and-optimize [EB/OL]. [ 2024-09-10 ]. https://arxiv.org/abs/2011.05354v2 https://arxiv.org/abs/2011.05354v2 .
SANG L W , XU Y L , LONG H , et al . Safety-aware semi-end-to-end coordinated decision model for voltage regulation in active distribution network [J]. IEEE Transactions on Smart Grid , 2023 , 14 ( 3 ): 1814 - 1826 .
ELMACHTOUB A N , LIANG J C N , MCNELLIS R . Decision trees for decision-making under the predict-then-optimize framework [C]// International Conference on Machine Learning , July 12-18 , 2020 .
MUÑOZ M A , MORALES J M , PINEDA S . Feature-driven improvement of renewable energy forecasting and trading [J]. IEEE Transactions on Power Systems , 2020 , 35 ( 5 ): 3753 - 3763 .
CHEN X B , YANG Y F , LIU Y K , et al . Feature-driven economic improvement for network-constrained unit commitment: a closed-loop predict-and-optimize framework [J]. IEEE Transactions on Power Systems , 2022 , 37 ( 4 ): 3104 - 3118 .
KHABIBRAKHMANOV I , LU S , HAMANN H F , et al . On the usefulness of solar energy forecasting in the presence of asymmetric costs of errors [J]. IBM Journal of Research and Development , 2016 , 60 ( 1 ): 1 - 7 .
CHEN X B , LIU Y K , WU L . Towards improving unit commitment economics: an add-on tailor for renewable energy and reserve predictions [J]. IEEE Transactions on Sustainable Energy , 2024 , 15 ( 4 ): 2547 - 2566 .
ZHANG H P , LI R , CHEN Y , et al . Risk-aware objective-based forecasting in inertia management [J]. IEEE Transactions on Power Systems , 2024 , 39 ( 2 ): 4612 - 4623 .
李瑞月 . 风光联合系统储能容量优化配置技术研究 [D]. 北京 : 华北电力大学 , 2022 .
LI Ruiyue . Research on optimal configuration technology of energy storage capacity of wind-solar combined system [D]. Beijing : North China Electric Power University , 2022 .
杨波 , 汤文成 , 吴福保 , 等 . 考虑CVaR的“新能源+储能”电厂日前市场投标策略 [J]. 电力系统保护与控制 , 2022 , 50 ( 9 ): 93 - 100 .
YANG Bo , TANG Wencheng , WU Fubao , et al . Day-ahead market bidding strategy for “renewable energy+energy storage” power plants considering conditional value-at-risk [J]. Power System Protection and Control , 2022 , 50 ( 9 ): 93 - 100 .
CRISTIANINI N , TAYLOR J S . An introduction to support vector machines and other kernel-based learning methods [M]. Cambridge, UK : Cambridge University Press , 2000 .
JEROME H F . Greedy function approximation: a gradient boosting machine [J]. Annals of Statistics , 2001 , 29 ( 5 ): 1189 - 1232 .
BREIMAN L . Random forests [J]. Machine learning , 2001 , 45 : 5 - 32 .
PAL S K , MITRA S . Multilayer perceptron, fuzzy sets, and classification [J]. IEEE Transactions on Neural Networks , 1992 , 3 ( 5 ): 683 - 697 .
KRAMER O . Dimensionality reduction by unsupervised k-nearest neighbor regression [C]// 2011 10th International Conference on Machine Learning and Applications and Workshops , December 18-21 , 2011 , Honolulu, USA .
KOHONEN T . The self-organizing map [J]. Neurocomputing , 1990 , 21 : 1 - 6 .
YANG Y T , CHEN H L , HEIDARI A A , et al . Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts [J]. Expert Systems with Applications , 2021 , 177 : 114864 .
The University of Oregon Solar Radiation Monitoring Lab (SRML) [EB/OL]. [ 2024-09-10 ]. http://is-solardata01.uoregon.edu/index.html http://is-solardata01.uoregon.edu/index.html .
DOWELL D C , ALEXANDER C R , JAMES E P , et al . The high-resolution rapid refresh (HRRR): an hourly updating convection-allowing forecast model: part Ⅰ motivation and system description [J]. Weather and Forecasting , 2022 , 37 ( 8 ): 1371 - 1395 .
LAGO J , MARCJASZ G , DE SCHUTTER B , et al . Forecasting day-ahead electricity prices: a review of state-of-the-art algorithms, best practices and an open-access benchmark [J]. Applied Energy , 2021 , 293 : 116983 .
WANG Y , DONG W , YANG Q . Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets [J]. Applied Energy , 2022 , 310 : 118528 .
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621