| 引用本文: | 杨 波,汤文成,吴福保,王洪亮,孙伟卿.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):93-101.[点击复制] |
| YANG Bo,TANG Wencheng,WU Fubao,WANG Hongliang,SUN Weiqing.Day-ahead market bidding strategy for "renewable energy + energy storage" power plantsconsidering conditional value-at-risk[J].Power System Protection and Control,2022,50(9):93-101[点击复制] |
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| 摘要: |
| 双碳背景下我国正积极构建以新能源为主体的新型电力系统，大力推进电力市场化改革。大量新能源电厂无序上网将会对电力系统的安全稳定运行带来巨大压力，也不利于其作为独立主体参与市场化竞争。将“新能源+储能”电厂作为价格接受者，以“报量不报价”的形式参与电力现货交易。采用随机优化方法构建新能源出力场景、日前及实时出清电价场景，采用K-medoids方法将数量众多的场景转化为数量有限的概率化确定性场景。以日前收益最大为目标，考虑条件风险价值并计及不平衡惩罚费用，建立“新能源+储能”电厂参与日前市场的最优投标策略模型并求解。最后，以某风电厂的历史出力数据以及现货市场电价数据开展数值仿真，仿真结果验证了所建投标策略模型的有效性。 |
| 关键词: “新能源+储能”电厂 不确定性 日前市场 条件风险价值 投标策略 |
| DOI：DOI: 10.19783/j.cnki.pspc.220113 |
| 投稿时间：2022-01-25修订日期：2022-03-21 |
| 基金项目:国家自然科学基金项目资助(51777126)；江苏省储能变流及应用工程技术研究中心开放基金项目资助(NY51202101352) |
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| Day-ahead market bidding strategy for "renewable energy + energy storage" power plantsconsidering conditional value-at-risk |
| YANG Bo,TANG Wencheng,WU Fubao,WANG Hongliang,SUN Weiqing |
| (1. School of Mechanical Engineering, Southeast University, Nanjing 211189, China; 2. Jiangsu Energy Storage Variable Current
and Application Engineering Technology Research Center (China Electric Power Research Institute), Nanjing 210003, China;
3. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China) |
| Abstract: |
| Under the background of dual carbon, China is actively building a new power system with renewable energy as the main body, and vigorously promoting the reform of power marketization. The disorderly access of a large number of renewable energy power plants will bring enormous pressure to the safe and stable operation of the power system, and will not be conducive to their participation in market competition as independent entities. This paper first takes "renewable energy + energy storage" power plants as price receivers, participates in the spot electricity transactions in the form of "quote volume without quotation". Secondly, the stochastic optimization method is used to construct renewable energy output scenarios, day-ahead and real-time clearing electricity price scenarios, and the K-medoids method is used to convert a large number of scenarios into a limited number of probabilistic deterministic scenarios. Then, with the goal of maximizing the day-ahead benefits, considering the conditional value-at-risk and taking into account the unbalanced penalty costs, an optimal bidding strategy model of "renewable energy + energy storage" power plants participating in the day-ahead market is established and solved. Finally, a numerical simulation is carried out with the historical output data of a wind power plant and the electricity price data in the spot market, and the simulation results verify the effectiveness of the proposed bidding strategy model.
This work is supported by the National Natural Science Foundation of China (No. 51777126). |
| Key words: “renewable energy + storage” power plant uncertainty day-ahead market conditional value-at-risk bidding strategy |
