Spot Market Bidding Strategy for a “Renewable Energy + Energy Storage” Power Station Considering Conditional Value at Risk

被引：0
|
作者：
Wang H. [1 ]
Sun W. [1 ]
Sang B. [2 ]
机构：
[1] School of Mechanical Engineering, University of Shanghai for Science and Technology, Yangpu District, Shanghai
[2] Jiangsu Energy Storage Variable Current and Application Engineering Technology Research Center, China Electric Power Research Institute, Jiangsu Province, Nanjing
来源：
Dianwang Jishu/Power System Technology | 2023年 / 47卷 / 09期
基金：
中国国家自然科学基金;
关键词：
conditional value at risk; rolling optimization; spot market; uncertainty; renewable energy + energy storage” power station;
D O I：
10.13335/j.1000-3673.pst.2022.1135
中图分类号：
学科分类号：
摘要：
The power generation of renewable energy power stations and the electricity prices in the spot markets have a great uncertainty, which makes the bidding decisions difficult, and it is easy to generate penalty costs or bad effects on the bidding decisions due to the large deviations between the actual power generation and the bidding power or between the actual electricity prices and the predicted electricity prices. Taking the above factors into consideration, the "renewable energy + energy storage" power station is used as the price receiver, and the optimal bidding strategy in the spot market is formulated under the background of the domestic double settlement model and the deviation assessment. First, the minimal gated memory network- quantile regression (MGM-QR) combination model is used to simulate the multiple uncertainties of the power station output and the electricity price. Taking the conditional value at risk (CVaR) as the risk measure to construct the electricity combination model of the day-ahead market bidding and the real-time market expected bidding of "renewable energy + energy storage" power stations. Second, in the real-time market stage, based on the ultra-short-term forecast results of the electricity price and output, a robust rolling optimization model is used to schedule the energy storage output and re-determine the bidding capacity to maximize the revenue. Finally, through the verification of the actual data in a certain area, the results show that the proposed method effectively meets the bidding decision-making needs of decision makers with different risk preferences, and ensures the income of the power station. © 2023 Power System Technology Press. All rights reserved.
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页码：3620 / 3628
页数：8
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