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考虑发用电相似性的海上风电中长期双边协商交易优化决策模型OA北大核心CSTPCD

Optimal Decision-making Model for Medium-and Long-term Bilateral Negotiation Transaction of Offshore Wind Power Considering Similarity Between Power Generation and Consumption

中文摘要英文摘要

海上风电是未来最有潜力的可再生能源之一,但其出力具有随机性和波动性.为了更好地促进海上风电的市场化消纳,文中基于海上风电商与负荷聚合商间的中长期双边协商交易构建了优化决策模型.首先,通过时间序列相似性评估方法,为目标海上风电寻找最优的用电负荷组合.其次,考虑需求响应备用容量配置和发用电曲线预测误差,构建了基于两阶段分布鲁棒优化的中长期交易优化决策模型,为海上风电配置适应其未来一段时间内出力特性的需求响应资源,并合理调整中长期交易曲线.最后,通过仿真算例验证了所提模型的有效性和实用性.

Offshore wind power is one of the most promising renewable energy sources in the future,but its output has the characteristics of randomness and volatility.To better promote the market-based consumption of offshore wind power,an optimal decision-making model is built based on the medium-and long-term bilateral negotiation transactions between offshore wind power generators and load aggregators.Firstly,through the time-series similarity assessment method,an optimal power consumption load combination is found for the target offshore wind power.Secondly,considering the reserve capacity configuration of the demand response and the forecasting error of the power generation and consumption curve,an optimal decision-making model for medium-and long-term transaction is constructed based on the two-stage distributionally robust optimization to allocate the demand response resources that adapt to the output characteristics of offshore wind power over a future period,and the medium-and long-term transaction curves are reasonably adjusted.Finally,the effectiveness and practicality of the proposed model are verified through the simulation cases.

谢敏;李弋升;董凯元;谢宇星;黄莹;刘明波

华南理工大学电力学院,广东省广州市 510640||广东省绿色能源技术重点实验室(华南理工大学),广东省广州市 510640

海上风电中长期交易双边协商时序曲线匹配分布鲁棒优化

offshore wind powermedium-and long-term transactionbilateral negotiationtime-series curve matchingdistributionally robust optimization

《电力系统自动化》 2024 (014)

42-51 / 10

广东省基础与应用基础研究基金资助项目(2022A1515240074);广东省重点领域研发计划资助项目(2021B0101230004). This work is supported by Guangdong Basic and Applied Basic Research Foundation(No.2022A1515240074)and Key Areas R&D Program of Guangdong Province(No.2021B0101230004).

10.7500/AEPS20231221004

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