| 引用本文: | 曾宪锴,杨苹,刘璐瑶,杨康,谭俊丰.电力现货市场环境下电动汽车充换电站的优化调控策略[J].电力自动化设备,2022,42(10): |
| ZENG Xiankai,YANG Ping,LIU Luyao,YANG Kang,TAN Junfeng.Optimal regulation strategy of battery charging and swapping station for electric vehicles under electricity spot market environment[J].Electric Power Automation Equipment,2022,42(10): |
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| 摘要: |
| 在构建新型电力系统的战略目标下，电力现货市场不断对电动汽车充换电站等需求侧资源提出新的需求与要求。基于南方电力现货市场系列试点规则，考虑现货电能量、调频辅助服务、多阶段需求响应等交易品种，建立了电动汽车充换电站的调控模型，设计了日前-日内-实时多阶段优化调控策略。日前阶段针对换电需求、调频调用电量和各交易品种价格的不确定性构建鲁棒优化问题，并采用二阶锥规划算法进行求解；日内阶段构建基于模型预测控制的滚动优化环节，实现对需求响应日内邀约的有效响应，同时改善日前调控计划的保守性；实时阶段以调控成本最低为目标，考虑需求响应实时邀约和电价波动，求解电池功率分配策略。仿真算例表明，所提策略可充分发挥充换电站的调节潜力，提升其经济效益。 |
| 关键词: 电动汽车 充换电站 电力现货市场 鲁棒优化 模型预测控制 多阶段优化 |
| DOI：10.16081/j.epae.202208018 |
| 分类号:TM732;U469.72 |
| 基金项目:广东省重点领域研发计划项目(2021B0101230003) |
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| Optimal regulation strategy of battery charging and swapping station for electric vehicles under electricity spot market environment |
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ZENG Xiankai1,2, YANG Ping1,2, LIU Luyao1, YANG Kang2, TAN Junfeng2
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1.School of Electric Power, South China University of Technology, Guangzhou 510640, China;2.Key Laboratory of Clean Energy Technology of Guangdong Province, South China University of Technology, Guangzhou 510640, China
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| Abstract: |
| Under the strategic goal of building the new-type power system, the electricity spot market continues to put forward new demands and requirements for demand-side resources such as battery charging and swapping station for electric vehicles. Based on pilot rules for the electricity spot market in southern China, considering the transaction varieties such as spot electric energy, frequency regulation auxiliary services and multi-stage demand response, the regulation model of battery charging and swapping station for electric vehicles is established, and the multi-stage optimal regulation strategy is designed, which includes day-ahead, intra-day and real-time stage. In the day-ahead stage, robust optimization is constructed to solve the uncertainty problems of battery swapping demand, electric energy used for frequency regulation and the price of various transaction varieties, and the solution is solved through the second-order cone programming algorithm. In the intra-day stage, the rolling optimization link based on model predictive control is constructed to ensure that intra-day demand response transactions are effectively engaged. In the real-time stage, aiming at the lowest control cost, considering the real-time invitation for the demand response and the fluctuation of electricity price, the battery power allocation strategy is solved. The simulation case shows that the proposed strategy can make full use of the regulation potential of the battery charging and swapping station and improve its economic benefits. |
| Key words: electric vehicles battery charging and swapping station electricity spot market robust optimization model predictive control multi-stage optimization |
