四川蜀道清洁能源集团有限公司,四川 成都 610041
王再闯(1988 —),男,工程师,博士,研究方向为新型电力系统中储能的应用(E-mail:714553929@qq.com);
郭明阳(1986 —),男,高级工程师,硕士,通信作者,研究方向为新能源并网技术(E-mail:62154778@qq.com)。
收稿:2025-01-08,
修回:2025-05-26,
网络首发:2025-07-14,
纸质出版:2026-03-10
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王再闯,郭明阳,刘斌等.基于策略性双向拍卖机制的共享储能与产销者电力交易模型[J].电力自动化设备,2026,46(03):41-50.
WANG Zaichuang,GUO Mingyang,LIU Bin,et al.Electric power trading model between shared energy storage and active energy participant based on strategic double auction mechanism[J].Electric Power Automation Equipment,2026,46(03):41-50.
王再闯,郭明阳,刘斌等.基于策略性双向拍卖机制的共享储能与产销者电力交易模型[J].电力自动化设备,2026,46(03):41-50. DOI: 10.16081/j.epae.202507017.
WANG Zaichuang,GUO Mingyang,LIU Bin,et al.Electric power trading model between shared energy storage and active energy participant based on strategic double auction mechanism[J].Electric Power Automation Equipment,2026,46(03):41-50. DOI: 10.16081/j.epae.202507017.
为了提升产销者之间的能源交易效率,基于共享储能构建一个新型产销者社区能源交易系统。该系统将产销者之间的能源交易转化为与共享储能的交易,基于策略性双向拍卖机制实现产销者之间的最优能源匹配。构建产销者与共享储能设备的能源交易框架,并将能源交易分为直接交易和缓冲交易。基于双向拍卖机制建立产销者与共享储能设备的两阶段能量交易模型:在阶段1,基于预测数据,以共享储能收益最大、产销者用能成本最小为目标进行日前优化,并进行投标信息上报;在阶段2,针对实时市场,以各参与者效用最大为目标对中标信息进行调节,并通过价格迭代实现投标均衡。基于实时均衡投标信息,采用均等分配方法分配中标资源。算例仿真结果验证了所提模型及方法的有效性。结果表明,所提方法能平抑社区净负荷波动,与基于经验的投标方法相比,其中标率提升了1 %,产销者的平均用能成本降低了2.9 %,储能的收益提高了15.8 %。
To enhance the energy trading efficiency among active energy participants,a new community energy trading system for active energy participants based on shared energy storage is constructed. The system transforms the energy trading among active energy participants into the transactions with shared energy sto-rage devices and achieves the optimal energy matching among active energy participants based on a strategic double auction mechanism. An energy trading framework for active energy participants and shared energy storage devices is constructed,and the energy trading is divided into direct trading and buffer trading. A two-stage energy trading model for active energy participants and shared energy storage devices is established based on the double auction mechanism. In stage 1,based on the predicted data,the day-ahead optimization is carried out with the goals of maximizing the revenue of shared energy storage and minimizing the energy consumption cost of active energy participants,and the bidding information is reported. In stage 2,for the real-time market,the winning bid information is adjusted with the goal of maximizing the utility of each participant,and the bidding equilibrium is achieved through price iteration. Based on the real-time equilibrium bidding information,the winning bid resources are allocated by using the equal-sharing method. The effectiveness of the proposed model and method is verified by the simulative results of example. The results show that,the proposed method can smooth the net load fluctuation of the community. Compared with the experience-based bidding method,the winning bid rate is increased by 1 %,the average energy consumption cost of active energy participants is reduced by 2.9 %,and the revenue of energy storage is increased by 15.8 %.
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