摘要
提高微电网风电的消纳能力是实现双碳目标的重要手段。该文构建了一种考虑极限场景和净负荷条件风险价值的微电网低碳分布鲁棒优化模型。首先,针对源-荷不确定性构造基于Wasserstein距离的概率分布模糊集,并考虑极限场景修正该模糊集,以提高模糊集的鲁棒性,减少场景数目,提高求解效率。其次,以包括供能成本、阶梯式碳交易成本和风险成本的微电网日运行成本最小为优化目标,建立分布鲁棒优化调度模型,并利用分段线性近似法和对偶理论将模型转换为线性规划问题求解。最后,通过算例仿真进行验证,结果表明,考虑极限场景的分布鲁棒优化方法具有较强的鲁棒性;引入阶梯式碳交易成本可以有效减少微电网的碳排放量,并提高风电的消纳能力;净负荷条件风险价值可以有效平衡微电网运行的经济性与风险性。
Improving the consumption capacity of microgrid wind power is an important means to achieve the carbon peak and carbon neutrality goal.We constructeda distributional robust optimization model for low carbon of microgrid considering the extreme scenarios and the risk value of payload conditions.Firstly,we construct a fuzzy set of probability distribution based on Wasserstein distance for source-load uncertainty,modify the fuzzy set by considering the extreme scenarios to improve the robustness of the fuzzy set,and improve the solution efficiency by reducing the number of scenarios.Secondly,a distributional robust optimization scheduling model is established with the objective of minimizing the daily operation cost,including energy supply cost,stepped carbon transaction cost and the risk value cost,and the model is converted to a linear programming problem solved by using segmented linear approximation and duality theory.Thirdly,the proposed model is validated by simulations.The results show that the distributional robust optimization method considering extreme scenarios has strong robustness,the introduction of stepped carbon trading cost can effectively reduce carbon emissions from microgrids and improve the absorption capacity of wind power,and payload condition value at risk can effectively balance the economy and risk of micro grid operation.
作者
王继东
边翊楠
许秋铭
孔祥玉
WANG Jidong;BIAN Yinan;XU Qiuming;KONG Xiangyu(Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China;Dalian Power Supply Company,State Grid Liaoning Electric Power Co.,Ltd.,Dalian 116000,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2024年第8期3477-3485,I0007,I0008,共11页
High Voltage Engineering
基金
国家自然科学基金(52277121)。
