计及不确定性和需求响应的风光燃储集成虚拟电厂随机调度优化模型

发布时间：2018-02-10 15:03

本文关键词： 虚拟电厂 CVaR 不确定性需求响应　出处：《电网技术》2017年11期 　论文类型：期刊论文

【摘要】：为促进以风光为代表的分布式能源发电并网,集成风电、光伏发电、燃气轮机、储能系统和激励型需求响应为虚拟电厂(virtual power plant,VPP),引入条件风险价值(conditional value at risk,CVaR)理论和置信度方法描述VPP运行不确定性,以运营收益最大化为目标函数,建立VPP常规调度优化模型,确定VPP运行收益的门槛值。然后,应用CVaR理论描述VPP运行目标函数中不确定性因素,应用置信度方法转换含不确定性变量的约束条件,建立考虑运行风险的VPP随机调度优化模型。最后,选择改进IEEE 30节点系统作为仿真系统,结果表明:价格型需求响应能够平缓用电负荷曲线,储能系统和激励型需求响应能够增加VPP运营收益。VPP常规调度收益为9 550.19元,风光并网电量为12.49 MW×h,风险规避调度收益为8 995.34元,风光并网电量为11.31 MW×h,这意味着CVaR理论和置信度方法能够用于描述VPP运行风险,通过设置门槛值和置信度反映决策者风险态度,为决策者提供风险控制工具。
[Abstract]:To promote grid-connected distributed energy generation, integrated wind power, photovoltaic power generation, gas turbines, The energy storage system and the demand response of incentive type are virtual power plan VPPs. The conditional value at risk (RV) theory and the confidence degree method are introduced to describe the uncertainty of VPP operation. Taking the maximization of operating income as the objective function, the conventional VPP scheduling optimization model is established. The threshold value of VPP operating income is determined. Then, the uncertain factors in VPP operation objective function are described by CVaR theory, and the constraint conditions with uncertain variables are transformed by confidence degree method. Finally, the improved IEEE 30-bus system is selected as the simulation system. The results show that the price-based demand response can smooth the power load curve. The energy storage system and the demand response of incentive type can increase the operating income of VPP. The regular dispatching income of VPP is 9 550.19 yuan, the power of wind and grid is 12.49 MW 脳 h, and the income of risk-averse dispatching is 8 995.34 yuan. The wind power is 11.31 MW 脳 h, which means that the CVaR theory and the confidence degree method can be used to describe the risk of VPP operation, and reflect the risk attitude of the decision maker by setting the threshold value and confidence degree, and provide the risk control tool for the decision maker.
【作者单位】：华北电力大学能源经济与环境研究所;国网节能服务有限公司;国网山西省电力公司经济技术研究院;
【基金】：国家自然科学基金项目(71573084) 北京市社会科学基金项目(16JDYJB044)~~
【分类号】：TM73

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