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计及不确定因素的需求侧灵活性资源优化调度
作者:
作者单位:

新能源电力系统国家重点实验室, 华北电力大学, 北京市 102206

摘要:

充分把握需求侧资源灵活性可以更好地实现日前优化调度以及合理地向电网提供辅助服务。然而,需求侧资源种类丰富、数量大、容量小、随机性强且分布于系统结构底层的不同位置,因此需要对其进行整合与量化。以含有高比例分布式能源的节点为研究对象,采用鲁棒边界的虚拟电池模型量化描述了具有不确定因素的集群电动汽车的灵活性与光伏出力。在此基础上,由交互平台参与日前电力市场,并采用协调优化策略合理地分配日前发用电计划与备用容量。算例验证了提出方法的有效性,既解决了由于高比例分布式能源导致的计算复杂问题,又降低了用户用电隐私信息暴露的风险。

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基金项目:

国家自然科学基金资助项目(51877078);北京市自然科学基金资助项目(3182037)

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作者简介:


Optimal Dispatch of Flexible Resource on Demand Side Considering Uncertainties
Author:
Affiliation:

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Abstract:

Sufficient grasp of flexibilities available from resources on demand side can better realize day-ahead optimal scheduling and reasonably provide ancillary services to power grids. However, the resources on demand side have abundant species, large quantities, small capacities and strong randomness. In addition, resources on demand side are usually distributed in different locations at the bottom of the system structure. Thus, these flexibilities need to be integrated and quantified. This paper takes the nodes with high-penetration of distributed energy as the research object. Virtual battery model with robust boundary is used to quantatively describe the flexibilities of aggregated electric vehicles and photovoltaic output considering their uncertainties. Based on the proposed virtual battery model, the transactive platform participates in the day-ahead electricity market and adopts a coordinated optimization strategy to reasonably allot energy schedule and reserve capacity. The case study verifies the effectiveness of the proposed method, which can reduce computational complexity caused by high-penetration distributed energy, and reduce the exposure risk of privacy electricity information for users.

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引用本文
[1]吴界辰,艾欣,胡俊杰,等.计及不确定因素的需求侧灵活性资源优化调度[J].电力系统自动化,2019,43(14):73-80. DOI:10.7500/AEPS20181109004.
WU Jiechen, AI Xin, HU Junjie, et al. Optimal Dispatch of Flexible Resource on Demand Side Considering Uncertainties[J]. Automation of Electric Power Systems, 2019, 43(14):73-80. DOI:10.7500/AEPS20181109004.
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  • 收稿日期:2018-11-09
  • 最后修改日期:2019-06-21
  • 录用日期:2019-05-07
  • 在线发布日期: 2019-06-20
  • 出版日期:
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