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考虑电能共享的综合能源楼宇群日前协同优化调度
胡鹏1,2, 艾欣1, 杨昭1, 吴界辰1, 郭良松2, 李庆彪2, 张润恩2
1.华北电力大学 电气与电子工程学院,北京 102206;2.国网乌鲁木齐县供电公司,新疆 乌鲁木齐 830012
摘要:
首先提出了一种含高比例分布式能源的智能园区运行框架,针对综合能源楼宇内冷热电联供系统、电动汽车、温控负荷等资源建立了计及用户用能需求的量化模型。进一步,基于电能双向流动特性,建立园区内多综合能源楼宇群电能共享模型,并通过智能园区代理商制定园区的日前优化调度策略。通过算例仿真,验证了所提方法可通过协调内部综合能源需求侧资源,进行楼宇间电能共享,实现电能供需就近平衡,达到最小化园区调度成本的优化目标。最后,讨论了有无变压器容量限制下的园区与电网的功率交互情况,所提调度策略可通过增加变压器容量约束来避免变压器越限对电网安全运行的不利影响。
关键词:  分布式能源  综合能源楼宇群  电能共享  智能园区  日前优化调度  综合能源系统
DOI:10.16081/j.epae.201908047
分类号:TM734
基金项目:国家重点研发计划项目(2016YFB0900500);北京市自然科学基金资助项目(3182037)
Day-ahead optimal scheduling for cluster building with integrated energy system considering power sharing
HU Peng1,2, AI Xin1, YANG Zhao1, WU Jiechen1, GUO Liangsong2, LI Qingbiao2, ZHANG Run'en2
1.College of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China;2.State Grid Urumqi County Power Supply Company, Urumqi 830012, China
Abstract:
An intelligent community operation framework with high penetration of distributed energy is firstly proposed. A quantitative model of the CCHP(Combined Cooling, Heating and Power),EV(Electric Vehicle) and TCLs(Thermostatically Controlled Loads) in the intelligent building, which takes the user’s energy demand into account, is established. Based on the bidirectional power flow characteristics of the electric energy, a power sharing model of multi-intelligent building group in the community is established and the intelligent community agent formulates a day-ahead scheduling strategy. The simulative results show that the proposed method can coordinate the internal schedulable resources, share the power between buildings, achieve the near balance of power supply and demand, and achieve the optimization goal of minimizing the community scheduling cost. Finally, the power interaction between the community and the grid under the limitation of transformer capacity is discussed. The proposed scheduling strategy can avoid the adverse impact of the transformer over-limit on the safe operation of the grid by considering the transformer capacity constraint.
Key words:  distributed energy resource  cluster building with integrated energy system  power sharing  intelligent community  day-ahead optimal scheduling  integrated energy system

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