Integrated Intelligent Energy ›› 2024, Vol. 46 ›› Issue (1): 28-37.doi: 10.3969/j.issn.2097-0706.2024.01.004
• Decision Support System based on Intelligent Algorithms • Previous Articles Next Articles
TIAN Zeyu(), SHA Zhaoyang, ZHAO Quanbin, YAN Hui, CHONG Daotong*(
)
Received:
2023-05-11
Revised:
2023-07-15
Published:
2024-01-25
Supported by:
CLC Number:
TIAN Zeyu, SHA Zhaoyang, ZHAO Quanbin, YAN Hui, CHONG Daotong. Research on control strategy for virtual power plants in response to thermostatically controlled loads[J]. Integrated Intelligent Energy, 2024, 46(1): 28-37.
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Table 2
Profit-based VPP coordinated control strategy
时间段 | 连入电网的储能组 | 电费 | 调度目的 |
---|---|---|---|
11:30—16:30 | C | 平值 | 电动公交车储能在谷电价时最大化充电,充电结束后储能组C剩余储能SOC=1 |
16:30—18:30 | A | 平值 | 储能车组A工作结束接入电网,同时此时段光伏机组输出功率下降,负载开始受到温控负载的影响,需求增大。为获得最大的收益,在此时段调度燃煤机组输出功率为额定功率,P=350 MW,储能组A充电,充电结束SOC,t=18:30 |
18:30—23:00 | A,C | 峰值 | 为获得最大收益,储能在峰值电价时段完全放电,此时段燃煤机组的输出功率信号为Ppeak。时段结束时,储能的设计容量剩余均为SOC=0.1,电池放电下限值 |
23:00—调峰时段结束 | C | 谷值 | 储能放电结束,但调峰时段仍在继续,需要满足系统功率需求,设定Pvalley= Pdemand,t=23:00为输出功率设定值 |
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