DOI:10.35833/MPCE.2021.000621 |
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Demand-side Management Based on Model Predictive Control in Distribution Network for Smoothing Distributed Photovoltaic Power Fluctuations |
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Net amount: 376 |
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Author:
Xu Jian1, Fu Haobo2, Liao Siyang1, Xie Boyu1, Ke Deping1, Sun Yuanzhang1, Li Xiong1, Peng Xiaotao1
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Author Affiliation:
1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China 2.Power Dispatching and Control Center of State Grid Jibei Electric Power Company Limited, Beijing 100054, China
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Foundation: |
This work was supported by the National Natural Science Foundation of China (No. U2066601). |
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Abstract: |
With the rapid increase of distributed photovoltaic (PV) power integrating into the distribution network (DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demand-side needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling (PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment. Thus, a demand-side management strategy based on model predictive control (MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally, the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power. |
Keywords: |
Demand-side management ; multi-time-scale optimization ; power fluctuation smoothing ; load control ; model predictive control (MPC). |
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Received:September 18, 2021
Online Time:2022/09/24 |
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