A corrective control framework for mitigating voltage fluctuations and congestion in distribution networks with high renewable energy penetration
The increasing integration of renewable energy sources into distribution networks introduces challenges such as voltage fluctuations and network congestion, complicating stable and efficient power delivery. To address these issues, we propose a model predictive control-based corrective control frame...
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Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2025-04-01
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Series: | International Journal of Electrical Power & Energy Systems |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525000596 |
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Summary: | The increasing integration of renewable energy sources into distribution networks introduces challenges such as voltage fluctuations and network congestion, complicating stable and efficient power delivery. To address these issues, we propose a model predictive control-based corrective control framework designed to manage voltage stability and congestion in distribution systems. This framework coordinates flexible devices with fast response, such as batteries and heating, ventilation, and air conditioning (HVAC) systems, to mitigate overloads and maintain voltage within specified limits. The proposed approach introduces a short-term preventive mechanism that dynamically addresses fluctuations while embedding local constraints of flexibility providers, which are encouraged through a reward structure. This novel framework enhances the flexibility and adaptability of modern power systems, supporting higher levels of renewable integration without compromising stability. Our results indicate that the framework can effectively alleviate operational issues in real time, offering a scalable solution for distributed networks with increasing renewable penetration. Through simulations on the modified IEEE 33 and 69 systems, our method reduces maximum line overload by over 25% and decreases maximum voltage violation by over 40%, demonstrating substantial operational improvements. |
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ISSN: | 0142-0615 |