Open Access

ARTICLE

Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

Huan Ma¹, Linlin Ma², Zengwei Wang^3,*, Zhendong Li³, Yuanzhen Zhu¹, Yutian Liu³

1 Power Grid Technology Center, State Grid Shandong Electric Power Research Institute, Jinan, 250003, China
2 Shandong Electric Power Dispatching and Control Center, State Grid Shandong Electric Power Company, Jinan, 250012, China
3 Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan, 250061, China

* Corresponding Author: Zengwei Wang. Email:

Energy Engineering 2024, 121(11), 3133-3160. https://doi.org/10.32604/ee.2024.055051

Received 14 June 2024; Accepted 14 August 2024; Issue published 21 October 2024

Abstract

With the increasing penetration of renewable energy in power system, renewable energy power ramp events (REPREs), dominated by wind power and photovoltaic power, pose significant threats to the secure and stable operation of power systems. This paper presents an early warning method for REPREs based on long short-term memory (LSTM) network and fuzzy logic. First, the warning levels of REPREs are defined by assessing the control costs of various power control measures. Then, the next 4-h power support capability of external grid is estimated by a tie line power prediction model, which is constructed based on the LSTM network. Finally, considering the risk attitudes of dispatchers, fuzzy rules are employed to address the boundary value attribution of the early warning interval, improving the rationality of power ramp event early warning. Simulation results demonstrate that the proposed method can generate reasonable early warning levels for REPREs, guiding decision-making for control strategy.

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