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推进能源整合:可再生能源、辅助服务与稳定性。

Advancing energy integration: renewable sources, ancillary services, and stability.

作者信息

Gulraiz Asif, Sajjad Haider Zaidi Syed, Mohammad Khan Bilal

机构信息

Electrical & Power Engineering Department, National University of Science & Technology (PNEC) , Karachi, Sindh, Pakistan.

Electrical Engineering Department, DHA Suffa University, Karachi, Sindh, Pakistan.

出版信息

PLoS One. 2025 Jun 2;20(6):e0324812. doi: 10.1371/journal.pone.0324812. eCollection 2025.

Abstract

The paper discusses the recent developments and challenges in the energy sector, particularly focusing on the integration of renewable energy sources into microgrids and conventional power systems. It highlights the importance of predicting future energy generation for effective grid integration and discusses the use of Artificial Neural Network (ANN) and Auto-Regressive Moving Average (ARMA) models for this purpose. Additionally, it explores the role of distributed generation in providing ancillary services traditionally offered by conventional power systems and analyzes the impact of renewable energy sources on core parameters like frequency and voltage stability. It also discusses the rapid growth of solar photovoltaic (PV) systems and the need to assess their impacts on distribution networks. Furthermore, it addresses the ongoing energy crisis, particularly in South Asia, and proposes solutions such as power factor correction through technologies like Static VAR Compensators (SVCs) to enhance system stability and efficiency, especially in medium and long transmission lines.

摘要

本文讨论了能源领域的最新发展和挑战,特别关注可再生能源融入微电网和传统电力系统的情况。它强调了预测未来发电量对于有效电网整合的重要性,并讨论了为此目的使用人工神经网络(ANN)和自回归移动平均(ARMA)模型。此外,它探讨了分布式发电在提供传统电力系统所提供的辅助服务方面的作用,并分析了可再生能源对频率和电压稳定性等核心参数的影响。它还讨论了太阳能光伏(PV)系统的快速增长以及评估其对配电网影响的必要性。此外,它还涉及当前的能源危机,特别是在南亚,并提出了诸如通过静止无功补偿器(SVC)等技术进行功率因数校正等解决方案,以提高系统稳定性和效率,特别是在中长输电线路中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca9/12129172/1f59774b3ef9/pone.0324812.g003.jpg

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