{"title":"Flexible Synthetic Inertia Optimization in Modern Power Systems","authors":"Peter Makolo, R. Zamora, Uvini Perera, T. Lie","doi":"10.3390/inventions9010018","DOIUrl":null,"url":null,"abstract":"Increasing the replacement of conventional synchronous machines by non-synchronous renewable machines reduces the conventional synchronous generator (SG) inertia in the modern network. Synthetic inertia (SI) control topologies to provide frequency support are becoming a new frequency control tactic in new networks. However, the participation of SI in the market of RES-rich networks to provide instant frequency support when required proposes an increase in the overall marginal operation cost of contemporary networks. Consequently, depreciation of operation costs by optimizing the required SI in the network is inevitable. Therefore, this paper proposes a flexible SI optimization method. The algorithm developed in the proposed method minimizes the operation cost of the network by giving flexible SI at a given SG inertia and different sizes of contingency events. The proposed method uses Box’s evolutionary optimizer with a self-tuning capability of the SI control parameters. The proposed method is validated using the modified New England 39-bus network. The results show that provided SIs support the available SG inertia to reduce the RoCoF values and maintain them within acceptable limits to increase the network’s resilience.","PeriodicalId":509629,"journal":{"name":"Inventions","volume":"58 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inventions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/inventions9010018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Increasing the replacement of conventional synchronous machines by non-synchronous renewable machines reduces the conventional synchronous generator (SG) inertia in the modern network. Synthetic inertia (SI) control topologies to provide frequency support are becoming a new frequency control tactic in new networks. However, the participation of SI in the market of RES-rich networks to provide instant frequency support when required proposes an increase in the overall marginal operation cost of contemporary networks. Consequently, depreciation of operation costs by optimizing the required SI in the network is inevitable. Therefore, this paper proposes a flexible SI optimization method. The algorithm developed in the proposed method minimizes the operation cost of the network by giving flexible SI at a given SG inertia and different sizes of contingency events. The proposed method uses Box’s evolutionary optimizer with a self-tuning capability of the SI control parameters. The proposed method is validated using the modified New England 39-bus network. The results show that provided SIs support the available SG inertia to reduce the RoCoF values and maintain them within acceptable limits to increase the network’s resilience.
在现代电网中,非同步可再生能源机器对传统同步机器的替代越来越多,从而降低了传统同步发电机(SG)的惯性。提供频率支持的合成惯性(SI)控制拓扑正在成为新网络中新的频率控制策略。然而,由于合成惯性(SI)参与了富含可再生能源的电网市场,可在需要时提供即时频率支持,因此会增加现代电网的整体边际运行成本。因此,通过优化网络中所需的 SI 来降低运营成本是不可避免的。因此,本文提出了一种灵活的 SI 优化方法。在给定的 SG 惯性和不同规模的突发事件条件下,该方法所开发的算法通过给出灵活的 SI,最大限度地降低了网络的运营成本。所提出的方法使用了具有自调整 SI 控制参数能力的 Box 进化优化器。利用修改后的新英格兰 39 总线网络对提出的方法进行了验证。结果表明,所提供的 SI 支持可用的 SG 惯性,以降低 RoCoF 值,并将其保持在可接受的范围内,从而提高网络的恢复能力。