Reliability assessment of generation capacity in modern power systems via analytical methodologies

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-22 DOI:10.1016/j.segan.2024.101509
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Abstract

With the recent transition to a low-carbon electrical power system (EPS), the large-scale utilization of renewable energy resources in electrical power generation introduces a substantial amount of uncertainty on the generation side of the EPS. This uncertainty, along with the inherent uncertainty of electricity demand, makes assessing generation reliability a very computationally intensive process. To enhance the computation efficiency of EPS generation reliability assessment, it is crucial to have an efficient probabilistic model of available generation capacities that strikes a balance between improved computational performance and model accuracy. In this paper, various probabilistic models are proposed to characterize the variability and uncertainty of conventional and renewable power generations (photovoltaic and wind). On the basis of these models, an analytical formulation of probabilistic reliability indices (RIs) is implemented. The computation time and accurate RIs values found using the Monte Carlo simulation method serve as the basis for reporting solving time improvements with corresponding losses in the accuracy of the RIs for different analytical methodologies. The results of multiple case studies of an EPS are presented, considering various combinations of conventional and renewable generation capacity, levels of renewable power penetration, and system reliability levels. The results indicate the practical implementation of analytical assessment methodologies compared to the simulation method in terms of accuracy and computational effort. This study is of immediate relevance and potential importance to operational reliability and generation expansion planning studies in EPSs.

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通过分析方法评估现代电力系统发电能力的可靠性
近年来,随着向低碳电力系统(EPS)的过渡,大规模利用可再生能源发电给 EPS 的发电侧带来了大量不确定性。这种不确定性加上电力需求的固有不确定性,使得评估发电可靠性成为一个非常耗费计算的过程。为了提高 EPS 发电可靠性评估的计算效率,必须建立一个有效的可用发电能力概率模型,在提高计算性能和模型准确性之间取得平衡。本文提出了各种概率模型,以描述常规发电和可再生能源发电(光伏发电和风力发电)的可变性和不确定性。在这些模型的基础上,实现了概率可靠性指数(RIs)的分析表述。使用蒙特卡洛模拟法计算的时间和准确的可靠性指数值是报告不同分析方法的解算时间改进和相应的可靠性指数准确性损失的基础。本文介绍了 EPS 的多个案例研究结果,考虑了常规发电和可再生能源发电能力的不同组合、可再生能源发电渗透率水平以及系统可靠性水平。结果表明,与模拟方法相比,分析评估方法在准确性和计算工作量方面都具有实用性。这项研究对 EPS 的运行可靠性和发电扩展规划研究具有直接意义和潜在重要性。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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