包含实时动态托管能力影响的主动配电网运行可靠性和非确定弹性估计

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS
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引用次数: 0

摘要

人们日益认识到,主动配电网络对实现可持续发展目标至关重要。传统上,托管容量被认为是规划分布式能源资源整合的静态措施。这项工作引入了动态寄存容量的概念,即根据不稳定的现代电网条件,反复重新评估寄存容量。动态托管容量的引入有助于测试从最小到 100 % 的功率注入变化,维持电力系统的管理参数限制。这就需要对运行可靠性进行评估,并提高态势感知能力,以实现最佳的功率注入和平衡。为实现基于动态托管能力的运行可靠性分析,提出了基于概率分布函数的混合蒙特卡罗模拟。由于该方法可在各种不确定因素下对系统性能进行全面、准确的评估,因此太阳能光伏发电和负载调整的改善率达到 85-90%。该框架的验证包括预测随时间变化的运行可靠性指数,以及与时间无关的可靠性指数,即动态负载损失概率、动态负载损失预期、动态负载损失持续时间、动态负载损失频率、动态电网裕度和动态电网依赖性。这使得电网裕度的评估结果提高了 30%,促进了可靠的不确定性处理能力。此外,由于太阳能光伏分布式能源资源的不确定性,还提出了期望最大化算法来评估非确定弹性。非确定性弹性评估测试了 80% 的反弹率,显示了更好的适应性和鲁棒性。整个分析在 MATLAB 中进行,使用台风硬件在环实时平台进行了验证,并与现有文献进行了比较,以证明其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity
Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.
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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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