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Utility Scale Photovoltaic Plant Variability Studies and Energy Storage Optimization for Ramp Rate Control

机译:公用事业规模的光伏电站可变性研究和能量存储优化,用于斜坡率控制

摘要

A major challenge in integrating high penetrations (>20%) of solar- and wind-energy rests in the grid's ability to cope with the intrinsic variability of these renewable resources. Although such high levels of penetration may be a decade or two away in most operating regions, we must find measures to manage the variability of these sources, especially when conventional market-based approaches are exhausted or ineffective. Furthermore, besides assuring reliability, effective integration of high levels of solar- and wind-power can reduce the `hidden' environmental costs and emissions associated with larger than necessary backup capacity. With large-scale PV plants (>250 MW) becoming significant generators on the grid in the near future, system operators became concerned about the plants' inherent variability, and questions were raised regarding the predictability and reliability of the output from such PV plants. In the first part of this research, the variability in the power output of six PV plants in the United States and Canada, with a total installed capacity of 195 MW (AC), is characterized. A new metric called the Daily Aggregate Ramp (DAR) is introduced to quantify, categorize, and compare daily variability across multiple sites. With this metric, and by harmonizing for climatic differences across the plants, we quantified the effect of geographic dispersion in reducing the cloud-induced power fluctuations. In addition, the reduction in variability was assessed by simulating a step by step increase of the plant size at the same location, using individual inverter data. Our data analysis showed maximum ramp rates 0.7, 0.58, 0.53, and 0.43 times the plant's capacity for 5, 21, 48, and 80 MW (AC) plants, respectively. After the variability in plant outputs was understood and quantified, we investigated algorithms for operating Energy Storage Units (ESU) to perform ramp rate control at the plant level. This task is designed to support proposed plans of grid balancing authorities to deal with ramps of variable energy resources (i.e., solar and wind). ESUs can be used to mitigate penalty fees caused by sharp ramps and perhaps allow for additional revenue streams by participating in grid balancing markets (e.g. frequency regulation). Consequently, we focused on building and optimizing ESU dispatch models for controlling ramp rates of individual PV plants within predetermined levels. The model comprised dispatch strategies tailored to specific fast response ESU technologies (e.g., flywheels, capacitors, batteries). The optimization involved trial and error testing of different combinations of ESU technologies, power and energy capacities, dispatch strategies and violation reduction requirements. For four PV plants (5, 21, 30.24 and 80 MW) in various North American locations, we found a required ESU power capacity of 2.2, 9, 12 and 22 MW respectively, to mitigate 99% of the violations of a 10%/minute ramp rate limit. These ESU capacities may add capital costs of about $0.35-0.63 per Watt PV for the 80 MW plant and $0.56-0.94 per Watt PV for the 5 MW plant. Lowering the mitigation requirement to 90% reduces the necessary ESU power capacity (and per Watt PV costs) to 1.1 MW ($0.27), 4.4 MW ($0.27), 6.4 MW ($0.27) and 10.8 MW ($0.18), respectively. Curtailment of power at the inverter during upward ramps reduces the number of violations even further and effectively decreases the necessary ESU capacity to approximately: 0.8, 3.1, 4.5, and 7.6 MW (for the 90% violation mitigation). It is noted that the reported ESU capacity additions and associated costs are based on the assumption of no forecasting or only a one-minute ahead forecasting of cloud-induced solar variability. If forward time forecasting is available, the optimization we developed should result in lower ESU capacity requirements as gradual ramp rate controls could be implemented in advance. Another way to reduce the costs associated with ramp-rate controls is to use the ESU for other revenue-generating activities, such as frequency regulation for which markets exist in different operating regions (e.g. the Real-Time Market of the New York Independent System Operator (NYISO)). Since ramp rate violations in the various facilities we studied, occurred in less than 2% of the time during the year, such additional uses of ESUs are possible.
机译:整合高渗透率(> 20%)的太阳能和风能的主要挑战在于电网应对这些可再生资源固有变化的能力。尽管在大多数运营区域中,如此高的渗透率可能相隔十年或两年,但我们必须找到措施来管理这些来源的可变性,尤其是当传统的基于市场的方法已经用尽或无效时。此外,除了确保可靠性外,高水平的太阳能和风能的有效整合还可以减少“隐式”的环境成本和与多余的备用容量相关的排放。随着大型光伏电站(> 250 MW)在不久的将来成为电网上的重要发电机,系统运营商开始担心电站的固有可变性,并提出了有关此类光伏电站输出的可预测性和可靠性的疑问。在本研究的第一部分中,对美国和加拿大的六个光伏电站(总装机容量为195 MW(AC))的输出功率变化进行了表征。引入了一种称为“每日汇总坡度(DAR)”的新指标,以量化,分类和比较多个站点之间的每日变化。使用此度量标准,并通过协调植物之间的气候差异,我们量化了地理分散在减少云引起的功率波动中的作用。此外,使用单个逆变器数据,通过在相同位置逐步模拟工厂规模的增加来评估可变性的降低。我们的数据分析显示,最大斜率分别是5、21、48和80 MW(AC)电厂容量的0.7、0.58、0.53和0.43倍。在了解并量化了工厂输出的可变性之后,我们研究了用于运行储能单元(ESU)来在工厂级别执行斜坡率控制的算法。该任务旨在支持电网平衡管理机构提出的计划,以应对可变能源(即太阳能和风能)的倾斜。 ESU可以用来减轻急剧增加带来的罚款,并且可以通过参与电网平衡市场(例如频率调节)来增加收入。因此,我们专注于建立和优化ESU调度模型,以将单个光伏电站的升温速率控制在预定水平内。该模型包括针对特定快速响应ESU技术(例如飞轮,电容器,电池)量身定制的调度策略。优化包括对ESU技术,电力和能源容量,调度策略和减少违规要求的不同组合进行试错测试。对于北美各个地区的四个光伏电站(5、21、30.24和80 MW),我们发现所需的ESU功率容量分别为2.2、9、12和22 MW,从而减轻了10%/分钟斜坡速率限制。这些ESU能力可能会增加80 MW电厂每瓦特PV约0.35-0.63美元的资本成本和5 MW电厂每瓦特PV 0.56-0.94美元的资本成本。将缓解要求降低到90%分别将必要的ESU功率容量(以及每瓦PV成本)分别降低到1.1兆瓦(0.27美元),4.4兆瓦(0.27美元),6.4兆瓦(0.27美元)和10.8兆瓦(0.18美元)。向上斜坡期间逆变器功率的减少进一步减少了违规次数,有效地将必要的ESU容量降低到大约:0.8、3.1、4.5和7.6 MW(对于90%的违规缓解)。请注意,报告的ESU容量增加和相关成本是基于没有预测或仅提前一分钟预测云引起的太阳变化的假设。如果可以提供远期预报,我们开发的优化将导致对ESU容量的需求降低,因为可以提前实施渐进式斜率控制。降低与斜坡率控制相关的成本的另一种方法是将ESU用于其他创收活动,例如对频率进行调节,针对这些频率,不同运营区域中存在市场(例如,纽约独立系统运营商的实时市场) (NYISO))。由于我们研究的各种设施中违反斜坡率的情况发生的时间不到一年的2%,因此可能会额外使用ESU。

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    van Haaren Rob;

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  • 年度 2014
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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