Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves
Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves
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DOI：
10.1016/j.apenergy.2017.08.141
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发表时间：
2017-11
期刊：
影响因子：
11.2
通讯作者：
Daniel Burillo;M. Chester;B. Ruddell;N. Johnson
中科院分区：
文献类型：
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作者：
Daniel Burillo;M. Chester;B. Ruddell;N. Johnson
气候非平稳性是电力基础设施可靠性面临的挑战；破纪录的热浪严重影响了峰值需求，降低了应急能力，并使城市面临因组件故障和安全威胁而停电的风险。美国电网在各种负荷、天气和电能质量条件下都能安全运行。然而，预计环境温度的升高可能会使电网的运行条件超出当前的可靠性公差范围。长期预测的进步，包括对气温上升和更严重热浪的预测，为推进长期基础设施规划的风险管理方法提供了机会。这在美国西南部尤为明显，这是一个相对炎热的地区，预计将经历显著的温度升高，影响电力负荷、发电和输送系统。发电能力通常是为了满足90个百分位（T90）最热的峰值需求，加上至少15%的额外储备，但如果气温高于预期，这可能不足以确保可靠的电力服务。这种ist90规划方法的问题在于，它需要一个固定的气候才能完全有效。实际上，年温差对系统性能的影响可能超过15%。目前的长期基础设施规划和风险管理过程是有偏见的气候数据选择，可能会严重低估峰值需求，高估发电能力，并导致热浪期间的重大停电。本研究利用缩小尺度的全球气候模型（GCMs）来评估非平平性对气温预报的影响，并开发了一种新的高级统计方法，与以前的预测方法相比，考虑了对峰值需求、发电量和当地储备边际（lrm）的后续影响。IPCC RCPs 4.5和8.5中对气温的预估是，到本世纪末可能升高6°C，亚利桑那州凤凰城和加利福尼亚州洛杉矶的最高温度分别为58°C和56°C。在最热的情况下，我们估计两个都会区的lrm将比各自的t90平均低30%，在洛杉矶（净进口国）的情况下，将需要5吉瓦的额外电力来满足电力需求。我们通过创建基于普通交流机组物理特性的峰值需求结构方程模型（SEM）来计算这些值；在历史数据不存在的前所未有的条件下，基于物理的模型对于预测需求是必要的。在25-40°C（104°F）范围内，SEM对峰值需求的预测接近于直线回归方法，但在较高温度下偏离较低。根据不同技术的电学和热学性能特点，建立了电厂发电容量降额因子模型。最后，我们讨论了降低LRM短缺风险的几个战略选择；包括实施技术、市场激励和城市形式，以减少峰值负荷和人均负荷变化，以及它们与其他几个利益相关者目标的权衡。
Climate non-stationarity is a challenge for electric power infrastructure reliability; recordbreaking heat waves significantly affect peak demand [1], lower contingency capacities, and expose cities to risk of blackouts due to component failures and security threats. The United States’ electric grid operates safely for a wide range of load, weather, and power quality conditions. Projected increases in ambient air temperatures could, however, create operating conditions that place the grid outside the boundaries of current reliability tolerances. Advancements in long-term forecasting, including projections of rising air temperatures and more severe heat waves, present opportunities to advance risk management methods for long-term infrastructure planning. This is particularly evident in the US Southwest—a relatively hot region expected to experience significant temperature increases affecting electric loads, generation, and delivery systems. Generation capacity is typically built to meet the 90th percentile (T90) hottest peak demand, plus an additional reserve margin of least 15%, but that may not be sufficient to ensure reliable power services if air temperatures are higher than expected. The problem with thisT90planning approach is that it requires a stationary climate to be completely effective. In reality, annual temperature differences can have more than a 15% effect on system performance. Current long-term infrastructure planning and risk management processes are biased climate data choices that can significantly underestimate peak demand, overestimate generation capacity, and result in major power outages during heat waves.This study used downscaled global climate models (GCMs) to evaluate the effects of non-stationarity on air temperature forecasts, and a new high-level statistical approach was developed to consider the subsequent effects on peak demand, power generation, and local reserve margins (LRMs) compared to previous forecasting methods. Air temperature projections in IPCC RCPs 4.5 and 8.5 are that increases up to 6 °C are possible by the end of century, with highs of 58 °C and 56 °C in Phoenix, Arizona and Los Angeles, California respectively. In the hottest scenarios, we estimated that LRMs for the two metro regions would be on average 30% less than at respectiveT90s, which in the case of Los Angeles (a net importer) would require 5 GW of additional power to meet electrical demand. We calculated these values by creating a structural equation model (SEM) for peak demand based on the physics of common AC units; physics-based models are necessary to predict demand under unprecedented conditions for which historical data do not exist. The SEM forecasts for peak demand were close to straight-line regression methods as in prior literature from 25–40 °C (104 °F), but diverged lower at higher temperatures. Power plant generation capacity derating factors were also modeled based on the electrical and thermal performance characteristics of different technologies. Lastly, we discussed several strategic options to reduce the risk of LRM shortages; including implementing technology, market incentives, and urban forms that reduce peak load and load variance per capita as well as their tradeoffs with several other stakeholder objectives.