摘要翻译：
在商业建筑密集的大都市地区，电力供应是严格的，尤其是在营业时间。使用电池的需求侧管理是一个很有希望的解决方案，以缓解高峰需求，但长的回报时间为大规模采用创造了障碍。在本文中，我们为建筑业主开发了一个设计阶段电池寿命周期成本评估工具和一个运行时控制器，并考虑了电池的退化。在设计阶段，假设对建筑物荷载分布的完美知识来估计理想的投资回收期。在运行时，采用随机规划和负荷预测来解决负荷中的不确定性，以实现电池的最佳运行。为了验证，我们使用纽约市服务的真实资费模型、Zn/MnO2电池和最先进的建筑模拟工具进行了数值实验。实验结果表明，在设计阶段评估和运行时控制之间存在较小的差距。为了进一步验证所提出的方法，我们应用了相同的资费模型，并对九个天气区和三类商业建筑进行了数值试验。实验结果表明，与通常的浅放电电池防止明显退化的做法相反，最佳深放电电池可以获得很好的回报时间。
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英文标题：
《Battery Life-Cycle Optimization and Runtime Control for Commercial
  Buildings Demand Side Management: A New York City Case Study》
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作者：
Yubo Wang, Zhen Song, Valerio De Angelis and Sanjeev Srivastava
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In metropolitan areas populated with commercial buildings, electric power supply is stringent especially during business hours. Demand side management using battery is a promising solution to mitigate peak demands, however long payback time creates barriers for large scale adoption. In this paper, we have developed a design phase battery life-cycle cost assessment tool and a runtime controller for the building owners, taking into account the degradation of battery. In the design phase, perfect knowledge on building load profile is assumed to estimate ideal payback time. In runtime, stochastic programming and load predictions are applied to address the uncertainties in loads for producing optimal battery operation. For validation, we have performed numerical experiments using the real-life tariff model serves New York City, Zn/MnO2 battery, and state-of-the-art building simulation tool. Experimental results shows a small gap between design phase assessment and runtime control. To further examine the proposed methods, we have applied the same tariff model and performed numerical experiments on nine weather zones and three types of commercial buildings. On contrary to the common practice of shallow discharging battery for preventing phenomenal degradation, experimental results show promising payback time achieved by optimally deep discharge a battery.
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PDF链接：
https://arxiv.org/pdf/1808.00095