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雷达卡
摘要翻译：
本文旨在研究大规模电动汽车(EV)充电与电力系统之间的相互作用。我们讨论了与电动汽车充电和融入电力系统有关的三个重要问题：（1）充电站的设置，(2)定价政策和能源管理策略，(3)电力交易市场和配电网络设计，以促进电动汽车和可再生能源(RES)融入电力系统。针对充电站布局问题，提出了一种基于多阶段消费者行为的电动汽车渗透率递增的布局策略，并将电动汽车充电行业建模为一个由少数充电服务提供商（Olidopolists）主导的寡头垄断市场。通过求解贝叶斯博弈，得到了每个服务提供商的最优布局策略。在电动汽车充电站的定价和能源管理方面，我们为充电服务提供商提供指导，以确定充电价格和管理电力储备，以平衡提高盈利能力、提高客户满意度和减少对电力系统的影响这三个相互竞争的目标。采用随机动态规划(SDP)算法和贪心算法（基准算法）推导出电价和购电策略。我们设计了一个新的电力交易市场和配电网，它支持无缝的RES集成、电网到车辆(G2V)、车辆到电网(V2G)、车辆到车辆(V2V)以及分布式发电(DG)和存储。我们将共享经济模式应用于电力部门，以刺激不同实体交换和货币化其未充分利用的电力。考虑用户剩余、电网阻塞和经济调度等因素，提出了一种基于适应度得分(FS)的供需匹配算法。
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英文标题：
《Engineering and Economic Analysis for Electric Vehicle Charging
Infrastructure --- Placement, Pricing, and Market Design》
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作者：
Chao Luo
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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：
This dissertation is to study the interplay between large-scale electric vehicle (EV) charging and the power system. We address three important issues pertaining to EV charging and integration into the power system: (1) charging station placement, (2) pricing policy and energy management strategy, and (3) electricity trading market and distribution network design to facilitate integrating EV and renewable energy source (RES) into the power system. For charging station placement problem, we propose a multi-stage consumer behavior based placement strategy with incremental EV penetration rates and model the EV charging industry as an oligopoly where the entire market is dominated by a few charging service providers (oligopolists). The optimal placement policy for each service provider is obtained by solving a Bayesian game. For pricing and energy management of EV charging stations, we provide guidelines for charging service providers to determine charging price and manage electricity reserve to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on the power system. Two algorithms --- stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) are applied to derive the pricing and electricity procurement strategy. We design a novel electricity trading market and distribution network, which supports seamless RES integration, grid to vehicle (G2V), vehicle to grid (V2G), vehicle to vehicle (V2V), and distributed generation (DG) and storage. We apply a sharing economy model to the electricity sector to stimulate different entities to exchange and monetize their underutilized electricity. A fitness-score (FS)-based supply-demand matching algorithm is developed by considering consumer surplus, electricity network congestion, and economic dispatch.
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PDF链接：
https://arxiv.org/pdf/1808.03897
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