摘要翻译：
目前，大多数ISO采用报价成本最小化(OCM)拍卖机制，使报价总成本最小化，然后采用基于位置边际价格（LMPs）或基于市场清算价格(MCP)的结算规则来确定对承诺单位的支付，这与拍卖机制不兼容，因为最小化成本与结算规则计算的支付成本不同。这种不一致可以急剧增加支付成本。另一方面，支付成本最小化(PCM)拍卖机制消除了这种不一致性；然而，PCM问题是一个非线性自参考NP难问题，计算量很大。针对基于价格的市场出清问题中快速增长的车辆到电网(V2G)渗透带来的额外复杂性，提出了PCM问题的混合整数非线性规划(MINLP)公式，并提出了一种基于广义benders分解(GBD)的V2G集成PCM问题的求解方法，并通过实例验证了该方法在收敛性和计算效率方面的良好性能。本文提出的基于GBD的模型处理方法可以处理决策变量和约束条件增加的放大模型，为PCM机制在大规模电力系统市场出清中的应用提供了便利。研究了V2G技术对OCM和PCM机制在MCPs和支付方面的影响，并利用数值结果比较了这两种机制的性能。
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英文标题：
《Price-Based Market Clearing with V2G Integration Using Generalized
  Benders Decomposition》
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作者：
Reza Jamalzadeh, Sajjad Abedi, Masoud Rashidinejad, and Mingguo Hong
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最新提交年份：
2018
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分类信息：

一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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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：
  Currently, most ISOs adopt offer cost minimization (OCM) auction mechanism which minimizes the total offer cost, and then, a settlement rule based on either locational marginal prices (LMPs) or market clearing price (MCP) is used to determine the payments to the committed units, which is not compatible with the auction mechanism because the minimized cost is different from the payment cost calculated by the settlement rule. This inconsistency can drastically increase the payment cost. On the other hand, payment cost minimization (PCM) auction mechanism eliminates this inconsistency; however, PCM problem is a nonlinear self-referring NP-hard problem which poses grand computational burden. In this paper, a mixed-integer nonlinear programing (MINLP) formulation of PCM problem are presented to address additional complexity of fast-growing penetration of Vehicle-to-Grid (V2G) in the price-based market clearing problem, and a solution method based on the generalized benders decomposition (GBD) is then proposed to solve the V2G-integrated PCM problem, and its favorable performance in terms of convergence and computational efficiency is demonstrated using case studies. The proposed GBD-based method can handle scaled-up models with the increased number of decision variables and constraints which facilitates the use of PCM mechanism in the market clearing of large-scale power systems. The impact of using V2G technologies on the OCM and PCM mechanisms in terms of MCPs and payments is also investigated, and by using numerical results, the performances of these two mechanisms are compared.
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PDF链接：
https://arxiv.org/pdf/1806.10684