智能电网配用电信息接入与负载调度研究
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摘要
电力网络是一个大型的互联的基础设施,负责把电能从发电站输送到千家万户。在过去的几十年间,虽然信息技术和控制技术发生了很大的变化,但日渐老化的电力网络并没有跟上技术变革的步伐。作为新一代的电力网络,智能电网以自动化的方式应用信息与通信技术,实现灵活、可靠、有效、安全、经济与环境友好的目标。
配用电网是智能电网的咽喉,保证智能配用电网中实时可靠的信息接入与合理优化的负载调度也是建设我国“坚强智能电网”的基石。来自智能电表的抄表数据将会多达数以万计的太字节,这对于智能电网通信网络收集、传输和存储如此大规模的数据带来了巨大的挑战。亟需先进的无线通信技术,如认知无线电技术,以保证抄表数据实时可靠的传输。另一方面,在未来的几十年里,用户的用电量还将继续增长。此外,电动汽车的广泛应用还可能使得电能需求量翻倍,合理的负载调度迫在眉睫。本文结合该方向的最新研究成果,以智能电网配用电信息接入与负载调度的关键技术研究为主要内容。本文的主要工作和贡献包括以下几个方面：
1.简要回顾了智能配用电网信息接入与负载调度问题的相关研究背景、概述和国内外研究现状。
2.研究基于认知无线电技术的配用电信息接入。为了克服频谱资源有限和利用不充分的矛盾,将认知无线电技术引入智能配用电网的通信,提高信息接入和传输的实时性与可靠性。由于电池供电的频谱感知传感器受能量约束,能量有效性成为传感器辅助认知无线电网络中的关键问题。优化调度各传感器的工作时间能够有效地延长网络寿命。把能量有效的协同频谱感知构建成一个调度问题,并证明其为NP完全问题。首先用贪心降解法将原问题简化为一系列整数线性规划子问题,然后分别提出三种不同的算法：隐枚举法、普通贪心法和λ贪心法来求解该子问题。仿真结果验证了上述算法的性能,同时考察了可调参数对算法性能的影响。
3.研究通信质量对配用电性能的影响。将认知无线电引入智能电网以改善通信质量。通过频谱感知与信道切换技术,智能电表可以决定通过原有的非授权信道或另外的授权信道传输数据,以减少通信中断。考虑到频谱感知的能耗以及不可靠通信对控制性能的影响,构建了感知-性能的权衡问题以期通过较小的通信代价获得较好的控制性能，为绿色智能电网铺平道路。分析了通信中断对需求响应管理控制性能的影响,包括减少供电商的利润和系统性能,但并不一定减少用户的效用。基于能量检测器进行频谱感知,证明存在唯一的最优频谱感知时间达到感知-性能之间最好的权衡,同时保证授权信道被充分保护。相关仿真结果验证了对应的理论分析。
4.研究基于博弈论和带时间耦合约束的负载调度。对于居民用户的用电调度问题,同时考虑用户之间的博弈关系以及时间耦合约束,构建成带耦合约束的博弈问题。提出的求解算法分为两部分：首先,通过对偶分解把原始问题解耦；然后,对于解耦后的博弈问题,通过最适反应法得到纳什均衡。该算法可扩展为在线版本,从而减少电价预测误差带来的影响。仿真结果表明提出的算法能够有效地转移高峰负载和降低峰均比,同时还能给用户带来利益。另外还考察了该算法的可扩展性以及用户个数对其性能的影响。
5.研究考虑未来电价不确定性的负载调度。在实时电价环境下,由于未来电价的不确定性,负载调度构建为带期望和时间耦合约束的优化问题。由于应用随机动态规划的算法复杂度太高很难得到显式解,提出用对偶分解和随机梯度的方法解决该问题。首先,把原始问题对偶分解为一系列单独的子问题,然后基于对未来电价的统计知识,每个子问题中的电价不确定性由随机梯度法处理。此外,还提出用在线算法进一步降低电价预测误差带来的影响。数值仿真结果验证了对应的理论分析。
最后对全文进行了总结,并对进一步的研究工作进行了展望。
The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. During the past decades, although the information and control technologies have changed a lot, the aging traditional grid still lagged. As widely considered to be the next generation of the power grid, the smart grid uses information and communications technology in an automated fashion to improve the agility, reliability, efficiency, security, economy and environ-mental friendliness.
Information access and load scheduling in the smart distribution grid are at the core of the future smart grid. The meter data from smart meters will be up to tens of thousands of terabytes in near future, which poses a significant challenge for smart grid communication networks to collect, transmit and store such large-scale data. Novel wireless communication technologies, such as cognitive radio, are expected to ensure reliable and real-time data transmission. On the other hand, the significant growth is expected in electricity consumption in the coming decades. Besides, the widespread adoption of electric vehicles will potentially double the energy demand. The smart distribution grid aims to address the ever-increasing load through appropriate scheduling. Based on the state-of-the-art study, this dissertation researches on communication and load scheduling in the smart distribution grid. The main work and contributions are summarized as follows:
1. A brief review of the background, overview, and related works on smart grid communication and load scheduling is provided.
2. Research on cognitive radio enabled smart grid communication. To tackle the conflict be-tween spectrum scarcity and under-utilization, cognitive radio is leveraged to improve the communication quality. Due to the energy constraint of battery-powered sensors, energy-efficiency arises as a critical issue in sensor-aided cognitive radio networks. An optimal scheduling of each sensor active time can effectively extend the network lifetime. The prob-lem of energy-efficient cooperative spectrum sensing is formulated as a scheduling problem, which is proved to be NP-complete. Greedy Degradation is employed to degrade it into a linear integer programming problem, and three approaches namely Implicit Enumeration, General Greedy and λ-Greedy are proposed to solve the subproblem. Simulation results are presented to verify the performance of our approaches, as well as to study the effect of adjustable parameters on the performance.
3. Research on the impact of communication quality on control performance in smart distri-bution grid. Cognitive radio is introduced into smart grid to improve the communication quality. By means of spectrum sensing and channel switching, smart meters can decide to transmit data on either an original unlicensed channel or an additional licensed channel, so as to reduce the communication outage. Considering the energy cost taxed by spectrum sensing together with the control performance degradation incurred by imperfect communications, the sensing-performance tradeoff problem is formulated between better control performance and lower communication cost, paving the way towards green smart grid. The impact of communication quality on control performance is also analyzed, which reduces the profit of power provider and the social welfare, although it may not always decrease the profit of power consumer. By employing the energy detector, it is proved that there exists a unique optimal sensing time which yields the maximum tradeoff revenue, under the constraint that the licensed channel is sufficiently protected. Numerical results are provided to validate theoretical analysis.
4. Research on load scheduling in a coupled-constraint game approach. The residential ener-gy consumption scheduling problem is formulated as a coupled-constraint game by taking the interaction among users and the temporally-coupled constraint into consideration. The proposed solution consists of two parts. Firstly, dual decomposition is applied to transform the original coupled-constraint game into a decoupled one. Then, Nash equilibrium of the decoupled game is proven to be achievable via best response, which is computed by gradient projection. The proposed solution is also extended to an online version, which is able to alleviate the impact of the price prediction error. Numerical results demonstrate that the pro-posed approach can effectively shift the peak-hour demand to off-peak hours, enhance the welfare of each user, and minimize the peak-to-average ratio. The scalability of the approach and the impact of the user number are also investigated.
5. Research on load scheduling with future price uncertainty. Under the real-time pricing en-vironment, due to the uncertainty of future prices, load scheduling is formulated as an opti- mization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, dual decomposition and stochastic gradient are proposed to solve the problem. That is, the pri-mal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, an online approach is proposed to fur-ther alleviate the impact of price prediction error. Numerical results are provided to validate theoretical analysis.
The conclusions are drawn with future work at the end of the dissertation.
配用电网是智能电网的咽喉,保证智能配用电网中实时可靠的信息接入与合理优化的负载调度也是建设我国“坚强智能电网”的基石。来自智能电表的抄表数据将会多达数以万计的太字节,这对于智能电网通信网络收集、传输和存储如此大规模的数据带来了巨大的挑战。亟需先进的无线通信技术,如认知无线电技术,以保证抄表数据实时可靠的传输。另一方面,在未来的几十年里,用户的用电量还将继续增长。此外,电动汽车的广泛应用还可能使得电能需求量翻倍,合理的负载调度迫在眉睫。本文结合该方向的最新研究成果,以智能电网配用电信息接入与负载调度的关键技术研究为主要内容。本文的主要工作和贡献包括以下几个方面：
1.简要回顾了智能配用电网信息接入与负载调度问题的相关研究背景、概述和国内外研究现状。
2.研究基于认知无线电技术的配用电信息接入。为了克服频谱资源有限和利用不充分的矛盾,将认知无线电技术引入智能配用电网的通信,提高信息接入和传输的实时性与可靠性。由于电池供电的频谱感知传感器受能量约束,能量有效性成为传感器辅助认知无线电网络中的关键问题。优化调度各传感器的工作时间能够有效地延长网络寿命。把能量有效的协同频谱感知构建成一个调度问题,并证明其为NP完全问题。首先用贪心降解法将原问题简化为一系列整数线性规划子问题,然后分别提出三种不同的算法：隐枚举法、普通贪心法和λ贪心法来求解该子问题。仿真结果验证了上述算法的性能,同时考察了可调参数对算法性能的影响。
3.研究通信质量对配用电性能的影响。将认知无线电引入智能电网以改善通信质量。通过频谱感知与信道切换技术,智能电表可以决定通过原有的非授权信道或另外的授权信道传输数据,以减少通信中断。考虑到频谱感知的能耗以及不可靠通信对控制性能的影响,构建了感知-性能的权衡问题以期通过较小的通信代价获得较好的控制性能，为绿色智能电网铺平道路。分析了通信中断对需求响应管理控制性能的影响,包括减少供电商的利润和系统性能,但并不一定减少用户的效用。基于能量检测器进行频谱感知,证明存在唯一的最优频谱感知时间达到感知-性能之间最好的权衡,同时保证授权信道被充分保护。相关仿真结果验证了对应的理论分析。
4.研究基于博弈论和带时间耦合约束的负载调度。对于居民用户的用电调度问题,同时考虑用户之间的博弈关系以及时间耦合约束,构建成带耦合约束的博弈问题。提出的求解算法分为两部分：首先,通过对偶分解把原始问题解耦；然后,对于解耦后的博弈问题,通过最适反应法得到纳什均衡。该算法可扩展为在线版本,从而减少电价预测误差带来的影响。仿真结果表明提出的算法能够有效地转移高峰负载和降低峰均比,同时还能给用户带来利益。另外还考察了该算法的可扩展性以及用户个数对其性能的影响。
5.研究考虑未来电价不确定性的负载调度。在实时电价环境下,由于未来电价的不确定性,负载调度构建为带期望和时间耦合约束的优化问题。由于应用随机动态规划的算法复杂度太高很难得到显式解,提出用对偶分解和随机梯度的方法解决该问题。首先,把原始问题对偶分解为一系列单独的子问题,然后基于对未来电价的统计知识,每个子问题中的电价不确定性由随机梯度法处理。此外,还提出用在线算法进一步降低电价预测误差带来的影响。数值仿真结果验证了对应的理论分析。
最后对全文进行了总结,并对进一步的研究工作进行了展望。
The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. During the past decades, although the information and control technologies have changed a lot, the aging traditional grid still lagged. As widely considered to be the next generation of the power grid, the smart grid uses information and communications technology in an automated fashion to improve the agility, reliability, efficiency, security, economy and environ-mental friendliness.
Information access and load scheduling in the smart distribution grid are at the core of the future smart grid. The meter data from smart meters will be up to tens of thousands of terabytes in near future, which poses a significant challenge for smart grid communication networks to collect, transmit and store such large-scale data. Novel wireless communication technologies, such as cognitive radio, are expected to ensure reliable and real-time data transmission. On the other hand, the significant growth is expected in electricity consumption in the coming decades. Besides, the widespread adoption of electric vehicles will potentially double the energy demand. The smart distribution grid aims to address the ever-increasing load through appropriate scheduling. Based on the state-of-the-art study, this dissertation researches on communication and load scheduling in the smart distribution grid. The main work and contributions are summarized as follows:
1. A brief review of the background, overview, and related works on smart grid communication and load scheduling is provided.
2. Research on cognitive radio enabled smart grid communication. To tackle the conflict be-tween spectrum scarcity and under-utilization, cognitive radio is leveraged to improve the communication quality. Due to the energy constraint of battery-powered sensors, energy-efficiency arises as a critical issue in sensor-aided cognitive radio networks. An optimal scheduling of each sensor active time can effectively extend the network lifetime. The prob-lem of energy-efficient cooperative spectrum sensing is formulated as a scheduling problem, which is proved to be NP-complete. Greedy Degradation is employed to degrade it into a linear integer programming problem, and three approaches namely Implicit Enumeration, General Greedy and λ-Greedy are proposed to solve the subproblem. Simulation results are presented to verify the performance of our approaches, as well as to study the effect of adjustable parameters on the performance.
3. Research on the impact of communication quality on control performance in smart distri-bution grid. Cognitive radio is introduced into smart grid to improve the communication quality. By means of spectrum sensing and channel switching, smart meters can decide to transmit data on either an original unlicensed channel or an additional licensed channel, so as to reduce the communication outage. Considering the energy cost taxed by spectrum sensing together with the control performance degradation incurred by imperfect communications, the sensing-performance tradeoff problem is formulated between better control performance and lower communication cost, paving the way towards green smart grid. The impact of communication quality on control performance is also analyzed, which reduces the profit of power provider and the social welfare, although it may not always decrease the profit of power consumer. By employing the energy detector, it is proved that there exists a unique optimal sensing time which yields the maximum tradeoff revenue, under the constraint that the licensed channel is sufficiently protected. Numerical results are provided to validate theoretical analysis.
4. Research on load scheduling in a coupled-constraint game approach. The residential ener-gy consumption scheduling problem is formulated as a coupled-constraint game by taking the interaction among users and the temporally-coupled constraint into consideration. The proposed solution consists of two parts. Firstly, dual decomposition is applied to transform the original coupled-constraint game into a decoupled one. Then, Nash equilibrium of the decoupled game is proven to be achievable via best response, which is computed by gradient projection. The proposed solution is also extended to an online version, which is able to alleviate the impact of the price prediction error. Numerical results demonstrate that the pro-posed approach can effectively shift the peak-hour demand to off-peak hours, enhance the welfare of each user, and minimize the peak-to-average ratio. The scalability of the approach and the impact of the user number are also investigated.
5. Research on load scheduling with future price uncertainty. Under the real-time pricing en-vironment, due to the uncertainty of future prices, load scheduling is formulated as an opti- mization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, dual decomposition and stochastic gradient are proposed to solve the problem. That is, the pri-mal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, an online approach is proposed to fur-ther alleviate the impact of price prediction error. Numerical results are provided to validate theoretical analysis.
The conclusions are drawn with future work at the end of the dissertation.
引文
[1]薛禹胜.综合防御由偶然故障演化为电力灾难——北美“8.14”大停电的警示[J].电力系统自动化,2003.27(18)：1-5.
[2]B Liscouski, W Elliot. Final report on the august 14,2003 blackout in the united states and Canada:Causes and recommendations[R].:U.S.-Canada Power System Outage Task Force,2004.
[3]韩祯祥,曹一家.电力系统的安全性及防治措施[J].电网技术，2004.28(9)：1-6.
[4]侯慧,尹项根,陈庆前,游大海,童光毅,邵德军.南方部分500kv主网架2008年冰雪灾害中受损分析与思考[J].电力系统自动化,2008.32(11)：12-15.
[5]梁志峰,葛睿,董昱,陈刚.印度“7.30”，“7.31”大停电事故分析及对我国电网调度运行工作的启示[J].电网技术,2013.37(7)：1841-1848.
[6]陈树勇,宋书芳,李兰欣,沈杰.智能电网技术综述[J].电网技术,2009.33(8)：1-7.
[7]U.S. Department of Energy. The smart grid:An introduction, http://energy.gov/oe/downloads/smart-grid-introduction-0.
[8]S Massoud Amin, Bruce F Wollenberg. Toward a smart grid:power delivery for the 21 st century[J]. IEEE Power and Energy Magazine,2005.3(5):34-41.
[9]Ali Vojdani. Smart integration[J]. IEEE Power and Energy Magazine,2008.6(6):71-79.
[10]Ali lpakchi, Farrokh Albuyeh. Grid of the future[J]. IEEE Power and Energy Magazine,2009.7(2):52-62.
[11]Hassan Farhangi. The path of the smart grid[J]. IEEE Power and Energy Magazine,2010.8(1):18-28.
[12]Khosrow Moslehi, Ranjit Kumar. A reliability perspective of the smart grid[J]. IEEE Transactions on Smart Grid,2010. 1(1):57-64.
[13]Xi Fang, Satyajayant Misra, Guoliang Xue, Dejun Yang. Smart grid - the new and improved power grid: A survey[J]. IEEE Communications Surveys Tutorials,2012.14(4):944-980.
[14]J. Momoh. Smart Grid:Fundamentals of Design and Analysis[M]. Wiley,2012.
[15]L.T. Berger, K. Iniewski. Smart Grid:Applications, Communications, and Security[M]. Wiley,2012.
[16]余贻鑫,栾文鹏.智能电网[J].电网与清洁能源,2009.25(1)：7-11.
[17]百度百科.智能电网http://baike.baidu.com/view/2222513.htm.
[18]余贻鑫.面向21世纪的智能配电网[J].南方电网技术研究,2006.2(6)：14-16.
[19]Federal Communications Commission. Report of the spectrum efficiency working group[R]. :Spectrum Policy Task Force, Nov.2002.
[20]Simon Haykin. Cognitive radio:brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications,2005.23(2):201-220.
[21]Qing Zhao, Brian M Sadler. A survey of dynamic spectrum access[J]. IEEE Signal Processing Magazine, 2007.24(3):79-89.
[22]Ian F Akyildiz, Won-Yeol Lee, Mehmet C Vuran, Shantidev Mohanty. A survey on spectrum management in cognitive radio networks[J]. IEEE Communications Magazine,2008.46(4):40-48.
[23]Amir Ghasemi, Elvino S Sousa. Spectrum sensing in cognitive radio networks:requirements, challenges and design trade-offs[J]. IEEE Communications Magazine,2008.46(4):32-39.
[24]Ying-Chang Liang, Yonghong Zeng, Edward CY Peh, Anh Tuan Hoang. Sensing-throughput tradeoff for cognitive radio networks[J]. IEEE Transactions on Wireless Communications,2008.7(4):1326-1337.
[25]Won-Yeol Lee, Ian F Akyildiz. Optimal spectrum sensing framework for cognitive radio networks [J]. IEEE Transactions on Wireless Communications,2008.7(10):3845-3857.
[26]Edward Chu Yeow Peh, Ying-Chang Liang, Yong Liang Guan, Yonghong Zeng. Optimization of cooper-ative sensing in cognitive radio networks:a sensing-throughput tradeoff view[J]. IEEE Transactions on Vehicular Technology,2009.58(9):5294-5299.
[27]Wei Zhang, Ranjan Mallik, K Letaief. Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks[J]. IEEE Transactions on Wireless Communications,2009.8(12):5761-5766.
[28]A Ghassemi, S Bavarian, L Lampe. Cognitive radio for smart grid communications[C]//Proc. IEEE S-martGridComm.2010:297-302.
[29]Pedram Samadi, A Mohsenian-Rad, Robert Schober, V Wong, Juri Jatskevich. Optimal real-time pricing algorithm based on utility maximization for smart grid[C]//Proc. IEEE SmartGridComm.2010:415-420.
[30]Na Li, Lijun Chen, Steven H Low. Optimal demand response based on utility maximization in power networks[C]//Proc. IEEE PES-GM.2011:1-8.
[31]Robert C Qiu, Zhen Hu, Zhe Chen, Nan Guo, Raghuram Ranganathan, Shujie Hou, Gang Zheng. Cogni-tive radio network for the smart grid:Experimental system architecture, control algorithms, security, and microgrid testbed[J]. IEEE Transactions on Smart Grid,2011.2(4):724-740.
[32]Rong Yu, Yan Zhang, Stein Gjessing, Chau Yuen, Shengli Xie, Mohsen Guizani. Cognitive radio based hierarchical communications infrastructure for smart grid[J]. IEEE Network,2011.25(5):6-14.
[33]Ruilong Deng, Sabita Maharjan, Xianghui Cao, Jiming Chen, Yan Zhang, Stein Gjessing. Sensing-delay tradeoff for communication in cognitive radio enabled smart grid[C]//Proc. IEEE SmartGridComm. 2011:155-160.
[34]Ruilong Deng, Jiming Chen, Xianghui Cao, Yan Zhang, Sabita Maharjan, Stein Gjessing. Sensing- performance tradeoff in cognitive radio enabled smart grid[J]. IEEE Transactions on Smart Grid,2013. 4(1):302-310.
[35]Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang. Vehicle-to-aggregator interaction game[J]. IEEE Transactions on Smart Grid,2012.3(1):434-442.
[36]E Bloustein. Assessment of customer response to real time pricing[R].:Rutgers-The State University of New Jersey,2005.
[37]A-H Mohsenian-Rad, Vincent WS Wong, Juri Jatskevich, Robert Schober. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid[C]//Proc. IEEE ISGT.2010:1-6.
[38]A Mohsenian-Rad, Vincent WS Wong, Juri Jatskevich, Robert Schober, Alberto Leon-Garcia. Au-tonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid[J]. IEEE Transactions on Smart Grid,2010. 1(3):320-331.
[39]A-H Mohsenian-Rad, Alberto Leon-Garcia. Optimal residential load control with price prediction in real-time electricity pricing environments[J]. IEEE Transactions on Smart Grid,2010.1 (2):120-133.
[40]Pedram Samadi, Hamed Mohsenian-Rad, Robert Schober, Vincent WS Wong. Advanced demand side management for the future smart grid using mechanism design[J]. IEEE Transactions on Smart Grid, 2012.3(3):1170-1180.
[41]Tung T Kim, H Vincent Poor. Scheduling power consumption with price uncertainty[J]. IEEE Transac-tions on Smart Grid,2011.2(3):519-527.
[42]Libin Jiang, Steven Low. Multi-period optimal energy procurement and demand response in smart grid with uncertain supply [C]//Proc. IEEE CDC-ECC.2011:4348-4353.
[43]Ruilong Deng, Zaiyue Yang, Jiming Chen, Navid Rahbari Asr, Mo-Yuen Chow. Residential energy con-sumption scheduling:A coupled-constraint game approach[J]. IEEE Transactions on Smart Grid,2014. 5(3):1340-1350.
[44]Ruilong Deng, Zaiyue Yang, Jiming Chen. Load scheduling with price uncertainty and coupling con-straints [C]//Proc. IEEE PES-GM.2013:1-5.
[45]Ruilong Deng, Zaiyue Yang, Jiming Chen, Mo-Yuen Chow. Load scheduling with price uncertainty and temporally-coupled constraints in smart grids[J]. IEEE Transactions on Power Systems, DOI:10.1109/T-PWRS.2014.2311127.
[46]ZubairMd Fadlullah, Mostafa M Fouda, Nei Kato, Akira Takeuchi, Noboru Iwasaki, Yousuke Nozaki. To-ward intelligent machine-to-machine communications in smart grid[J]. IEEE Communications Magazine, 2011.49(4):60-65.
[47]Vehbi C Gungor, Dilan Sahin, Taskin Kocak, Salih Ergut. Concettina Buccella, Carlo Cecati, Gerhard P Hancke. Smart grid technologies:communication technologies and standards[J]. IEEE Transactions on Industrial Informatics,2011.7(4):529-539.
[48]Zhong Fan, Parag Kulkarni, Sedat Gormus, Costas Efthymiou, Georgios Kalogridis, Mahesh Sooriya-bandara, Ziming Zhu, Sangarapillai Lambotharan, W Chin. Smart grid communications:Overview of research challenges, solutions, and standardization activities[J]. IEEE Communications Surveys & Tuto-rials,2013.15(1):21-38.
[49]Anindya Majumder, James Caffery Jr. Power line communications[J]. IEEE Potentials,2004.23(4):4-8.
[50]Palak P Parikh, Mitalkumar G Kanabar, Tarlochan S Sidhu. Opportunities and challenges of wireless communication technologies for smart grid applications[C]//in Proc. IEEE PES-GM.2010:1-7.
[51]Yao-Win Hong, W-J Huang, Fu-Hsuan Chiu, C-CJ Kuo. Cooperative communications in resource-constrained wireless networks[J]. IEEE Signal Processing Magazine,2007.24(3):47-57.
[52]Dusit Niyato, Ping Wang. Cooperative transmission for meter data collection in smart grid[J]. IEEE Communications Magazine,2012.50(4):90-97.
[53]David L Donoho. Compressed sensing[J]. IEEE Transactions on Information Theory,2006.52(4):1289-1306.
[54]Husheng Li, Rukun Mao, Lifeng Lai, Robert C Qiu. Compressed meter reading for delay-sensitive and secure load report in smart grid[C]//Proc. IEEE SmartGridComm.2010:114-119.
[55]Husheng Li, Weiyi Zhang. Qos routing in smart grid[C]//Proc. IEEE GLOBECOM.2010:1-6.
[56]Benefits of demand response in electricity markets and recommendations for achieving them[R].:U.S. Department of Energy,2006.
[57]Josep M Guerrero, Poh Chiang Loh, Mukul Chandorkar, Tzung-Lin Lee. Advanced control architec-tures for intelligent microgrids -part I:Decentralized and hierarchical control[J]. IEEE Transactions on Industrial Electronics,2013.60(4):1254-1262.
[58]Josep M Guerrero, Poh Chiang Loh, Tzung-Lin Lee, Mukul Chandorkar. Advanced control architec-tures for intelligent microgrids - part Ⅱ:Power quality, energy storage, and ac/dc microgrids[J]. IEEE Transactions on Industrial Electronics,2013.60(4):1263-1270.
[59]Assessment of demand response and advanced metering[R].:Federal Energy Regulatory Commission, 2012.
[60]P Jazayeri, A Schellenberg, WD Rosehart, JADJ Doudna, SAWS Widergren, D Lawrence, JAMJ Mickey, SAJS Jones. A survey of load control programs for price and system stability[J]. IEEE Transactions on Power Systems,2005.20(3):1504-1509.
[61]M.H. Albadi, EF E1-Saadany. A summary of demand response in electricity markets[J]. Electric Power Systems Research,2008.78(11):1989-1996.
[62]Peter Palensky, Dietmar Dietrich. Demand side management:Demand response, intelligent energy sys-tems, and smart loads[J]. IEEE Transactions on Industrial Informatics,2011.7(3):381-388.
[63]T. Ericson. Direct load control of residential water heaters[J]. Energy Policy,2009.37(9):3502-3512.
[64]HA Aalami, M.P. Moghaddam, GR Yousefi. Demand response modeling considering interrupt-ible/curtailable loads and capacity market programs[J]. Applied Energy,2010.87(1):243-250.
[65]J. Saebi, J. Mohammadi, H. Taheri, S.S. Nayer. Demand bidding/buyback modeling and its impact on market clearing price[C]//Proc. IEEE EnergyCon.2010:791-796.
[66]R. Tyagi, J.W. Black. Emergency demand response for distribution system contingencies[C]//Proc. IEEE PES Transmission and Distribution Conference and Exposition.2010:1-4.
[67]S Datchanamoorthy, S Kumar, Y Ozturk, G Lee. Optimal time-of-use pricing for residential load con-trol[C]//Proc. IEEE SmartGridComm.2011:375-380.
[68]Ontario Energy Board. Time-of-use (tou) prices for smart meters. http://www.ontarioenergyboard.ca/OEB/Consumers/.
[69]K. Herter. Residential implementation of critical-peak pricing of electricity [J]. Energy Policy,2007. 35(4):2121-2130.
[70]H. Allcott. Real time pricing and electricity markets[J]. Harvard University, Jan,2009.
[71]Ameren Illinois Power Corporation. Real time prices for residential customer. http://www2.ameren.corn/RetailEnergy/realtimeprices.aspx.
[72]Severin Borenstein. Equity effects of increasing-block electricity pricing[R].:Center for the Study of Energy Markets, University of California Energy Institute,2008.
[73]Danping Ren, Hui Li, Yuefeng Ji. Home energy management system for the residential load control based on the price prediction[C]//Proc. IEEE GreenCom.2011:1-6.
[74]Francesco De Angelis, Matteo Boaro, Danilo Fuselli, Stefano Squartini, Francesco Piazza, Qinglai Wei. Optima] home energy management under dynamic electrical and thermal constraints[J]. IEEE Transac-tions on Industrial Informatics,2013.9(3):1518-1527.
[75]Libin Jiang, Steven Low. Real-time demand response with uncertain renewable energy in smart grid[C]//Proc. IEEE Allerton Conference on Communication, Control, and Computing.2011:1334-1341.
[76]Chris Hutson, Ganesh Kumar Venayagamoorthy, Keith A Corzine. Intelligent scheduling of hybrid and electric vehicle storage capacity in a parking lot for profit maximization in grid power transaction-s[C]//Proc. IEEE Energy 2030 Conference.2008:1-8.
[77]S Hatami, M Pedram. Minimizing the electricity bill of cooperative users under a quasi-dynamic pricing model[C]//Proc. IEEE SmartGridComm.2010:421-426.
[78]Supun Tiptipakorn, Wei-Jen Lee. A residential consumer-centered load control strategy in real-time elec-tricity pricing environment[C]//Proc. IEEE NAPS.2007:505-510.
[79]Pengwei Du, Ning Lu. Appliance commitment for household load scheduling[J]. IEEE Transactions on Smart Grid,2011.2(2):411-419.
[80]Hadi Goudarzi, Safar Hatami, Massoud Pedram. Demand-side load scheduling incentivized by dynamic energy prices[C]//Proc. IEEE SmartGridComm.2011:351-356.
[81]Juan Manuel Carrasco, Leopoldo Garcia Franquelo, Jan T Bialasiewicz, Eduardo Galvan, RC Portillo Guisado, Ma AM Prats, Jose Ignacio Leon, Narciso Moreno-Alfonso. Power-electronic systems for the grid integration of renewable energy sources:A survey [J]. IEEE Transactions on Industrial Electronics, 2006.53(4):1002-1016.
[82]Jan T Bialasiewicz. Renewable energy systems with photovoltaic power generators:Operation and mod-eling[J]. IEEE Transactions on Industrial Electronics,2008.55(7):2752-2758.
[83]Hristiyan Kanchev, Di Lu, Frederic Colas, Vladimir Lazarov, Bruno Francois. Energy management and operational planning of a microgrid with a pv-based active generator for smart grid applications[J]. IEEE Transactions on Industrial Electronics,2011.58(10):4583-4592.
[84]Frede Blaabjerg, Remus Teodorescu, Marco Liserre, Adrian V Timbus. Overview of control and grid synchronization for distributed power generation systems[J]. IEEE Transactions on Industrial Electronics, 2006.53(5):1398-1409.
[85]Carlo Cecati, Costantino Citro, Antonio Piccolo, Pierluigi Siano. Smart operation of wind turbines and diesel generators according to economic criteria[J]. IEEE Transactions on Industrial Electronics,2011. 58(10):4514-4525.
[86]Andrew J Roscoe, G Ault. Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response[J].1ET Renewable Power Generation,2010.4(4):369-382.
[87]Miao He, Sugumar Murugesan, Junshan Zhang. Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration[C]//Proc. IEEE INFOCOM.2011:461-465.
[88]Michael Caramanis, Justin M Foster. Management of electric vehicle charging to mitigate renewable generation intermittency and distribution network congestion[C]//Proc. IEEE CDC/CCC.2009:4717-4722.
[89]JA Pecas Lopes, PM Rocha Almeida, FJ Soares. Using vehicle-to-grid to maximize the integration of intermittent renewable energy resources in islanded electric grids[C]//Proc. IEEE Clean Electrical Power. 2009:290-295.
[90]Ahmed Yousuf Saber, Ganesh Kumar Venayagamoorthy. Plug-in vehicles and renewable energy sources for cost and emission reductions[J]. IEEE Transactions on Industrial Electronics,2011.58(4):1229-1238.
[91]Jorge Moreno, Micah E Ortuzar, LW Dixon. Energy-management system for a hybrid electric vehi-cle, using ultracapacitors and neural networks[J]. IEEE Transactions on Industrial Electronics,2006. 53(2):614-623.
[92]Micah Ortuzar, Jorge Moreno, Juan Dixon. Ultracapacitor-based auxiliary energy system for an elec-tric vehicle:Implementation and evaluation[J]. IEEE Transactions on Industrial Electronics,2007. 54(4):2147-2156.
[93]Zahra Amjadi, Sheldon S Williamson. Power-electronics-based solutions for plug-in hybrid electric ve-hicle energy storage and management systems[J]. IEEE Transactions on Industrial Electronics,2010. 57(2):608-616.
[94]Sergio Vazquez, Srdjan M Lukic, Eduardo Galvan, Leopoldo G Franquelo, Juan M Carrasco. Energy storage systems for transport and grid applications[J]. IEEE Transactions on Industrial Electronics,2010. 57(12):3881-3895.
[95]Fabian Kennel, D Gorges, Steven Liu. Energy management for smart grids with electric vehicles based on hierarchical mpc[J]. IEEE Transactions on Industrial Informatics,2013.9(3):1528-1537.
[96]Perukrishnen Vytelingum, Thomas D Voice, Sarvapali D Ramchurn, Alex Rogers, Nicholas R Jennings. Agent-based micro-storage management for the smart grid[C]//Proc.1FAAMAS.2010:39-46.
[97]CC Chan, KT Chau. An overview of power electronics in electric vehicles[J]. IEEE Transactions on Industrial Electronics,1997.44(1):3-13.
[98]Ali Emadi, Young Joo Lee, Kaushik Rajashekara. Power electronics and motor drives in electric, hy-brid electric, and plug-in hybrid electric vehicles[J]. IEEE Transactions on Industrial Electronics,2008. 55(6):2237-2245.
[99]Kevin J Dyke, Nigel Schofield, Mike Barnes. The impact of transport electrification on electrical network-s[J]. IEEE Transactions on Industrial Electronics,2010.57(12):3917-3926.
[100]Ferdinando Luigi Mapelli, Davide Tarsitano, Marco Mauri. Plug-in hybrid electric vehicle:Modeling, prototype realization, and inverter losses reduction analysis[J]. IEEE Transactions on Industrial Electron-ics,2010.57(2):598-607.
[101]Wencong Su, Habiballah Eichi, Wente Zeng, Mo-Yuen Chow. A survey on the electrification of trans-portation in a smart grid environment[J]. IEEE Transactions on Industrial Informatics,2012.8(l):1-10.
[102]Kristien Clement, Edwin Haesen, Johan Driesen. Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids[C]//Proc. IEEE/PES PSCE.2009:1-7.
[103]Kristien Clement-Nyns, Edwin Haesen, Johan Driesen. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J]. IEEE Transactions on Power Systems,2010.25(1):371-380.
[104]Ahmed Yousuf Saber, Ganesh Kumar Venayagamoorthy. Unit commitment with vehicle-to-grid using particle swarm optimization[C]//Proc. IEEE PowerTech.2009:1-8.
[105]Hao Liang, Bong Jun Choi, Weihua Zhuang, Xuemin Shen. Towards optimal energy store-carry-and-deliver for phevs via v2g system[C]//Proc. IEEE INFOCOM.2012:1674-1682.
[106]Walid Saad, Zhu Han, HVincent Poor, Tamer Basar. Game-theoretic methods for the smart grid:an overview of microgrid systems, demand-side management, and smart grid communications[J]. IEEE Signal Processing Magazine,2012.29(5):86-105.
[107]Lijun Chen, Na Li, SH Low, JC Doyle. Two market models for demand response in power network-s[C]//Proc. IEEE SmartGridComm.2010:397-402.
[108]Christian Ibars, Monica Navarro, Lorenza Giupponi. Distributed demand management in smart grid with a congestion game[C]//Proc. IEEE SmartGridComm.2010:495-500.
[109]Zhong Fan. Distributed demand response and user adaptation in smart grids[C]//Proc. IFIP/IEEE Integrat-ed Network Management.2011-.726-729.
[110]Perukrishnen Vytelingum, Sarvapali D Ramchurn, Thomas D Voice, Alex Rogers, Nicholas R Jennings. Trading agents for the smart electricity grid[C]//Proc. IFAAMAS.2010:897-904.
[111]Bhuvaneswari Ramachandran, Sanjeev K Srivastava, Chris S Edrington, David A Cartes. An intelligent auction scheme for smart grid market using a hybrid immune algorithm[J]. IEEE Transactions on Indus-trial Electronics,2011.58(10):4603-4612.
[112]Lei Rao, Xue Liu, Le Xie, Zhan Pang. Hedging against uncertainty:A tale of internet data center opera-tions under smart grid environment[J]. IEEE Transactions on Smart Grid,2011.2(3):555-563.
[113]Luis M Costa, George Kariniotakis. A stochastic dynamic programming model for optimal use of local energy resources in a market environment[C]//Proc. IEEE Power Tech.2007:449-454.
[114]Albert Molderink. Vincent Bakker, Maurice GC Bosman, Johann L Hurink, Gerard JM Smit. Management and control of domestic smart grid technology [J]. IEEE Transactions on Smart Grid,2010.1(2):109-119.
[115]Shalinee Kishore, Lawrence V Snyder. Control mechanisms for residential electricity demand in smart-grids[C]//Proc. IEEE SmartGridComm.2010:443-448.
[116]Roger N Anderson, Albert Boulanger, Warren B Powell, Warren Scott. Adaptive stochastic control for the smart grid[J]. Proceedings of the IEEE,2011.99(6):1098-1115.
[117]Daniel O'Neill, Marco Levorato, Andrea Goldsmith, Urbashi Mitra. Residential demand response using reinforcement learning[C]//Proc. IEEE SmartGridComm.2010:409-414.
[118]Xian Liu. Economic load dispatch constrained by wind power availability:A wait-and-see approach[J]. IEEE Transactions on Smart Grid,2010. 1(3):347-355.
[119]Masood Parvania, Mahmud Fotuhi-Firuzabad. Demand response scheduling by stochastic scuc[J]. IEEE Transactions on Smart Grid,2010.1 (1):89-98.
[120]Jichen Zhang, J David Fuller, Samir Elhedhli. A stochastic programming model for a day-ahead electricity market with real-time reserve shortage pricing[J']. IEEE Transactions on Power Systems,2010.25(2):703-713.
[121]Michael Angelo A Pedrasa, Ted D Spooner, Iain F MacGill. Coordinated scheduling of residential dis-tributed energy resources to optimize smart home energy services[J]. IEEE Transactions on Smart Grid, 2010.1(2):134-143.
[122]Joydeep Mitra, Shashi B Patra, Satish J Ranade. Reliability stipulated microgrid architecture using particle swarm optimization[C]//Proc. IEEE PMAPS.2006:1-7.
[123]M Pedrasa, Ted D Spooner, lain F MacGill. Scheduling of demand side resources using binary particle swarm optimization[J]. IEEE Transactions on Power Systems,2009.24(3):1173-1181.
[124]Jun Sun, Vasile Palade, Xiao-Jun Wu, Wei Fang, Zhenyu Wang. Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization[J]. IEEE Transactions on Industrial Informatics,2014.10(1):222-232.
[125]Ruilong Deng, Shibo He, Jiming Chen, Juncheng Jia, Weihua Zhuang, Youxian Sun. Energy-efficient spectrum sensing by optimal periodic scheduling in cognitive radio networks[J]. IET communications, 2012.6(6):676-684.
[126]Ruilong Deng, Jiming Chen, Chau Yuen, Peng Cheng, Youxian Sun. Energy-efficient cooperative spec-trum sensing by optimal scheduling in sensor-aided cognitive radio networks[J]. IEEE Transactions on Vehicular Technology,2012.61(2):716-725.
[127]Peng Cheng, Ruilong Deng, Jiming Chen. Energy-efficient cooperative spectrum sensing in sensor-aided cognitive radio networks[J]. IEEE Wireless Communications,2012.19(6):100-105.
[128]Ruilong Deng, Jiming Chen, Jialu Fan, Youxian Sun. A general approach for building linux on sensor node imote2[J]. Computer Standards & Interfaces,2012.34(1):101-108.
[129]Mi Wen, Kuan Zhang, Jingsheng Lei, Xiaohui Liang, Ruilong Deng, Xuemin Sherman Shen. CIT:A credit-based incentive tariff scheme with fraud-traceability for smart grid[J]. Security and Communication Networks, DOI:10.1002/sec.895.
[130]Danijela Cabric, Shridhar Mubaraq Mishra, Robert W Brodersen. Implementation issues in spectrum sensing for cognitive radios[C]//Proc. IEEE Asilomar conference on Signals, systems and computers. 2004:772-776.
[131]Tevfik Yucek, Huseyin Arslan. A survey of spectrum sensing algorithms for cognitive radio application-s[J]. IEEE Communications Surveys & Tutorials,2009.11(1):116-130.
[132]Anant Sahai, Niels Hoven, Rahul Tandra. Some fundamental limits on cognitive radio[C]//Proc. Allerton Conference on Communication, Control, and Computing.2004:1662-1671.
[133]Martin BH Weiss, Simon Delaere, William H Lehr. Sensing as a service:An exploration into practical implementations of dsa[C]//Proc. IEEE Symposium on New Frontiers in Dynamic Spectrum.2010:1-8.
[134]Amir Ghasemi, Elvino S Sousa. Collaborative spectrum sensing for opportunistic access in fading envi-ronments[C]//Proc. IEEE DySPAN.2005:131-136.
[135]Shridhar Mubaraq Mishra, Anant Sahai, Robert W Brodersen. Cooperative sensing among cognitive radios[C]//Proc. IEEE ICC.2006:1658-1663.
[136]Vikram Krishnamurthy, Michael Maskery, George Yin. Decentralized adaptive filtering algorithms for sensor activation in an unattended ground sensor network[J]. IEEE Transactions on Signal Processing, 2008.56(12):6086-6101.
[137]Bertrand Mercier, Viktoria Fodor, Ragnar Thobaben, Mikael Skoglund, Visa Koivunen, Saska Lindfors, Jussi Ryynanen, Erik G Larsson, Chiara Petrioli, Giancarlo Bongiovanni, et al. Sensor networks for cognitive radio:Theory and system design[C]//Proc. ICT Mobile and Wireless Communications Summit. 2008:1-8.
[138]Vladimirl Kostylev. Energy detection of a signal with random amplitude[C]//Proc. IEEE ICC.2002:1606-1610.
[139]Saman Atapattu, Chintha Tellambura, Hai Jiang. Energy detection based cooperative spectrum sensing in cognitive radio networks[J]. IEEE Transactions on Wireless Communications,2011.10(4):1232-1241.
[140]Hai Ngoc Pham, Yan Zhang, Paal E Engelstad, Tor Skeie, Frank Eliassen. Optimal cooperative spectrum sensing in cognitive sensor networks[C]//Proc. ACM IWCMC.2009:1073-1079.
[141]Hai Ngoc Pham, Yan Zhang, Paal E Engelstad, Tor Skeie, Frank Eliassen. Energy minimization ap-proach for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks[C]//Proc. IEEE WICON.2010:1-9.
[142]Yi Zou, Krishnendu Chakrabarty. A distributed coverage-and connectivity-centric technique for selecting active nodes in wireless sensor networks[J]. IEEE Transactions on Computers,2005.54(8):978-991.
[143]P Berman, G Calinescu, C Shah, A Zelikovsky. Efficient energy management in sensor networks[J]. Ad Hoc and Sensor Networks,2005.2:71-90.
[144]Mihaela Cardei, My T Thai, Yingshu Li, Weili Wu. Energy-efficient target coverage in wireless sensor networks[C]//Porc. IEEE INFOCOM.2005:1976-1984.
[145]H. Kellerer, U. Pferschy, D. Pisinger. Knapsack Problems[M]. Springer,2004.
[146]Salman Mohagheghi, James Stoupis, Zhenyuan Wang, Zhao Li, Hormoz Kazemzadeh. Demand re-sponse architecture:Integration into the distribution management system[C]//Proc. IEEE SmartGridCom-m.2010:501-506.
[147]S.P. Boyd, L. Vandenberghe. Convex Optimization[M]. Cambridge University Press,2004.
[148]A.J. Wood, B.F. Wollenberg. Power Generation, Operation, and Control[M]. Wiley,1996.
[149]Steven E Widergren, Maria L Paget, Thomas J Secrest, Patrick J Balducci, Alice C Orrell, Cary N Bloyd. Using smart grids to enhance use of energy-efficiency and renewable-energy technologies[R].:Pacific Northwest National Laboratory (PNNL), Richland, WA (US),2011.
[150]Goran Strbac. Demand side management:Benefits and challenges[J]. Energy Policy,2008.36(12):4419-4426.
[151]Alec Brooks, Ed Lu, Dan Reicher, Charles Spirakis, Bill Weihl. Demand dispatch[J]. IEEE Power and Energy Magazine,2010.8(3):20-29.
[152]D. Fudenberg, J. Tirole. Game Theory[M]. Mit Press,1991.
[153]James O Neel, Jeffrey H Reed, Robert P Gilles. Convergence of cognitive radio networks[C]//Proc. IEEE WCNC. volume 4.2004:2250-2255.
[154]Eitan Altman, Zwi Altman. S-modular games and power control in wireless networks[J]. IEEE Transac-tions on Automatic Control,2003.48(5):839-842.
[155]J Ben Rosen. Existence and uniqueness of equilibrium points for concave n-person games[J]. Economet-rica,1965.33(3):520-534.
[156]Changyu Wang, Naihua Xiu. Convergence of the gradient projection method for generalized convex minimization[J]. Computational Optimization and Applications,2000.16(2):111-120.
[157]Daniel P Palomar, Mung Chiang. A tutorial on decomposition methods for network utility maximiza-tion[J]. IEEE Journal on Selected Areas in Communications,2006.24(8):1439-1451.
[158]Shibo He, Jiming Chen, David KY Yau, Youxian Sun. Cross-layer optimization of correlated data gather-ing in wireless sensor networks[J]. IEEE Transactions on Mobile Computing,2012.11(11):1678-1691.
[159]Steven H Low, David E Lapsley. Optimization flow control-I:basic algorithm and convergence[J]. IEEE/ACM Transactions on Networking,1999.7(6):861-874.
[160]Miao Wang, Hao Liang, Ruilong Deng, Ran Zhang, X Shen. Vanet based online charging strategy for electric vehicles[C]//Proc. IEEE GLOBECOM.2013:5115-5120.
[161]K. Zhou, L. Cai. Randomized phev charging under distribution grid constraints[J]. IEEE Transactions on Smart Grid,2014.5(2):879-887.
[162]Z. Yang, L. Sun, J. Chen, Q. Yang, K. Xing, X. Chen. Maximization for plug-in electric taxi with uncertain future electricity prices[J]. IEEE Transactions on Power Systems, accepted.
[163]Ruilong Deng, Zaiyue Yang. Cooperative transmission game for smart grid communication[C]//Proc. IEEE ICCA. accepted,2014.
[164]Bo Chai, Jiming Chen, Zaiyue Yang, Yan Zhang. Demand response management with multiple utility companies:A two-level game approach[J]. IEEE Transactions on Smart Grid,2014.5(2):722-731.
[165]Nerea Ruiz, Inigo Cobelo, Jose Oyarzabal. A direct load control model for virtual power plant manage-ment[J]. IEEE Transactions on Power Systems,2009.24(2):959-966.
[166]Sanjeev Kumar Aggarwal, Lalit Mohan Saini, Ashwani Kumar. Electricity price forecasting in deregulated markets:A review and evaluation[J], International Journal of Electrical Power & Energy Systems,2009. 31(1):13-22.
[167]Antonio J Conejo, Juan M Morales, Luis Baringo. Real-time demand response model[J]. IEEE Transac-tions on Smart Grid,2010. 1(3):236-242.
[168]Dong C Park, MA El-Sharkawi, RJ Marks, LE Atlas, MJ Damborg, et al. Electric load forecasting using an artificial neural network[J]. IEEE Transactions on Power Systems,1991.6(2):442-449.
[169]Poramate Tarasak. Optimal real-time pricing under load uncertainty based on utility maximization for smart grid[C]//Proc. IEEE SmartGridComm.2011:321-326.
[170]L. Yang, J. Zhang, H. V. Poor. Risk-aware day-ahead scheduling and real-time dispatch for electric vehicle charging[J]. IEEE Transactions on Smart Grid,2014.5(2):693-702.
[171]D.P. Bertsekas. Nonlinear programming[M]. Athena Scientific,1999.
[172]Junshan Zhang, Dong Zheng, Mung Chiang. The impact of stochastic noisy feedback on distributed network utility maximization[J]. IEEE Transactions on Information Theory,2008.54(2):645-665.
[173]J.C. Spall. Introduction to Stochastic Search and Optimization:Estimation, Simulation, and Control [M]. Wiley,2005.
[174]Natural Resources Canada. Energy consumption of major household appliances shipped in Canada, sum-mary report-trends for 1990-2009. http://oee.nrcan.gc.ca/publications/statistics/cama11/index.cfm.
[175]Miao He, Lei Yang, Junshan Zhang, Vijay Vittal. A spatio-temporal analysis approach for short-term forecast of wind farm generation[J]. IEEE Transactions on Power Systems, DOI:10.1109/TP-WRS.2014.2299767.
[176]M.R. Garey, D.S. Johnson. Computers and Intractability:A Guide to the Theory of NP-Completeness[M]. W.H. Freeman,1979.
[2]B Liscouski, W Elliot. Final report on the august 14,2003 blackout in the united states and Canada:Causes and recommendations[R].:U.S.-Canada Power System Outage Task Force,2004.
[3]韩祯祥,曹一家.电力系统的安全性及防治措施[J].电网技术，2004.28(9)：1-6.
[4]侯慧,尹项根,陈庆前,游大海,童光毅,邵德军.南方部分500kv主网架2008年冰雪灾害中受损分析与思考[J].电力系统自动化,2008.32(11)：12-15.
[5]梁志峰,葛睿,董昱,陈刚.印度“7.30”，“7.31”大停电事故分析及对我国电网调度运行工作的启示[J].电网技术,2013.37(7)：1841-1848.
[6]陈树勇,宋书芳,李兰欣,沈杰.智能电网技术综述[J].电网技术,2009.33(8)：1-7.
[7]U.S. Department of Energy. The smart grid:An introduction, http://energy.gov/oe/downloads/smart-grid-introduction-0.
[8]S Massoud Amin, Bruce F Wollenberg. Toward a smart grid:power delivery for the 21 st century[J]. IEEE Power and Energy Magazine,2005.3(5):34-41.
[9]Ali Vojdani. Smart integration[J]. IEEE Power and Energy Magazine,2008.6(6):71-79.
[10]Ali lpakchi, Farrokh Albuyeh. Grid of the future[J]. IEEE Power and Energy Magazine,2009.7(2):52-62.
[11]Hassan Farhangi. The path of the smart grid[J]. IEEE Power and Energy Magazine,2010.8(1):18-28.
[12]Khosrow Moslehi, Ranjit Kumar. A reliability perspective of the smart grid[J]. IEEE Transactions on Smart Grid,2010. 1(1):57-64.
[13]Xi Fang, Satyajayant Misra, Guoliang Xue, Dejun Yang. Smart grid - the new and improved power grid: A survey[J]. IEEE Communications Surveys Tutorials,2012.14(4):944-980.
[14]J. Momoh. Smart Grid:Fundamentals of Design and Analysis[M]. Wiley,2012.
[15]L.T. Berger, K. Iniewski. Smart Grid:Applications, Communications, and Security[M]. Wiley,2012.
[16]余贻鑫,栾文鹏.智能电网[J].电网与清洁能源,2009.25(1)：7-11.
[17]百度百科.智能电网http://baike.baidu.com/view/2222513.htm.
[18]余贻鑫.面向21世纪的智能配电网[J].南方电网技术研究,2006.2(6)：14-16.
[19]Federal Communications Commission. Report of the spectrum efficiency working group[R]. :Spectrum Policy Task Force, Nov.2002.
[20]Simon Haykin. Cognitive radio:brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications,2005.23(2):201-220.
[21]Qing Zhao, Brian M Sadler. A survey of dynamic spectrum access[J]. IEEE Signal Processing Magazine, 2007.24(3):79-89.
[22]Ian F Akyildiz, Won-Yeol Lee, Mehmet C Vuran, Shantidev Mohanty. A survey on spectrum management in cognitive radio networks[J]. IEEE Communications Magazine,2008.46(4):40-48.
[23]Amir Ghasemi, Elvino S Sousa. Spectrum sensing in cognitive radio networks:requirements, challenges and design trade-offs[J]. IEEE Communications Magazine,2008.46(4):32-39.
[24]Ying-Chang Liang, Yonghong Zeng, Edward CY Peh, Anh Tuan Hoang. Sensing-throughput tradeoff for cognitive radio networks[J]. IEEE Transactions on Wireless Communications,2008.7(4):1326-1337.
[25]Won-Yeol Lee, Ian F Akyildiz. Optimal spectrum sensing framework for cognitive radio networks [J]. IEEE Transactions on Wireless Communications,2008.7(10):3845-3857.
[26]Edward Chu Yeow Peh, Ying-Chang Liang, Yong Liang Guan, Yonghong Zeng. Optimization of cooper-ative sensing in cognitive radio networks:a sensing-throughput tradeoff view[J]. IEEE Transactions on Vehicular Technology,2009.58(9):5294-5299.
[27]Wei Zhang, Ranjan Mallik, K Letaief. Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks[J]. IEEE Transactions on Wireless Communications,2009.8(12):5761-5766.
[28]A Ghassemi, S Bavarian, L Lampe. Cognitive radio for smart grid communications[C]//Proc. IEEE S-martGridComm.2010:297-302.
[29]Pedram Samadi, A Mohsenian-Rad, Robert Schober, V Wong, Juri Jatskevich. Optimal real-time pricing algorithm based on utility maximization for smart grid[C]//Proc. IEEE SmartGridComm.2010:415-420.
[30]Na Li, Lijun Chen, Steven H Low. Optimal demand response based on utility maximization in power networks[C]//Proc. IEEE PES-GM.2011:1-8.
[31]Robert C Qiu, Zhen Hu, Zhe Chen, Nan Guo, Raghuram Ranganathan, Shujie Hou, Gang Zheng. Cogni-tive radio network for the smart grid:Experimental system architecture, control algorithms, security, and microgrid testbed[J]. IEEE Transactions on Smart Grid,2011.2(4):724-740.
[32]Rong Yu, Yan Zhang, Stein Gjessing, Chau Yuen, Shengli Xie, Mohsen Guizani. Cognitive radio based hierarchical communications infrastructure for smart grid[J]. IEEE Network,2011.25(5):6-14.
[33]Ruilong Deng, Sabita Maharjan, Xianghui Cao, Jiming Chen, Yan Zhang, Stein Gjessing. Sensing-delay tradeoff for communication in cognitive radio enabled smart grid[C]//Proc. IEEE SmartGridComm. 2011:155-160.
[34]Ruilong Deng, Jiming Chen, Xianghui Cao, Yan Zhang, Sabita Maharjan, Stein Gjessing. Sensing- performance tradeoff in cognitive radio enabled smart grid[J]. IEEE Transactions on Smart Grid,2013. 4(1):302-310.
[35]Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang. Vehicle-to-aggregator interaction game[J]. IEEE Transactions on Smart Grid,2012.3(1):434-442.
[36]E Bloustein. Assessment of customer response to real time pricing[R].:Rutgers-The State University of New Jersey,2005.
[37]A-H Mohsenian-Rad, Vincent WS Wong, Juri Jatskevich, Robert Schober. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid[C]//Proc. IEEE ISGT.2010:1-6.
[38]A Mohsenian-Rad, Vincent WS Wong, Juri Jatskevich, Robert Schober, Alberto Leon-Garcia. Au-tonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid[J]. IEEE Transactions on Smart Grid,2010. 1(3):320-331.
[39]A-H Mohsenian-Rad, Alberto Leon-Garcia. Optimal residential load control with price prediction in real-time electricity pricing environments[J]. IEEE Transactions on Smart Grid,2010.1 (2):120-133.
[40]Pedram Samadi, Hamed Mohsenian-Rad, Robert Schober, Vincent WS Wong. Advanced demand side management for the future smart grid using mechanism design[J]. IEEE Transactions on Smart Grid, 2012.3(3):1170-1180.
[41]Tung T Kim, H Vincent Poor. Scheduling power consumption with price uncertainty[J]. IEEE Transac-tions on Smart Grid,2011.2(3):519-527.
[42]Libin Jiang, Steven Low. Multi-period optimal energy procurement and demand response in smart grid with uncertain supply [C]//Proc. IEEE CDC-ECC.2011:4348-4353.
[43]Ruilong Deng, Zaiyue Yang, Jiming Chen, Navid Rahbari Asr, Mo-Yuen Chow. Residential energy con-sumption scheduling:A coupled-constraint game approach[J]. IEEE Transactions on Smart Grid,2014. 5(3):1340-1350.
[44]Ruilong Deng, Zaiyue Yang, Jiming Chen. Load scheduling with price uncertainty and coupling con-straints [C]//Proc. IEEE PES-GM.2013:1-5.
[45]Ruilong Deng, Zaiyue Yang, Jiming Chen, Mo-Yuen Chow. Load scheduling with price uncertainty and temporally-coupled constraints in smart grids[J]. IEEE Transactions on Power Systems, DOI:10.1109/T-PWRS.2014.2311127.
[46]ZubairMd Fadlullah, Mostafa M Fouda, Nei Kato, Akira Takeuchi, Noboru Iwasaki, Yousuke Nozaki. To-ward intelligent machine-to-machine communications in smart grid[J]. IEEE Communications Magazine, 2011.49(4):60-65.
[47]Vehbi C Gungor, Dilan Sahin, Taskin Kocak, Salih Ergut. Concettina Buccella, Carlo Cecati, Gerhard P Hancke. Smart grid technologies:communication technologies and standards[J]. IEEE Transactions on Industrial Informatics,2011.7(4):529-539.
[48]Zhong Fan, Parag Kulkarni, Sedat Gormus, Costas Efthymiou, Georgios Kalogridis, Mahesh Sooriya-bandara, Ziming Zhu, Sangarapillai Lambotharan, W Chin. Smart grid communications:Overview of research challenges, solutions, and standardization activities[J]. IEEE Communications Surveys & Tuto-rials,2013.15(1):21-38.
[49]Anindya Majumder, James Caffery Jr. Power line communications[J]. IEEE Potentials,2004.23(4):4-8.
[50]Palak P Parikh, Mitalkumar G Kanabar, Tarlochan S Sidhu. Opportunities and challenges of wireless communication technologies for smart grid applications[C]//in Proc. IEEE PES-GM.2010:1-7.
[51]Yao-Win Hong, W-J Huang, Fu-Hsuan Chiu, C-CJ Kuo. Cooperative communications in resource-constrained wireless networks[J]. IEEE Signal Processing Magazine,2007.24(3):47-57.
[52]Dusit Niyato, Ping Wang. Cooperative transmission for meter data collection in smart grid[J]. IEEE Communications Magazine,2012.50(4):90-97.
[53]David L Donoho. Compressed sensing[J]. IEEE Transactions on Information Theory,2006.52(4):1289-1306.
[54]Husheng Li, Rukun Mao, Lifeng Lai, Robert C Qiu. Compressed meter reading for delay-sensitive and secure load report in smart grid[C]//Proc. IEEE SmartGridComm.2010:114-119.
[55]Husheng Li, Weiyi Zhang. Qos routing in smart grid[C]//Proc. IEEE GLOBECOM.2010:1-6.
[56]Benefits of demand response in electricity markets and recommendations for achieving them[R].:U.S. Department of Energy,2006.
[57]Josep M Guerrero, Poh Chiang Loh, Mukul Chandorkar, Tzung-Lin Lee. Advanced control architec-tures for intelligent microgrids -part I:Decentralized and hierarchical control[J]. IEEE Transactions on Industrial Electronics,2013.60(4):1254-1262.
[58]Josep M Guerrero, Poh Chiang Loh, Tzung-Lin Lee, Mukul Chandorkar. Advanced control architec-tures for intelligent microgrids - part Ⅱ:Power quality, energy storage, and ac/dc microgrids[J]. IEEE Transactions on Industrial Electronics,2013.60(4):1263-1270.
[59]Assessment of demand response and advanced metering[R].:Federal Energy Regulatory Commission, 2012.
[60]P Jazayeri, A Schellenberg, WD Rosehart, JADJ Doudna, SAWS Widergren, D Lawrence, JAMJ Mickey, SAJS Jones. A survey of load control programs for price and system stability[J]. IEEE Transactions on Power Systems,2005.20(3):1504-1509.
[61]M.H. Albadi, EF E1-Saadany. A summary of demand response in electricity markets[J]. Electric Power Systems Research,2008.78(11):1989-1996.
[62]Peter Palensky, Dietmar Dietrich. Demand side management:Demand response, intelligent energy sys-tems, and smart loads[J]. IEEE Transactions on Industrial Informatics,2011.7(3):381-388.
[63]T. Ericson. Direct load control of residential water heaters[J]. Energy Policy,2009.37(9):3502-3512.
[64]HA Aalami, M.P. Moghaddam, GR Yousefi. Demand response modeling considering interrupt-ible/curtailable loads and capacity market programs[J]. Applied Energy,2010.87(1):243-250.
[65]J. Saebi, J. Mohammadi, H. Taheri, S.S. Nayer. Demand bidding/buyback modeling and its impact on market clearing price[C]//Proc. IEEE EnergyCon.2010:791-796.
[66]R. Tyagi, J.W. Black. Emergency demand response for distribution system contingencies[C]//Proc. IEEE PES Transmission and Distribution Conference and Exposition.2010:1-4.
[67]S Datchanamoorthy, S Kumar, Y Ozturk, G Lee. Optimal time-of-use pricing for residential load con-trol[C]//Proc. IEEE SmartGridComm.2011:375-380.
[68]Ontario Energy Board. Time-of-use (tou) prices for smart meters. http://www.ontarioenergyboard.ca/OEB/Consumers/.
[69]K. Herter. Residential implementation of critical-peak pricing of electricity [J]. Energy Policy,2007. 35(4):2121-2130.
[70]H. Allcott. Real time pricing and electricity markets[J]. Harvard University, Jan,2009.
[71]Ameren Illinois Power Corporation. Real time prices for residential customer. http://www2.ameren.corn/RetailEnergy/realtimeprices.aspx.
[72]Severin Borenstein. Equity effects of increasing-block electricity pricing[R].:Center for the Study of Energy Markets, University of California Energy Institute,2008.
[73]Danping Ren, Hui Li, Yuefeng Ji. Home energy management system for the residential load control based on the price prediction[C]//Proc. IEEE GreenCom.2011:1-6.
[74]Francesco De Angelis, Matteo Boaro, Danilo Fuselli, Stefano Squartini, Francesco Piazza, Qinglai Wei. Optima] home energy management under dynamic electrical and thermal constraints[J]. IEEE Transac-tions on Industrial Informatics,2013.9(3):1518-1527.
[75]Libin Jiang, Steven Low. Real-time demand response with uncertain renewable energy in smart grid[C]//Proc. IEEE Allerton Conference on Communication, Control, and Computing.2011:1334-1341.
[76]Chris Hutson, Ganesh Kumar Venayagamoorthy, Keith A Corzine. Intelligent scheduling of hybrid and electric vehicle storage capacity in a parking lot for profit maximization in grid power transaction-s[C]//Proc. IEEE Energy 2030 Conference.2008:1-8.
[77]S Hatami, M Pedram. Minimizing the electricity bill of cooperative users under a quasi-dynamic pricing model[C]//Proc. IEEE SmartGridComm.2010:421-426.
[78]Supun Tiptipakorn, Wei-Jen Lee. A residential consumer-centered load control strategy in real-time elec-tricity pricing environment[C]//Proc. IEEE NAPS.2007:505-510.
[79]Pengwei Du, Ning Lu. Appliance commitment for household load scheduling[J]. IEEE Transactions on Smart Grid,2011.2(2):411-419.
[80]Hadi Goudarzi, Safar Hatami, Massoud Pedram. Demand-side load scheduling incentivized by dynamic energy prices[C]//Proc. IEEE SmartGridComm.2011:351-356.
[81]Juan Manuel Carrasco, Leopoldo Garcia Franquelo, Jan T Bialasiewicz, Eduardo Galvan, RC Portillo Guisado, Ma AM Prats, Jose Ignacio Leon, Narciso Moreno-Alfonso. Power-electronic systems for the grid integration of renewable energy sources:A survey [J]. IEEE Transactions on Industrial Electronics, 2006.53(4):1002-1016.
[82]Jan T Bialasiewicz. Renewable energy systems with photovoltaic power generators:Operation and mod-eling[J]. IEEE Transactions on Industrial Electronics,2008.55(7):2752-2758.
[83]Hristiyan Kanchev, Di Lu, Frederic Colas, Vladimir Lazarov, Bruno Francois. Energy management and operational planning of a microgrid with a pv-based active generator for smart grid applications[J]. IEEE Transactions on Industrial Electronics,2011.58(10):4583-4592.
[84]Frede Blaabjerg, Remus Teodorescu, Marco Liserre, Adrian V Timbus. Overview of control and grid synchronization for distributed power generation systems[J]. IEEE Transactions on Industrial Electronics, 2006.53(5):1398-1409.
[85]Carlo Cecati, Costantino Citro, Antonio Piccolo, Pierluigi Siano. Smart operation of wind turbines and diesel generators according to economic criteria[J]. IEEE Transactions on Industrial Electronics,2011. 58(10):4514-4525.
[86]Andrew J Roscoe, G Ault. Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response[J].1ET Renewable Power Generation,2010.4(4):369-382.
[87]Miao He, Sugumar Murugesan, Junshan Zhang. Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration[C]//Proc. IEEE INFOCOM.2011:461-465.
[88]Michael Caramanis, Justin M Foster. Management of electric vehicle charging to mitigate renewable generation intermittency and distribution network congestion[C]//Proc. IEEE CDC/CCC.2009:4717-4722.
[89]JA Pecas Lopes, PM Rocha Almeida, FJ Soares. Using vehicle-to-grid to maximize the integration of intermittent renewable energy resources in islanded electric grids[C]//Proc. IEEE Clean Electrical Power. 2009:290-295.
[90]Ahmed Yousuf Saber, Ganesh Kumar Venayagamoorthy. Plug-in vehicles and renewable energy sources for cost and emission reductions[J]. IEEE Transactions on Industrial Electronics,2011.58(4):1229-1238.
[91]Jorge Moreno, Micah E Ortuzar, LW Dixon. Energy-management system for a hybrid electric vehi-cle, using ultracapacitors and neural networks[J]. IEEE Transactions on Industrial Electronics,2006. 53(2):614-623.
[92]Micah Ortuzar, Jorge Moreno, Juan Dixon. Ultracapacitor-based auxiliary energy system for an elec-tric vehicle:Implementation and evaluation[J]. IEEE Transactions on Industrial Electronics,2007. 54(4):2147-2156.
[93]Zahra Amjadi, Sheldon S Williamson. Power-electronics-based solutions for plug-in hybrid electric ve-hicle energy storage and management systems[J]. IEEE Transactions on Industrial Electronics,2010. 57(2):608-616.
[94]Sergio Vazquez, Srdjan M Lukic, Eduardo Galvan, Leopoldo G Franquelo, Juan M Carrasco. Energy storage systems for transport and grid applications[J]. IEEE Transactions on Industrial Electronics,2010. 57(12):3881-3895.
[95]Fabian Kennel, D Gorges, Steven Liu. Energy management for smart grids with electric vehicles based on hierarchical mpc[J]. IEEE Transactions on Industrial Informatics,2013.9(3):1528-1537.
[96]Perukrishnen Vytelingum, Thomas D Voice, Sarvapali D Ramchurn, Alex Rogers, Nicholas R Jennings. Agent-based micro-storage management for the smart grid[C]//Proc.1FAAMAS.2010:39-46.
[97]CC Chan, KT Chau. An overview of power electronics in electric vehicles[J]. IEEE Transactions on Industrial Electronics,1997.44(1):3-13.
[98]Ali Emadi, Young Joo Lee, Kaushik Rajashekara. Power electronics and motor drives in electric, hy-brid electric, and plug-in hybrid electric vehicles[J]. IEEE Transactions on Industrial Electronics,2008. 55(6):2237-2245.
[99]Kevin J Dyke, Nigel Schofield, Mike Barnes. The impact of transport electrification on electrical network-s[J]. IEEE Transactions on Industrial Electronics,2010.57(12):3917-3926.
[100]Ferdinando Luigi Mapelli, Davide Tarsitano, Marco Mauri. Plug-in hybrid electric vehicle:Modeling, prototype realization, and inverter losses reduction analysis[J]. IEEE Transactions on Industrial Electron-ics,2010.57(2):598-607.
[101]Wencong Su, Habiballah Eichi, Wente Zeng, Mo-Yuen Chow. A survey on the electrification of trans-portation in a smart grid environment[J]. IEEE Transactions on Industrial Informatics,2012.8(l):1-10.
[102]Kristien Clement, Edwin Haesen, Johan Driesen. Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids[C]//Proc. IEEE/PES PSCE.2009:1-7.
[103]Kristien Clement-Nyns, Edwin Haesen, Johan Driesen. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J]. IEEE Transactions on Power Systems,2010.25(1):371-380.
[104]Ahmed Yousuf Saber, Ganesh Kumar Venayagamoorthy. Unit commitment with vehicle-to-grid using particle swarm optimization[C]//Proc. IEEE PowerTech.2009:1-8.
[105]Hao Liang, Bong Jun Choi, Weihua Zhuang, Xuemin Shen. Towards optimal energy store-carry-and-deliver for phevs via v2g system[C]//Proc. IEEE INFOCOM.2012:1674-1682.
[106]Walid Saad, Zhu Han, HVincent Poor, Tamer Basar. Game-theoretic methods for the smart grid:an overview of microgrid systems, demand-side management, and smart grid communications[J]. IEEE Signal Processing Magazine,2012.29(5):86-105.
[107]Lijun Chen, Na Li, SH Low, JC Doyle. Two market models for demand response in power network-s[C]//Proc. IEEE SmartGridComm.2010:397-402.
[108]Christian Ibars, Monica Navarro, Lorenza Giupponi. Distributed demand management in smart grid with a congestion game[C]//Proc. IEEE SmartGridComm.2010:495-500.
[109]Zhong Fan. Distributed demand response and user adaptation in smart grids[C]//Proc. IFIP/IEEE Integrat-ed Network Management.2011-.726-729.
[110]Perukrishnen Vytelingum, Sarvapali D Ramchurn, Thomas D Voice, Alex Rogers, Nicholas R Jennings. Trading agents for the smart electricity grid[C]//Proc. IFAAMAS.2010:897-904.
[111]Bhuvaneswari Ramachandran, Sanjeev K Srivastava, Chris S Edrington, David A Cartes. An intelligent auction scheme for smart grid market using a hybrid immune algorithm[J]. IEEE Transactions on Indus-trial Electronics,2011.58(10):4603-4612.
[112]Lei Rao, Xue Liu, Le Xie, Zhan Pang. Hedging against uncertainty:A tale of internet data center opera-tions under smart grid environment[J]. IEEE Transactions on Smart Grid,2011.2(3):555-563.
[113]Luis M Costa, George Kariniotakis. A stochastic dynamic programming model for optimal use of local energy resources in a market environment[C]//Proc. IEEE Power Tech.2007:449-454.
[114]Albert Molderink. Vincent Bakker, Maurice GC Bosman, Johann L Hurink, Gerard JM Smit. Management and control of domestic smart grid technology [J]. IEEE Transactions on Smart Grid,2010.1(2):109-119.
[115]Shalinee Kishore, Lawrence V Snyder. Control mechanisms for residential electricity demand in smart-grids[C]//Proc. IEEE SmartGridComm.2010:443-448.
[116]Roger N Anderson, Albert Boulanger, Warren B Powell, Warren Scott. Adaptive stochastic control for the smart grid[J]. Proceedings of the IEEE,2011.99(6):1098-1115.
[117]Daniel O'Neill, Marco Levorato, Andrea Goldsmith, Urbashi Mitra. Residential demand response using reinforcement learning[C]//Proc. IEEE SmartGridComm.2010:409-414.
[118]Xian Liu. Economic load dispatch constrained by wind power availability:A wait-and-see approach[J]. IEEE Transactions on Smart Grid,2010. 1(3):347-355.
[119]Masood Parvania, Mahmud Fotuhi-Firuzabad. Demand response scheduling by stochastic scuc[J]. IEEE Transactions on Smart Grid,2010.1 (1):89-98.
[120]Jichen Zhang, J David Fuller, Samir Elhedhli. A stochastic programming model for a day-ahead electricity market with real-time reserve shortage pricing[J']. IEEE Transactions on Power Systems,2010.25(2):703-713.
[121]Michael Angelo A Pedrasa, Ted D Spooner, Iain F MacGill. Coordinated scheduling of residential dis-tributed energy resources to optimize smart home energy services[J]. IEEE Transactions on Smart Grid, 2010.1(2):134-143.
[122]Joydeep Mitra, Shashi B Patra, Satish J Ranade. Reliability stipulated microgrid architecture using particle swarm optimization[C]//Proc. IEEE PMAPS.2006:1-7.
[123]M Pedrasa, Ted D Spooner, lain F MacGill. Scheduling of demand side resources using binary particle swarm optimization[J]. IEEE Transactions on Power Systems,2009.24(3):1173-1181.
[124]Jun Sun, Vasile Palade, Xiao-Jun Wu, Wei Fang, Zhenyu Wang. Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization[J]. IEEE Transactions on Industrial Informatics,2014.10(1):222-232.
[125]Ruilong Deng, Shibo He, Jiming Chen, Juncheng Jia, Weihua Zhuang, Youxian Sun. Energy-efficient spectrum sensing by optimal periodic scheduling in cognitive radio networks[J]. IET communications, 2012.6(6):676-684.
[126]Ruilong Deng, Jiming Chen, Chau Yuen, Peng Cheng, Youxian Sun. Energy-efficient cooperative spec-trum sensing by optimal scheduling in sensor-aided cognitive radio networks[J]. IEEE Transactions on Vehicular Technology,2012.61(2):716-725.
[127]Peng Cheng, Ruilong Deng, Jiming Chen. Energy-efficient cooperative spectrum sensing in sensor-aided cognitive radio networks[J]. IEEE Wireless Communications,2012.19(6):100-105.
[128]Ruilong Deng, Jiming Chen, Jialu Fan, Youxian Sun. A general approach for building linux on sensor node imote2[J]. Computer Standards & Interfaces,2012.34(1):101-108.
[129]Mi Wen, Kuan Zhang, Jingsheng Lei, Xiaohui Liang, Ruilong Deng, Xuemin Sherman Shen. CIT:A credit-based incentive tariff scheme with fraud-traceability for smart grid[J]. Security and Communication Networks, DOI:10.1002/sec.895.
[130]Danijela Cabric, Shridhar Mubaraq Mishra, Robert W Brodersen. Implementation issues in spectrum sensing for cognitive radios[C]//Proc. IEEE Asilomar conference on Signals, systems and computers. 2004:772-776.
[131]Tevfik Yucek, Huseyin Arslan. A survey of spectrum sensing algorithms for cognitive radio application-s[J]. IEEE Communications Surveys & Tutorials,2009.11(1):116-130.
[132]Anant Sahai, Niels Hoven, Rahul Tandra. Some fundamental limits on cognitive radio[C]//Proc. Allerton Conference on Communication, Control, and Computing.2004:1662-1671.
[133]Martin BH Weiss, Simon Delaere, William H Lehr. Sensing as a service:An exploration into practical implementations of dsa[C]//Proc. IEEE Symposium on New Frontiers in Dynamic Spectrum.2010:1-8.
[134]Amir Ghasemi, Elvino S Sousa. Collaborative spectrum sensing for opportunistic access in fading envi-ronments[C]//Proc. IEEE DySPAN.2005:131-136.
[135]Shridhar Mubaraq Mishra, Anant Sahai, Robert W Brodersen. Cooperative sensing among cognitive radios[C]//Proc. IEEE ICC.2006:1658-1663.
[136]Vikram Krishnamurthy, Michael Maskery, George Yin. Decentralized adaptive filtering algorithms for sensor activation in an unattended ground sensor network[J]. IEEE Transactions on Signal Processing, 2008.56(12):6086-6101.
[137]Bertrand Mercier, Viktoria Fodor, Ragnar Thobaben, Mikael Skoglund, Visa Koivunen, Saska Lindfors, Jussi Ryynanen, Erik G Larsson, Chiara Petrioli, Giancarlo Bongiovanni, et al. Sensor networks for cognitive radio:Theory and system design[C]//Proc. ICT Mobile and Wireless Communications Summit. 2008:1-8.
[138]Vladimirl Kostylev. Energy detection of a signal with random amplitude[C]//Proc. IEEE ICC.2002:1606-1610.
[139]Saman Atapattu, Chintha Tellambura, Hai Jiang. Energy detection based cooperative spectrum sensing in cognitive radio networks[J]. IEEE Transactions on Wireless Communications,2011.10(4):1232-1241.
[140]Hai Ngoc Pham, Yan Zhang, Paal E Engelstad, Tor Skeie, Frank Eliassen. Optimal cooperative spectrum sensing in cognitive sensor networks[C]//Proc. ACM IWCMC.2009:1073-1079.
[141]Hai Ngoc Pham, Yan Zhang, Paal E Engelstad, Tor Skeie, Frank Eliassen. Energy minimization ap-proach for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks[C]//Proc. IEEE WICON.2010:1-9.
[142]Yi Zou, Krishnendu Chakrabarty. A distributed coverage-and connectivity-centric technique for selecting active nodes in wireless sensor networks[J]. IEEE Transactions on Computers,2005.54(8):978-991.
[143]P Berman, G Calinescu, C Shah, A Zelikovsky. Efficient energy management in sensor networks[J]. Ad Hoc and Sensor Networks,2005.2:71-90.
[144]Mihaela Cardei, My T Thai, Yingshu Li, Weili Wu. Energy-efficient target coverage in wireless sensor networks[C]//Porc. IEEE INFOCOM.2005:1976-1984.
[145]H. Kellerer, U. Pferschy, D. Pisinger. Knapsack Problems[M]. Springer,2004.
[146]Salman Mohagheghi, James Stoupis, Zhenyuan Wang, Zhao Li, Hormoz Kazemzadeh. Demand re-sponse architecture:Integration into the distribution management system[C]//Proc. IEEE SmartGridCom-m.2010:501-506.
[147]S.P. Boyd, L. Vandenberghe. Convex Optimization[M]. Cambridge University Press,2004.
[148]A.J. Wood, B.F. Wollenberg. Power Generation, Operation, and Control[M]. Wiley,1996.
[149]Steven E Widergren, Maria L Paget, Thomas J Secrest, Patrick J Balducci, Alice C Orrell, Cary N Bloyd. Using smart grids to enhance use of energy-efficiency and renewable-energy technologies[R].:Pacific Northwest National Laboratory (PNNL), Richland, WA (US),2011.
[150]Goran Strbac. Demand side management:Benefits and challenges[J]. Energy Policy,2008.36(12):4419-4426.
[151]Alec Brooks, Ed Lu, Dan Reicher, Charles Spirakis, Bill Weihl. Demand dispatch[J]. IEEE Power and Energy Magazine,2010.8(3):20-29.
[152]D. Fudenberg, J. Tirole. Game Theory[M]. Mit Press,1991.
[153]James O Neel, Jeffrey H Reed, Robert P Gilles. Convergence of cognitive radio networks[C]//Proc. IEEE WCNC. volume 4.2004:2250-2255.
[154]Eitan Altman, Zwi Altman. S-modular games and power control in wireless networks[J]. IEEE Transac-tions on Automatic Control,2003.48(5):839-842.
[155]J Ben Rosen. Existence and uniqueness of equilibrium points for concave n-person games[J]. Economet-rica,1965.33(3):520-534.
[156]Changyu Wang, Naihua Xiu. Convergence of the gradient projection method for generalized convex minimization[J]. Computational Optimization and Applications,2000.16(2):111-120.
[157]Daniel P Palomar, Mung Chiang. A tutorial on decomposition methods for network utility maximiza-tion[J]. IEEE Journal on Selected Areas in Communications,2006.24(8):1439-1451.
[158]Shibo He, Jiming Chen, David KY Yau, Youxian Sun. Cross-layer optimization of correlated data gather-ing in wireless sensor networks[J]. IEEE Transactions on Mobile Computing,2012.11(11):1678-1691.
[159]Steven H Low, David E Lapsley. Optimization flow control-I:basic algorithm and convergence[J]. IEEE/ACM Transactions on Networking,1999.7(6):861-874.
[160]Miao Wang, Hao Liang, Ruilong Deng, Ran Zhang, X Shen. Vanet based online charging strategy for electric vehicles[C]//Proc. IEEE GLOBECOM.2013:5115-5120.
[161]K. Zhou, L. Cai. Randomized phev charging under distribution grid constraints[J]. IEEE Transactions on Smart Grid,2014.5(2):879-887.
[162]Z. Yang, L. Sun, J. Chen, Q. Yang, K. Xing, X. Chen. Maximization for plug-in electric taxi with uncertain future electricity prices[J]. IEEE Transactions on Power Systems, accepted.
[163]Ruilong Deng, Zaiyue Yang. Cooperative transmission game for smart grid communication[C]//Proc. IEEE ICCA. accepted,2014.
[164]Bo Chai, Jiming Chen, Zaiyue Yang, Yan Zhang. Demand response management with multiple utility companies:A two-level game approach[J]. IEEE Transactions on Smart Grid,2014.5(2):722-731.
[165]Nerea Ruiz, Inigo Cobelo, Jose Oyarzabal. A direct load control model for virtual power plant manage-ment[J]. IEEE Transactions on Power Systems,2009.24(2):959-966.
[166]Sanjeev Kumar Aggarwal, Lalit Mohan Saini, Ashwani Kumar. Electricity price forecasting in deregulated markets:A review and evaluation[J], International Journal of Electrical Power & Energy Systems,2009. 31(1):13-22.
[167]Antonio J Conejo, Juan M Morales, Luis Baringo. Real-time demand response model[J]. IEEE Transac-tions on Smart Grid,2010. 1(3):236-242.
[168]Dong C Park, MA El-Sharkawi, RJ Marks, LE Atlas, MJ Damborg, et al. Electric load forecasting using an artificial neural network[J]. IEEE Transactions on Power Systems,1991.6(2):442-449.
[169]Poramate Tarasak. Optimal real-time pricing under load uncertainty based on utility maximization for smart grid[C]//Proc. IEEE SmartGridComm.2011:321-326.
[170]L. Yang, J. Zhang, H. V. Poor. Risk-aware day-ahead scheduling and real-time dispatch for electric vehicle charging[J]. IEEE Transactions on Smart Grid,2014.5(2):693-702.
[171]D.P. Bertsekas. Nonlinear programming[M]. Athena Scientific,1999.
[172]Junshan Zhang, Dong Zheng, Mung Chiang. The impact of stochastic noisy feedback on distributed network utility maximization[J]. IEEE Transactions on Information Theory,2008.54(2):645-665.
[173]J.C. Spall. Introduction to Stochastic Search and Optimization:Estimation, Simulation, and Control [M]. Wiley,2005.
[174]Natural Resources Canada. Energy consumption of major household appliances shipped in Canada, sum-mary report-trends for 1990-2009. http://oee.nrcan.gc.ca/publications/statistics/cama11/index.cfm.
[175]Miao He, Lei Yang, Junshan Zhang, Vijay Vittal. A spatio-temporal analysis approach for short-term forecast of wind farm generation[J]. IEEE Transactions on Power Systems, DOI:10.1109/TP-WRS.2014.2299767.
[176]M.R. Garey, D.S. Johnson. Computers and Intractability:A Guide to the Theory of NP-Completeness[M]. W.H. Freeman,1979.