中文版 | English
题名

计及点对点能源交易的光储充一体站的能量管理与经济运行

其他题名
ENERGY MANAGEMENT AND ECONOMIC OPERATION FOR INTEGRATED CHARGING STATIONS UNDER PEER-TO-PEER ENERGY TRADING
姓名
姓名拼音
LIU Yujie
学号
12032202
学位类型
硕士
学位专业
080902 电路与系统
学科门类/专业学位类别
08 工学
导师
嘉有为
导师单位
电子与电气工程系
论文答辩日期
2023-05-18
论文提交日期
2023-06-25
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

光储充一体化集成是一种新型的充电站建设模式,具有绿色、高效的特点。然而,光伏和储能系统分别具有间歇性和高成本等特点,因此光储充站的能量管理策略对其经济运行至关重要。光储充一体站可以通过协同光伏、储能系统和电动汽车的充放电来实现经济运行。由于售电侧市场的开放,出现了一系列新型灵活的能源交易形式,例如点对点交易。点对点能源交易方式不仅可以提升多充电站系统的运行稳定性和灵活性,还可以促进可再生能源的就地消纳与高效利用。

目前,关于考虑点对点能源交易的充电站能量管理及经济运行问题的研究相对较少,主要面临以下挑战。首先,需要使用组合优化模型来考虑充电站动态负荷需求、传输损失和最佳交易电价。其次,预测充电站在不同时间段内作为买方或卖方的角色是一项复杂的任务。第三,需要设计一个最优和公平的定价方案。第四,传统集中式求解算法存在效率低、隐私泄漏风险大等缺陷,需要改进传统的求解算法进一步保护隐私性。

鉴于上述挑战,本文提出了一个去中心化的充电站点对点能源交易框架,旨在降低充电站的能源成本,实现其经济运行。在该框架中,充电站包含了可再生能源、电池储能系统和电动汽车等基本单元。其次,充电站的角色是动态的,它既可以作为能源供应者,也可以作为能源需求者。此外,本文改进了传统ADMM算法,提出了一种基于对偶共识的去中心化交替方向乘法器算法(DC-ADMM)来解决点对点交易问题。该算法在原始变量更新中不需要协调器,具有较高的隐私保护和较少的通信负担。为了实现点对点能源交易数据的透明化和可追溯性,本文提出了基于区块链平台的点对点交易机制,帮助供需双方高效建立和完成能源交易。在区块链平台上,构建了一个完整的点对点交易流程。最后,通过算例验证,本文所提策略为充电站带来了显著的成本降低,贡献了部分可用的分布式能源,使得整个社会福利最大化。同时,本文所提出的方法保证了交易效率和能源交易的隐私性和安全性。

其他摘要

The integrated solar-storage-charge-discharge is a new construction mode of charging stations, which has the characteristics of green and efficient. However, due to the intermittent and high-cost characteristics of photovoltaic and energy storage respectively, Therefore, it is necessary to do a good job in energy management. Integrated solar-storage-charge-discharge charging stations can operate economically through collaboration between PV, ESS and EV charging and discharging.  The opening up of the electricity market has made possible a range of new and flexible forms of energy trading, such as peer-to-peer trading. On the one hand, peer-to-peer energy transaction mode aims to improve the stability and flexibility of the economic operation of the multi-charging station system, and on the other hand, to realize the local consumption and efficient utilization of renewable energy.

At present, the energy management and economic operation of charging stations considering peer-to-peer energy transactions are rarely studied, because of the following challenges. First, a combinatorial optimization model must be used to consider the dynamic load demand of charging stations, transmission loss and optimal transaction price. Second, anticipating the role of charging stations as buyers or sellers in advance is a tricky task. Third, the traditional centralized solution algorithm has technical defects such as low efficiency and high risk of privacy leakage. It is necessary to improve the traditional solution algorithm to further protect privacy.

Considering the above challenges, this paper aims to propose a decentralized peer-to-peer energy trading framework for charging stations to reduce the energy cost of charging stations and realize the economical operation of charging stations. The proposed framework considers multiple dynamic components, including renewable energy, battery storage systems, and electric vehicles. In this framework, the role of charging stations as buyers or sellers is endogenously determined. Secondly, the traditional ADMM algorithm is improved, and a decentralized alternating direction multiplier algorithm (DC-ADMM) based on dual consensus is proposed to solve the peer-to-peer transaction problem. In the original variable update, no coordinator is needed, which has higher privacy protection and communication burden reduction. Finally, in order to solve the trust problem of energy transactions and realize the transparency and traceability of peer-to-peer energy transaction data. This paper proposes a peer-to-peer transaction mechanism based on the blockchain platform to help the supply and demand sides to efficiently establish and complete energy transactions. On the blockchain platform, a complete peer-to-peer transaction process is constructed. Finally, an example is given to verify that the method proposed in this paper brings significant cost reduction to the charging station, contributes part of the available distributed energy, maximizes the entire social welfare, ensures the transaction efficiency, and also protects the privacy and security of the energy transaction.

关键词
其他关键词
语种
中文
培养类别
独立培养
入学年份
2020
学位授予年份
2023-06
参考文献列表

[1] 杨经纬, 张宁, 王毅, 等. 面向可再生能源消纳的多能源系统: 述评与展望[J]. 电力系统自动化, 2018, 42(4): 11-24.
[2] 邹才能, 熊波, 薛华庆, 等. 新能源在碳中和中的地位与作用[J]. 石油勘探与开发, 2021, 48(2): 411-420.
[3] 袁志逸, 李振宇, 康利平, 等. 中国交通部门低碳排放措施和路径研究综述[J]. 气候变化研究进展, 2021, 17(1): 27.
[4] 刘动, 孟晨旭, 潘正阳, 等. 居民小区经营性电动汽车充电站投资建设研究[J]. 电气技术, 2022, 23(6): 104-108.
[5] 李瑞忠, 陈铮, 苏宏田. 2018 年我国能源消费形势分析[J]. 煤炭经济研究, 2019, 39(7): 4-9.
[6] YUAN M, MAI J, LIU X, et al. Current Implementation and Development Countermeasures of Green Energy in China’s Highway Transportation[J]. Sustainability, 2023, 15(4): 3024.
[7] LI J, CHEN S, WU Y, et al. How to make better use of intermittent and variable energy? A review of wind and photovoltaic power consumption in China[J]. Renewable and Sustainable Energy Reviews, 2021, 137: 110626.
[8] LI C, DONG Z, CHEN G, et al. Data-driven planning of electric vehicle charging infrastructure: a case study of Sydney, Australia[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3289-3304.
[9] 张宇轩, 郭力, 刘一欣, 等. 电动汽车充电负荷概率分布的数值建模方法[J]. 电力系统自动化, 2021.
[10] 张洪财, 胡泽春, 宋永华, 等. 考虑时空分布的电动汽车充电负荷预测方法[J]. 电力系统自动化, 2014, 38(1): 13-20.
[11] 陈丽丹, 聂涌泉, 钟庆. 基于出行链的电动汽车充电负荷预测模型[J]. 电工技术学报, 2015, 30(4): 216-225.
[12] 冯仕杰, 刘韬, 潘萨, 等. 基于分层优化的电动汽车有序充电策略[J]. 电气工程学报, 2021, 16(3): 137-144.
[13] 邵尹池, 穆云飞, 余晓丹, 等. “车-路-网” 模式下电动汽车充电负荷时空预测及其对配电网潮流的影响[J]. 中国电机工程学报, 2017, 37(18): 5207-5219.
[14] NANSAI K, TOHNO S, KONO M, et al. Effects of electric vehicles (EV) on environmental loads with consideration of regional differences of electric power generation and charging characteristic of EV users in Japan[J]. Applied energy, 2002, 71(2): 111-125.
[15] XIANG Y, JIANG Z, GU C, et al. Electric vehicle charging in smart grid: A spatial-temporal simulation method[J]. Energy, 2019, 189: 116221.
[16] 陈静鹏, 艾芊, 肖斐. 基于集群响应的规模化电动汽车充电优化调度[J]. 电力系统自动化, 2016, 40(22): 43-48.
[17] ZHANG X, KONG X, YAN R, et al. Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior[J]. Energy, 2023, 264: 126274.
[18] NEUMANN H M, SCHAR D, BAUMGARTNER F. The potential of photovoltaic carports to cover the energy demand of road passenger transport[J]. Progress in Photovoltaics: Research and Applications, 2012, 20(6): 639-649.
[19] BIRNIE III D P. Solar-to-vehicle (S2V) systems for powering commuters of the future[J]. Journal of Power Sources, 2009, 186(2): 539-542.
[20] GIANNOULI M, YIANOULIS P. Study on the incorporation of photovoltaic systems as an auxiliary power source for hybrid and electric vehicles[J]. Solar Energy, 2012, 86(1): 441-451.
[21] LEIBLE V, BESSLER W G. Passive hybridization of photovoltaic cells with a lithium-ion battery cell: An experimental proof of concept[J]. Journal of Power Sources, 2021, 482: 229050.
[22] SHAMSDIN S H, SEIFI A, ROSTAMI-SHAHRBABAKI M, et al. Plug-in electric vehicle optimization and management charging in a smart parking lot[C]//2019 IEEE Texas power and energy conference (TPEC). IEEE, 2019: 1-7.
[23] MOHAMED A, SALEHI V, MA T, et al. Real-time energy management algorithm for plug-in hybrid electric vehicle charging parks involving sustainable energy[J]. IEEE Transactions on Sustainable Energy, 2013, 5(2): 577-586.
[24] CHEN Y, MEI S, ZHOU F, et al. An energy sharing game with generalized demand bidding: Model and properties[J]. IEEE Transactions on Smart Grid, 2019, 11(3): 2055-2066.
[25] CHAUDHARI K, UKIL A, KUMAR K N, et al. Hybrid optimization for economic deployment of ESS in PV-integrated EV charging stations[J]. IEEE Transactions on Industrial Informatics, 2017, 14(1): 106-116.
[26] YAO L, DAMIRAN Z, LIM W H. Optimal charging and discharging scheduling for electric vehicles in a parking station with photovoltaic system and energy storage system[J]. Energies, 2017, 10(4): 550.
[27] KOUKA K, MASMOUDI A, ABDELKAFI A, et al. Dynamic energy management of an electric vehicle charging station using photovoltaic power[J]. Sustainable Energy, Grids and Networks, 2020, 24: 100402.
[28] ELDEEB H, FADDEL S, MOHAMMED O A. Multi-objective optimization technique for the operation of grid tied PV powered EV charging station[J]. Electric Power Systems Research, 2018, 164: 201-211.
[29] MEHTA R, SRINIVASAN D, TRIVED A. Optimal charging scheduling of plug-in electric vehicles for maximizing penetration within a workplace car park[C]//2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016: 3646-3653.
[30] ZHANG P, SHAO W, QU H, et al. Study on charging strategy of electric vehicle parking lot based on improved PSO[C]//2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016: 4456-4460.
[31] MEHRJERDI H, HEMMATI R. Stochastic model for electric vehicle charging station integrated with wind energy[J]. Sustainable Energy Technologies and Assessments, 2020, 37: 100577.
[32] YAO L, LIM W H, TSAI T S. A real-time charging scheme for demand response in electric vehicle parking station[J]. IEEE Transactions on Smart Grid, 2016, 8(1): 52-62.
[33] WANG R, WANG P, XIAO G. Two-stage mechanism for massive electric vehicle charging involving renewable energy[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 4159-4171.
[34] PENA-BELLO A, PARRA D, HERBERZ M, et al. Integration of prosumer peer-to-peer trading decisions into energy community modelling[J]. Nature Energy, 2022, 7(1): 74-82.
[35] 朱浩昊, 朱继忠, 李盛林, 等. 电-热综合能源系统优化调度综述[J]. Journal of Global Energy Interconnection, 2022, 5(4).
[36] 司方远, 汪晋宽, 韩英华, 等. 信息物理融合的智慧能源系统多级对等协同优化[J]. 自动化学报, 2019, 45(1): 84-97.
[37] 徐敏, 刘早富, 康哲. 计及光热电站和 “源荷” 双侧博弈的综合能源系统优化运行[J]. 电工电能新技术, 2022, 41(9): 27-39.
[38] 冯宜伟, 毋智军, 王鑫. 智能微网能源管理系统研究综述[J]. Smart Grid, 2020, 10: 312.
[39] AFRASIABI M, MOHAMMADI M, RASTEGAR M, et al. Stochastic distributed microgrid energy management based on over-relaxed alternative direction method of multipliers[J]. IET Renewable Power Generation, 2020, 14(14): 2639-2648
[40] ZHNAG C, WU J, ZHOU Y, et al. Peer-to-Peer energy trading in a Microgrid. Applied Energy[J], 2018, 220: 1-12.
[41] LONG C, WU J, ZHANG C, et al. Peer-to-peer energy trading in a community microgrid[C]//2017 IEEE power & energy society general meeting. IEEE, 2017: 1-5.
[42] LIU N, YU X,WANG C, et al. Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers. IEEE Transactions on Power Systems[J], 2017, 32(5): 3569-3583.
[43] SALDARRIAGA-ZULUAGA S D, LOPEZ-LEZAMA J M, MUNOZ-GALEANO N. Optimal coordination of over-current relays in microgrids using unsupervised learning techniques[J]. Applied Sciences, 2021, 11(3): 1241.
[44] ALOBAIDI A H, KHODAYAR M E, SHAHIDEHPOUR M. Decentralized energy management for unbalanced networked microgrids with uncertainty[J]. IET Generation, Transmission & Distribution, 2021, 15(13): 1922-1938.
[45] 张伟亮, 张辉, 支娜, 等. 考虑网络损耗的基于模型预测直流微电网群能量优化策略[J]. 电力系统自动化, 2021, 45(13): 49-56.
[46] CHEN Y, HAO L, YIN G. Distributed Energy Management of the Hybrid AC/DC Microgrid with High Penetration of Distributed Energy Resources Based on ADMM[J]. Complexity, 2021, 2021.
[47] 汪超群, 韦化, 吴思缘, 等. 七种最优潮流分解协调算法的性能比较[J]. 电力系统自动化, 2016, 40(6): 49-57.
[48] LI Q, LIAO Y, WU K, et al. Parallel and distributed optimization method with constraint decomposition for energy management of microgrids[J]. IEEE Transactions on Smart Grid, 2021, 12(6): 4627-4640.
[49] WANG Z,YU X, MU Y, et al. A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System. Applied Energy[J],2020, 260: 114327.
[50] AN J, LEE M, YEOM S, et al. Determining the Peer-to-Peer electricity trading price and strategy for energy prosumers and consumers within a microgrid. Applied Energy[J], 2020, 261: 114335.
[51] 金之钧, 白振瑞, 杨雷. 能源发展趋势与能源科技发展方向的几点思考[J]. 中国科学院院刊, 2020, 35(5): 576-582.
[52] RAJASEKARAN A S, AZEES M, AL-TURJMAN F. A comprehensive survey on blockchain technology[J]. Sustainable Energy Technologies and Assessments, 2022, 52: 102039.
[53] UR REHMAN M H, SALAH K, DAMIANI E, et al. Trust in blockchain cryptocurrency ecosystem[J]. IEEE Transactions on Engineering Management, 2019, 67(4): 1196-1212.
[54] ZAGHLOUL E, LI T, MUTKA M W, et al. Bitcoin and blockchain: Security and privacy[J]. IEEE Internet of Things Journal, 2020, 7(10): 10288-10313.
[55] TOYODA K, MACHI K, OHTAKE Y, et al. Function-level bottleneck analysis of private proof-of-authority ethereum blockchain[J]. IEEE Access, 2020, 8: 141611-141621.
[56] 龚钢军, 杨晟, 王慧娟, 等. 综合能源服务区块链的网络架构, 交互模型与信用评价[J]. 中国电机工程学报, 2020, 40(18).
[57] 赵升, 徐小舒, 吴征天. 区块链技术在分布式能源交易领域的创新应用[J]. 电器与能效管理技术, 2020 (11): 1.
[58] VIEIRA G, ZHANG J. Peer-to-peer energy trading in a microgrid leveraged by smart contracts[J]. Renewable and Sustainable Energy Reviews, 2021, 143: 110900.
[59] ESMAT A, DE VOS M, GHIASSI-FARROKHFAL Y, et al. A novel decentralized platform for peer-to-peer energy trading market with blockchain technology[J]. Applied Energy, 2021, 282: 116123.
[60] KUMARI A, SHUKLA A, GUPTA R, et al. ET-DeaL: A P2P smart contract-based secure energy trading scheme for smart grid systems[C]//IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2020: 1051-1056.
[61] 郭庆来, 王博弘, 田年丰, 等. 能源互联网数据交易: 架构与关键技术[J]. 电工技术学报, 2020, 35(11): 2285-2295.
[62] 张妍, 王龙泽, 吴靖, 等. 区块链与综合能源系统: 应用及展望[J]. 中国科学基金, 2020, 34(1): 31-37.
[63] HE Y, SONG Z, LIU Z. Fast-charging station deployment for battery electric bus systems considering electricity demand charges[J]. Sustainable Cities and Society, 2019, 48: 101530.
[64] 赵唯嘉, 张宁, 康重庆, 等. 光伏发电出力的条件预测误差概率分布估计方法[J]. 电力系统自动化, 2015 (16): 8-15.
[65] LOFBERG J. YALMIP: A toolbox for modeling and optimization in MATLAB[C]//2004 IEEE international conference on robotics and automation (IEEE Cat. No. 04CH37508). IEEE, 2004: 284-289.
[66] LYU C, JIA Y W, XU Z. Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles[J]. Applied Energy, 2021, 299: 117243.
[67] PAIHO S, KILJANDER J, SARALA R, et al. Towards cross-commodity energy-sharing communities–A review of the market, regulatory, and technical situation[J]. Renewable and Sustainable Energy Reviews, 2021, 151: 111568.
[68] 朱建全, 刘海欣, 叶汉芳, 等. 园区综合能源系统优化运行研究综述[J]. 高电压技术, 2022, 48(7): 2469-2482.
[69] 欧阳丽炜, 王帅, 袁勇, 等. 智能合约: 架构及进展[J]. 自动化学报, 2019, 45(3): 445-457.
[70] 平健, 陈思捷, 张宁, 等. 基于智能合约的配电网去中心化交易机制[J]. 中国电机工程学报, 2017, 37(13): 3682-3690.
[71] VYTELINGUM P, DASH R K, DAVID E, et al. A risk-based bidding strategy for continuous double auctions[J]. 2004.

所在学位评定分委会
电子科学与技术
国内图书分类号
TM732
来源库
人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/543896
专题工学院_电子与电气工程系
推荐引用方式
GB/T 7714
刘宇杰. 计及点对点能源交易的光储充一体站的能量管理与经济运行[D]. 深圳. 南方科技大学,2023.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
12032202-刘宇杰-电子与电气工程(4222KB)----限制开放--请求全文
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[刘宇杰]的文章
百度学术
百度学术中相似的文章
[刘宇杰]的文章
必应学术
必应学术中相似的文章
[刘宇杰]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。