[1] LONNGREN K E, BAI E-W. On the global warming problem due to carbon dioxide[J]. Energy Policy, 2008, 36(4): 1567-1568.
[2] International Energy Agency. Global EV Outlook 2021-Accelerating ambitions despite the pandemic[R]. IEA, 2021.
[3] 国务院办公厅. 关于印发《新能源汽车产业发展规划(2021-2035年)》的通知：国办发
[2020]39号[EB/OL]. (2020-10-20)
[2020-11-02]. http://www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm.
[4] ZHENG Y, SHAO Z, ZHANG Y, et al. A systematic methodology for mid-and-long term electric vehicle charging load forecasting: the case study of Shenzhen, China[J]. Sustainable Cities and Society, 2020, 56: 102084.
[5] ZHANG H, HU X, HU Z, et al. Sustainable plug-in electric vehicle integration into power systems[J]. Nature Reviews Electrical Engineering, 2024, 1(1): 35-52.
[6] CAI S, MATSUHASHI R. Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization[J]. Applied Energy, 2022, 319: 119233.
[7] 郑爽. 国际碳价政策进展及对我国的启示[J]. 中国能源, 2019, 41(10): 33-37.
[8] WEN F, ZHAO H, ZHAO L, et al. What drive carbon price dynamics in China?[J]. International Review of Financial Analysis, 2022, 79: 101999.
[9] LIN B, JIA Z. Impacts of carbon price level in carbon emission trading market[J]. Applied energy, 2019, 239: 157-170.
[10] ZHOU F, HUANG Z, ZHANG C. Carbon price forecasting based on CEEMDAN and LSTM[J]. Applied energy, 2022, 311: 118601.
[11] LI G, NING Z, YANG H, et al. A new carbon price prediction model[J]. Energy, 2022, 239: 122324.
[12] HAO Y, TIAN C, WU C. Modelling of carbon price in two real carbon trading markets[J]. Journal of Cleaner Production, 2020, 244: 118556.
[13] 王一蓉,陈浩林,林立身,等.考虑电力行业碳排放的全国碳价预测[J/OL].中国电力,1-9
[2024-04-27].http://kns.cnki.net/kcms/detail/11.3265.TM.20231213.1618.006.html.
[14] WILBERFORCE T, OLABI A, SAYED E T, et al. Progress in carbon capture technologies[J]. Science of The Total Environment, 2021, 761: 143203.
[15] 袁桂丽, 刘骅骐, 禹建芳. 含碳捕集热电机组的虚拟电厂热电联合优化调度[J]. 中国电机工程学报, 2022, 42(12): 4440-4449.
[16] 彭元, 娄素华, 吴耀武, 等. 考虑储液式碳捕集电厂的含风电系统低碳经济调度[J]. 电工技术学报, 2021, 36(21): 4508-4516.
[17] 周任军, 孙洪, 唐夏菲, 等. 双碳量约束下风电–碳捕集虚拟电厂低碳经济调度[J]. 中国电机工程学报, 2018, 38(6): 1675-1683.
[18] 崔杨, 邓贵波, 曾鹏. 计及碳捕集电厂低碳特性的含风电电力系统源-荷多时间尺度调度方法[J]. 中国电机工程学报, 2022, 42(16): 5869-5886.
[19] WU J, ZHANG Q, LU Y, et al. Source-load coordinated low-carbon economic dispatch of microgrid including electric vehicles[J]. Sustainability, 2023, 15(21): 15287.
[20] LYU X, LIU T, LIU X, et al. Low-carbon robust economic dispatch of park-level integrated energy system considering price-based demand response and vehicle-to-grid[J]. Energy, 2023, 263: 125739.
[21] COLMENAR S A, MUÑOZ G A, ROSALES A E, et al. Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario[J]. Energy, 2019, 183: 61-74.
[22] YUAN Q, YE Y, TANG Y, et al. Low carbon electric vehicle charging coordination in coupled transportation and power networks[J]. IEEE Transactions on Industry Applications, 2022, 59(2): 2162-2172.
[23] ZHANG S, LI X, LIU L. Low carbon economic dispatch method of power system based on wind power and electric vehicle coordination[C]. 2022 China International Conference on Electricity Distribution (CICED), 2022: 750-754.
[24] LEI X, SHANG Y, SHAO Z, et al. Grid integration of electric vehicles for optimal marginal revenue of distribution system operator in spot market[J]. Energy Reports, 2022, 8: 1061-1068.
[25] ZHENG Y, YU H, SHAO Z, et al. Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets[J]. Applied Energy, 2020, 280: 115977.
[26] GAO X, CHAN K W, XIA S, et al. A multiagent competitive bidding strategy in a pool-based electricity market with price-maker participants of WPPs and EV aggregators[J]. IEEE transactions on industrial informatics, 2021, 17(11): 7256-7268.
[27] HAKIMI S M, HASANKHANI A, SHAFIE-KHAH M, et al. Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market[J]. Applied Energy, 2021, 298: 117215.
[28] CHENG S, GU C, LI S, et al. EV flexibility trading in the electricity market[J], 2022.
[29] ZHAO Y, FENG C, LIN Z, et al. Development of optimal bidding strategy for an electric vehicle aggregator in a real-time electricity market[C]. 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 2018: 288-293.
[30] SABER H, RANJBAR H, EHSAN M, et al. Transactive charging management of electric vehicles in office buildings: a distributionally robust chance-constrained approach[J]. Sustainable Cities and Society, 2022, 87: 104171.
[31] LEARD B, MCCONNELL V. Progress and potential for electric vehicles to reduce carbon emissions[R]. Resources for the Future Washington, DC, USA, 2020.
[32] LIU Z, ZHENG W, QI F, et al. Optimal dispatch of a virtual power plant considering demand response and carbon trading[J]. Energies, 2018, 11(6): 1488.
[33] WANG J, WU Z, DU E, et al. Constructing a V2G-enabled regional energy internet for cost-efficient carbon trading[J]. CSEE Journal of Power and Energy Systems, 2020, 6(1): 31-40.
[34] LIU X. Research on bidding strategy of virtual power plant considering carbon-electricity integrated market mechanism[J]. International Journal of Electrical Power & Energy Systems, 2022, 137: 107891.
[35] WEN Y, CHEN Y, WANG P, et al. Photovoltaic–electric vehicles participating in bidding model of power grid that considers carbon emissions[J]. Energy reports, 2022, 8: 3847-3855.
[36] SINGH K N, DAS N, MAURYA M, et al. An intelligent bidding strategy based on social welfare of virtual power plant considering carbon trading[C]. 2022 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE), 2022: 1-5.
[37] YAN Y, LIN Z, WEN F, et al. Bidding strategy of a microgrid considering risk interdependence between electricity and carbon markets[C]. 2019 IEEE Power & Energy Society General Meeting (PESGM), 2019: 1-5.
[38] YANG S X, WANG X F, NING W Q, et al. An optimization model for charging and discharging battery-exchange buses: consider carbon emission quota and peak-shaving auxiliary service market[J]. Sustainable Cities and Society, 2021, 68: 102780.
[39] BIBER A, FELDER M, WIELAND C, et al. Negative price spiral caused by renewables? Electricity price prediction on the German market for 2030[J]. The Electricity Journal, 2022, 35(8): 107188.
[40] SONG Y, LIU T, LI Y, et al. Paths and policy adjustments for improving carbon-market liquidity in China[J]. Energy Economics, 2022, 115: 106379.
[41] LIN J, KAHRL F, YUAN J, et al. Economic and carbon emission impacts of electricity market transition in China: a case study of Guangdong province[J]. Applied Energy, 2019, 238: 1093-1107.
[42] LIU X, JIN Z. An analysis of the interactions between electricity, fossil fuel and carbon market prices in Guangdong, China[J]. Energy for Sustainable Development, 2020, 55: 82-94.
[43] ZHU B, HUANG L, YUAN L, et al. Exploring the risk spillover effects between carbon market and electricity market: a bidimensional empirical mode decomposition based conditional value at risk approach[J]. International Review of Economics & Finance, 2020, 67: 163-175.
[44] SONG Y, LIU T, YE B, et al. Linking carbon market and electricity market for promoting the grid parity of photovoltaic electricity in China[J]. Energy, 2020, 211: 118924.
[45] WOODY M, CRAIG M T, VAISHNAV P T, et al. Optimizing future cost and emissions of electric delivery vehicles[J]. Journal of Industrial Ecology, 2022, 26(3): 1108-1122.
[46] 张安安, 周奇, 李茜, 等.“双碳”目标下火电厂CO2计量技术研究现状与展望[J].发电技术, 2024, 45(01): 51-61.
[47] WANG C, LU Z, QIAO Y. A consideration of the wind power benefits in day-ahead scheduling of wind-coal intensive power systems[J]. IEEE Transactions on Power Systems, 2012, 28(1): 236-245.
[48] WEISSER D. A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies[J]. Energy, 2007, 32(9): 1543-1559.
[49] 柳君波,徐向阳,李思雯.中国电力行业的全周期碳足迹[J].中国人口·资源与环境,2022,32(01):31-41.
[50] LI B, SONG Y, HU Z. Carbon flow tracing method for assessment of demand side carbon emissions obligation[J]. IEEE Transactions on Sustainable Energy, 2013, 4(4): 1100-1107.
[51] 周天睿, 康重庆, 徐乾耀, 等. 电力系统碳排放流分析理论初探[J]. 电力系统自动化, 2012, 36(7): 38-43.
[52] KANG C, ZHOU T, CHEN Q, et al. Carbon emission flow from generation to demand: a network-based model[J]. IEEE Transactions on Smart Grid, 2015, 6(5): 2386-2394.
[53] 汪超群, 陈懿, 迟长云, 等. 基于潮流分布矩阵的电力系统碳排放流计算方法[J]. 科学技术与工程, 2022, 22(12): 4835-4842.
[54] 袁书林, 马瑞. 基于电力系统碳排放流理论的碳排放分摊模型研究[J]. 现代电力, 2014, 31(6): 70-75.
[55] 陈达, 鲜文军, 吴涛, 等. 混合电力市场下碳排放流的分配[J]. 电网技术, 2016, 40(6): 1683-1688.
[56] 周天睿, 康重庆. 基于碳排放流的配电系统低碳优化运行方法研究[J]. Journal of Global Energy Interconnection, 2019, 2(3).
[57] YANG C, LIANG G, LIU J, et al. A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems[J]. Applied Energy, 2023, 350: 121712.
[58] CHENG Y, ZHANG N, ZHANG B, et al. Low-carbon operation of multiple energy systems based on energy-carbon integrated prices[J]. IEEE Transactions on Smart Grid, 2019, 11(2): 1307-1318.
[59] LI J, HE X, LI W, et al. Low-carbon optimal learning scheduling of the power system based on carbon capture system and carbon emission flow theory[J]. Electric Power Systems Research, 2023, 218: 109215.
[60] ABAPOUR S, NAZARI-HERIS M, MOHAMMADI-IVATLOO B, et al. Game theory approaches for the solution of power system problems: a comprehensive review[J]. Archives of computational methods in engineering, 2020, 27: 81-103.
[61] CHURKIN A, BIALEK J, POZO D, et al. Review of cooperative game theory applications in power system expansion planning[J]. Renewable and Sustainable Energy Reviews, 2021, 145: 111056.
[62] CONG H, WANG X, JIANG C. Robust coalitional game theoretic optimisation for cooperative energy hubs with correlated wind power[J]. IET Renewable Power Generation, 2019, 13(13): 2391-2399.
[63] GENG Q, HU Y, HE J, et al. Optimal operation of integrated community energy system based on Nash bargaining[J]. Journal of Electric Power Construction, 2020, 41(1): 114-125.
[64] FANG F, YU S, LIU M. An improved Shapley value-based profit allocation method for CHP-VPP[J]. Energy, 2020, 213: 118805.
[65] YANG S, TAN Z, ZHAO R, et al. Operation optimization and income distribution model of park integrated energy system with power-to-gas technology and energy storage[J]. Journal of cleaner production, 2020, 247: 119090.
[66] KRISTIANSEN M, KORPAS M, SVENDSEN H G. A generic framework for power system flexibility analysis using cooperative game theory[J]. Applied energy, 2018, 212: 223-232.
[67] HE J, LI Y, LI H, et al. Application of game theory in integrated energy system systems: a review[J]. Ieee Access, 2020, 8: 93380-93397.
[68] FAN S, LI Z, WANG J, et al. Cooperative economic scheduling for multiple energy hubs: a bargaining game theoretic perspective[J]. IEEE Access, 2018, 6: 27777-27789.
[69] WANG H, ZHANG C, LI K, et al. Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage[J]. Energy, 2021, 221: 119777.
[70] 彭天海, 蔡萱, 瞿子涵, 等. 计及大规模电网分级分区解耦的区域电网供电碳排放因子计算模型研究[J]. 中国电机工程学报, 2024, 44(03): 894-905.
[71] 贾晓强, 李德智, 陈洪银. 考虑碳交易的工业园区多源冷热电联供系统优化运行策略[J]. 动力工程学报, 2023, 43(08): 1095-1102.
[72] 林卓然, 王守相, 王绍敏, 等. 考虑阶梯型碳交易机制的区域电-热综合能源系统分布协同调度方法[J]. 电网技术, 2023, 47(01): 217-229.
[73] WANG L, SHI Z, DAI W, et al. Two-stage stochastic planning for integrated energy systems accounting for carbon trading price uncertainty[J]. International Journal of Electrical Power & Energy Systems, 2022, 143: 108452.
[74] CHEN Y, YAO Y, ZHANG Y. A robust state estimation method based on SOCP for integrated electricity-heat system[J]. IEEE Transactions on Smart Grid, 2020, 12(1): 810-820.
[75] JAVANMARD M E, GHADERI S, SANGARI M S. Integrating energy and water optimization in buildings using multi-objective mixed-integer linear programming[J]. Sustainable Cities and Society, 2020, 62: 102409.
[76] LEI X, YU H, YU B, et al. Bridging electricity market and carbon emission market through electric vehicles: optimal bidding strategy for distribution system operators to explore economic feasibility in China's low-carbon transitions[J]. Sustainable Cities and Society, 2023, 94: 104557.
[77] TRANBERG B, CORRADI O, LAJOIE B, et al. Real-time carbon accounting method for the European electricity markets[J]. Energy Strategy Reviews, 2019, 26: 100367.
[78] REZAEIMOZAFAR M, ESKANDARI M, SAVKIN A V. A self-optimizing scheduling model for large-scale EV fleets in microgrids[J]. IEEE Transactions on Industrial Informatics, 2021, 17(12): 8177-8188.
[79] BRIGHT J M. Solcast：validation of a satellite-derived solar irradiance dataset[J]. Solar Energy，2019，189：435-449.
[80] SHANG Y, LIU M, SHAO Z, et al. Internet of smart charging points with photovoltaic Integration: a high-efficiency scheme enabling optimal dispatching between electric vehicles and power grids[J]. Applied Energy, 2020, 278: 115640.
[81] SAMPAIO P G V, GONZALEZ M O A. Photovoltaic solar energy: conceptual framework[J]. Renewable and sustainable energy reviews, 2017, 74: 590-601.
[82] YANG H, YANG X, LI G. Forecasting carbon price in China using a novel hybrid model based on secondary decomposition, multi-complexity and error correction[J]. Journal of Cleaner Production, 2023, 401: 136701.
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