计及需求响应的气电互联虚拟电厂多目标调度优化模型

doi:10.3969/j.issn.1000-7229.2020.02.001

摘要/Abstract

摘要： 随着分布式能源渗透率的逐步提高，虚拟电厂技术逐渐成为解决可再生能源规模化发展的关键技术，文章重点研究气电互联虚拟电厂多目标优化问题。首先，文章将风电机组、光伏发电机组、燃气轮机、电转气设备、储气装置等集成为虚拟电厂，同时联动公共网络和天然气网络，负荷侧包括电力负荷、氢气燃料电池汽车负荷、天然气负荷，并在终端负荷引入需求响应技术进行调节。然后，以运行经济效益、削峰填谷效应、二氧化碳排放作为优化目标，结合功率平衡等约束条件，构建气电互联虚拟电厂多目标模型，通过为各目标函数赋权将多目标优化模型转化为单目标模型进行求解。最后，为了验证所建立模型的有效性和可行性，选取了某地区虚拟电厂作为算例进行分析，比较4种情景下运行结果。算例结果表明：1）所提气电互联虚拟电厂多目标优化模型能够实现经济性、稳定性、环保性多方面综合效益最优；2）电转气和需求响应具有协同削峰填谷效应，提高系统运行稳定性；3）电转气设备增加了清洁能源并网量，减小碳排放量；4）虚拟电厂与公共网络相连能够实现能量间灵活互动，有利于制定合理购售电策略优化虚拟电厂运行。

关键词: 虚拟电厂, 电转气, 多目标优化, 需求响应

Abstract: With the gradual increase of distributed energy permeability, virtual power plant technology has gradually become a key technology to solve the large-scale development of renewable energy. This paper focuses on multi-objective optimization of gas-electricity interconnected virtual power plant. Firstly, this paper integrates wind power, photovoltaic power generation, convention gas turbine, power-to-gas, gas storage tank, etc. into virtual power plants. At the same time, it links the public power grid and natural gas network. The load side includes electricity load, hydrogen fuel-cell vehicle load and natural gas load. The demand response is introduced to adjust the terminal load. Then, combined with power balance and other constraints, the multi-objective model of the gas-electricity interconnected virtual power plant is constructed with the optimization objectives of economic benefit of operation, peak regulation, and carbon dioxide emission. The multi-objective optimization model is transformed into a single-objective model by weighing each objective function. Finally, in order to verify the validity and feasibility of the established model, a virtual power plant in a certain area is selected as an example to compare the operation results of four scenarios. The results show that: 1) The multi-objective optimization model of the gas-electricity interconnected virtual power plant can achieve the optimal comprehensive benefits of economy, stability and environmental protection； 2) Power-to-gas and demand response have the synergistic effect of peak regulation, which improves the stability of the system； 3) Power-to-gas increases the amount of grid-connected clean energy and reduces carbon emissions； 4) The connection between the virtual power plant and the public network enables flexible energy interaction, and the rational purchase and sale of electricity strategies can optimize operation.

Key words: virtual power plant, power-to-gas, multi-objective optimization, demand response

中图分类号:

TM 73

张军六，樊伟，谭忠富，鞠立伟，德格吉日夫，杨莘博，孙婧霞. 计及需求响应的气电互联虚拟电厂多目标调度优化模型[J]. 电力建设, 2020, 41(2): 1-10.

ZHANG Junliu,FAN Wei, TAN Zhongfu, JU Liwei, DE Gejirifu,YANG Shenbo, SUN Jingxia. Multi-Objective Optimization Model of Gas-Electricity Interconnected Virtual Power Plant Considering Demand Response[J]. Electric Power Construction, 2020, 41(2): 1-10.

参考文献

［1］杨挺, 翟峰, 赵英杰, 等. 泛在电力物联网释义与研究展望［J］. 电力系统自动化, 2019, 43(13): 9-20, 53.
YANG Ting, ZHAI Feng, ZHAO Yingjie, et al. Explanation and prospect of ubiquitous electric power Internet of Things［J］. Automation of Electric Power Systems, 2019, 43(13): 9-20, 53.
［2］王宣元,刘敦楠,刘蓁，等.泛在电力物联网下虚拟电厂运营机制及关键技术［J］.电网技术，2019，43（9）：3175-3183.
WANG Xuanyuan, LIU Dunnan, LIU Zhen, et al. Operational mechanism and key technologies of virtual power plants under ubiquitous power Internet of Things ［J］. Power System Technology,2019，43（9）：3175-3183.
［3］周贤正,陈玮,郭创新.考虑供能可靠性与风光不确定性的城市多能源系统规划［J］.电工技术学报,2019，34（17）：3672-3686.ZHOU Xianzheng, CHEN Wei, GUO Chuangxin. Urban multi-energy system planning considering energy supply reliability and wind-light uncertainty ［J］. Transactions of China Electrical Technology,2019，34（17）：3672-3686.
［4］赵继超, 钱康, 许文超, 等. 风光联合并网引起电压随机波动的概率评估［J］. 电力系统及其自动化学报, 2016, 28(9): 82-87.
ZHAO Jichao, QIAN Kang, XU Wenchao, et al. Statistical assessment of voltage random fluctuation for joint grid integration of wind and PV power［J］. Proceedings of the CSU-EPSA, 2016, 28(9): 82-87.
［5］丁明, 林玉娟. 考虑风光荷不确定性的随机生产模拟［J］. 太阳能学报, 2018, 39(10): 2937-2944.
DING Ming, LIN Yujuan. Probabilistic production simulation considering randomness of renewable wind power, photovoltaic and load［J］. Acta Energiae Solaris Sinica, 2018, 39(10): 2937-2944.
［6］盛四清, 孙晓霞. 利用风蓄联合削峰的电力系统经济调度［J］. 电网技术, 2014, 38(9): 2484-2489.
SHENG Siqing, SUN Xiaoxia. An economic dispatching strategy of peak load shifting by wind farm and pumped storage plant［J］. Power System Technology, 2014, 38(9): 2484-2489.
［7］ ZHAO H R, WU Q W, HU S J, et al. Review of energy storage system for wind power integration support［J］. Applied Energy, 2015, 137: 545-553.
［8］ LIU J, SUN W, HARRISON G. Optimal low-carbon economic environmental dispatch of hybrid electricity-natural gas energy systems considering P2G［J］. Energies, 2019, 12(7): 1355.
［9］ WANG C F, DONG S, XU S J, et al. Impact of power-to-gas cost characteristics on power-gas-heating integrated system scheduling［J］. IEEE Access, 2019, 7: 17654-17662.
［10］ GE S Y, LIU X O, LIU H, et al. Research on unit commitment optimization of high permeability wind power generation and P2G［J］. Journal of Renewable and Sustainable Energy, 2018, 10(3): 1-14.
［11］曾红, 刘天琪, 何川, 等. 含电转气设备的气电互联综合能源系统多目标优化［J］. 电测与仪表, 2019, 56(8): 99-107.
ZENG Hong, LIU Tianqi, HE Chuan, et al. Multi-objective optimization for integrated natural-gas and electricity energy system considering power-to-gas［J］. Electrical Measurement & Instrumentation, 2019, 56(8): 99-107.
［12］窦迅, 赵文浩, 郎伊紫禾, 等. 计及电转气技术的天然气-电力耦合系统运行研究综述［J］. 电网技术, 2019, 43(1): 165-173.
DOU Xun, ZHAO Wenhao, LANG Yizihe, et al. A review of operation of natural gas-electricity coupling system considering power-to-gas technology［J］. Power System Technology, 2019, 43(1): 165-173.
［13］ TAN Z F, FAN W, LI H F, et al. Dispatching optimization model of gas-electricity virtual power plant considering uncertainty based on robust stochastic optimization theory［J］. Journal of Cleaner Production, 2020, 247: 119-106.
［14］方燕琼, 艾芊, 范松丽. 虚拟电厂研究综述［J］. 供用电, 2016, 33(4): 8-13.
FANG Yanqiong, AI Qian, FAN Songli. A review on virtual power plant［J］. Distribution & Utilization, 2016, 33(4): 8-13.
［15］傅中兴, 邹蓉蓉, 王佳宁, 等. 商业型与技术型虚拟电厂优化调度技术研究［J］. 机电信息, 2019(18): 96-97.
［16］王莹, 王宣元. 能源转型与虚拟电厂的未来［J］. 华北电业, 2018(9): 16-19.
［17］ SOARES J, FOTOUHI GHAZVINI M A, VALE Z, et al. A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads［J］. Applied Energy, 2016, 162: 1074-1088.
［18］ LIANG Z M, ALSAFASFEH Q, JIN T, et al. Risk-constrained optimal energy management for virtual power plants considering correlated demand response［J］. IEEE Transactions on Smart Grid, 2019, 10(2): 1577-1587.
［19］彭院院,周任军,曾子琪，等.参与气电市场的虚拟电厂内部优化随机模型［J/OL］.中国电力，2019［2019-09-02］.http://kns.cnki.net/kcms/detail/11.3265.TM.20190815.1418.002.html.
PENG Yuanyuan, ZHOU Renjun, ZENG Ziqi, et al. Internal optimization stochastic model of virtual power plant participating in gas and electricity market ［J/OL］. Electric Power,2019［2019-09-02］.http://kns.cnki.net/kcms/detail/11.3265.TM.20190815.1418.002.html.
［20］周任军, 吕佳, 张武军, 等. 气电虚拟电厂多能源市场竞标策略［J］. 中国电力, 2018, 51(7): 120-127.
ZHOU Renjun, L Jia, ZHANG Wujun, et al. Bidding strategies for gas-electricity virtual power plants in multi-energy market［J］. Electric Power, 2018, 51(7): 120-127.
［21］曹俊波. 风功率预测与多种市场下虚拟电厂电—热—气协调优化调度［D］. 长沙: 长沙理工大学, 2018.
CAO Junbo. Prediction of wind power and optimization of electric thermal gas coordinated operation in virtual power plant under multiple market［D］. Changsha: Changsha University of Science & Technology, 2018.
［22］王冠, 李鹏, 焦扬, 等. 计及风光不确定性的虚拟电厂多目标随机调度优化模型［J］. 中国电力, 2017, 50(5): 107-113.
WANG Guan, LI Peng, JIAO Yang, et al. Multi-objective stochastic scheduling optimization model for virtual power plant considering uncertainty of wind and photovoltaic power［J］. Electric Power, 2017, 50(5): 107-113.
［23］鞠立伟. 需求响应参与清洁能源集成消纳与效益评价模型研究［D］. 北京: 华北电力大学, 2017.
JU Liwei. The research on the optimization mode and benefit evaluation mechanism for clean energy absorptive considering demand response［D］. Beijing: North China Electric Power University, 2017.

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