Abstract：With the large-scale of the micro energy network containing renewable energy integrated into the regional integrated energy system (RIES), its fluctuation and uncertainty confront the flexibilityof the system with significant challenge. In view of this problem, a multi-player bi-level interactive decision-making model was proposed based on the co-operating market mechanism. Firstly, the flexible ramping requirements in RIES operation were specified, and the applicability of flexible ramping product (FRP) to meet the requirements was analyzed from the market aspect. Secondly, the FRP market was introduced to co-operate with the electrical and thermal energy market, then a co-operating market mechanism was carried out to stimulate the market players in both source and demand side to dispatch the flexible ramping resources. In order to solve the Nash equilibrium of the market, a multi-player interactive decision model of non-cooperative game of multiple market players in the external level and clearing of RIES operators in the internal level was established. Q-learning algorithm and path tracking interior point method were adopted to solve the external and internal layer models, respectively. Finally, a numerical example of combined electrical-thermal RIES was used to carry out a comparative analysis of multiple scenarios. The results show that the proposed method can reduce renewable energy curtailment, the total energy cost and the average energy price, thus improving the system operational flexibility effectively.
王雪纯, 陈红坤, 陈磊. 提升区域综合能源系统运行灵活性的多主体互动决策模型[J]. 电工技术学报, 2021, 36(11): 2207-2219.
Wang Xuechun, Chen Hongkun, Chen Lei. Multi-Player Interactive Decision-Making Model for Operational Flexibility Improvement of Regional Integrated Energy System. Transactions of China Electrotechnical Society, 2021, 36(11): 2207-2219.
[1] 余晓丹, 徐宪东, 陈硕翼, 等. 综合能源系统与能源互联网简述[J]. 电工技术学报, 2016, 31(1): 1-13. Yu Xiaodan, XuXiandong, Chen Shuoyi, et al. A briefreview to integrated energy system and energy-yinternet[J]. Transactions of China Electrotechnical Society, 2016, 31(1): 1-13. [2] 韩佶,苗世洪,李超,等. 计及相关性的电-气-热综合能源系统概率最优能量流[J]. 电工技术学报,2019, 34(5): 1055-1067. Han Ji, Miao Shihong, Li Chao, et al.Probabilistic optimal energy flow of electricity-gas-heat integrated energy system considering correlation[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 1055-1067. [3] Wang Beibei, Hobbs B F.Real-time markets for flexiramp: a stochastic unit commitment-based analysis[J]. IEEE Transactions on Power Systems, 2016, 31(2): 846-860. [4] 郭鸿业, 陈启鑫, 夏清, 等. 电力市场中的灵活调节服务:基本概念、均衡模型与研究方向[J]. 中国电机工程学报, 2017, 37(11): 3057-3066. GuoHongye, Chen Qixin, Xia Qing, et al. Flexible ramping product in electricity markets: basic concept, equilibrium model and research prospect[J]. Proceedings of the CSEE, 2017, 37(11): 3057-3066. [5] 王成山, 李鹏, 于浩. 智能配电网的新形态及其灵活性特征分析与应用[J]. 电力系统自动化, 2018, 42(10):13-21. Wang Chengshan, Li Peng, Yu Hao.Development and characteristic analysis of flexibility in smart distribution network[J]. Automation of Electric Power Systems, 2018, 42(10): 13-21. [6] 王蓓蓓, 丛小涵, 高正平, 等. 高比例新能源接入下电网灵活性爬坡能力市场化获取机制现状分析及思考[J]. 电网技术, 2019, 43(8): 2691-2701. Wang Beibei, Cong Xiaohan, GaoZhengping, et al. Status analysis and thoughts of market-oriented acquisition mechanism on flexible ramp capability for power grid with high proportion of renewable energy[J]. Power System Technology, 2019, 43(8): 2691-2701. [7] Cornelius A.Assessing the impact of flexible ramp capability products in the midcontinent ISO[D]. Durham, North Carolina: Duke University, 2014. [8] Wu Chengye, Hug G, Kar S.A functional approach to assessing flexible ramping products' impact on electricity market[C]//Proceedings of 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, 2015: 1-5. [9] Chen Qixin, ZouPeng, Wu Chenye, et al. A nash-cournot approach to assessing flexible ramping products[J]. Applied Energy, 2017, 206: 42-50. [10] Sreekumar S, Sharma K C, Bhakar R.Gumbel copula based multi interval ramp product for power system flexibility enhancement[J]. International Journal of Electrical Power and Energy Systems, 2019, 112: 417-427. [11] 林鸿基, 闫园, 文福栓, 等. 高比例可再生能源电力系统中计及灵活调节产品的实时调度[J]. 电力建设, 2019, 40(10): 18-27. Lin Hongji, Yan Yuan, Wen Fushuan, et al.Real-time dispatch considering flexible ramping products in a power system with high proportion of renewable energy generation[J]. Electric Power Construction, 2019, 40(10): 18-27. [12] 林凯骏, 吴俊勇, 刘迪, 等. 基于双层Stackelberg博弈的微能源网能量管理优化[J]. 电网技术, 2019, 43(3): 974-981. Lin Kaijun, Wu Junyong, Liu Di, et al.Energy management optimization of micro energy grid based on hierarchical Stackelberg game theory[J]. Power System Technology, 2019, 43(3): 943-981. [13] 郝然, 艾芊, 姜子卿. 区域综合能源系统多主体非完全信息下的双层博弈策略[J]. 电力系统自动化, 2018, 42(4): 194-201. Hao Ran, Ai Qian, Jiang Ziqing.Bi-level game strategy for multiagent with incomplete information in regional integrated energy system[J]. Automation of Electric Power Systems, 2018, 42(4): 194-201. [14] 施锦月, 许健, 曾博, 等. 基于热电比可调模式的区域综合能源系统双层优化运行[J]. 电网技术, 2018, 40(10): 2959-2966. Shi Jinyue, XuJian, Zeng Bo, et al. A bi-level optimal operation for energy hub based on regulating heat-to-electric ratio mode[J]. Power System Technology, 2018, 40(10): 2959-2966. [15] Wei Feng, Jing Zhaoxia, Wu P Z, et al.A Stackelberg game approach for multiple energies trading in integrated energy systems[J]. Applied Energy, 2017, 200: 315-329. [16] 陈柏翰, 冯伟, 孙凯, 等. 冷热电联供系统多元储能及孤岛运行优化调度方法[J]. 电工技术学报, 2019, 34(15): 3231-3243. Chen Bohan, Feng Wei, Sun Kai, et al.Multi-energy storage system and islanded optimal dispatch method of CCHP[J]. Transactions of China Electrotechnical Society, 2019, 34(15): 3231-3243. [17] 王洪坤, 王守相, 潘志新, 等. 含高渗透分布式电源配电网灵活性提升优化调度方法[J]. 电力系统自动化, 2018, 42(15): 86-93. Wang Hongkun, Wang Shouxiang, Pan Zhixin, et al.Optimized dispatching method for flexibility improvement of distribution network with high-penetration distributed generation[J]. Automation of Electric Power Systems, 2018, 42(15): 86-93. [18] Wang Qin, Hodge B.Enhancing power system operational flexibility with flexible ramping products: a review[J]. IEEE Transactions on Industrial Informatics, 2017, 13(4): 1652-1664. [19] 张义志, 王小君, 和敬涵,等. 考虑供热系统建模的综合能源系统最优能流计算方法[J]. 电工技术学报, 2019, 34(3): 562-570. Zhang Yizhi, Wang Xiaojun, He Jinghan, et al.Optimal energy flow calculation method of integrated energy system considering thermal system modeling[J]. Transactions of China Electrotechnical Society, 2019, 34(3): 562-570. [20] 刘国静, 韩学山, 王尚, 等. 基于强化学习方法的风储合作决策[J]. 电网技术, 2016, 40(9): 2729-2736. Liu Guojing, Han Xueshan, Wang Shang, et al.Optimal decision-making in the cooperation of wind power and energy storage based on reinforcement learning algorithm[J]. Power System Technology, 2016, 40(9): 2729-2736. [21] Zhang Xiaoshun, Bao Tao, Yu Tao, et al.Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid[J]. Energy, 2017, 133: 348-365. [22] 王锡凡, 方万良, 杜正春. 现代电力系统分析[M]. 北京: 科学出版社, 2003. [23] 李昊飞, 余涛, 瞿凯平, 等. 综合负荷聚合商参与的配电—气能源系统供需互动均衡模型[J]. 电力系统自动化, 2019, 43(19): 32-41. Li Haofei, Yu Tao, QuKaiping, et al. Interactive equilibrium supply and demand model for electricity-gas energy distribution system with participation of integrated load aggregator[J]. Automation of Electric Power Systems, 2019, 43(19): 32-41.
2021, Vol. 36 
