Low-Carbon Optimal Dispatch of Electric-Hydrogen-Heat System in Park Based on Alternating Direction Method of Multipliers
Kong Lingguo1, Shi Lihao1, Shi Zhenyu2, Wang Shibo1, Cai Guowei1
1. China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China; 2. Engineering and Training Teaching Center Northeast Electric Power University Jilin 132012 China
Abstract：With the transition to low-carbon energy and the promotion of the "double carbon" goal, the full use of clean energy has become the consensus of social development. Hydrogen energy, which has no carbon emissions and can realize combined heat and power supply, is an important way to achieve the goal of "double carbon". In order to improve the flexibility and coordination of users' energy use, the renewable energy-based electric-hydrogen multi-energy complementary park energy system has become the focus of attention of experts and scholars, so how to achieve economic and optimal operation of the park electric-hydrogen-thermal decarbonized energy system. To solve this problem, this paper proposes a double-layer energy optimal scheduling method of electric-hydrogen-thermal energy in park based on the alternating direction method of multipliers (ADMM). By combining distributed (rooftop photovoltaic, park building electricity, thermal load) and centralized (battery storage, hydrogen storage, electric heat storage) energy, it realizes precise and optimal interaction of point-to-point energy of multiple subjects. First, the mathematical model of each building is established based on the power interaction cost between buildings and grid, equipment operation and maintenance cost, power interaction cost between buildings and carbon benefit, and the final economic function of the upper level optimization is obtained by decoupling the interaction power between buildings through ADMM. Second, as each building solves its own optimization problem synchronously, its interactive power values need to be coordinated by a centralized energy optimization scheduling center to achieve a balance between supply and demand. Third, the lower layer optimization uses the building interaction energy and the residual power of each building obtained from the upper layer to solve the exact interaction power among buildings and the optimal output of each centralized energy equipment in the park based on mixed integer programming with the objective of minimizing the park operation cost. Simulation results for the electric-hydrogen-thermal system of the park show that each building, as an independent entity, can provide certain electric power support to other buildings with insufficient PV power generation when its own PV power generation is sufficient, or it can be absorbed by the electrolyzer and converted into hydrogen energy or stored by battery storage. When there is a power shortage in the park's energy system, fuel cells and battery storage act as power providers to meet the park's load in conjunction with the grid. In addition, while hydrogen production by electrolysis of water and waste heat of fuel cell power generation are matched with electric boilers to meet the heat load of the park, the remaining heat is stored by the heat storage tank, which fully improves the utilization rate of clean energy. The park has a certain support capacity externally, and the energy system of the park can share a certain load for the grid when the external load peaks. The park calculates carbon emission reductions and carbon benefits based on the output of each equipment. The following conclusions can be drawn from the simulation analysis: (1) Each building in the park can solve its own optimization problem in parallel, solve the desired interactive power and upload it to the centralized energy optimization scheduling center, while taking into account the privacy of user data. (2) Realize the precise interaction of energy from multiple buildings and multiple energy sources (electricity, hydrogen, heat) in the park. solving for the interactive power values available for energy sharing in each building with the support of the ADMM algorithm. The lower layer is based on a mixed integer planning model to accurately solve for the interactive power between buildings and the output of each controllable energy source in the park according to the system operation economy. (3) The adjustable equipment of the park energy system has the ability to support external flexibility while retaining the corresponding margin, and the system can calculate the carbon emission reduction and carbon revenue of the park according to the output of each equipment, so as to realize the low-carbon optimal scheduling of the park electricity-hydrogen-heat system.
孔令国, 史立昊, 石振宇, 王士博, 蔡国伟. 基于交替方向乘子法的园区电-氢-热系统低碳优化调度[J]. 电工技术学报, 2023, 38(11): 2932-2944.
Kong Lingguo, Shi Lihao, Shi Zhenyu, Wang Shibo, Cai Guowei. Low-Carbon Optimal Dispatch of Electric-Hydrogen-Heat System in Park Based on Alternating Direction Method of Multipliers. Transactions of China Electrotechnical Society, 2023, 38(11): 2932-2944.
[1] Zhu Dafeng, Yang Bo, Liu Qi, et al.Energy trading in microgrids for synergies among electricity, hydrogen and heat networks[J]. Applied Energy, 2020, 272: 115225. [2] 袁铁江, 孙传帅, 谭捷, 等. 考虑氢负荷的新型电力系统电源规划[J]. 中国电机工程学报, 2022, 42(17): 6316-6325. Yuan Tiejiang, Sun Chuanshuai, Tan Jie, et al.Generation planning of new power system considering hydrogen load[J]. Proceedings of the CSEE, 2022, 42(17): 6316-6325. [3] 沈小军, 聂聪颖, 吕洪. 计及电热特性的离网型风电制氢碱性电解槽阵列优化控制策略[J]. 电工技术学报, 2021, 36(3): 463-472. Shen Xiaojun, Nie Congying, Lü Hong.Coordination control strategy of wind power-hydrogen alkaline electrolyzer bank considering electrothermal charact-eristics[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 463-472. [4] 李奇, 赵淑丹, 蒲雨辰, 等. 考虑电氢耦合的混合储能微电网容量配置优化[J]. 电工技术学报, 2021, 36(3): 486-495. Li Qi, Zhao Shudan, Pu Yuchen, et al.Capacity optimization of hybrid energy storage microgrid considering electricity-hydrogen coupling[J]. Trans-actions of China Electrotechnical Society, 2021, 36(3): 486-495. [5] 孔令国, 蔡国伟, 李龙飞, 等. 风光氢综合能源系统在线能量调控策略与实验平台搭建[J]. 电工技术学报, 2018, 33(14): 3371-3384. Kong Lingguo, Cai Guowei, Li Longfei, et al.Online energy control strategy and experimental platform of integrated energy system of wind, photovoltaic and hydrogen[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3371-3384. [6] 范宏, 于伟南, 柳璐, 等. 双碳目标下考虑电氢互补的智慧园区多楼宇协调调度[J]. 电力系统自动化, 2022, 46(21): 42-51. Fan Hong, Yu Weinan, Liu Lu, et al.Method of Multi-building coordinated dispatch in smart park considering electricity and hydrogen complementary with dual carbon targets[J]. Automation of Electric Power Systems, 2022, 46(21): 42-51. [7] Lu Tianguang, Wang Zhaoyu, Ai Qian, et al.Interactive model for energy management of clustered microgrids[J]. IEEE Transactions on Industry Applications, 2017, 53(3): 1739-1750. [8] Meng Lexuan, Shafiee Q, Trecate G F, et al.Review on control of DC microgrids and multiple microgrid clusters[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2017, 5(3): 928-948. [9] Han Yang, Zhang Ke, Li Hong, et al.MAS-based distributed coordinated control and optimization in microgrid and microgrid clusters: a comprehensive overview[J]. IEEE Transactions on Power Electronics, 2018, 33(8): 6488-6508. [10] Boroojeni K, Amini M H, Nejadpak A, et al.A novel cloud-based platform for implementation of oblivious power routing for clusters of microgrids[J]. IEEE Access, 2016, 5: 607-619. [11] Che Liang, Zhang Xiaping, Shahidehpour M, et al.Optimal interconnection planning of community microgrids with renewable energy sources[J]. IEEE Transactions on Smart Grid, 2017, 8(3): 1054-1063. [12] Faia R, Soares J, Pinto T, et al.Optimal model for local energy community scheduling considering peer to peer electricity transactions[J]. IEEE Access, 2021, 9: 12420-12430. [13] Stennikov V, Barakhtenko E, Mayorov G, et al.Coordinated management of centralized and distributed generation in an integrated energy system using a multi-agent approach[J]. Applied Energy, 2022, 309: 118487. [14] Liu Zhijian, Li Ying, Fan Guangyao, et al.Co-optimization of a novel distributed energy system integrated with hybrid energy storage in different nearly zero energy community scenarios[J]. Energy, 2022, 247: 123553. [15] 王程, 刘念. 基于交替方向乘子法的互联微电网系统分布式优化调度[J]. 电网技术, 2016, 40(9): 2675-2681. Wang Cheng, Liu Nian.Distributed optimal dispatching of interconnected microgrid system based on alternating direction method of multipliers[J]. Power System Technology, 2016, 40(9): 2675-2681. [16] 李佩杰, 陆镛, 白晓清, 等. 基于交替方向乘子法的动态经济调度分散式优化[J]. 中国电机工程学报, 2015, 35(10): 2428-2435. Li Peijie, Lu Yong, Bai Xiaoqing, et al.Decentralized optimization for dynamic economic dispatch based on alternating direction method of multipliers[J]. Proceedings of the CSEE, 2015, 35(10): 2428-2435. [17] 王皓, 艾芊, 吴俊宏, 等. 基于交替方向乘子法的微电网群双层分布式调度方法[J]. 电网技术, 2018, 42(6): 1718-1727. Wang Hao, Ai Qian, Wu Junhong, et al.Bi-level distributed optimization for microgrid clusters based on alternating direction method of multipliers[J]. Power System Technology, 2018, 42(6): 1718-1727. [18] 杨昭, 艾欣. 考虑电能共享的综合能源建筑群分布式优化调度[J]. 电网技术, 2020, 44(10): 3770-3776. Yang Zhao, Ai Xin.Distributed optimal scheduling for integrated energy building clusters considering energy sharing[J]. Power System Technology, 2020, 44(10): 3770-3776. [19] 吴成辉, 林声宏, 夏成军, 等. 基于模型预测控制的微电网群分布式优化调度[J]. 电网技术, 2020, 44(2): 530-538. Wu Chenghui, Lin Shenghong, Xia Chengjun, et al.Distributed optimal dispatch of microgrid cluster based on model predictive control[J]. Power System Technology, 2020, 44(2): 530-538. [20] 牟晨璐, 丁涛, 李立, 等. 基于分层分布式调度的多园区服务商与综合能源供应商两级协调优化运行模型[J]. 电网技术, 2021, 45(11): 4336-4348. Mu Chenlu, Ding Tao, Li Li, et al.Coordinated optimization operation model of multiple load-serving entities and energy supply companies based on two-level distributed scheduling[J]. Power System Technology, 2021, 45(11): 4336-4348. [21] 冯昌森, 任冬冬, 沈佳静, 等. 计及多能共享的互联微能源网的分布式协同优化调度[J]. 电力系统自动化, 2022, 46(11): 47-57. Feng Changsen, Ren Dongdong, Shen Jiajing, et al.Distributed coordinated optimal scheduling of interconnected micro-energy grids considering multi-energy sharing[J]. Automation of Electric Power Systems, 2022, 46(11): 47-57. [22] 兰鹏, 沈晓东, 吴刚, 等. 基于交替方向乘子法的输-配-天然气系统分布式优化调度[J]. 电力系统自动化, 2021, 45(23): 21-30. Lan Peng, Shen Xiaodong, Wu Gang, et al.Distributed optimal scheduling for transmission-distribution-natural-gas system based on alternating direction method of multipliers[J]. Automation of Electric Power Systems, 2021, 45(23): 21-30. [23] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516. Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal configuration of hydrogen storage in industrial park integrated energy system based on stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516. [24] 孔令国, 于家敏, 蔡国伟, 等. 基于模型预测控制的离网电氢耦合系统功率调控[J]. 中国电机工程学报, 2021, 41(9): 3139-3149. Kong Lingguo, Yu Jiamin, Cai Guowei, et al.Power regulation of off-grid electro-hydrogen coupled system based on model predictive control[J]. Proceedings of the CSEE, 2021, 41(9): 3139-3149. [25] 孔令国. 风光氢综合能源系统优化配置与协调控制策略研究[D]. 北京: 华北电力大学(北京), 2017. [26] Boyd S, Parikh N, Chu E, et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends® in Machine Learning, 2011, 3(1): 1-122.
2023, Vol. 38 
