Carbon Emissions Constraint Distributed Low-Carbon Economic Dispatch of Power System
Li Junhui1, Shao Yan1, Zhu Xingxu1, Guo Qi2, Qi Jun3
1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China; 2. Branch of Power Dispatching Control Inner Mongolia Power (Group) Co. Ltd Hohhot 010020 China; 3. Alxa Electirc Power Supply Company Inner Mongolia Power Group Co. Ltd Alxa 750306 China
Abstract：In a multi-area interconnected power system, to meet the needs of emission reduction policies, carbon emission constraints can be added to the economic dispatch model to regulate the output of the unit and achieve low-carbon economic dispatch of the system. This method is the most common in centralized economic dispatch. Because centralized optimal dispatch requires great communication bandwidth and large computing and information storage requirements, distributed economic dispatch has become a research hotspot. In recent years, scholars have made some achievements in the research of distributed economic dispatch, but the methods involved can only solve the economic dispatch problem with a global equality constraint and several local constraints. For the economic dispatch problem with global inequality constraints, these methods are not applicable. Therefore, this paper proposes a new distributed low-carbon economic scheduling method. The proposed method can solve the economic scheduling problem with global equality constraints and global inequality constraints, which is more universal than the existing distributed economic scheduling methods. First, a low-carbon economic dispatch model is constructed to determine the objective function and the corresponding equality constraints and inequality constraints. Then, based on the duality principle, the original problem is transformed into a corresponding dual problem. Then, the dual problem model is decomposed by the variable decomposition method, so that the objective function and constraints are transformed into optimization sub-problems related to a single region. Finally, based on ADMM, an iterative solution framework for the collaborative optimization of each region is designed. To improve the convergence of the algorithm and avoid the problem of penalty factor parameter selection, a dynamic multiplier update strategy is adopted. The emission reduction factor is set at 15%. According to the method proposed in this paper, the IEEE 6 node test system and the 72-node test system are optimized for distributed low-carbon economy scheduling. In the example of the IEEE 6 node system, the convergence condition is reached when iterating 247 times. The relative error of distributed economic dispatch is 0.88% compared with centralized operation cost, and the calculation result can be regarded as consistent. The carbon emission of the system is reduced from 224 t to 190.06 t, which is reduced by 15.15% and meets the carbon emission constraint. Then, the 72-node test system is analyzed, and the distributed solution effects of different network topology types and partition methods are compared. The universality of the method to different network topologies and the applicability under different partition conditions of the same test system is verified. Finally, the communication fault in the interconnected area verifies that the method meets the needs of “plug and play” in the optimized area. Through the example analysis, it is verified that the method can effectively reduce the amount of information transmission between regions, fully guarantee the requirements of information privacy of each regional unit, meet the demand of “plug and play” in the optimization region, and realize the distributed low-carbon economic dispatch of the multi-area interconnected system. The next work will improve the algorithm to accelerate its convergence speed, and further enrich and improve the model of controllable units within each region.
[1] 刘哲远, 邢海军, 程浩忠, 等. 考虑碳排放流及需求响应的综合能源系统双层优化调度[J]. 高电压技术, 2023, 49(1): 169-178. Liu Zheyuan, Xing Haijun, Cheng Haozhong, et al.Bi-level optimal scheduling of integrated energy system considering carbon emission flow and demand response[J]. High Voltage Engineering, 2023, 49(1): 169-178. [2] 盛戈皞, 钱勇, 罗林根, 等. 面向新型电力系统的电力设备运行维护关键技术及其应用展望[J]. 高电压技术, 2021, 47(9): 3072-3084. Sheng Gehao, Qian Yong, Luo Lingen, et al.Key technologies and application prospects for operation and maintenance of power equipment in new type power system[J]. High Voltage Engineering, 2021, 47(9): 3072-3084. [3] 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. Peng Yuan, Lou Suhua, Wu Yaowu, et al.Low-carbon economic dispatch of power system with wind power considering solvent-storaged carbon capture power plant[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4508-4516. [4] 李忠文, 程志平, 张书源, 等. 考虑经济调度及电压恢复的直流微电网分布式二次控制[J]. 电工技术学报, 2021, 36(21): 4482-4492. Li Zhongwen, Cheng Zhiping, Zhang Shuyuan, et al.Distributed secondary control for economic dispatch and voltage restoration of DC microgrid[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4482-4492. [5] 夏世威, 邹唯薇, 张茜, 等. 基于交替方向乘子法的电力系统分散式经济调度[J]. 电力系统自动化, 2019, 43(6): 100-106. Xia Shiwei, Zou Weiwei, Zhang Qian, et al.Decentralized economic dispatch for power system based on alternating direction method of multipliers[J]. Automation of Electric Power Systems, 2019, 43(6): 100-106. [6] 陈厚合, 王子璇, 张儒峰, 等. 含虚拟电厂的风电并网系统分布式优化调度建模[J]. 中国电机工程学报, 2019, 39(9): 2615-2625. Chen Houhe, Wang Zixuan, Zhang Rufeng, et al.Decentralized optimal dispatching modeling for wind power integrated power system with virtual power plant[J]. Proceedings of the CSEE, 2019, 39(9): 2615-2625. [7] 喻洁, 李扬, 夏安邦. 兼顾环境保护与经济效益的发电调度分布式优化策略[J]. 中国电机工程学报, 2009, 29(16): 63-68. Yu Jie, Li Yang, Xia Anbang.Distributed optimization of generation dispatch schedule considering environmental protection and economic profits[J]. Proceedings of the CSEE, 2009, 29(16): 63-68. [8] Kar S, Hug G.Distributed robust economic dispatch in power systems: a consensus innovations approach[C]//2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 2012: 1-8. [9] 谢俊, 陈凯旋, 岳东, 等. 基于多智能体系统一致性算法的电力系统分布式经济调度策略[J]. 电力自动化设备, 2016, 36(2): 112-117. Xie Jun, Chen Kaixuan, Yue Dong, et al.Distributed economic dispatch based on consensus algorithm of multi agent system for power system[J]. Electric Power Automation Equipment, 2016, 36(2): 112-117. [10] 马锴, 于洋庆, 朱善迎, 等. 基于梯度下降和一致性的电网分布式经济调度算法[J]. 中国科学: 信息科学, 2018, 48(10): 1364-1380. Ma Kai, Yu Yangqing, Zhu Shanying, et al.Distributed algorithm for economic dispatch based on gradient descent and consensus in power grid[J]. Scientia Sinica (Informationis), 2018, 48(10): 1364-1380. [11] 陈伟俊, 罗洪林, 彭建文. 随机ADMM算法及其在电力系统凸经济调度问题中的应用[J]. 应用数学和力学, 2021, 42(9): 979-988. Chen Weijun, Luo Honglin, Peng Jianwen.The random ADMM and its application to convex economic dispatch problems of power systems[J]. Applied Mathematics and Mechanics, 2021, 42(9): 979-988. [12] Wang Yamin, Wu Lei, Wang Shouxiang.A fully-decentralized consensus-based ADMM approach for DC-OPF with demand response[J]. IEEE Transactions on Smart Grid, 2017, 8(6): 2637-2647. [13] 杨珺, 侯俊浩, 刘亚威, 等. 分布式协同控制方法及在电力系统中的应用综述[J]. 电工技术学报, 2021, 36(19): 4035-4049. Yang Jun, Hou Junhao, Liu Yawei, et al.Distributed cooperative control method and application in power system[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4035-4049. [14] 史佳琪, 胡浩, 张建华. 计及多个独立运营商的综合能源系统分布式低碳经济调度[J]. 电网技术, 2019, 43(1): 127-136. Shi Jiaqi, Hu Hao, Zhang Jianhua.Distributed low-carbon economy scheduling for integrated energy system with multiple individual energy-hubs[J]. Power System Technology, 2019, 43(1): 127-136. [15] 杨青润, 李澄, 丁涛, 等. 基于交替方向乘子法的电力系统完全分布式动态经济调度方法[J]. 控制理论与应用, 2018, 35(5): 709-716. Yang Qingrun, Li Cheng, Ding Tao, et al.Fully distributed dynamic economical dispatching method for power system based on alternating direction multiplier method[J]. Control Theory & Applications, 2018, 35(5): 709-716. [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] Xing Hao, Mou Yuting, Fu Minyue, et al.Distributed bisection method for economic power dispatch in smart grid[J]. IEEE Transactions on Power Systems, 2015, 30(6): 3024-3035. [18] 李国庆, 王冲, 雷顺波, 等. 考虑碳捕集技术的电力系统双层优化配置[J]. 电力自动化设备, 2023, 43(1): 25-31. Li Guoqing, Wang Chong, Lei Shunbo, et al.Bi-level optimal allocation of power system considering carbon capture technology[J]. Electric Power Automation Equipment, 2023, 43(1): 25-31. [19] 王澹, 蒋传文, 李磊, 等. 考虑碳排放权分配及需求侧资源的安全约束机组组合问题研究[J]. 电网技术, 2016, 40(11): 3355-3362. Wang Tan, Jiang Chuanwen, Li Lei, et al.Security constrained unit commitment problem considering carbon emission allocation and demand side resources[J]. Power System Technology, 2016, 40(11): 3355-3362. [20] 陈哲, 王橹裕, 郭创新, 等. 基于风险的多区互联电力系统分布式鲁棒动态经济调度[J]. 电力系统自动化, 2021, 45(23): 113-122. Chen Zhe, Wang Luyu, Guo Chuangxin, et al.Risk-based distributed robust dynamic economic dispatch for interconnected multi-regional power systems[J]. Automation of Electric Power Systems, 2021, 45(23): 113-122. [21] Stephen B, Neal P, Eric C, et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers, 2011, 3(1): 1-122. [22] Matt K, Eric C, Javad L, et al.Dynamic network energy management via proximal message passing[J]. Dynamic Network Energy Management Via Proximal Message Passing, 2014, 1(2): 73-126.
2023, Vol. 38 
