权超,冯怿彬,赵鲁臻,陶炳权,谢杭,杨浩然,祁兵
QUAN Chao,FENG Yibin,ZHAO Luzhen,TAO Bingquan,XIE Hang,YANG Haoran,QI Bing

• 1:国网浙江省电力有限公司宁波供电公司
• 2:华北电力大学电气与电子工程学院

摘要(Abstract)：

海量需求侧数据的传输需求使得通信网络的压力成倍增加，容易造成通信延迟、拥塞、中断等现象，影响供需互动业务实时性，不利于负荷调控的进一步实施。针对上述问题，以实现通信受限情况下新能源发电的精准消纳为目标，提出一种计及通信资源优化的负荷精细化调控策略。首先基于信息物理融合技术，建立考虑通信网络影响的温控负荷调控机制。进而基于通信网络模型和计及通信时延的改进温控负荷模型，采用自适应权重与反向学习策略相结合的改进粒子群算法，实现考虑通信资源均衡的温控负荷精细化调控。最后通过算例仿真，验证了所提算法能够合理分配通信资源，使得温控负荷在通信链路时延较大的情况下仍具备良好的消纳能力。
Due to the transmission demand for massive demand-side data, the pressure on communication networks has doubled and redoubled, leading to communication delays, congestion, interruptions, and other problems, which impact the real-time interaction of supply and demand services and hinder further implementation of load scheduling. To address the aforementioned issues and achieve precise consumption of new energy generation under communication constraints, a refined load scheduling strategy considering communication resource optimization is proposed. Firstly, based on information physical fusion technology, a scheduling mechanism for thermostaticallycontrolled loads considering the influence of communication networks is established. Subsequently, by use of a communication network model and an improved thermostatically-controlled load model that takes account of communication delay, an improved particle swarm optimization(PSO) that combines adaptive weighting and reverse learning is utilized, a refined thermostatically-controlled load scheduling considering communication resource balance is achieved. Finally, numerical simulation demonstrates that the proposed method can reasonably allocate communication resources and thermostatically-controlled loads maintain good consumption capability even under significant communication link delays.

关键词(KeyWords)： 温控负荷;负荷调控;新能源消纳;通信时延;AO-MO粒子群优化算法
thermostatically-controlled load;load scheduling;new energy consumption;communication delay;AO-MO particle swarm optimization

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211NB230004）

作者(Author): 权超,冯怿彬,赵鲁臻,陶炳权,谢杭,杨浩然,祁兵
QUAN Chao,FENG Yibin,ZHAO Luzhen,TAO Bingquan,XIE Hang,YANG Haoran,QI Bing

DOI: 10.19585/j.zjdl.202408009

参考文献(References)：

• [1] MENG Y,FAN S,SHEN Y,et al.Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy[J].Applied Energy,2023,350:121681.
• [2]刘任，刘洋，许立雄，等.计及分布式需求响应的多微电网系统协同优化策略[J].电力建设，2023,44(5):72-83.LIU Ren,LIU Yang,XU Lixiong,et al. Multi-microgrid system collaborative optimization strategy considering distributed demand respons[J].Electric Power Construction,2023,44(5):72-83.
• [3]杨悦，宋良泰.考虑用户需求响应的空调负荷日前-实时聚合调控[J].全球能源互联网，2023,6(5):529-537.YANG Yue,Song Liangtai.Day ahead and real time aggregate regulation of air conditioning load on demand response[J]. Journal of Global Energy Interconnection,2023,6(5):529-537.
• [4]李力，董密，宋冬然，等.分布式的温控负荷集群负荷跟随控制[J].中国电机工程学报，2023,43(21):8270-8282.LI Li,DONG Mi,SONG Dongran,et al.Distributed loadfollowing control for thermostatically controlled loads clusters[J]. Proceedings of the CSEE,2023,43(21):8270-8282.
• [5]刘衣萍，于鹤洋，王晨旭，等.考虑用户行为的居民空调负荷需求响应[J].浙江电力，2023,42(3):1-8.LIU Yiping,YU Heyang,WANG Chenxu,et al.Demand response of residential air conditioning load based on user behavior[J].Zhejiang Electric Power,2023,42(3):1-8.
• [6]张天伟，王蓓蓓，贲树俊，等.兼顾降负荷和反弹抑制的负荷聚合商下空调集群调控策略[J].电力系统及其自动化学报，2019,31(3):50-60.ZHANG Tianwei,WANG Beibei,BEN Shujun,et al.Control strategies for air-conditioning group under load aggregator considering load reduction and rebound suppression[J]. Proceedings of the CSU-EPSA,2019,31(3):50-60.
• [7]曹望璋，祁兵，李彬，等.面向需求响应的电力通信网业务均衡与优化[J].中国电机工程学报，2020,40(23):7635-7643.CAO Wangzhang,QI Bing,LI Bin,et al.Balance and optimization of service in electric power communication network oriented to demand response[J].Proceedings of the CSEE,2020,40(23):7635-7643.
• [8]陈启鑫，吕睿可，郭鸿业，等.面向需求响应的电力用户行为建模：研究现状与应用[J].电力自动化设备，2023,43(10):23-37.CHEN Qixin,LüRuike,GUO Hongye,et al.Electricity user behavior modeling for demand response:research status quo and applications[J]. Electric Power Automation Equipment,2023,43(10):23-37.
• [9] WANG J,ZHANG H,ZHOU Y. Intelligent under frequency and under voltage load shedding method based on the active participation of smart appliances[J]. IEEE Transactions on Smart Grid,2017,8(1):353-361.
• [10]孙毅，张辰，李泽坤，等.计及多区域用户差异化PMV的柔性负荷多功率级调控策略[J].中国电机工程学报，2021,41(22):7574-7585.SUN Yi,ZHANG Chen,LI Zekun,et al. Flexible load multi-power level control strategy taking into account the differentiated PMV of multi-region users[J].Proceedings of the CSEE,2021,41(22):7574-7585.
• [11]马庆刘，喻鹏，吴佳慧，等.基于深度强化学习的综合能源业务通道优化机制[J].北京邮电大学学报，2020,43(2):87-93.MA Qingliu,YU Peng,WU Jiahui,et al.A integrated energy service channel optimization mechanism based on deep reinforcement learning[J].Journal of Beijing University of Posts and Telecommunications,2020,43(2):87-93.
• [12]刘林，张运洲，王雪，等.能源互联网目标下电力信息物理系统深度融合发展研究[J].中国电力，2019,52(1):2-9.LIU Lin,ZHANG Yunzhou,WANG Xue,et al.Research on deep integration of power cyber physical system under energy internet goals[J].Electric Power,2019,52(1):2-9.
• [13] RADAIDEH A,VAIDYA U,AJJARAPU V.Sequential set-point control for heterogeneous thermostatically controlled loads through an extended Markov chain abstraction[J].IEEE Transactions on Smart Grid,2019,10(1):116-127.
• [14]刘萌，褚晓东，张文，等.基于多样性保持的空调负荷群调度控制策略[J].中国电机工程学报，2014,34(22):3674-3682.LIU Meng,CHU Xiaodong,ZHANG Wen,et al.Dispatch and control strategies for air-conditioning load groups based on maintenance of load diversity[J].Proceedings of the CSEE,2014,34(22):3674-3682.
• [15]李珂，邰能灵，张沈习.基于改进粒子群算法的配电网综合运行优化[J].上海交通大学学报，2017,51(8):897-902.LI Ke,TAI Nengling,ZHANG Shenxi. Comprehensive optimal dispatch of distribution network based on improved particle swarm optimization algorithm[J]. Journal of Shanghai Jiao Tong University,2017,51(8):897-902.
• [16]梁静，葛士磊，瞿博阳，等.求解电力系统经济调度问题的改进粒子群优化算法[J].控制与决策，2020,35(8):1813-1822.LIANG Jing,GE Shilei,QU Boyang,et al.Improved particle swarm optimization algorithm for solving power system economic dispatch problem[J].Control and Decision,2020,35(8):1813-1822.
• [17]蒙璟，李训聿，丁霞燕.基于改进自适应权重多目标粒子群算法的分布式电源优化配置[J].电力科学与技术学报，2020,35(2):55-60.MENG Jing,LI Xunyu,DING Xiayan.Optimal allocation of distributed generation based on improved adaptive weight multi-objective particle swarm optimization[J].Journal of Electric Power Science and Technology,2020,35(2):55-60.
• [18]刘广生，李成鑫，侯治吉，等.计及用户舒适度的空调负荷可调节能力评估及响应策略[J].电力系统自动化，2023,43(21):8270-8282.LIU Guangsheng,LI Chengxin,HOU Zhiji,et al.Evaluation of adjustable capacity and response strategy for air conditioning load considering comfort of customers[J].Automation of Electric Power Systems,2023,43(21):8270-8282.