[0001] 本发明设计电力系统运行领域，涉及一种考虑配电系统负荷波动的日前鲁棒调度方法。技术背景
[0002] 近年来，节能和减少碳排放导致可再生能源的快速发展。随着风力发电(wind power generation,WPG)等可再生能源(renewable energy resource,RES)的渗透率逐步提高，能量存储系统(energy storage system,ESS)已用于应对RES引入的挑战。压缩空气储能(compressed air energy storage,CAES)是一种大规模的物理储能技术，为电力负荷的调峰和削峰填谷提供了新的解决方案，并缓解了可再生能源过剩的问题。相关研究已经[1‑2]进行 。CAES使用高压，等温空气压缩机/膨胀机，避免了碳排放，相比使用化石燃料的传[3]
统系统效率更高 。CAES具有大规模存储容量，高爬坡率和短启动时间，可以减轻可再生能[4‑6]
源发电的波动。目前，已成为可再生能源储能的良好选择 。
[0003] 不确定性是WPG的主要问题，这对WPG参与电力市场带来了巨大挑战。WPG的不确定[7]性随着预测时间的增加而增加 。在具有不确定性的调度问题中采用随机优化和鲁棒优[8]
化。随机优化方法无法得到精确解。随着场景的增加，计算量也将大幅增加 。鲁棒优化已成为在不确定环境下实现具有合理实用性，经济性和可靠性的解决方案的有效决策工具。
文献[9]提出基于风险条件值(conditional value at risk,CVaR)的分布式鲁棒方程来获得风电储备需求。文献[10]通过将随机模型转化为可以有效求解的确定性双线性矩阵不等式问题，消除了调度模型中风电预测误差的随机变量。鲁棒最优调度(ROD)已被用于可再生[11‑12]
发电系统 。
[0004] 文献[13]结果表明，混合风力发电系统可以在波动的风速条件下提供平稳的功率输出。采用CAES的小型风力涡轮机的建模和实验研究在[13]中提出。该系统在具有1kW [14‑15]WPG‑CAES系统的实验室中进行测试。数百兆瓦的CAES系统也已开展研究 。文献[16]中使用'PLEXOS'对电力系统运行的经济效益以及WPG‑CAES系统的收益和总发电成本进行了评估。文献[17]为参与能源旋转和非旋转备用市场的CAES设施提出了一种自我调度方法。
文献[18]通过双层规划以确定性方法优化了分布式发电和CAES在孤岛微网的发电规模。此外，所提出的模型考虑了旋转储备以响应负载和可再生能源输出的不确定性。遗传算[19] [20] [21]
法 、粒子群算法 、神经网络 等智能算法常用于经济调度问题。智能算法的一个缺点是很难收敛到全局优化解决方案。动态规划(Dynamic programming,DP)方法具有获得全局[22]
最优控制策略的能力。DP可以处理不连续和非线性约束 。分布式动态规划在文献[23]中用于传统发电系统的经济调度。因此，本发明采用鲁棒动态规划解决WPG‑CAES系统中的日前调度问题。
[0005] 参考文献
[0006] [1]Luo,X.,Wang,J.,Krupke,C.,et al.:‘Modelling study,efficiency analysis and optimisation of large‑scale Adiabatic Compressed Air Energy Storage systems with low‑temperature thermal storage’,Applied Energy,2016,162:589‑600.
[0007] [2]Colthrope,A.,‘Canadian firms NRStor and Hydrostor partner up on utility‑scale adiabatic compressed air storage’,https://www.energy‑storage.news/news/canadian‑firms‑nrstor‑and‑hyd rostor‑partner‑up‑on‑utility‑scale‑adiabatic‑c,accessed 12December 2017.
[0008] [3]Perry,Y.L.,Mohsen,S.:‘An approach to reduce the flow requirement for a liquid piston near‑isothermal air compressor/expander in a compressed air energy storage system’,IET Renewable Power Generation,2015,10,(10),pp.1506‑1514.
[0009] [4]National Renewable Energy Laboratory.:‘Cost and performance data for power generation technologies’,NREL technology report,2012.
[0010] [5]Klumpp,F.:‘Comparison of pumped hydro,hydrogen storage and compressed air energy storage for integrating high shares of renewable energies‑Potential,cost‑comparison and ranking’,J.Energy Storage,2016,8,pp.119‑128.
[0011] [6]Shafiee,S.,Zareipour,H.,Knight,A.M.,Amjady,N.,and Mohammadi‑Ivatloo,B.:‘Risk‑constrained bidding and offering strategy for a merchant compressed air energy storage plant’,IEEE Transactions on Power System,2017,32,(2),pp.946‑957.
[0012] [7]Meng,K.,Yang,H.,Dong,Z.,Guo,W.,Wen,F.,Xu,Z.:‘Flexible operational planning framework considering multiple wind energy forecasting service providers’,IEEE Transactions on Sustainable Energy,2016,7,(2),pp.708‑717[0013] [8]CONEJO,A.J., M.,MORALES,J.M.:‘Decision making under uncertainty in electricity markets’,USA:Springer,2010.
[0014] [9]Wang,Z.,Bian,Q.,Xin,H.and Gan,D.:‘A distributionally robust co‑ordinated reserve scheduling model considering CVaR‑based wind power reserve requirements’,IEEE Transactions on Sustainable Energy,2016,7,(2),pp.625‑636.[0015] [10]Li,Y.,Li,Z.,Wen,F.,Mohammad,S.:‘Minimax‑regret robust co‑optimization for enhancing the resilience of integrated power distribution and natural gas system’,accepted for publication by IEEE Transactions on Sustainable Energy.
[0016] [11]Peng,C.,Xie,P.,Pan,L.and Yu,R.:‘Flexible Robust Optimization Dispatch for HybridWind/Photovoltaic/Hydro/Thermal Power System’,IEEE Transactions on Smart Grid,2016,7,(2),pp.751‑762.
[0017] [12]Wei,W.,Liu,F.and Mei,S.:‘Distributionally Robust Co‑Optimization of Energy and Reserve Dispatch’,IEEE Transactions on Sustainable Energy,2016,7,(1),pp.289‑300.
[0018] [13]Christopher,K.,Wang,J.,Jonathan,C.and Lou,X.:‘Modeling and Experimental Study of a Wind Turbine System in Hybrid Connection With Compressed Air Energy Storage’,IEEE Transactions on Energy Conversion,2017,32,(1),pp.137‑145.
[0019] [14]Evans,A.,Strezov,V.and Evans,T.J.:‘Assessment of utility energy storage options for increased renewable energy penetration’,Renewable&Sustainable Energy Reviews,2012,16,(6),pp.4141–4147.
[0020] [15]‘A.CAES.(2013).APEX CAES’,http://www.apexcaes.com,accessed 31 October 2013.
[0021] [16]Brendan,C.,Aidan,D.,Alan,O.,Michael,C.and Vasilis F.:‘Assessing the Economic Benefits of Compressed Air Energy Storage for Mitigating Wind Curtailment’,IEEE Transactions on Sustainable Energy.2015,6,(3),pp.1021‑1028.[0022] [17]Soroush,S.,Hamidreza,Z.and Andrew,M.K.:‘Considering Thermodynamic Characteristics of a CAES Facility in Self‑Scheduling in Energy and Reserve Markets’,IEEE Transactions on Smart Grid,2018,9,(4),pp.3476‑3485.[0023] [18]Zhang,J.,Li,K.,Wang,M.,et al.:‘A Bi‑Level Program for the Planning of an Islanded Microgrid Including CAES’,IEEE Transactions on Industry Applications.2016,52,(4),pp.2768‑2777.
[0024] [19]Damousis,I.G.,Bakirtzis,A.G.and Dokopoulos.P.S.:‘Network constrained economic dispatch using real‑coded genetic algorithm’,IEEE Transactions on Power System,2003,18,(1),pp.198‑205.
[0025] [20]Selvakumar,A.I.and Thanushkodi,K.:‘A new particle swarm optimization solution to nonconvex economic dispatch problems’,IEEE Transactions on Power System,2007,22,(1),pp.42–51.
[0026] [21]Umamaheswari,K.and Devarajan,N.:‘Hybrid weighted probabilistic neural network and biogeography based optimization for dynamic economic dispatch of integrated multiple‑fuel and wind power plants’,International Journal of Electrical Power&Energy Systems,2016,(77),pp.385‑394.
[0027] [22]Zhang,S.,Xiong,R.:‘Adaptive energy management of a plug‑in hybrid electric vehicle based on driving pattern recognition and dynamic programming’,Applied Energy,2015,(155),pp.68‑78.
[0028] [23]Xu,Y.,Zhang,W.and Liu,W.:‘Distributed Dynamic Programming‑Based Approach for Economic Dispatch in Smart Grids’,IEEE Transactions on Industrial Informatics,2015,11,(1),pp.166‑175.
