基于增量交换的主动配电网分布式多目标最优潮流

doi:10.3969/j.issn.2097-0706.2024.02.006

摘要/Abstract

摘要：

接入高比例可再生能源的主动配电系统，配电网络运营商和能源运营商之间存在竞争与合作的复杂利益关系。为了提高分布式能源利用率，提出了一种基于增量交换的分布式多目标最优潮流（MO-OPF）算法。在MO-OPF模型中，网络运营商和能源运营商分布追求自己的运营目标，同时确保整个网络的电压和支路功率安全限制得到满足。该算法能够以分散的方式求解MO-OPF问题，而不会向网络运营商暴露任何能源运营商的私有信息。其核心思想是利用耦合变量增量的二次函数来描述能源运营商对网络运营商的影响，网络运营商通过求解累积二次规划问题来计算耦合变量的增量。以IEEE 33节点配电系统为例，验证所提方法不仅可以提供与使用集中式优化相同的帕累托最优解，而且具有电压调节能力。

关键词: 分布式多目标优化, 主动配电网, 最优潮流, 可再生能源, 增量交换

Abstract:

With the access of high-proportion renewable energy to active distribution systems，the benefit allocation and competition between distribution network operators and energy system operators are complex. To increase the utilization rate of distributed energy resources, an increment-exchange-based decentralized multi-objective optimal power flow（MO-OPF）algorithm for distribution grids is proposed. During the solving of an MO-OPF model, network operators and multi-energy system operators will pursue their own operation objectives, while satisfying the voltages and branch power limits throughout the power grid. Since the proposed algorithm is decentralized，it can solve MO-OPF problems without exposing any private information of energy system operators to network operators. The core of the algorithm is to describe the impact of energy system operators on network operators with quadratic functions of coupling variables increments, which are obtained by network operators through accumulated quadratic programming. The case study on an IEEE 33-bus distribution system is carried out to demonstrate that the proposed algorithm can not only provide the Pareto optimal solution as the centralized optimization model does, but can also regulate the system voltage.

Key words: decentralized multi-objective optimization, active distribution network, optimal power flow, renewable energy, increment exchange

中图分类号:

TK01

陆文甜. 基于增量交换的主动配电网分布式多目标最优潮流[J]. 综合智慧能源, 2024, 46(2): 43-48.

LU Wentian. Increment-exchange-based decentralized multi-objective optimal power flow algorithm for active distribution grids[J]. Integrated Intelligent Energy, 2024, 46(2): 43-48.

图/表 5

参考文献 25

[1]	BOLOGNANI S, CARLI R, CAVRARO G, et al. Distributed reactive power feedback control for voltage regulation and loss minimization[J]. IEEE Transactions on Automatic Control, 2015, 60(4): 966-981. doi: 10.1109/TAC.2014.2363931
[2]	WANG Z Y, CHEN B K, WANG J H, et al. Coordinated energy management of networked microgrids in distribution systems[J]. IEEE Transactions on Smart Grid, 2015, 6(1):45-53. doi: 10.1109/TSG.2014.2329846
[3]	WANG S S, WANG K, TENG F, et al. An affine arithmetic-based multi-objective optimization method for energy storage systems operating in active distribution networks with uncertainties[J]. Applied Energy, 2018, 223: 215-228. doi: 10.1016/j.apenergy.2018.04.037
[4]	NIKNAM T, MEYMAND H Z, MOJARRAD H D. An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants[J]. Energy, 2011, 36(1): 119-132. doi: 10.1016/j.energy.2010.10.062
[5]	NIKNAM T, MEYMAND H Z, MOJARRAD H D, et al. Multi-objective daily operation management of distribution network considering fuel cell power plants[J]. IET Renewable Power Generation, 2011, 5(5): 356-367. doi: 10.1049/iet-rpg.2010.0190
[6]	LV T G, AI Q. Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources[J]. Applied Energy, 2016, 163: 408-422. doi: 10.1016/j.apenergy.2015.10.179
[7]	GUO L, WANG N, LU H, et al. Multi-objective optimal planning of the stand-alone microgrid system based on different benefit subjects[J]. Energy, 2016, 116: 353-363. doi: 10.1016/j.energy.2016.09.123
[8]	AMELI A, BAHRAMI S, KHAZAELI F, et al. A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner's and distribution company's viewpoints[J]. IEEE Transactions on Power Delivery, 2014, 29(4): 1831-1840. doi: 10.1109/TPWRD.2014.2300845
[9]	GAO Y J, HU X B, YANG W H, et al. Multi-objective bilevel coordinated planning of distributed generation and distribution network frame based on multiscenario technique considering timing characteristics[J]. IEEE Transactions on Sustainable Energy 2017, 8(4):1415-1429. doi: 10.1109/TSTE.2017.2680462
[10]	GUGGILAM S S, DALL'ANESE E, CHEN Y C, et al. Scalable optimization methods for distribution networks with high PV integration[J]. IEEE Transactions on Smart Grid, 2016, 7(4): 2061-2070. doi: 10.1109/TSG.2016.2543264
[11]	HADDADIAN H, NOROOZIAN R. Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices[J]. Applied Energy, 2017, 185: 650-663. doi: 10.1016/j.apenergy.2016.10.120
[12]	QI Q, WU J Z, LONG C. Multi-objective operation optimization of an electrical distribution network with soft open point[J]. Applied Energy, 2017, 208: 734-744. doi: 10.1016/j.apenergy.2017.09.075
[13]	MORSHED M J, HMIDA J B, FEKIH A. A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems[J]. Applied Energy, 2018, 211: 1136-1149. doi: 10.1016/j.apenergy.2017.11.101
[14]	ZHANG S X, CHENG H Z, LI K, et al. Multi-objective distributed generation planning in distribution network considering correlations among uncertainties[J]. Applied Energy, 2018, 226: 743-755. doi: 10.1016/j.apenergy.2018.06.049
[15]	DI SOMMA M, YAN B, BIANCO N, et al. Multi-objective design optimization of distributed energy systems through cost and exergy assessments[J]. Applied Energy, 2017, 204: 1299-1316. doi: 10.1016/j.apenergy.2017.03.105
[16]	DAVOODI E, BABAEI E, MOHAMMADI-IVATLOO B. An efficient covexified SDP model for multi-objective optimal power flow[J]. International Journal of Electrical Power & Energy Systems, 2018, 102: 254-264. doi: 10.1016/j.ijepes.2018.04.034
[17]	WEI P C, HE F C, LI L, et al. Multi-objective problem based operation and emission cots for heat and power hub model through peak load management in large scale users[J]. Energy Conversion and Management, 2018, 171: 411-426. doi: 10.1016/j.enconman.2018.05.025
[18]	ROMAN C, ROSEHART W. Evenly distributed Pareto points in multi-objective optimal power flow[J]. IEEE Transactions on Power Systems, 2006, 21(2):1011-1012. doi: 10.1109/TPWRS.2006.873010
[19]	MESSAC A, ISMAIL-YAHAYA A, MATTSON C A. The normalized normal constraint method for generating the Pareto frontier[J]. Structural and Multidisciplinary Optimization, 2003, 25: 86-98. doi: 10.1007/s00158-002-0276-1
[20]	LI Q, LIU M B, LIU H Y. Piecewise normalized normal constraint method applied to minimization of voltage deviation and active power loss in an AC-DC hybrid power system[J]. IEEE Transactions on Power Systems, 2014, 30(3): 1243-1251. doi: 10.1109/TPWRS.2014.2343625
[21]	张兴科, 魏朝阳, 王康平, 等. 面向高比例光伏并网的火电爬坡压力缓解策略[J]. 综合智慧能源, 2022, 44(1): 1-8. doi: 10.3969/j.issn.2097-0706.2022.01.001
	ZHANG Xingke, WEI Chaoyang, WANG Kangping, et al. Strategies for relieving ramp pressure of thermal power units with high-proportion photovoltaic power connecting to the grid[J]. Integrated Intelligent Energy, 2022, 44(1): 1-8. doi: 10.3969/j.issn.2097-0706.2022.01.001
[22]	石立宝, 翟放. 考虑风-光-荷不确定性的数据驱动型机组组合模型[J]. 综合智慧能源, 2022, 44(1): 18-25. doi: 10.3969/j.issn.2097-0706.2022.01.003
	SHI Libao, ZHAI Fang. Data-driven unit commitment model incorporating the uncertainty of wind-PV-load[J]. Integrated Intelligent Energy, 2022, 44(1): 18-25. doi: 10.3969/j.issn.2097-0706.2022.01.003
[23]	路晓敏, 张明, 邓星, 等. 考虑多重不确定性的多站融合容量优化配置方法[J]. 综合智慧能源, 2022, 44(1): 31-38. doi: 10.3969/j.issn.2097-0706.2022.01.005
	LU Xiaomin, ZHANG Ming, DENG Xing, et al. Optimal capacity configuration for multi-station integration considering multiple uncertainties[J]. Integrated Intelligent Energy, 2022, 44(1): 31-38. doi: 10.3969/j.issn.2097-0706.2022.01.005
[24]	KHOSHFETRAT PAKAZAD S, HANSSON A, ANDERSEN M S, et al. Distributed primal-dual interior-point methods for solving tree-structured coupled convex problems using message-passing[J]. Optimization Methods and Software, 2017, 32(3): 401-435. doi: 10.1080/10556788.2016.1213839
[25]	TAN S W, LIN S J, YANG L Q, et al. Multi-objective optimal power flow model for power system operation dispatching[C]// 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2013: 1-6.