基于自适应蝠鲼觅食优化算法的分布式电源选址定容

doi:10.16183/j.cnki.jsjtu.2021.397

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (12): 1673-1688.doi: 10.16183/j.cnki.jsjtu.2021.397

所属专题：《上海交通大学学报》2021年“电气工程”专题；《上海交通大学学报》2021年12期专题汇总专辑

基于自适应蝠鲼觅食优化算法的分布式电源选址定容

杨博¹, 俞磊¹, 王俊婷¹, 束洪春¹, 曹璞璘¹(), 余涛²^,³

1.昆明理工大学电力工程学院, 昆明 650500
2.华南理工大学电力学院, 广州 510640
3.广东省电网智能量测与先进计量企业重点实验室, 广州 510640

收稿日期:2021-10-08 出版日期:2021-12-28 发布日期:2021-12-30
通讯作者: 曹璞璘 E-mail:pulincao_kust@sina.com
作者简介:杨博(1988-),男,云南省昆明市人,教授,主要从事新能源发电/储能系统优化与控制,以及人工智能在智能电网中的应用研究.
基金资助:
国家自然科学基金(61963020);云南省基础研究计划(202001AT070096);国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U2066212)

Optimal Sizing and Placement of Distributed Generation Based on Adaptive Manta Ray Foraging Optimization

YANG Bo¹, YU Lei¹, WANG Junting¹, SHU Hongchun¹, CAO Pulin¹(), YU Tao²^,³

1. School of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
2. School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
3. Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510640, China

Received:2021-10-08 Online:2021-12-28 Published:2021-12-30
Contact: CAO Pulin E-mail:pulincao_kust@sina.com

摘要/Abstract

摘要：

建立了考虑有功功率损耗、电压分布、污染排放、分布式电源(DG)成本以及气象条件的DG选址定容规划模型,其中选址、定容工作分别是一个离散、连续变量,是一个高度非线性、含离散优化变量的复杂模型.因此,应用自适应蝠鲼觅食优化 (AMRFO) 算法获取最优Pareto解集,其具有丰富多样的搜索机制,个体更新机制以及先进的Pareto解筛选机制,针对该模型能够获得更加优异的高质量解.为回避权重系数人为设置主观性带来的影响,采用基于马氏距离的理想决策点法进行Pareto最优解集决策.最后,基于IEEE 33, 69节点配电网和孤网运行的IEEE 33, 69节点配电网进行仿真分析.研究结果表明:与传统的多目标智能优化算法相比,AMRFO算法能够获得分布更加广泛、均匀的Pareto前沿,在兼顾经济性的同时,配电网的电压分布、有功功率损耗的改善效果显著优于其他算法.

关键词: 配电网, 分布式电源, 选址定容, 自适应蝠鲼觅食优化算法

Abstract:

In this paper, an optimal sizing and placement model for distributed generation (DG) is established, which includes active power losses, voltage profile, pollution emission, DG costs, and meteorological conditions. Since optimizing placement and sizing are discrete and continuous variables respectively, the model established is a highly nonlinear complex one with discrete optimization variables. Therefore, the adaptive manta ray foraging optimization (AMRFO) algorithm is applied to obtain the optimal Pareto front, which has a rich and diverse search mechanism, individual updating mechanism, and advanced Pareto solution selection mechanism. For this model, a better solution of high quality can be obtained. In order to avoid the influence of subjective setting of weight coefficient, the ideal point method based on Mahalanobis distance is used to make Pareto front decision. Finally, the simulation based on the IEEE 33, 69-bus distribution network and the IEEE 33, 69-bus distribution network in isolated network operation are implemented. The results show that compared with the traditional multi-objective intelligent optimization algorithm, AMRFO algorithm can obtain a more widely distributed and uniform Pareto front. While considering the economy, the optimized distribution network voltage profile and active power losses can be significantly improved.

Key words: distribution network, distributed generation (DG), optimal sizing and placement, adaptive manta ray foraging optimization (AMRFO) algorithm

中图分类号:

TM76

杨博, 俞磊, 王俊婷, 束洪春, 曹璞璘, 余涛. 基于自适应蝠鲼觅食优化算法的分布式电源选址定容[J]. 上海交通大学学报, 2021, 55(12): 1673-1688.

YANG Bo, YU Lei, WANG Junting, SHU Hongchun, CAO Pulin, YU Tao. Optimal Sizing and Placement of Distributed Generation Based on Adaptive Manta Ray Foraging Optimization[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1673-1688.

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参考文献 26

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DG种类	投资成本/ (美元·kW^-1)	运维成本/ (美元·kW·h^-1)	CO₂/ (kg·kW·h^-1)	SO₂/ (kg·kW·h^-1)	NO_x/ (kg·kW·h^-1)
燃料电池	3500~10000	0.5~1.0	0.502	3.629×10^-6	0.5216
微型燃汽轮机	700~1100	0.5~1.6	3.445	3.629×10^-6	0.1996×10^-3
光伏系统	4500~6000	每年初始投资成本的1%	-	-	-
风力发电机	800~3500	每年初始投资成本的1.5%~2%	-	-	-

节点	年平均风速/ (m·s^-1)	年平均辐射量/ (MJ·m^-2)	节点	年平均风速/ (m·s^-1)	年平均辐射量/ (MJ·m^-2)
1	3.464	0.6179	18	4.437	0.6912
2	2.765	0.3120	19	4.616	0.3916
3	2.315	0.1270	20	4.675	0.5796
4	3.037	0.6776	21	4.893	0.5712
5	4.670	0.6893	22	1.487	0.5941
6	4.016	0.6949	23	4.712	0.6056
7	4.452	0.6991	24	3.967	0.6087
8	2.792	0.6963	25	4.595	0.6195
9	3.359	0.6984	26	3.808	0.6862
10	4.513	0.6752	27	4.156	0.6329
11	2.304	0.6981	28	2.654	0.6812
12	2.882	0.4695	29	2.679	0.6020
13	3.694	0.6262	30	4.458	0.6108
14	2.108	0.6695	31	2.779	0.2820
15	4.566	0.6983	32	3.1125	0.6993
16	3.683	0.7054	33	4.383	0.5712
17	1.333	0.6741

节点	年平均风速/ (m·s^-1)	年平均辐射量/ (MJ·m^-2)	节点	年平均风速/ (m·s^-1)	年平均辐射量/ (MJ·m^-2)
1	2.575	0.8029	36	2.891	0.4583
2	3.270	0.5895	37	3.157	0.5970
3	4.504	0.8487	38	4.112	0.9929
4	2.833	0.2275	39	3.957	0.8437
5	2.241	0.4133	40	2.279	0.4362
6	1.754	0.6670	41	1.404	0.25791
7	3.791	0.7575	42	3.387	0.7879
8	5.066	0.7679	43	5.762	0.9933
9	4.979	0.7717	44	4.437	0.9004
10	5.15	0.7279	45	4.416	0.9204
11	4.154	0.6508	46	2.151	0.5995
12	3.754	0.6141	47	3.191	0.7420
13	3.079	0.5775	48	3.612	0.7454
14	2.658	0.9079	49	2.041	0.6408
15	1.529	0.8395	50	1.670	0.4720
16	1.312	0.4912	51	1.191	0.5133
17	2.312	0.4862	52	3.133	0.5358
18	4.779	0.9379	53	4.804	0.8354
19	4.233	0.6991	54	2.387	0.5945
20	4.070	0.8316	55	1.879	0.2441
21	4.320	0.9141	56	2.766	0.4808
22	2.875	0.8666	57	1.033	0.4033
23	3.225	0.8379	58	0.908	0.1520
24	2.904	0.7133	59	0.883	0.3716
25	2.116	0.5120	60	2.162	0.8954
26	3.066	0.6204	61	2.216	0.5895
27	2.141	0.7229	62	1.066	0.3920
28	1.875	0.33291	63	1.612	0.42166
29	2.608	0.62791	64	1.125	0.66751
30	1.883	0.38254	65	1.358	1.01625
31	0.416	0.19208	66	1.595	0.65666
32	1.937	0.58833	67	2.058	0.64833
33	3.037	0.57666	68	2.225	0.29875
34	4.037	0.68125	69	1.425	0.49208
35	4.287	0.95416

算法	光伏系统							燃料电池			微型燃气轮机				风电机组
算法	#1容量/ kW	#1安装节点		#2容量/ kW		#2安装节点		容量/ kW	安装节点		容量/ kW		安装节点		#1容量/ kW		#1安装节点	#2容量/ kW	#2安装节点
AMRFO	289.79	11		419.74		25		395.14	30		399.55		12		101.39		26	69.38	16
MRFO	317.94	11		347.85		25		303.26	8		399.67		30		60.99		23	212.44	12
NSGA-II	649.20	3		401.55		32		8	16		382.85		9		334.64		31	453.68	22
MOPSO	218.14	16		134.63		15		50.64	18		50.41		6		519.05		25	414.64	21
算法	目标函数适应度值											目标函数权重分配
算法	f₁/kW		f₂(p.u.)		f₃/kg		f₄/美元			f₅(p.u.)		ω_f₁		ω_f₂		ω_f₃		ω_f₄	ω_f₅
AMRFO	2023.26		28.26		5.14×10⁷		8.76×10⁷			0.4837		0.1929		0.2194		0.2144		0.1947	0.1783
MRFO	2167.09		33.98		4.44×10⁷		7.82×10⁷			0.5373		0.1822		0.2244		0.1845		0.2005	0.2082
NSGA-II	4061.87		53.2		2.09×10⁷		4.97×10⁷			0.5685		0.2082		0.1994		0.1875		0.1619	0.2428
MOPSO	27706.8		70.51		6.49×10⁶		1.45×10⁷			0.4251		0.2184		0.2173		0.1823		0.1993	0.1824

算法	光伏系统							燃料电池			微型燃气轮机				风电机组
算法	#1容量/ kW	#1安装节点		#2容量/ kW		#2安装节点		容量/ kW	安装节点		容量/ kW		安装节点		#1容量/ kW		#1安装节点	#2容量/ kW	#2安装节点
AMRFO	78.3	35		298.09		61		398	62		261.23		17		26.27		52	120.94	53
MRFO	416.19	58		370.51		61		385	62		379.23		12		269.88		30	162.38	21
NSGA-II	285.55	44		186.27		61		50.23	23		50.14		63		383.4		31	270.79	66
MOPSO	298.79	11		419.74		25		395	30		399.551		12		101.4		26	69.38	16
算法	目标函数适应度值											目标函数权重分配
算法	f₁/kW		f₂(p.u.)		f₃/kg		f₄/美元			f₅(p.u.)		ω_f₁		ω_f₂		ω_f₃		ω_f₄	ω_f₅
AMRFO	672.49		20.54		4.44×10⁷		7.11×10⁷			0.4278		0.2321		0.1923		0.2043		0.1559	0.2151
MRFO	1664.82		25.22		7.59×10⁷		8.54×10⁷			0.7341		0.2067		0.1886		0.2071		0.2074	0.1899
NSGA-II	3621.59		42.19		6.49×10⁵		1.45×10⁷			0.5912		0.2389		0.1871		0.1586		0.1552	0.2601
MOPSO	1568.83		63.68		5.15×10⁷		8.76×10⁷			0.6246		0.1929		0.2194		0.2144		0.1947	0.1783

基于自适应蝠鲼觅食优化算法的分布式电源选址定容

Optimal Sizing and Placement of Distributed Generation Based on Adaptive Manta Ray Foraging Optimization

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算法	光伏系统							燃料电池			微型燃气轮机				风电机组
算法	#1容量/ kW	#1安装节点		#2容量/ kW		#2安装节点		容量/ kW	安装节点		容量/ kW		安装节点		#1容量/ kW		#1安装节点	#2容量/ kW	#2安装节点
AMRFO	389.44	11		591.31		32		931	2		1496.99		6		285.4		18	205.05	23
MRFO	996.28	11		808.86		32		745	2		1480.38		6		290.1		18	244.47	23
NSGA-II	803.02	5		789.94		31		816	2		1455.51		31		207.54		3	200.57	22
MOPSO	806.63	11		269.74		32		1472	25		1500		2		200.05		19	200	23
算法	目标函数适应度值											目标函数权重分配
算法	f₁/kW		f₂(p.u.)		f₃/kg		f₄/美元			f₅(p.u.)		ω_f₁		ω_f₂		ω_f₃		ω_f₄	ω_f₅
AMRFO	1637.18		20.76		8.11×10⁷		2.62×10⁸			0.429		0.2311		0.1423		0.1931		0.1465	0.2867
MRFO	2964.11		26.67		7.30×10⁷		2.47×10⁸			0.429		0.2145		0.2249		0.1638		0.1736	0.2229
NSGA-II	6656.76		24.86		7.52×10⁷		2.49×10⁸			0.5658		0.1857		0.1204		0.2144		0.2131	0.2663
MOPSO	3638.18		21.22		1.03×10⁸		3.18×10⁸			0.429		0.5065		0.1323		0.1459		0.1185	0.0966

算法	光伏系统							燃料电池			微型燃气轮机				风电机组
算法	#1容量/ kW	#1安装节点		#2容量/ kW		#2安装节点		容量/ kW	安装节点		容量/ kW		安装节点		#1容量/ kW		#1安装节点	#2容量/ kW	#2安装节点
AMRFO	987.98	31		504.89		61		949.14	17		777.86		2		340.98		43	399.03	37
MRFO	554.04	33		613.11		62		1151.47	2		837.24		64		463.36		49	283.74	30
NSGA-II	995.24	56		240.67		61		760.78	2		1337.41		8		200.26		29	426.32	3
MOPSO	842.87	44		898.96		61		890.52	2		1129.28		11		330.91		37	429.84	3
算法	目标函数适应度值											目标函数权重分配
算法	f₁/kW		f₂(p.u.)		f₃/kg		f₄/美元			f₅(p.u.)		ω_f₁		ω_f₂		ω_f₃		ω_f₄	ω_f₅
AMRFO	1711.12		23.87		6.13×10⁷		1.90×10⁸			0.5996		0.2093		0.1674		0.1953		0.2182	0.2095
MRFO	3043.14		30.89		7.13×10⁷		2.15×10⁸			0.5555		0.166521		0.181594		0.184374		0.19593	0.27158
NSGA-II	2240.92		24.57		6.95×10⁷		2.29×10⁸			0.5612		0.2206		0.1655		0.1879		0.2615	0.1642
MOPSO	2482.11		27.345		6.89×10⁷		2.23×10⁸			0.4443		0.2913		0.1692		0.1659		0.1631	0.2102