基于改进粒子群算法在多能互补微网中的应用

doi:10.16180/j.cnki.issn1007-7820.2022.10.012

Abstract

Abstract:

The multi-energy complementary microgrid system has multi-dimensional variables and multiple constraints, which leads to the complex and high-cost calculation of the system comprehensive annual cost. In the calculation process, because the system needs power balance, it is easy to cause the problem that the output of various equipment exceeds the specified limit. In view of this problem, an improved particle swarm optimization algorithm is proposed. The algorithm comprehensively considers the power balance of electricity-heat-cooling multiple loads in the system, and introduces a secondary limit on the basis of the particle swarm algorithm. The algorithm pulls the equipment output beyond the power limit back to the constraint boundary, and dynamically optimizes the reduction coefficient of the equipment output to avoid the over-limit problem caused by the internal power balance of the system. Through the analysis of examples, the genetic algorithm, the algorithm before the improvement, and the algorithm after the improvement are applied to the multi-energy complementary microgrid system, and the equipment output and the comprehensive annualized cost in different scenarios are compared and analyzed. The experimental results prove the effectiveness and universal applicability of the proposed algorithm.

Key words: multi-energy complementary microgrid, multi-dimensional variable, multiple constraints, power balance, comprehensive annualized cost, particle swarm algorithm, quadratic limit, genetic algorithm

CLC Number:

TP391

ZHAO Jian,YUAN Boxun,NI Lingfan,LIN Shunfu,WANG Wei. Application of Improved Particle Swarm Optimization Algorithm in Multi-Energy Complementary Microgrid[J].Electronic Science and Technology, 2022, 35(10): 72-78.

Figures/Tables 12

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Table 1.

Table 2.

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Figure 3.

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Table 6.

Figure 6.

References 18

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粒子个数	迭代次数	y	x	用时/s
5	5	3.285 5	1.068 1	0.012 4
10	10	1.243 0	1.061 1	0.014 7
20	20	0.003 9	1.049 4	0.016 3
50	50	0.000 9	1.050 1	0.015 5
100	100	0.000 0	1.050 0	0.017 1

算法名称	y₁	x₁	x₂	用时/s
PSO算法	0.00	1.050	2.080	5.825 7
遍历算法	0.25	1.050	2.075	0.638 0

算法名称	y₂	x₁	x₂	x₃	用时/s
PSO算法	0.126	1.053 4	2.079 2	5.020 5	5.905
遍历算法	0.500	1.050 0	2.075 0	5.025 0	57.160

设备	投资成本	维护成本系数	效率	运行周期/年
风机	6 000元/kW	0.050元/kW	——	20
光伏	12 700元/kW	0.039元/kW	——	25
热电联供机组	3 300元/kW	0.059元/kW	电:0.3 热:0.45	20
燃气锅炉	1 780元/kW	0.026元/kW	0.872	20
余热锅炉	672元/kW	0.013元/kW	0.80	20
电制冷机	1 180元/kW	0.013元/kW	3.5	20
吸收式制冷机	1 780元/kW	0.013元/kW	1.44	20
蓄电池	972元/(kW·h)	0.013元/(kW·h)	0.75	20
热储	102元(kW·h)	0.026元/(kW·h)	0.9	20
冷储	136元/(kW·h)	0.026元/(kW·h)	0.9	20

设备名称	功率限制/kW
系统与电网的交换功率	-12≤P₁≤12
热电联供机组	0≤P₂≤30
燃气锅炉	0≤P₃≤30
电制冷机	0≤P₄≤30
吸收式制冷机	0≤P₅≤30
蓄电池	-5≤P₆≤5
热储	-7.5≤P₇≤7.5
冷储	-12≤P₈≤12

Application of Improved Particle Swarm Optimization Algorithm in Multi-Energy Complementary Microgrid

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场景数量	改进PSO成本/元	遗传算法成本/元
1种场景	1.079×10⁸	1.125×10⁸
4种场景	1.089×10⁸	1.142×10⁸
16种场景	1.094×10⁸	1.167×10⁸
36种场景	1.097×10⁸	1.169×10⁸

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