Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (12): 3681-3692.doi: 10.13413/j.cnki.jdxbgxb.20230119

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Optimization model of distribution network system by considering multi⁃types distributed power generation

Wen-bin HAO(),Zhi-gao MENG(),Yong ZHANG,Bo XIE,Ling-yun HE,Pan PENG,Yan TU,Yi-ming HU   

  1. Chengdu Power Supply Company，State Grid Sichuan Electric Power Company，Chengdu 610021，China
  • Received:2023-02-09 Online:2024-12-01 Published:2025-01-24
  • Contact: Zhi-gao MENG E-mail:hwb760817@163.com;mengzhigao718@163.com

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Abstract:

In order to improve the power supply reliability of the distribution network system， solve the current problems of insufficient power supply and high production cost， an annual power balance analysis was conducted for the distribution network system with hybrid power sources. First， the scene tree generation method was used to sample and select the wind power， photovoltaic， and load data within the year. Three typical days were selected in each month according to the probability principle to reflect the characteristics of wind and wind power generation and load throughout the year. Secondly， considering the operating constraints of wind and solar power plants， gas turbine units， batteries， and continuous and discrete reactive power compensation devices， an optimization model of the distribution network system was established to minimize with the goal of minimizing the annual operating cost. The optimization solution problem is a second-order cone programming model. The Yalmip optimization solution tool was used to model the distribution network system， and the Gurobi solver was used to solve the power balance problem of each typical day. Based on the solar radiation， temperature， wind speed， power load of Chengdu， the design example of the IEEE 33-node distribution network system is used to finally realize the production capacity distribution and economic analysis of the annual power balance.

Key words: power balance, distribution network planning, wind power and photovoltaic power, scene analysis

CLC Number: 

  • TM734

Fig.1

Typical daily load output curves of photovoltaic， wind power and electric load in February"

Fig.2

Branch power flow model"

Fig.3

Algorithmic solution process"

Fig.4

IEEE 33 bus system"

Table 1

Typical daily production in February"

典型日

场景

概率

光伏产能/ （MW·h-1）风机产能/ （MW·h-1）

燃气轮机

产能/（MW·h-1）

10.428 578.966 10.160 719.257 1
20.392 8610.093 40.154 618.013 7
30.178 578.942 30.311 118.910 5

Table 2

Typical daily production in June"

典型日

场景

概率

光伏产能/ （MW·h-1）风机产能/ （MW·h-1）燃气轮机产能/（MW·h-1）
10.115.356 10.369 624.398 6
20.533 3315.397 20.204 925.518 4
30.366 6714.390 80.370 438.963 8

Table 3

Typical daily production in December"

典型日

场景

概率

光伏产能/ （MW·h-1）风机产能/ （MW·h-1）燃气轮机产能/（MW·h-1）
10.354 846.989 90.449 322.396 9
20.483 877.186 50.428 121.540 0
30.161 297.238 00.388 521.605 7

Fig.5

Typical daily energy production ratio"

Fig.6

Typical daily battery state of charge"

Table 4

Typical daily economic operation costs"

月份

风机发电

成本/元

光伏发电

成本/元

蓄电池运行成本/元燃气轮机运行成本/元总成本/元
运维成本/元

污染气体治理

成本/元

燃料成本/元

污染气体治理

成本/元

全年9 565.21334 866.37160 441.971 298.16 527 902.767 779 819.917 883 894.25
2153.1121 066.5814 035.436 237.14389 695.8246 552.5477 504.94
6113.3636 058.60473.19210.2778677 102.8984 349.5794 613.81
12704.717 670.151 454.52646.37504 051.6360 214.1584 741.47

Fig.7

Typical daily electricity production and consumption"

Table 5

Annual production capacity and economic comparison"

机组产能/MW成本/元
光伏4 185.83334 866.4
风机170.539 565.21
燃气轮机8 774.147 307 722.7
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