Download Reactive Power Optimization for Distribution System

2016China International Conference on Electricity Distribution (CICED 2016)
Xi’an, 10-13 Aug, 2016
Reactive Power Optimization for Distribution System WithDistributed
Generations Based on AHSPSO Algorithm
ZHANG XUE, WANG XIN CHAO, QI XIAO WU
School of Electrical Engineering, Shandong University
Abstract:
Distributed generation (DG) has significant effects on the voltage quality and economic operation of distribution
network. Taking reactive powers supplied by DGs and reactive power compensation equipment as control
variables, the reactive power optimization problem in distribution power system with DGs is researched and an
adaptive harmony search-particle swarm optimization (AHSPSO) algorithm is proposed. Considering economic
factors, the multi-objective reactive power optimization model of distribution power system with DGs is established
firstly. Then a new hybrid algorithm - AHSPSO algorithm is proposed based on the improved harmony search (HS)
algorithm and particle swarm optimization (PSO) algorithm. Compared with original HS and PSO in a modified
IEEE 33-bus system simulation analysis, the results show that the proposed algorithm has stronger global search
capability, higher convergence accuracy and faster convergence rate. Besides, taking DG as a mean of reactive power
compensation can effectively reduce the power loss and improve the node voltage level. AHSPSO algorithm is suitable
for solving the reactive power optimization problem in distribution power system with DGs.
1.
Introduction
In this paper, reactive power optimization of distribution network with DG is taken as the main topic. The
adaptability of the intelligent optimization algorithm to solve the problem is analysed, and for the reactive power
optimization problem of distribution network with DG, a hybrid optimization algorithm - adaptive harmony
search-particle swarm optimization (AHSPSO) algorithm is presented.
2.
Mathematical model of multi-objective reactive power optimization in distribution network with DGs
When DG is connected to the distribution network as a reactive power optimization mean combined with the
traditional reactive power optimization method, not only the impact of the network loss and voltage quality need
to be considered, but also the investment of reactive power compensation equipment needs to be considered.
2.1
Objective function
In this paper, the reactive power QDG supplied by the DG to the power grid and the output of reactive power
compensation equipment QC are taken as the control variables, and the load node voltages are taken as the state
variables. To minimize active power loss Ploss, the best voltage quality (the minimum node voltage offset Vad)
and equipment investment Pinvest (minimum the output of reactive power compensation equipment QC) are taken
as goals to establish multi-objective reactive power optimization objective functions.
2.2
Equality constraints
The equations of the power flow equilibrium of the system are considered as the equality constraints.
2.3
Inequality constraints
Inequality constraints can be divided into control variables constraints and state variables constraints.
2.4
Normalization and weighting method
The final objective function is shown as the following formula:
n
min F  1Ploss +2Vad +3Pinvest +1C pl  2  U j
j 1
CICED2016
Session 2
Paper No 319
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2016China International Conference on Electricity Distribution (CICED 2016) Xi’an Sep. 2016
3.
The basic principle of AHSPSO algorithm
3.1
Basic harmony search algorithm
3.2
Particle swarm optimization algorithm
3.3
Adaptive harmony search-particle swarm optimization algorithm
In this chapter, harmony search algorithm is improved firstly, and then it is combined with PSO
algorithm, finally, a hybrid algorithm - adaptive harmony search-particle swarm optimization algorithm is
proposed.
4.
Application of AHSPSO algorithm in reactive power optimization
QDG and QC, as control variables, together constitute the harmony in harmony memory. When evaluating the
merits of the harmony, take the objective function of reactive power optimization as the evaluation standard.
5.
Example analysis
In this paper, taking the modified IEEE33-bus system as example to simulate and analyze.
5.1
Parameter settings
5.2
Simulation result and analysis
AHSPSO, IHS and PSO algorithm are used to solve the multi-objective reactive power optimization
problem in distribution network with DG respectively, and every algorithm is run continuously 50 times under
the same basic conditions, the average of the optimization results are shown in Table 1.Table 2 presents the
optimal values of control variables after optimizations with different algorithms.
Table 2 The values of control variables after
Table１Comparison of optimal results of
optimizations with different algorithms
different algorithms
Ploss/MW
decline
rate of
Ploss
Vad
decline
rate of
Vad
QC
Before
0.1282
-
5.1559
-
0
PSO
IHS
AHSPSO
0.0731
0.0714
0.0696
42.98%
44.31%
45.71%
2.9406
2.8442
2.6756
42.97%
44.84%
48.11%
0.924
0.984
1.050
6.
control
variables
DG1（MVar）
DG2（MVar）
C1（Mvar）
C2（MVar）
node
number
2
14
6
30
PSO
IHS
AHSPSO
0.216
0.472
0.519
0.405
0. 399
0. 470
0.600
0.384
0.500
0.500
0.600
0.450
Conclusion
The results of the simulation analysis on IEEE33-bus system show that: 1) using DG combined with
traditional means of reactive power compensation to optimize reactive power can greatly reduce power loss,
improve the voltage level of nodes, and increase the compensation effect; 2) the algorithm proposed in this paper
can solve reactive power optimization of distribution system containing DG effectively and quickly. The
algorithm will have better application prospect in reactive power optimization of power system.
Keywords:
Distribution system, distributed generation, reactive power optimization, particle swarm optimization, harmony
search
Author’s brief introduction and contact information:
Zhang Xue（1989-）, F, Master Degree Candidate, Research on reactive power optimization of distribution
network. Email: zhangxue920201@126.com
Wang Xinchao（1962-）, M, Associate Professor, Research on relay protection of power system and so on. Email:
wangxinchao@sdu.edu.cn
Qi Xiaowu（1991-）, M, Master Degree Candidate, Research on transformer condition monitoring. Email:
qixiaowu123@163.com
CICED2016 Session 2
Paper No 319
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