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A model for the Optimal Allocation of
Voltage Regulators and Capacitors
based on MILP Applied to Distribution
Systems in Steady State
Code: 19.021
M. S. Medrano, S. M. C. Tome, L. L. Martins, P. L. Cavalcante,
H. Iwamoto, M. R. R. Malveira and T. M de Moraes
CPqD and Energisa
10/11/2017 1
Objectives
10/11/2017 2
Development of a model for the simultaneous allocation of Cs and VRs in the electrical network
Minimization of power losses, equipmentinvestments and O&M costs
Tests with different linearizations of model’snonlinear constraints
Introduction
10/11/2017 3
• Utilities are constantly working to provide better voltageand reactive power levels
• Voltage levels – Voltage Regulator
• Reactive power – Capacitor
• Equipment allocation is very important and it is not a trivialtask
• Optimization models can help to improve the power qualitytogether with the return on investment
Introduction
10/11/2017 4
Methodology
10/11/2017 5
As
su
mp
tio
ns
Steady state distribution system – radial operation
Balanced distribution system load-flowbalanced monophasic equivalent
Different load level - Constant active (P) and reactive(Q) power
Fixed or switched capacitor, with different capacities (kVAr)
Voltage regulators with regulator range (r%) and fixednumber of steps (𝒏𝒕)
Acquisition (investment) and O&M costs according to the equipment and its capacity
Objective function
Min { p1 + p2 + p3 }
Constraints
• Equations for the linearized power flow
• Related to the capacitor and voltage regulator allocation
• Voltage and current limit
𝑉𝑚𝑖𝑛2 ≤ 𝑉𝑖,𝑑
𝑞𝑑𝑟≤ 𝑉𝑚𝑎𝑥
2 and 𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
≤ 𝐼𝑚𝑎𝑥2
Methodology
10/11/2017 6
Power
Losses costs
Investments
(new equipment)
costs
O&M costs
• Optmized model solved using MILP
Methodology
10/11/2017 7
MIL
P
Objective function to minimize, composed by a linear equation
Feasible area defined by linear constraints
Integer and real variables
Some of the previous constraints are nonlinear
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
8
𝐼𝑖𝑗,𝑑2
𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝑉𝑖,𝑑
2𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝐼𝑖𝑗,𝑑
2
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
9
𝐼𝑖𝑗,𝑑2
𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝑉𝑖,𝑑
2𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝐼𝑖𝑗,𝑑
2
Constant loads for
each load level d
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
10
𝐼𝑖𝑗,𝑑2
𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝑉𝑖,𝑑
2𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝐼𝑖𝑗,𝑑
2
Reactive power due to the installation of fixed and switched capacitors
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
11
𝐼𝑖𝑗,𝑑2
𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝑉𝑖,𝑑
2𝐼𝑖𝑗,𝑑2
𝑉𝑖,𝑑2 𝐼𝑖𝑗,𝑑
2
Nonlinearities
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
12
𝑉𝑖,𝑑2 𝑉𝑖,𝑑
2
𝑉𝑖,𝑑2
Nonlinearities
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
13
𝑉𝑖,𝑑2
Nonlinearities
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
Power flow equations:
𝑗𝑖∈𝐴
𝑃𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑃𝑖𝑗,𝑑 + 𝑅𝑖𝑗 + 𝑃𝑖,𝑑𝑆 = 𝑃𝑖,𝑑
𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
𝑗𝑖∈𝐴
𝑄𝑗𝑖,𝑑 −
𝑖𝑗∈𝐴
𝑄𝑖𝑗,𝑑 + 𝑋𝑖𝑗 + 𝑄𝑖,𝑑𝑆 + 𝑄𝑖,𝑑
𝐵𝐶 = 𝑄𝑖,𝑑𝐷 ∀𝑖 ∈ 𝐴, ∀𝑑 ∈ 𝐷
− 2 𝑅𝑖𝑗 . 𝑃𝑖𝑗,𝑑 + 𝑋𝑖𝑗 . 𝑄𝑖𝑗,𝑑 − 𝑍𝑖𝑗2 . − = 0 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
. = 𝑃𝑖𝑗,𝑑2 + 𝑄𝑖𝑗,𝑑
2 ∀𝑖𝑗 ∈ 𝑉, ∀𝑑 ∈ 𝐷
Methodology
14
Nonlinearities
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
Voltage regulator model:
• Voltage regulators are modeled using the expression:
𝑽𝒋,𝒅 = 𝟏 + 𝒓%𝒏𝒕𝒊𝒋,𝒅
𝒏𝒕. 𝑽𝒋,𝒅,
where 𝑛𝑡𝑖𝑗,𝑑 is an integer variable with −𝑛𝑡 ≤ 𝑛𝑡𝑖𝑗,𝑑≤ 𝑛𝑡
Methodology
10/11/2017 15
Re
gula
tor
ran
ge 𝑉𝑗,𝑑 𝑉𝑗,𝑑
1 + 𝑟% . 𝑉𝑗,𝑑
1 − 𝑟% . 𝑉𝑗,𝑑
𝑉𝑗,𝑑
𝑛𝑡 𝑡𝑎𝑝𝑠
−𝑛𝑡 𝑡𝑎𝑝𝑠
• In addition, for each theoretical system, the model was solved
considering the following approximations for the term 𝑉𝑖,𝑑𝑞𝑑𝑟
. 𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
:
The results were compared with those obtained using a piecewise linear
function with 5 parts:
• Maximum relative error;
• Average relative error;
• Standard deviation of relative errors.
Experimental results
10/11/2017 16
𝑉𝑚𝑎𝑥𝑞𝑑𝑟
= 𝑉𝑛𝑜𝑚𝑞𝑑𝑟
𝑉𝑚𝑖𝑛𝑞𝑑𝑟
𝑉𝑚𝑒𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛𝑞𝑑𝑟
+ 𝑉𝑚𝑎𝑥𝑞𝑑𝑟
Experimental results
10/11/2017 17
Numerical results were obtained considering three theoreticaldistribution systems, composed by:
70, 202 and 400 nodes
Different capacities were considered:
Fixed capacitor – 300, 600 and 900 kVAr
Switched capacitor– 300, 600 and 900 kVAr
Voltage regulators that enables ±10% regulator range and 33 steps
Minimum distance between the nodes was set in 100m andbetween capacitors was set in 500m
• Distribution system with 70 nodes
Experimental results
10/11/2017 18
0,08
3,69
0,04
0,44
0,04
1,5
0,010,28
0,12
1,57
0,04
0,79
1 2 3 4
Average relative errors (%)
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟𝑃𝑖𝑗,𝑑 𝑄𝑖𝑗,𝑑
0,31
1,41
0,06
1,28
0,16
0,77
0,02
0,95
0,34
1,32
0,07
2,84
1 2 3 4
Standard deviations – averageerrors
Série1 Série2 Série3𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
• Distribution system with 70 nodes
Experimental results
10/11/2017 19
1,33
5,07
0,29
14,72
0,672,1
0,08
8,98
1,35
4,57
0,24
30,87
1 2 3 4
Maximum relative errors (%)
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝑃𝑖𝑗,𝑑 𝑄𝑖𝑗,𝑑
Only in onebranch
• Distribution system with 202 nodes
Experimental results
10/11/2017 20
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
0,01
1,66
0,05
0,3
0,01
0,85
0,01 0,060,01
1,5
0,04
0,23
1 2 3 4
Standard deviations – averageerrors
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
0,01
3,97
0,03 0,150,01
1,33
0,01 0,040,01
3,41
0,03 0,13
1 2 3 4
Average relative errors (%)
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
• Distribution system with 202 nodes
0,02
7,41
0,3
3,43
0,01
3,05
0,070,39
0,02
6,35
0,21
2,96
1 2 3 4
Maximum relative errors (%)
Experimental results
10/11/2017 21
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝑃𝑖𝑗,𝑑 𝑄𝑖𝑗,𝑑
• Distribution system with 400 nodes
Experimental results
10/11/2017 22
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
0,27
3,65
0,05
0,770,51
1,47
0,010,180,25
3,71
0,05
0,57
1 2 3 4
Average relative errors (%)
0,33
1,88
0,1
2,43
0,64
1,02
0,02
0,560,31
1,69
0,07
1,67
1 2 3 4
Standard deviations – averageerrors
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
• Distribution system with 400 nodes
0,71
7,45
0,61
19,27
1,343,09
0,16
3,91
0,66
6,32
0,24
14,33
1 2 3 4
Maximum relative errors (%)
Experimental results
10/11/2017 23
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑖𝑛2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑒𝑑2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
= 𝑉𝑚𝑎𝑥2 𝐼𝑖𝑗,𝑑
𝑞𝑑𝑟
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
𝑃𝑖𝑗,𝑑 𝑄𝑖𝑗,𝑑
Conclusions
10/11/2017 24
The model ensures that the set of equipment is the mostappropiate for the voltage and reactive control
The approximation for the product 𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
was analyzed
using two different approaches and the relative errors(maximum and average) and the standard deviation werecompared.
The use of 𝑉𝑚𝑒𝑑𝑞𝑑𝑟
to approximate 𝑉𝑖,𝑑𝑞𝑑𝑟
in the product
𝑉𝑖,𝑑𝑞𝑑𝑟
𝐼𝑖𝑗,𝑑𝑞𝑑𝑟
presented lower errors, with indices under 10%