Post on 15-May-2023
Estimation of the Battery Capacity in the Microgrid of a Nearly Zero Energy Building
According to the Desired Degree of Energy Autonomy
Christos Mademlis*, Nikolaos Jabbour, Evangelos Tsioumas, Markos Koseoglou, and Dimitrios Papagiannis
School of Electrical and Computer EngineeringAristotle University of Thessaloniki, Thessaloniki, Greece
Presentation Summary
Introduction to BSS sizing and planning, and nZEB concept.
2Electrical Machines & Power Electronics Laboratory A.U.Th
Aim of this paper.
Optimization variables.
Simulation results.
Conclusions.
Proposed estimation methodology and algorithm.
Introduction to nZEB and Sizing
3
RES and BSS sizing
RES development and availability
Diversity of offered technologies (WT, PV, ESS)
Battery Management Systems (BMS)
Urgent need for coordination and cost reduction
Nearly Zero Energy Building (nZEB)
High energy performance
Higher priority to energy savings and efficiency improvement
End-Use energy consumption reduction
Electrical Machines & Power Electronics Laboratory A.U.Th
Aim of this paper
4
…is to propose an integrated method based on the genetic-algorithm technique toestimate the proper capacity size of a BSS that should be installed in a nZEB, inorder to reach the desired degree of energy autonomy.
Specifically: Grid-connected nZEBs
Balance Cost-Efficiency
Genetic algorithm technique
Adaptive cost function
Various operating scenarios
Electrical Machines & Power Electronics Laboratory A.U.Th
Control Variables (1)
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The energy autonomy degree (EAD)
The BSS utilization degree (BUD)
EAD RES
cons
EE
BUD RESnom
eBSS
EC
The satisfaction degree of the energy peak demands (EPD)
The satisfaction degree of the power peak demands (PPD)
_PPD pe consnompBSS
EC
_EPD pp consnom
eBSS
EC
Control Variables (2)
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EAD RES
cons
EE
BUD RESnom
eBSS
EC
_PPD pe consnompBSS
EC
_EPD pp consnom
eBSS
EC
... are used as target values for the energy autonomy and BSS utilization
are considered in the optimization algorithm to define the cost function
1 2CF (EPD) + (PPD)w w...maximized by the utilizing the genetic algorithm technique in order to determine the optimal sizing of the BSS,
... are utilized as scoring variables for the achievement of the above target values for the EAD and BUD
Simulation Results (1)
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0
1000
2000
3000
4000
5000
6000
7000
8:15PM
7:15AM
6:15PM
5:15AM
4:15PM
3:15AM
2:15PM
1:15AM
12:15PM
11:15PM
10:15AM
9:15PM
8:15AM
7:15PM
6:15AM
5:15PM
4:15AM
3:15PM
2:15AM
1:15PM
12:15AM
11:15AM
10:15PM
9:15AM
Power con
sumption (W
)
Time (duration of 6 days)
1st flat
0
1000
2000
3000
4000
5000
6000
7000
3:34PM
10:01AM
4:28AM
10:55PM
5:21PM
11:48AM
6:15AM
12:42AM
7:08PM
1:35PM
8:02AM
2:29AM
8:55PM
3:22PM
9:49AM
4:16AM
10:42PM
5:09PM
11:36AM
6:03AM
12:29AM
6:56PM
1:23PM
7:50AM
2:16AM
Power con
sumption (W
)
Time (duration of 6 days)
2nd flat
0
1000
2000
3000
4000
5000
6000
7000
6:46PM
12:59PM
7:12AM
1:25AM
7:38PM
1:51PM
8:04AM
2:17AM
8:30PM
2:43PM
8:56AM
3:09AM
9:22PM
3:35PM
9:48AM
4:01AM
10:14PM
4:27PM
10:40AM
4:53AM
11:06PM
5:19PM
11:32AM
5:45AM
Power co
nsum
ption (W
)
Time (duration of 6 days)
3rd flat1st flat of 60m2 at the 1stfloor of the building
2nd flat of 80m2 at the 2ndfloor of the building
3rd flat of 140m2 at the 3rd floor of the building.
Simulation Results (2)
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3, 0.71
2.5, 0.59
2, 0.47
1.5, 0.35
3, 0.3
2.5, 0.35
2, 0.44
1.5, 0.59
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
3 2.5 2 1.5
Varia
tion
of t
he EA
D an
d BU
D
RES nominal power (kW)
EAD
BUD
3, 0.62
2.5, 0.52
2, 0.41
1.5, 0.31
1, 0.21
3, 0.76
2.5, 0.64
2, 0.51
1.5, 0.38
1, 0.25
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
3 2.5 2 1.5 1
Variation of the
EPD an
d PP
D
BSS nominal power/BSS nominal energy (kW/kWh)
EPD
PPD
3, 1.5 2.5, 1.24
2, 0.99
1.5, 0.75
3, 0.297 2.5, 0.35
2, 0.45
1.5, 0.6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
3 2.5 2 1.5
Varia
tion of EAD
and
BUD
RES nominal power (kW)
EAD
BUD
3, 4.34
2.5, 3.6
2, 2.9
1.5, 2.17
3, 1.0392.5, 0.86 2, 0.69
1.5, 0.52
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
3 2.5 2 1.5
Varia
tion of the
EPD an
d PP
D
BSS nominal power/BSS nominal energy (kW/kWh)
EPD
PPD
6, 1.91
5.5, 1.755, 1.6
4.5, 1.434, 1.27
3.5, 1.123, 0.96
2.5, 0.8
6, 0.23 5.5, 0.25 5, 0.28 4.5, 0.3 4, 0.35
3.5, 0.4 3, 0.462.5, 0.55
0
0.5
1
1.5
2
2.5
6 5.5 5 4.5 4 3.5 3 2.5Variatio
n o fthe
EAD
and
BUD
RES nominal power (kW)
ΕΑD
BUD
6, 1.97
5.5, 1.815, 1.64
4.5, 1.48
4, 1.313.5, 1.15
3, 0.992.5, 0.82
6, 1.895.5, 1.73
5, 1.57
4.5, 1.414, 1.26
3.5, 1.1
3, 0.94 2.5, 0.79
0
0.5
1
1.5
2
2.5
3
6 5.5 5 4.5 4 3.5 3 2.5
Varia
tion of the
EPD an
d PP
D
BSS nominal power/BSS nominal energy (kW/kWh)
EPD
PPD
2nd flat1st flat 3rd flat
Simulation Results (2)
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3, 0.71
2.5, 0.59
2, 0.47
1.5, 0.35
3, 0.3
2.5, 0.35
2, 0.44
1.5, 0.59
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
3 2.5 2 1.5
Varia
tion of the
EAD
and
BUD
RES nominal power (kW)
EAD
BUD
3, 0.62
2.5, 0.52
2, 0.41
1.5, 0.31
1, 0.21
3, 0.76
2.5, 0.64
2, 0.51
1.5, 0.38
1, 0.25
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
3 2.5 2 1.5 1
Varia
tion of the
EPD an
d PP
D
BSS nominal power/BSS nominal energy (kW/kWh)
EPD
PPD
3, 1.5 2.5, 1.24
2, 0.99
1.5, 0.75
3, 0.297 2.5, 0.35
2, 0.45
1.5, 0.6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
3 2.5 2 1.5
Varia
tion of EAD an
d BU
D
RES nominal power (kW)
EAD
BUD
3, 4.34
2.5, 3.6
2, 2.9
1.5, 2.17
3, 1.0392.5, 0.86 2, 0.69 1.5, 0.52
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
3 2.5 2 1.5
Varia
tion of the
EPD an
d PP
D
BSS nominal power/BSS nominal energy (kW/kWh)
EPD
PPD
6, 1.91
5.5, 1.755, 1.6
4.5, 1.434, 1.27
3.5, 1.123, 0.96
2.5, 0.8
6, 0.23 5.5, 0.25 5, 0.28 4.5, 0.3 4, 0.35
3.5, 0.4 3, 0.462.5, 0.55
0
0.5
1
1.5
2
2.5
6 5.5 5 4.5 4 3.5 3 2.5
Variatio
n o fthe
EAD
and
BUD
RES nominal power (kW)
ΕΑD
BUD
6, 1.97
5.5, 1.815, 1.64
4.5, 1.48
4, 1.313.5, 1.15
3, 0.992.5, 0.82
6, 1.895.5, 1.73
5, 1.57
4.5, 1.414, 1.26
3.5, 1.1
3, 0.94 2.5, 0.79
0
0.5
1
1.5
2
2.5
3
6 5.5 5 4.5 4 3.5 3 2.5
Varia
tion of the
EPD an
d PP
D
BSS nominal power/BSS nominal energy (kW/kWh)
EPD
PPD
2nd flat1st flat 3rd flat
From the above analysis, it is concluded that for a given value of the EAD and at any specific selection of the nominal power and energy of the RES, there is a certain value for the size of the BSS that can optimize the BUD, EPD and PPD variables
Simulation Results (3)
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TABLE I
OPTIMAL SIZING OF THE BSS
Variable 1st flat 2nd flat 3rd flat
BSS nominal power 2.9 kW 2.5 kW 5.3kW
BSS nominal energy capacity 3.1 kWh 2.8 kWh 5 kWh
Graphical Interface of the calculation program
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Conclusions
13
An integrated BSS estimation method for a nZEB’s microgrid, based on the genetic-algorithm technique, has been proposed.
It is based on a correct balance between the installation cost and the potentialefficiency improvement of the nZEB.
Selective simulation results from a real nZEB microgrid have been presented tovalidate the effectiveness of the suggested method.
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