Estimation of the Battery Capacity in the Microgrid of a ... - PCIM Asia

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


Control Variables (1)


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)


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

Algorithm of the BSS estimation method


Simulation Results (1)


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

)


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

)


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.



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


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


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


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


EPD

PPD

2nd flat1st flat 3rd flat



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


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


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


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


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


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


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



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


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.


14

Thank you for your attention


Christos Mademlis*, Nikolaos Jabbour, Evangelos Tsioumas, Markos Koseoglou, and Dimitrios Papagiannis

School of Electrical and Computer EngineeringAristotle University of Thessaloniki

Thessaloniki, Greece

Estimation of the Battery Capacity in the Microgrid of a ... - PCIM Asia

Documents

Transcript of Estimation of the Battery Capacity in the Microgrid of a ... - PCIM Asia