MULTI-AGENT SYSTEM GRID OPTIMIZATION

Monjur Mourshed

Piccadilly circus by LS Lowry

LOW VOLTAGE GRID CHALLENGES

Centralised Distributed Holonic / dynamic

Generation node

Distribution node

Control node

Control boundary

Distribution bus

Transmission bus Integration of ICT and energy infrastructures together with the integration of renewable energy, and

decentralisation and deregulation will lead to more dynamic management of the grid, resulting in a

holonic architecture (i.e. system in a system) to allow

for new business models

Past Future

What

Understanding

of demand

and

production

Where

The source of

demand

(load)

generation

When

Time of day,

type of day,

season

Interoperability

and standards

Reliability and

security

Self-organising

architecture

Distributed

optimization

Business

models

Last-mile connectivity and enabling technologies

Business focused use cases

Flexibility

Impact

Interaction

Coincidence

Localisation

MULTI AGENT SYSTEMS

💻📟

💻

📟

📟

Modelling interactions between physical entities

Relationships between energy producers and consumers

Consideration for changing roles - prosumers

Topology

MAS HOLONIC PLATFORM FOR OPTIMIZATION AND MANAGEMENT

DSO

= Local DispatcherDistrict Manager

Home

Home

Storage

Smart

Appliance

District

DER

District

StorageHome Home

Smart

Appliance

Home

DER

Contracted energy flow

consumption

production

management

+ Flexibility Pool Manager

CEA

MAS2TERING AGENT HIERARCHY

Control and optimization strategies are based on:

• Actor roles and goals

• Business functions

• Statutory requirementsCEA

PROBLEM FORMULATION: AN EXAMPLE OF LOCAL DISPATCHING

1. Energy balance inside a district

a. Consider energy flows between actors

→ optimise balance and demand to DSO

a. Topology of the grid:

→ take into account line capacity constraints→ minimise power losses

a. Consider quality of supply:

→ Electricity quality (voltage)

2. Energy flows between districts

a. Consider dynamically rearrangeable topology

→ optimise balance, demand to DSO and topology

→ minimise power losses

IMPLEMENTING OPTIMISATION IN AGENTS

💻

📟

Data acquisition

Simulation/ data fusion

Processed data – (objectives)

CI forecasting / simulation

Computational Intelligence (evolutionary)

based optimisation

Optimum schedule for control and

optimisation

SITUATION ASSESSMENT AND FORECASTING: DISAGGREGATED

Temp (t), Hum (RH)

Weather DER generation Appliances Seasonal

Solar irradiation (I)

Wind (v, θ)

Cloud cover (C)

Wind (turbine char.)

PV (type, size, loc.)

Deferrable

White goods

Storage

EV

Behaviour

Disaggregated forecasting

Effect is accounted

for by weather

parameters if

machine learning

or CI is used for

forecasting

FORECASTING AND OPTIMISATION AT HOME WITH PV

Implemented

In a pilot house

February – March

2015

Optimised ANN-

Priority Based

Intelligent Appliance

Scheduler

…………………………………………………

DB

Device Initial

Configurations

Weather Data

Web-Services

D1 D2 DN

Device Controller

Challenges

• Disaggregated (device) forecasting very

sensitive to occupant behaviour

• Weather data (spatial and temporal) has

significant effect on demand and

production

• Solar and wind vary significantly

FORECASTING OF GRID ENERGY USAGE AND EFFECTS OF WEATHER VARIABLES

GEUij= S(k i

jARk − PVGi

j𝑖𝑓 PVGi

j< S(k i

jARk

0 𝑒𝑙𝑠𝑒

Variable ID Sensitive Variables Regression Coefficient

1 OUTDOOR TEMPERATURE 148.786

2 WIND DIRECTION -63.531

3 DIFFUSE SOLAR RADIATION 47.426

4 OUTDOOR HUMIDITY -39.018

5 DIRECT SOLAR RADIATION 33.437

6 WIND SPEED 26.344

7 AIR TEMPERATURE 11.426

8 AIR RELATIVE HUMIDITY 10.555

9 VISIBILITY 0.214

10 MSL PRESSURE -0.018

11 SOLAR HOUR DURATION -0.000646

12 Constant -24937.734

FORECASTING AT HOME

a) The training results of the best performed ANN for the demand of Device 1.

b) The training results of the best performed ANN for the demand of Device 2.

c) The training results of the best performed ANN for the

demand of Device 3.

d) The training results of the best performed ANN for the

demand of Device 4.

e) The training results of the best performed ANN for the Indoor Air Temperature.

f) The training results of the best performed ANN for the PV electricity generation.

a) The training results of the best performed ANN for the demand of Device 1.

b) The training results of the best performed ANN for the demand of Device 2.

c) The training results of the best performed ANN for the

demand of Device 3.

d) The training results of the best performed ANN for the

demand of Device 4.

e) The training results of the best performed ANN for the Indoor Air Temperature.

f) The training results of the best performed ANN for the PV electricity generation.

a) The training results of the best performed ANN for the demand of Device 1.

b) The training results of the best performed ANN for the demand of Device 2.

c) The training results of the best performed ANN for the

demand of Device 3.

d) The training results of the best performed ANN for the

demand of Device 4.

e) The training results of the best performed ANN for the Indoor Air Temperature.

f) The training results of the best performed ANN for the PV electricity generation.

Fridge

Tumble Dr.

Washing M.

Plug load PV Generation

Indoor temp.

RESULTS

25% reduction scenario10% reduction scenario

Original

Optimised

Original

Optimised

WIND POWER FORECASTING

0

5

10

15

20

25

30

0

1000

2000

3000

4000

5000

6000

7000

8000

304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324

ave watts

max watts

ave m/s

max m/s

0

1000

2000

3000

4000

5000

6000

7000

8000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

wind m/s

power curve

Cp=0.4

power 240-260 compass

leaflet power

power 210-140 compass

FORECASTING AND PREDICTION: CHALLENGES

Intermittent Spatial Temporal SeasonalSystem

dependent

URBAN AREA FORECASTING - 2

Figure 3 The labels and lcation of the building corners

on Quickbird (0.6 GSD) test image

Figure 4 (a) 3D visualisation of solar angles: Sun

Elevation, Azimuth and Zenith angle with the

formation of a cast shadow of the urban structure, (b)

a shadow pattern of the actual illumination direction

and its opposite direction, which is composed of the

cast direction of the shadow with the geometric angles

of the relationships within the image space

THE EVALUATION OF THE 3D MODEL

OF URBAN STRUCTURES

To evaluate and test the uncertainty in the creation of

a 3D model of detached buildings, we investigated the

area of the base (building footprint) of a 3D solid box

(which is also the same area of the top of a 3D model).

The shape of the buildings mostly represents a

rectangle shape, and therefore the area must be a

regular rectangular shape. This means the geometry of

the 3D solid boxes has right angles in each corner

between each of the two edges (in the 2D plan) within

the area of the base of the model. Accordingly, the

reference data (e.g. the shape of polygon) of each

building of test image was manually prepared by a

qualified human operator using ArcGIS and their areas

were calculated. Essentially, the reference data is the

data that was derived from high precision

measurements of their field measurements or aerial

photography or satellite imagery with a high spatial

resolution, including a local coordinate system and

widely used for comparison. Thereafter, the

comparison was based on an object-based assessment

between two area values of the building footprints of

the reference and computed area values from the

created 3D model were conducted. The results of the

comparison are shown in figure 6, which presents

some discrepancies between the true and measured

values. These discrepancies between the base area of

the 3D model (measured value) and the building

footprint area of the reference of the geometric

building (true value) were taken into account and

corrected using Ecotect software.

Figure 5 3D model of urban structures results of the

proposed approach with different azimuth (Az) and

Elevation (El) of MATLAB visualisation, (a) 3D

buildings visualised by the top view, (b)

Comprehensive view Az:71 & El:28, (c) X-side view

Az:-20, El:36, (d) Y-side view Az:-66, El:36, (e) Back-

sight view Az:147, El:36, (f) Front-sight view Az:7,

El:36

ASSESSMENT OF SOLAR ENERGY

POTENTIAL

The verification of the correct geometry is

implemented for the 3D model of urban structures in

Figure 7a and the true sun path and shadows of

buildings in Figure 7b. We consider the climate data

to assess, simulate and analyse the solar radiation on

building roofs and surfaces in Ecotect software. The

assessment is performed in terms of simulation and

(a)

(b)

(a) (b)

(c) (d)

(e) (f)

Figure 3 The labels and lcation of the building corners

on Quickbird (0.6 GSD) test image

Figure 4 (a) 3D visualisation of solar angles: Sun

Elevation, Azimuth and Zenith angle with the

formation of a cast shadow of the urban structure, (b)

a shadow pattern of the actual illumination direction

and its opposite direction, which is composed of the

cast direction of the shadow with the geometric angles

of the relationships within the image space

THE EVALUATION OF THE 3D MODEL

OF URBAN STRUCTURES

To evaluate and test the uncertainty in the creation of

a 3D model of detached buildings, we investigated the

area of the base (building footprint) of a 3D solid box

(which is also the same area of the top of a 3D model).

The shape of the buildings mostly represents a

rectangle shape, and therefore the area must be a

regular rectangular shape. This means the geometry of

the 3D solid boxes has right angles in each corner

between each of the two edges (in the 2D plan) within

the area of the base of the model. Accordingly, the

reference data (e.g. the shape of polygon) of each

building of test image was manually prepared by a

qualified human operator using ArcGIS and their areas

were calculated. Essentially, the reference data is the

data that was derived from high precision

measurements of their field measurements or aerial

photography or satellite imagery with a high spatial

resolution, including a local coordinate system and

widely used for comparison. Thereafter, the

comparison was based on an object-based assessment

between two area values of the building footprints of

the reference and computed area values from the

created 3D model were conducted. The results of the

comparison are shown in figure 6, which presents

some discrepancies between the true and measured

values. These discrepancies between the base area of

the 3D model (measured value) and the building

footprint area of the reference of the geometric

building (true value) were taken into account and

corrected using Ecotect software.

Figure 5 3D model of urban structures results of the

proposed approach with different azimuth (Az) and

Elevation (El) of MATLAB visualisation, (a) 3D

buildings visualised by the top view, (b)

Comprehensive view Az:71 & El:28, (c) X-side view

Az:-20, El:36, (d) Y-side view Az:-66, El:36, (e) Back-

sight view Az:147, El:36, (f) Front-sight view Az:7,

El:36

ASSESSMENT OF SOLAR ENERGY

POTENTIAL

The verification of the correct geometry is

implemented for the 3D model of urban structures in

Figure 7a and the true sun path and shadows of

buildings in Figure 7b. We consider the climate data

to assess, simulate and analyse the solar radiation on

building roofs and surfaces in Ecotect software. The

assessment is performed in terms of simulation and

(a)

(b)

(a) (b)

(c) (d)

(e) (f)

Figure 3 The labels and lcation of the building corners

on Quickbird (0.6 GSD) test image

Figure 4 (a) 3D visualisation of solar angles: Sun

Elevation, Azimuth and Zenith angle with the

formation of a cast shadow of the urban structure, (b)

a shadow pattern of the actual illumination direction

and its opposite direction, which is composed of the

cast direction of the shadow with the geometric angles

of the relationships within the image space

THE EVALUATION OF THE 3D MODEL

OF URBAN STRUCTURES

To evaluate and test the uncertainty in the creation of

a 3D model of detached buildings, we investigated the

area of the base (building footprint) of a 3D solid box

(which is also the same area of the top of a 3D model).

The shape of the buildings mostly represents a

rectangle shape, and therefore the area must be a

regular rectangular shape. This means the geometry of

the 3D solid boxes has right angles in each corner

between each of the two edges (in the 2D plan) within

the area of the base of the model. Accordingly, the

reference data (e.g. the shape of polygon) of each

building of test image was manually prepared by a

qualified human operator using ArcGIS and their areas

were calculated. Essentially, the reference data is the

data that was derived from high precision

measurements of their field measurements or aerial

photography or satellite imagery with a high spatial

resolution, including a local coordinate system and

widely used for comparison. Thereafter, the

comparison was based on an object-based assessment

between two area values of the building footprints of

the reference and computed area values from the

created 3D model were conducted. The results of the

comparison are shown in figure 6, which presents

some discrepancies between the true and measured

values. These discrepancies between the base area of

the 3D model (measured value) and the building

footprint area of the reference of the geometric

building (true value) were taken into account and

corrected using Ecotect software.

Figure 5 3D model of urban structures results of the

proposed approach with different azimuth (Az) and

Elevation (El) of MATLAB visualisation, (a) 3D

buildings visualised by the top view, (b)

Comprehensive view Az:71 & El:28, (c) X-side view

Az:-20, El:36, (d) Y-side view Az:-66, El:36, (e) Back-

sight view Az:147, El:36, (f) Front-sight view Az:7,

El:36

ASSESSMENT OF SOLAR ENERGY

POTENTIAL

The verification of the correct geometry is

implemented for the 3D model of urban structures in

Figure 7a and the true sun path and shadows of

buildings in Figure 7b. We consider the climate data

to assess, simulate and analyse the solar radiation on

building roofs and surfaces in Ecotect software. The

assessment is performed in terms of simulation and

(a)

(b)

(a) (b)

(c) (d)

(e) (f)

calculation of the cumulative values of the incident

solar radiation and total monthly solar exposure in

Figure 8. The total cumulative solar radiation

calculation and simulation were implemented on all

surfaces (rooftops and walls) of buildings across every

month covering the entire year in different directions.

The goal behind this simulation is to calculate and

assess the availability of incident solar radiation

(insolation) on surfaces within our model. To achieve

this goal, we used the weather file of our study area in

Ankara, which was created based on hourly recorded

data. We also consider the overshadowing and shading

calculation for the geometry of the buildings and their

surroundings.

Table 1

Performance results of corner detection of buildings

from the VHR satellite image (Quickbird test image)

Table 2

Performance results of the estimation of buildings

heights from the VHR satellite image (test image)

No.

of

buildings

The

measured

value of

building

height (m)

The

estimated

value of

building

height (m)

Differences

M-E

(m)

1 3.70 3.40 0.30

2 4.80 5.10 -0.30

3 4.00 5.70 -1.70

4 5.60 5.60 0.00

5 4.00 4.10 -0.10

6 5.60 4.80 0.80

7 5.60 4.80 0.80

RMSE -0.20

Mean -0.029 Standard Deviation 0.79

RESULTS AND DICUSSION

We visualise the assessment of the solar energy

potential of urban structures, which are derived from

the Quickbird test image in Figure 9. The extraction of

the 3D model of urban structures demonstrates that

our approach can provide remarkable assessment and

quite representative results. The assessment of the

solar energy potential for rooftops and facades based

on the solar 3D model of urban structures using

Ecotect software provides an opportunity to carry out

a fair quantitative comparison.

Figure 5 Evaluation of the areas of the polygons of the

extracted 3D model of urban buildings and the

reference data. The correction was implemented to the

extracted areas of the 3D model based on restoring

the right angles at the intersection of two

perpendicular straight lines

Figure 6 (a) The modified geometry of the 3D model

of the detached buildings in Ecotect, (b) the setting of

the sun path and the cast shadows of the detached

buildings in Ecotect

Figure 7 Solar radiation assessment for urban

structures (a) in the same direction of the solar

radiation, (b) in the opposite direction of the solar

radiation

According to Figure 9, the results illustrate the

differences between the amount of solar radiation

received by buildings’ roofs and surfaces (walls). The

lower values of incident solar radiation were found

within the buildings’ facades in the north direction in

Figure 9a. The facades in the east and west directions

present convergent values of solar radiation whereas

the inconsistent values were observed within the

facades in the south direction. In contrast, the amount

of received solar radiation by rooftops was higher than

other surfaces which exhibits large values of

insolation due to the fact that the regions of the

rooftops were full exposed to sunlight in Figure 10. In

particular, because the selected study area lacks dense

vegetation cover, such as trees, they receive more

solar radiation and thus have less overshadowing

vegetation cover over the rooftops.

ID

Diff. of

Location

(Pixels)

Accu.

(%)

ID

Diff. of

Location

(Pixels)

Accu.

(%)

1 0 100 15 0 100

2 0 100 16 0 100

3 8.98 91.02 17 0 100

4 2.84 97.16 18 7.25 92.75

5 0 100 19 66.15 33.85

6 0 100 20 65.36 34.65

7 0 100 21 0 100

8 0 100 22 0 100

9 7.40 92.60 23 6.06 93.94

10 3.31 96.69 24 6.06 93.94

11 9.86 90.64 25 0 100

12 7.40 92.60 26 13.80 86.20

13 0 100 27 14.86 85.13

14 0 100 28 5.52 94.48

(b) (a)

(b) (a)

calculation of the cumulative values of the incident

solar radiation and total monthly solar exposure in

Figure 8. The total cumulative solar radiation

calculation and simulation were implemented on all

surfaces (rooftops and walls) of buildings across every

month covering the entire year in different directions.

The goal behind this simulation is to calculate and

assess the availability of incident solar radiation

(insolation) on surfaces within our model. To achieve

this goal, we used the weather file of our study area in

Ankara, which was created based on hourly recorded

data. We also consider the overshadowing and shading

calculation for the geometry of the buildings and their

surroundings.

Table 1

Performance results of corner detection of buildings

from the VHR satellite image (Quickbird test image)

Table 2

Performance results of the estimation of buildings

heights from the VHR satellite image (test image)

No.

of

buildings

The

measured

value of

building

height (m)

The

estimated

value of

building

height (m)

Differences

M-E

(m)

1 3.70 3.40 0.30

2 4.80 5.10 -0.30

3 4.00 5.70 -1.70

4 5.60 5.60 0.00

5 4.00 4.10 -0.10

6 5.60 4.80 0.80

7 5.60 4.80 0.80

RMSE -0.20

Mean -0.029 Standard Deviation 0.79

RESULTS AND DICUSSION

We visualise the assessment of the solar energy

potential of urban structures, which are derived from

the Quickbird test image in Figure 9. The extraction of

the 3D model of urban structures demonstrates that

our approach can provide remarkable assessment and

quite representative results. The assessment of the

solar energy potential for rooftops and facades based

on the solar 3D model of urban structures using

Ecotect software provides an opportunity to carry out

a fair quantitative comparison.

Figure 5 Evaluation of the areas of the polygons of the

extracted 3D model of urban buildings and the

reference data. The correction was implemented to the

extracted areas of the 3D model based on restoring

the right angles at the intersection of two

perpendicular straight lines

Figure 6 (a) The modified geometry of the 3D model

of the detached buildings in Ecotect, (b) the setting of

the sun path and the cast shadows of the detached

buildings in Ecotect

Figure 7 Solar radiation assessment for urban

structures (a) in the same direction of the solar

radiation, (b) in the opposite direction of the solar

radiation

According to Figure 9, the results illustrate the

differences between the amount of solar radiation

received by buildings’ roofs and surfaces (walls). The

lower values of incident solar radiation were found

within the buildings’ facades in the north direction in

Figure 9a. The facades in the east and west directions

present convergent values of solar radiation whereas

the inconsistent values were observed within the

facades in the south direction. In contrast, the amount

of received solar radiation by rooftops was higher than

other surfaces which exhibits large values of

insolation due to the fact that the regions of the

rooftops were full exposed to sunlight in Figure 10. In

particular, because the selected study area lacks dense

vegetation cover, such as trees, they receive more

solar radiation and thus have less overshadowing

vegetation cover over the rooftops.

ID

Diff. of

Location

(Pixels)

Accu.

(%)

ID

Diff. of

Location

(Pixels)

Accu.

(%)

1 0 100 15 0 100

2 0 100 16 0 100

3 8.98 91.02 17 0 100

4 2.84 97.16 18 7.25 92.75

5 0 100 19 66.15 33.85

6 0 100 20 65.36 34.65

7 0 100 21 0 100

8 0 100 22 0 100

9 7.40 92.60 23 6.06 93.94

10 3.31 96.69 24 6.06 93.94

11 9.86 90.64 25 0 100

12 7.40 92.60 26 13.80 86.20

13 0 100 27 14.86 85.13

14 0 100 28 5.52 94.48

(b) (a)

(b) (a)

Images Geometry Prediction

Kadhim N, Mourshed M, Bray, M (2015)

THANK YOU