Smart energy management algorithm for load smoothing and

Norwegian University of Science and TechnologyDepartment of Electric Power Engineering

SPYRIDON CHAPALOGLOU

Trondheim, May 2019

Smart energy management algorithm for load

smoothing and peak shaving

based on load forecasting, of an island’s power system

PhD Candidate

Chapaloglou, S., Nesiadis A., Iliadis P., Atsonios K., Nikolopoulos N., Grammelis P., Yiakopoulos C., Antoniadis I., Kakaras E.,

Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system.

Applied Energy, 2019. 238: p. 627-642.

Contents

1. Problem description and Scope

2. Developed Algorithm

3. Algorithm implementation + Energy system

modelling and simulation

4. Conclusions and future work

Objective Algorithm Simulation

Scope

Problem Description

• Restricted renewable integration in Islanded systems due to power balance issues

• PV peak generation mismatch with peak demand

• Curtailment issues from Diesel Gen. technical minimum and robustness of the grid

• Seasonal variations of the load pattern affect the scheduling strategy

Source: California ISO: What the duck curve tells us about managing a green grid

Typical Spring day

The “Duck” chart

1.

2.


Scope

Possible Solution: Load Curve smoothing

Consumer Perspective

Demand Response Techniques

Producer Perspective

Optimized dispatch planning

• Reduced Peak Demand

• Load Shifting

• Reduced GenSets number

• Steadier operation

Difficult in an island level

especially with large

residential and touristic

factors

Production = Consumption

€Power production is more

easily managed if forecasting

is combined with storage


€

In this study

Diesel Engine

Output:

Scheduled power

+ Residual power

PV installation

BESS+ -

Input:

Solar

Radiation

Objective:

Balance of System

Provide power

@ Night Peak

Hours

Output:

Electrical

Power

SP

System’s Load

Neural

Network

ModuleP

eak

sha

vin

g A

lgo

rith

m

Past years Load values

Case Study

Forecasting

Scheduling

Developed AlgorithmControl Structure

Dynamic Simulation

Possible Power Flows

1. DG Load

2. PV Load

3. PV BESS

4. BESS Load

𝑃𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙(𝑡) =

𝑖=1

𝑁𝑝

൯𝑃𝑖𝑛𝑓𝑙𝑜𝑤,𝑖(𝑡 −

𝑖=1

𝑁𝑐

൯𝑃𝑜𝑢𝑡𝑓𝑙𝑜𝑤,𝑖(𝑡 ≤ 𝜀

Np=3 :energy sources

Nc=2 : energy sinks

ε=0.001 kW: balance residual

Integrated Framework


Energy Balance

Load Forecasting

Module

20

Hid

den

neu

ron

sPrevious day hourly load data

Day before previous day hourly load data

24

Input Layer

24

24Previous day hourly temperature values

Binary index value for the day of week

Binary index value for the weekend day

7

1

Load @ hour=1

Load @ hour=2

Load @ hour=3

Load @ hour=24

Output Layer

.

.

.

Peaks

Neural Network Inputs Model Output


1. Levenberg-Marquardt training algorithm

2. Sigmoid logistic activation function

3. Linear output activation function

Artificial Neural Network Model Parameters

Datasets

Training: 2014, 2015

Testing: 2016

Short-term load forecasting (hourly basis)

Forecasting results

Training + Testing DatasetTesting Dataset (1 year)

Training Dataset (1 week)

True

Forecast

𝑅2 = 1 −σ𝑖=1𝑁 𝑦𝑖 − 𝑡𝑖

2

σ𝑖=1𝑁 ൯(𝑡𝑖

2 = 𝟎. 𝟗𝟗𝟏𝟖

𝑀𝐴𝑃𝐸 =1

𝑁

i=1

𝑁𝐹𝑜𝑟𝑒𝑐𝑠𝑡𝑒𝑑 𝐿𝑜𝑎𝑑𝑖 − 𝑇𝑟𝑢𝑒 𝐿𝑜𝑎𝑑𝑖

𝑇𝑟𝑢𝑒 𝐿𝑜𝑎𝑑𝑖= 𝟏. 𝟕𝟏𝟓% < 𝟐%

Model Results


Linear Correlation

Hours of the Day

Lo

ad [

MW

]

Day

s o

f th

e M

on

th

Month of the Year

Lo

ad [

MW

]

Hours of the Day

2nd Cluster (value=0) – Load pattern without clear peak

1st Cluster (value=1) – Load pattern with clear peak

Hierarchical Clustering Dendrogram

Cluster=0 (Don’t Perform Peak Shave)

Cluster=1 (Perform Peak Shave)

Pattern Recognition Module

Methodology Objective Algorithm Simulation

€

Input variables

24 hours Load &PV forecasts 1. Calculate shaved area (sha)

2. Find intersection points

combined curve vs load curve

P [

MW

]3. Calculate surplus area (spa)

Offset=Pbase

New peak=Ppeak-0.1

Offset_new = Offset_old+dP

spa>= sha

a) Offset value

b) 24 h DG dispatch plan

YES

NO

Cluster =1NO peak

shaving

NO

YES

Ηours

Clustering

Forecasting

Peak Shaving (1/3)𝒕𝟏

𝒕𝟐First Stage of the Algorithm

1. Peak Shave Level determination (10%)

2. Intersection points 𝒕𝟏 and 𝒕𝟐 found

3. Shaved amount of energy (sha) calculation

4. Offset level initialization

5. Combined Power Curve calculation

6. Offset level update until cross section points

are found

𝐸1→2 𝑀𝑊ℎ = න𝑡1

𝑡2

𝑃 𝑡 𝑑𝑡

𝑃𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 𝑀𝑊 =

൝𝑃𝑃𝑉 + 𝑃𝑙𝑜𝑎𝑑 , 𝑓𝑜𝑟 𝑃𝑙𝑜𝑎𝑑 < 𝑃𝑜𝑓𝑓𝑠𝑒𝑡𝑃𝑃𝑉 + 𝑃𝑜𝑓𝑓𝑠𝑒𝑡, 𝑓𝑜𝑟 𝑃𝑙𝑜𝑎𝑑 ≥ 𝑃𝑜𝑓𝑓𝑠𝑒𝑡

𝑃offset 𝑀𝑊 = min𝑖=1÷24

𝑃𝑙𝑜𝑎𝑑,𝑖

𝒕𝟏 𝒕𝟐


Load Curve

PV Curve

𝑬𝒑𝒆𝒂𝒌

Peak Shaving (2/3)

7. Offset level update by:

8. Localization of the cross points tI and tII

9. Calculation of the surplus energy (spa)

10. Continue updating until spa is positive and until:

Second Stage of the Algorithm – The “elevator” concept

න𝑡𝐼

𝑡𝐼𝐼

൯(𝑃𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 − 𝑃𝑙𝑜𝑎𝑑 𝑑𝑡 ≥ න𝑡1

𝑡2

𝑃𝑙𝑜𝑎𝑑 𝑑𝑡

න𝑡𝐼

𝑡𝐼𝐼

𝑃 𝑡 𝑑𝑡 ≈

𝑘=2

𝑁)𝑃 𝑡𝑘−1 + 𝑃(𝑡𝑘

2𝛥𝑡𝑘

spa = E21 - E22 ≈ Epeak = sha

Termination:

E21

E22


𝒕𝑰 𝒕𝑰𝑰

Start of the day

Initial SoC

Discharge Charge Discharge for peak

Final SoC

Peak Energy

…Previous day Next day…

(1)

E22 E21

(2) (3) (4)

(1)

(2)

(3)

(4)

Are

aca

lcu

lati

on

Hours of the Day

Hours of the Day

Hours of the Day

𝑃 = 𝑃𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 − 𝑃𝑙𝑜𝑎𝑑

Cross points

𝒕𝑰

𝒕𝑰𝑰

Peak Reduction level

Epeak

𝑃′𝑜𝑓𝑓𝑠𝑒𝑡 = 𝑃𝑜𝑓𝑓𝑠𝑒𝑡 + 𝒅𝑷

Po

wer

Whole year performance

Objective Algorithm SimulationPeak Shaving (3/3)

Days of the Year

Res

idu

al

erro

r(s

pa

-sh

a)

Hours of the Day

Po

wer

Peak Reduction level

Load Curve

PV Curve

>

Offset level

>>

Updates: dP=1 kW

Day: 35

Day: 97

Day: 94Day: 241

Day: 248

Day

:2

90

Day: 301

• Generator peak power production is limited from the final offset level and peak shave level

• Part of the produced PV energy is injected during midday and the rest during peak hours

• The load of the diesel is smooth during the off-peak hours

Peak Shaving algorithm animation

Day 25 (Winter) Day 70 (Spring)

Day 312 (Autumn)

Pow

er[M

W]

Hours

Load Curve

PV Curve

Objective Algorithm SimulationAlgorithm results

Day 195 (Summer)

• Method converges but

no peak shave is applied

• These profiles are

identified from pattern

recognition module

Dynamic system

modellingObjective Algorithm Simulation

Peak demand [MW] 1.5

PV capacity [kWp] 300

Thermal units capacity [MW] 2

Battery power rating [kW] 160

Battery capacity [Ah] 2000

Power electronic losses 10%

• Li-Ion cells (𝑂𝐶𝑉 ≈ 4 𝑉)

• Terminal Voltage = f(SoC, 𝑵𝒆𝒒𝒗)

• Capacity fading:

1. Equivalent charging cycles (Ah

throughput)

2. Lifetime storage (Arrhenious

expotential fading)

• Initial SoC=50% (t=0)

Battery modelSystem SpecificationsPV production model

0

0.2

0.4

0.6

0.8

1

1.2

0

50

100

150

200

250

1115

229

343

457

571

685

799

913

1027

1141

1255

1369

1483

1597

1711

1825

1939

2053

2167

2281

2395

2509

2623

2737

2851

2965

3079

3193

3307

3421

3535

3649

3763

3877

3991

4105

4219

4333

4447

4561

4675

4789

4903

5017

5131

5245

5359

5473

5587

5701

5815

5929

6043

6157

6271

6385

6499

6613

6727

6841

6955

7069

7183

7297

7411

7525

7639

7753

7867

7981

8095

8209

8323

8437

8551

8665

Dir

ect

So

lar

Ra

dia

tio

n [

kW

/m

2]

PV

po

we

r o

utp

ut

[kW

]

Hours

Solar Data

1. Input variables: T, 𝑰𝒃,𝒉𝒐𝒓 , 𝑰𝒅,𝒉𝒐𝒓2. Panel surface plane: 𝑰𝒑,𝒕𝒐𝒕3. Panel efficiency model: f(T, 𝑰𝒑)

• Weather dataset: MERA (global)

• Tilt: 0˚, Azimuth: 180˚

• 2-axis tracking

APROS simulation

platform

Apros Dynamic Simulation Model

Idle

Logic Controller 1

Logic Controller 2

Logic Controller 3

Logic Controller 4

Logic Controller 5

Logic Controller Purpose/Description Condition

4

“System

Surplus

Check”

Responsible for positive

forecasting error compensation

and not charging the battery with

non-renewable energy

𝑃𝑃𝑉 + 𝑃𝑆𝑃 − 𝑃𝐿 > 0.1

and

𝑃𝑆𝑃 − 𝑃𝐿 ≤ 0.001


2 “PV Check”Check whether there is

available PV energy𝑃𝑃𝑉 > 0.001


1 “System Check”Monitor the balance of

system and the residual sign𝑃𝑃𝑉 + 𝑃𝑆𝑃 − 𝑃𝐿 > 0


5. “Idle”

Responsible for the idling

state management of the

battery𝑦𝑜𝑢𝑡 = ቊ

0, 𝑓𝑜𝑟 𝑖𝑑𝑙𝑒1, 𝑓𝑜𝑟 𝑛𝑜 𝑖𝑑𝑙𝑒


3 “State”

Responsible for changing

the switch positions in the

electrical network𝑦𝑜𝑢𝑡 = ቊ

0, 𝑓𝑜𝑟 𝑐ℎ𝑎𝑟𝑔𝑒1, 𝑓𝑜𝑟 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒


Dynamic simulation

results (1/3)

• Typical Week results

• Two level approach (offset +

peak) is achieved

• Smoother Operation

• Peak Shaving of the maximum

power demand from the

GenSets

• Smooth Level of operation

during off-peak hours

• Reduced Ramp-up rate and

magnitude


• BESS charged with

renewable PV energy

• Maximum BESS SoC

after the PV peak

• Maximum BESS

discharge power right

after SoC maximum

value

• Planned trajectory

close to simulated

cc

Charg

e

Discharge

Objective Algorithm SimulationDynamic simulation

results (2/3)

𝑘𝑢𝑟𝑡𝑜𝑠𝑖𝑠 =σ𝑘=1𝐾 𝑥 𝑘 − 𝑥𝑚

4

𝐾 − 1 𝑥𝑠𝑡𝑑4 Old kurtosis=2.5416

New kurtosis=2.1319

Difference= -16.12%

Objective Algorithm SimulationDynamic simulation

results (3/3)

New diesel power distribution

Old diesel power distribution

Old diesel power fitted

probability density function

Old diesel power fitted

probability density function

Kurtosis has to do with the

extent to which a frequency

distribution is peaked or flat

• Measure of how “impulse/sharp”

variations of the signal

(how outlier-prone is the distribution)

• Reduction of the kurtosis factor =>

More smooth distribution of signal

values around the mean value

Conclusions and

future workObjective Algorithm Simulation

1. Integration of forecasting and clustering in the scheduling strategy

2. Possibility for downsizing of the thermal units (lower peak)

3. Ensuring maximum PV utilization (0 curtailment events)

4. Smoother operation of DG during off-peak hours (steady operation)

5. Avoiding the “duck” shape evolution of the load curve

6. Reduction of the ramp-up rate

7. Reduction of the kurtosis factor of the load distribution

1. Optimization of the peak reduction level

2. Parametric investigation of the battery size

3. PV time-series forecasting implementation

Conclusions

Future work

Thank you for your attention!

AcknowledgementsThis work has been carried out in the framework of the European Union’s Horizon 2020 research and

innovation programme under grant agreement No 731249 (Smart Islands Energy Systems -

SMILE)

phone number: +30 6979471675

e-mail: [email protected]

Questions?

Chapaloglou, S., Nesiadis A., Iliadis P., Atsonios K., Nikolopoulos N., Grammelis P., Yiakopoulos C., Antoniadis I., Kakaras E.,

Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system.

Applied Energy, 2019. 238: p. 627-642.

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Smart energy management algorithm for load smoothing and