Workshop on Distributed Energy Management Systems - JST ... · Workshop on Distributed Energy...

Home Energy Management System on Smart Grid

under Uncertainty

Workshop on Distributed Energy Management Systems

- JST CREST EMS Hayashiʼs super team / NSF CURENT -

1

Yoshiharu AMANO

Waseda University , Tokyo, Japan

ACROSS: Advanced Collaborative Research Organization for Smart Society

11th December, 2015 13:30-18:30

2Research topics by eight Japanese Universities researchers

Open EMS Simulation Model

Distribution NW Simulator

Prof. Hayashi (PI)

Development of integrated collaborative EMS method

Prof. Ishii

Cyber security

Prof. Suzuki

PHV-HEMS

HPWH-HEMS

Discrete Structure Manipulation

Assoc. Prof. Baba

• Determination of NW

configuration by switches

• HPWH model reflecting actual

characteristic

• Feeder voltage control

• Detection of cyber attacks

against voltage control

• Operational charge-discharge

plan for PHV-HEMS

Prof. Amano

Operational planning for

residential energy systems

Assoc. prof. Fujimoto

Multiple scenario Forecast

Prof. Minato

(JST ERATO) Development of collaborative EMS

Ph.D.student Mr. Yoshizawa

Voltage control in Grid EM

Robust distributed optimal control

Prof. Ohmori

• Distributed collaborative

control b/w BESS and PVs

Geoscience Information delivery

Prof. Shimoda

Demand profiles for EMS

Economic analysis

• Simulation of demand change

against DR based on electricity

price

• Generation of energy demands

for EMS based on multi-agent

model

Prof. Ohashi

• Analysis and delivery of

geophysical values using

satellite data

Prof. Nakajima

Internationalcolaboration

Universities (NSF, DFG, RCN)

3What is “ACROSS” ?

ACROSS: Advanced Collaborative Research Organization for Smart Society

PA-SST(Promotion Associationfor Smart Society Technology)

Infrastructure companies

Social Frame-work

Advanced

research at university

Creating the NEW social value from the view point of energy consumers/customers and global market

Implementation to Society, National projects, etc.

SG-SST(Study Group forSmart Society Technology)Manufacturing companies with various development technology

Technology,

Products

Yasuhiro HAYASHIChairperson of ACROSSResearch Institute for Advanced Network Technology (RIANT)

Toshiyuki OKANO

The Smart Life Science Institute

Ayu WASHIZU

Institute for Economic Analysis of Next-generation Science and Technology

Yushi KAMIYA

Research Institute of Electric-driven Vehicles

Shin-ichi TANABE

Research Institute for Building Environmental Design

Shinji WAKAO

Research Institute for Photovoltaic Power Generation System

Yoshiharu AMANO

Research Institute for Power and Energy Systems

7 Research Institutesin WASEDA Univ.

Shinjuku EMS R&D Center

Innovation of technology fusion

http://www.waseda.jp/across/en/top/

18 companies17 companies

Energy Management System （EMS）【Optimal cooperation of distributed EMSs】

BEMS （Building）GEMS （Grid） HEMS （Home） CEMS （Community）

4Vision of Smart Grid in Japan after 3.11

￥

Thermal Power

Hydro Power

Buildings with PV／CGS／Battery

Substation

Wind Farm

EVBattery

PV

Smart House

Fuel Cell

Electric Power NW(Power Grid)

Smart Building

PV Power Station

Pumped-up Hydro Power

Renewable Energy Sources

Power Quality Issue

(frequency, voltage)Scheme for electricity saving

With incentive

Smart Community

Impact

Control by ICT

AC

Energy Cost

HEMS

Smart meter HP Water

Heater

Energystorage

EnvironmentalImpact of

Smart Grid

Evaluation

value

5Target of Our Research

Advanced HEMS

E

VA

CH

P

Peak shift by DR

based on TOU and FIT

Reverse PV power flow

HEMS

HWHP

BESS

PV

EV

ACSmart

meter

INV

Advanced GEMS

E

V

time

[W]

① Next Day NW Load and PV Forecast② Next Hour V control Parameters Plan③ Real Time Voltage Control⇒ Expansion of PV installation

① Next Day Load and PV Forecast② Next Day Operational Plan③ Real time Equipment Control

⇒ Expansion of demand suppression

Demand suppression

PV introduction

Vo

ltag

e

Line length

Upper limit

Lower limit

LVRLRT, SVRCooperative

EMS Method of

GEMS & HEMSPV,

Dem

and [

MW

]

0 3 6 9 12 15 18 21

PV

24

100/200V

6.6kV

Pricing

P flow

6Research Framework

Test Platform of cooperative EMS method

Amounts of

• PV introduction

• Peak demand suppression

Control possibility of

• PV introduction

• Peak demand suppression

Propose of cooperative EMS method by GEMS and HEMS

Forecast model

GEMS/HEMSPlanning model

GEMS/HEMSDynamic model

GEMS/HEMS

System Link

Real time

voltage control

by GEMS/HEMS

Distribution NW simulator

Load

Pole Transformer

PV system

6,600[V]

100/200[V]

Integrated simulation model

Grid EMS model

Home EMS model

Total

demand

Target

value

Time

Consumer’s

demand

PV introduction

Theoretical aspect Practical aspect

Introduction

EvaluationEvaluationEvaluation of EMS method

7H-EMS

ObjectiveScenario-based evaluation for “smart community” regarding energy

managementFramework for Design and Analysis of Distributed Energy Systems

Subject : Energy System in residential unit

Super structure model

Fuel

Electricity

Utilities

Natural Resources

Space heating/cooling

Domestic Hot water

Electricity

Optimal Configuration/Operation

Buildings with PV／CGS／Battery

EVBattery

PV

Smart House

Fuel Cell

Smart Building

Smart Community

AC

HP Water Heater

Uncertainty

8Home Energy Management

AC PV

FC

EV/PHEV BESS

LED

HPWH

Smart meter

TOU

TemperatureObjective

Constraint

Minimize・Primary energy consumption・Electricity cost

Predicted Mean Vote (PMV)？

How should we use?

How to simultaneously control appliances?

9Evaluation of Stochastic Optimization of Operational Planning Scheme

Residential Energy Systems

① Forecast ② Operational Planning ③ Control

Multiple scenarios

S1

PVEnergy Demand

S5

・・・・

Status of energy devices（For the next day at 15 min. step）（For the next day at 15 min. step）（At every 15 min. ）

Status of energy devices

・・・・

Minimize Cost (For next day)

Plan device’s operational statusFC

BESS

Energy Demand PV

TOU Current measured data

FC

BESS

Minimize Cost (At present time)

Modify

Present

time

HEMS

10Modeling and evaluation

MILP model with super structure

Identification by Test-bed

Evaluation on the simulator: ANSWER

• PEM Fuel Cell cogenerationsystem

• CO2 Heat Pump water heater• Air conditioner• Photovoltaic power

generator• Electrical battery

11Optimization model

MINP model with super structure Energy conversion devices + storage units

12Forcast Operational Planning Control




Time index

Binary variable of Equipment’s ON/OFF status

Scenario probabilityCost conversion coefficient vector

Energy flow vector supplied to equipmentOperational strategy vector

Energy flow vector supplied from equipment

Exogamous variable vector in each scenario(Energy demand and PV output etc.)

Equipment’s index

Forecast scenario index


Optimal control problem by Mixed Integer Linear Programming (MILP)

Equipment’s index

Time index

Binary variable of Equipment’s ON/OFF status

Decision variable

15

Demand profile analysis

16Analysis on demand profile

Annual average(E:15.49, Q:13.05) Daily Average(E:11.79, Q:9.93)

Mode(E:8, Q:4)


CGS issue: prime mover’s heat-to-power ratio is almost constant. It’s around 1.4, but…


• Human factor > Climate factor

19Optimal Installation

Polymer electrolyte

membrane fuel cell CGS

(PEFC-CGS)

Mixed Integer Linear Programming (MILP)

Gas boiler(GB)

Gas engine CGS(GE-CGS)

Heat pump water heater(HP-S)


HP-S

PEFC-CGS

GE-CGS

C-S

300250200150100500

Primary energy consumption MJ/day

HP-S

PEFC-CGS

GE-CGS

C-S

1000

800

600

400

200

0

Ele

ctr

icit

y d

em

an

d W

h/3

0m

in

20151050

Time

8000

4000

0 Ho

t w

ate

r d

em

an

d W

h/3

0m

in

1000

800

600

400

200

0

Ele

ctr

icit

y d

em

an

d W

h/3

0m

in

20151050

Time

8000

4000

0 Ho

t w

ate

r d

em

an

d W

h/3

0m

in

Gas

20.25%

6.54%

2.56%

1.57%

2.39%

−6.62%

Primary energy reduction ratio

（Energy saving ratio） %：

E:12.4

Q:35.96

kWh/day

E:15.99

Q:4.73

kWh/day


40

30

20

10

0

Ho

t w

ate

r d

em

an

d G

J/y

ea

r

403020100

Electricity demand GJ/year

12

10

8

6

4

2

0

Pri

ma

ry e

ne

rgy

re

du

cti

on

ra

tio

%

1

1.5


70

60

50

40

30

20

10

0

Ho

t w

ate

r d

em

an

d k

Wh

/da

y

706050403020100

Electricity demand kWh/day

20

15

10

5

0

-5

-10

Pri

ma

ry e

ne

rgy

re

du

cti

on

ra

tio

%706050403020100

Hot water demand kWh/day

25

20

15

10

5

0

-5

-10

Pri

ma

ry e

ne

rgy

re

du

cti

on

ra

tio

%

706050403020100

25

20

15

10

5

0

-5

-10

100

80

60

40

20

0

Co

ntr

ibu

tio

n r

ati

o %

From the viewpoint of CGSDaily scale analysis is needed

1

1.5Heat-to-Power ratio of FC unit： 1.43


50

40

30

20

10

0

Ho

t w

ate

r d

em

an

d k

Wh

/day

403020100


403020100


20

15

10

5

0

-5

-10

Pri

mary

en

erg

y r

ed

uc

tio

n r

ati

o %

(a) (b)

200

Freq. days

50

40

30

20

10

0

Ho

t w

ate

r d

em

an

d k

Wh

/day

400

Freq. days

50

40

30

20

10

0

Ho

t w

ate

r d

em

an

d k

Wh

/day

Effect from time-series profile?


The relationship between demand time-series and

energy saving characteristics of PEFC-CGS

25Operational planning problem using Stochastic programming

120

100

80

60

40

Pri

ma

ry e

ne

rgy

co

ns

um

pti

on

MJ

/da

y

30252015105

Number of input scenarios

30

25

20

15

10

5

0

Ga

p o

f p

rim

ary

e

ne

rgy

co

ns

um

pti

on

%

SS(15-min) WS(15-min) Gap

• Gap was saturated at around 10 scenarios

26

Identification of equipment model

27FC Test Facility

Simulate hot water demand by control valve

Simulate electricity demand by load device

1sec logging, 1min modeling

28FC model

Status transition model

29FC Steady State Charcteristics

1.0

0.8

0.6

0.4

0.2

0.0

Ele

ctric

ity a

nd

hot w

ate

r outp

ut of FC

unit k

W

2.52.01.51.00.50.0

Gas consumption kW

ath,2=0.62067bth,2=-0.23418

ae,2=0.38495

be,2=-0.021025

R2=0.9966

R2=0.9970

R2=0.9486

ae,1=0.85897

be,1=-0.44051

Hot water output Electricity output

60

50

40

30

20

10

0

Net ele

ctr

ic a

nd

heat re

covery

effic

iencie

s o

f FC

unit %

1.00.80.60.40.20.0

Electricity output of FC unit kW

Heat recovery efficiency Net electric efficiency

30FC Dynamic Performance

0.8

0.6

0.4

0.2

0.0

Ele

ctr

icity kW

126001240012200120001180011600Time sec

Set point of load device0.8

0.6

0.4

0.2

0.0

Ele

ctr

icity kW

670066806660664066206600Time sec

Electricity output of FC unit

(a) (b)

1. Load following-up: 0.81W/s → 48.6W/min → 729W/15min

2. Load following-down: Immediately

1sec

31

Optimal configuration in Long-term horizon

32Long-term horizon

Renewal of energy system

33Long-term horizon

Degradation of Storage unit(Lithium-ion battery)

33

Joongpyo Shim, Kathryn A. Striebel, “Characterization of high-power lithium-ion cells during constant current cycling”, J. of Power Source, 2003

=0.07 %/cycle

BT degradation ∝1) Depth of discharge2) # of Cycle3) Charge/Discharge

rate

34Long-term horizon

Li-ion battery performance degradation model for MINLP problem

Purchased electricity Electricity

demand

PV

Conversion factor

BT

of electricity

A household

Surplus electricityPrimary energyconsumption

PCU

The available capacity of the BT

→ 𝑒BT 𝑡 ≤ 1 − 𝑁𝛽 𝑡 𝐸BTMAX

→ 𝑒BT 𝑡 ≤ 𝑓 𝑒BT 0, … , 𝑡 − 1 , 𝑒ሶin 0, … , 𝑡 − 1 , 𝑒ሶout 0, … , 𝑡 − 1

35Long-term horizon


Energy minimization

Cost minimization

36Long-term horizon


36

PV-BT(1)：Energy saving > Economic

PV-BT(4)： Energy saving < Economic

PV-BT(8)： Energy saving > Economic

37Long-term horizon


37

DOD degradation

Cycle degradation

PV-BT(1)： the difference between the economy case and the energy saving case was caused by the cycle degradation.

PV-BT(4)： Cycle degradation energy ≒ costDOD degradation energy > cost

PV-BT(8)： Total amount of BT capacity degradation energy ≒ cost Cycle degradation energy > cost

38Long-term horizon


38

(c)Economy (d)Energy saving

DOD degradation occurred

Intermediate season, first year

39

PV output mitigation prevention potential by collaborating control between G-EMS and H-EMS

40Long-term horizon

41Settings for numerical experiment60

50

40

30

20

10

0

Ele

ctr

icit

y r

ate

Yen

/kW

h

3 7 13 16 23

Time

Electricity rate PV-FIT

1. The day in May.It’s light load period known as 1 week vacation called "Golden week“

2. Cost-driven operation using PV-FIT3. 2030s Japanese situation

Radial distribution systemAll house have PV

4. Just 1 household has H-EMS

42Settings for numerical experiment

120

100

80

60

40

20

0

Pri

ma

ry e

ne

rgy

c

on

su

mp

tio

n w

ith

ou

t s

ell

ing

PV

po

we

r M

J/d

Without grid info.

30

25

20

15

10

5

0

PV

po

we

r s

en

t to

gri

d

kW

h/d

Without suppression

30

25

20

15

10

5

0

PV

ou

tpu

t k

Wh

/d

With grid info.-800

-600

-400

-200

0

Op

era

tin

g c

os

t w

ith

pro

fit

fro

m P

V-F

IT

Ye

n/d

Without suppression

(a) (b) (c) (d)

55% drop

(84 yen/d)

73% drop

(491 yen/d)

62% suppression

(16 kWh/d)

3kWh mitigation of suppression

43

Smart Society

EMS for Home, Building, (appliances, energy supply systems) model regarding grid conditions

Community-scale modeling

Collaboration of EMSs;G-EMS, H-EMS, B-EMS…

44

Thank you for your attention.

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