Proceedings VCM 2012 101 đề xuất bộ điều khiển thích nghi nhiệt độ cho thiết bị HVAC để quản lý

Development of an adaptive temperature control for HVAC to intelligent energy management system in buildings at DaNang city Đề xuất bộ điều khiển thích nghi nhiệt độ cho thiết bị HVAC đ

Development of an adaptive temperature control for HVAC to intelligent energy management system in buildings at DaNang city

Đề xuất bộ điều khiển thích nghi nhiệt độ cho thiết bị HVAC để quản lý thông minh hệ thống năng lượng trong các tòa nhà tại TP Đà Nẵng

1 Vietnam Electricity-EVN nminhtri@cdmt.vn

2 QuyNhon University, Vietnam nhanh@qnu.edu.vn; huynhduchoan@qnu.edu

3 IDEA, Grenoble-INPG, France Senior Member, IEEE

Abstract:

In order to use loads in an active and intelligent way to resolve technical problems in the networks or contribute to ancillary services (smart grid), this paper presents a new method of air-conditioning control that allows to reduce the peak consumption by maintaining thermal comforts This control is based on the variable set-point temperature of air conditioning adapted to the permissible power This power can be fixed by outdoor signal from DNO (Distribution Network Operators) In addition, this method relies support on a wireless sensor network (WNS) which allows to measure in realtime simultaneously the internal temperatures and the power consumption The protocol Zigbee is used for the communication between wireless temperature sensors The proposed air-conditioning control is tested by simulation under EMTP-RV with satisfied results for a distribution network containing air-conditioners at DaNang These results show that the proposed solution can

be efficiently applied for a group of loads, buildings (such as a virtual consumer) in distribution networks in order to reduce the peak consumption in the distribution network

Keywords— Air conditioning, direct load control, adaptive control, peak load reduction, distribution

network, Zigbee, wireless sensor network, WNS

Toám tắt:

Để giải quyết các vấn đề về kỹ thuật, dịch vụ trên lưới điện cần có phương pháp điều chỉnh nhu cầu sử dụng tải một cách chủ động và linh hoạt, bài viết này trình bày một phương pháp mới để điều khiển thiết bị điều hòa không khí; cho phép giảm tiêu thụ cao điểm mà vẫn duy trì các tiện nghi về nhiệt Điều khiển này dựa trên việc thiết lập nhiệt độ của điều hòa không khí thích ứng với công suất làm việc cho phép Công suất này

có thể được cố định bởi tín hiệu bên ngoài(Nhà cung cấp và phân phối điện) Ngoài ra, phương pháp này có sự

hỗ trợ của một mạng cảm biến không dây (WNS) cho phép đo trong thời gian thực đồng thời nhiệt độ và công suất tiêu thụ điện Giao thức Zigbee cho phép giao tiếp giữa các cảm biến nhiệt độ không dây Việc kiểm soát bằng phương pháp đề xuất được thử nghiệm trên phần mêm EMTP-RV trên một mạng lưới phân phối có điều hòa nhiệt độ tại Đà Nẵng Những kết quả này cho thấy rằng các giải pháp được đề xuất có thể được áp dụng quản lý hiệu quả cho một nhóm tải, các tòa nhà… để giảm công suất tiêu thụ trong giờ cao điểm hoặc khi điều

độ yêu cầu của lưới điện phân phối

1 Introduction:

oad management is defined as sets of

objectives designed to control and modify the

patterns of demands of various consumers of a

power utility Load management permits to limit

or shift peak load from on-peak to off-peak time

periods Load management is dedicated to

control systems which monitor and plan the

energy demand of a building or larger zone They can be programmed to control lighting,

refrigeration equipment, pumps, valves and motors (Fig 1)

The sector of the building presents one of the greatest potentials of energy efficiency and reduction of the gas emissions The use of the loads in an active and intelligent way and optimal load management is one of the major L

concerns of the managers, the providers and the

consumers of energy

The peak consumption reduction is one of the

most effective solutions of energy management

systems This reduction presents many interests:

 For the customers: reduce the bill for the

subscription and consumption in peak hours,

 For the DNO (Distribution Network

Operator): avoid the congestion and the

problems caused by overloads,

 For the energy provider: limit the purchase

of an expensive energy

In order to develop the intelligent electric

distribution networks in the future (Smart Grid),

direct load control for controllable loads plays

an important role Loads become active and

intelligent The loads participate to resolve

technical problems in the networks or contribute

to ancillary services such as voltage control,

congestion management…

The air-conditioning is a controllable load

For tropical countries in summer, it takes an

important part in the tertiary and residential

buildings Demand Side Management (DSM)

considers air-conditioning load as one of the

most suitable loads to implement direct

customer load control in order to exercise peak

demand control as well as energy consumption

control in supply systems [1-8] In DSM, the

air-conditioning units located at customer premises

are directed to enter energy/demand saving

control modes by means of control signals

issued by DNO from sub stations either via

remote radio link or via power line carrier

communication link at distribution level when

the utility wants to exercise demand control

during periods of power shortage Therefore the

management of air conditioners has an important

potential to reduce the peaks of consumption

and permits to contribute to ancillary services in

distribution

Figure 1 Energy and load management system

In Vietnam, the electric power consumption

is always superior to the electric power

production The load sheddings in on-peak

periods in order to avoid over load or blackouts

are inevitable The air-conditioning takes an important part in the tertiary and residential buildings This is why the direct load control of air conditioning presents one of the best solutions to reduce peak consumption

In this paper, an adaptive control of air-conditioning units is proposed Then the proposed solution is applied for a distribution network in order to reduce the peak load consumption and to avoid congestion

2 Load control 2.1 Techniques of load control

Techniques of load control can be presented

in the following: Time- Of- Use-Tariff, Interruptible Load Tariffs, Distribution System Loss Reduction (ex: reactive compensation)

2.2 Measurement for load management

Load control can be realised for lighting load and HVAC Load control strategies can be presented as following:

 Fixed Priority Strategy Priority strategy sheds the least important loads first and the most important last The last load shed is the first to be restored

 Fixed Priority Strategy Priority strategy sheds the least important loads first and the most important last The last load shed is the first to be restored

 Rotate Strategy The rotating sequence provides for an equal distribution of power to all controlled loads

 Combination Fixed/Rotate Strategy This is the most versatile and powerful strategy because so many combinations are possible

 Adaptive Control Strategy This is an intelligent strategy by using an adaptive control for variable set-point values

3 Principle of the adaptive control of air-conditioner

3.1 Model of air conditioner

The operation of an air conditioner is based

on the phase change of a fluid refrigerant: evaporation occurs with heat absorption, condensation with heat production Therefore, in any air conditioner there is:

- An exchanger evaporator where cooling

associated with the evaporation of refrigerant is

transmitted to the ambient air;

- A compressor compressing the gaseous

fluids, increasing pressure and temperature;

- An exchanger condenser where gas

transfers its heat by condensing;

- A relief valve decreasing the pressure of the

liquid refrigerant before its evaporation in the

heat exchanger

In this paper, the electrical analogue model

for an air conditioned house proposed in [7] is

used Fig 2 shows the model of an air

conditioning developed with EMTP-RV From

this model, we propose a new method based on

the adaptive control of air-conditioner for load

management system

Iac S(t) Is

To

Iinst

Figure 2 Electrical analogue model for an air

conditioned house

Where:

 Rw, Cw: the equivalent thermal conduction

resistance and thermal storage capacity of

the house (wall, base, roof)

 Rc, Ci: the equivalent thermal conduction

resistance of the average air infiltration and

thermal capacity of the air inside the house

 To, Tw, Ti: the exterior temperature, the

wall temperature and interior temperature

 Is : the current source of two components

(solar irradiation and the portion of internal

heat sources involved in this indirect heating

of air)

 Iinst: the current source of heat source

produced by lamp, computer, the body…

 Iac: The heat removed by the air

conditioner

 S(t): The switching function (= 1 when the

compressor motor is ON and = 0 when the

compressor motor is OFF)

In Fig 2, the equivalent thermal conduction

resistance of inside-wall and outside-wall (Rw)

are assumed to be equal

The differential equation system is obtained

by applying Kirchoff’law at the nodes:

Where Tw and Ti are the unknown variables

RwCw

Tw RwCw

To RwCw

Ti Cw

Is dt

Rw Rc

Ci Ti RwCi Tw RcCi To Ci t IacS Ci Iinst dt

Where Tw and Ti are the unknown variables The Fig 3 shows the EMTP-RV model of the air conditioner that is built from this differential equation system In order to connect to distribution network, the air conditioner is modeled with EMTP-RV by a current injection

f(u) 1

Fm1

f(u) 1

Fm12

f(u) 1

Fm13

f(u) 1

Fm14

+

+ +

sum6

f(u) 1

Fm 15

f(u) 1

Fm 16

f(u) 1

Fm 17

f(u) 1

Fm 18

f(u) 1

Fm 20

+

+ + +

sum7

!h

Int3

!h

Int4

f(u) 1 3

Fm22

c

#P_AC#

C1

+

+

sum8

c

#T_Set#

C4

scope

T _int

c

C10

1

Ftb3

f(u)

Fm23 Ftb4

c

C11

0

scope

T _ext

scope

Solar

f(u) 1

Fm24

-1

Gain1

scope

Q_AC

-1

Gain2

scope

scp6

f(s)

fs1

f(u) 1

Fm25

scope

P_AC

P_Depas

f(u)=0 1

Relay Iinst

scope

Figure 3 Air conditioner modelized using

EMTP-RV

3.2 Proposed adaptive control

In general, a modern air conditioner is equipped with a temperature regulator (called classical control) This regulator is used to maintain the temperature in a specified value and carried out by a thermostat (a bimetal or an electronic thermostat) The classical controls can ensure the thermal comfort, but this method is not able to vary the power consumption and can also cause an excess from a permissible power fixed by outdoor signal from DNO (Distribution Network Operators)

In order to reduce the peak consumption for a network in presence of air conditioners, this part presents a development of an adaptive temperature control for air conditioning

In the normal operation (without excess of contractual demand or without outdoor signal from DNO or energy provider), the regulator operates like a normal temperature regulator to ensure thermal comfort (Ex: Tset-point ±1 where Tset-point is constant and fixed)

Classical

regulator

Air conditioning

Meteorologies Conditions

P_permissible

Temperature and Fuzzy

-T_room

P_total

Temperature

Set-point

Thermal model

of building ( surfaces, walls, windows )

+

Adaptive module

+

-Adaptive conversion

ΔP  ΔT

Figure 4 Principle of the proposed

air conditioning control

In case of excess of contractual demand or

with outdoor signal (ex: congestion signal

generated by DNO), the regulator switches to

the adaptive regulator mode with a variable

set-point value of temperature in order to limit the

peak of consumption to a predefined level The

principle of the proposed air conditioning

regulator is represented in Fig.4

In case of excess of contractual demand or

with outdoor signal, this signal converts it into a

temperature variation corresponding to required

reduction power This value is transmitted to

each air conditioning in order to modify the

set-point temperature In a distribution network with

different houses, the permissible power signal of

DNO is generated from sub stations

4 Real-time control using Zigbee sensor

network for energy management system

in buildings

In the light of developments in

microelectro-mechanical systems (MEMS), along with

progress made in communication and embedded

smart sensors, the residential sector has a huge

possibilities of creating networks between home

appliances, sensors and wireless media, enable

the control of domestic equipment locally or

remotely via the Internet The development of

WNS, with the Zigbee technology allows us to

establish more sophisticated control based on the

combination of measured information and

intelligent control in order to improve the use of

electrical equipments

The advantages of this type of technology

ZigBee include:

 Elimination of all costs related to the

physical connection of devices

communication interface between several

devices, using a communication protocol supported by numerous manufacturers

 Ability to automatically reconfigure the communication network each time a new element is added

This application relies on a wireless sensor network (WNS) which allows simultaneous, real-time measurement of indoor temperature

communication protocol is used between wireless temperature sensors and equipment

Table 1 Electrical appliances of the

apartement

Name Description

air

from the control unit ZigBee

layers

ZigBee layers for communication

This system comprises an array of wireless temperature sensors, a wireless electrical power sensor, radiators equipped adaptive controls, and

a central control unit (Fig.5)

Figure 5 Architecture of system

The Wireless Electrical Power Sensor comprises two components: a power sensor and

a communication unit The first component is

transmits this information to the central cont rol unit The wireless electrical power sensor receives the consum ption information and detects excessive power (beyond the authorized power limit)

air conditioning

air conditioning

The central control unit : in our system, the

control unit is a computer equipped with a

wireless communication module and control

software The central control unit analyzes the

information received The central control unit

measurements and makes decisions to control

the air conditionings in an intelligent manner,

consumption below the authorized power limit

The array of Wireless Temperature Sensors is

programmed to measure temperature within the

building at all times After collection, this

information is transmitted to the central control

unit via a ZigBee communication link

5 Application for a distribution network at

DaNang city

The proposed temperature control is used for

air conditioner load in a LV rural distribution

network at DaNang city as shown in Fig 7 This

network is connected with a MV network via a

100 kVA, 22/0.4 kV transformer This network

contains 12 air conditioners with 5kW for

individual houses Other loads (lightning,

washing-machine…) are considered as an equivalent load

in each house Fig 6 shows the daily variation

of these loads (active and reactive power) The maximal active power is 5 kW The power factor of equivalent load is 0.93 The total consumption of each house includes two parts:

consumption by this equivalent load

presented in Fig 8 Total equivalent power obtained from solar irradiation (Is) for each house is showed by (Fig 9) We suppose that the load variation, the exterior temperature variation and the solar irradiation are identical for all houses in this network

Figure 6 Daily variation of residential loads in each house without air conditioners

1

R3

LF LF1

Sl ack: 20.5kVRMSLL/_0

Phase :0

5 nF

C1

p1 p2 ALM7 0_1 30m PI

p1 p2

AL M70_ 185 m PI

p1 p2

AL M70_ 100 0m PI

p1 p2 ALM70 _34 6m PI

p1 p2

AL M70_4 16 PI

p1 p2 ALM7 0_1 30m PI

p1 p2 ALM7 0_2 51m PI

p1 p2 ALM3 5_1 45m

PI

p1 p2 ALM35 _15 7m PI

p1 p2 ALM35 _12 1m PI

p1 p2

AL M35_ 130 m PI

p1 p2 ALM35_ 127 m PI p1 p2

AL9 5_5 0S_ 470 m PI

1 2

DY_ 1

20 /0 4 2 +

S_HT A

20.5kVRMSLL /_0

Sl ack:LF1

p V_ pu V4

p V_pu V5 p

V_ pu V3

p V_ pu V2

P

50 Hz

Q

50 Hz

Loa d_AirCond iti oni ng

L_AC_ 5

Lo ad_ Ai rCon di ti on in g

L_ AC_4

Loa d_Ai rCond iti oni ng L_AC_3

Loa d_AirCond iti oni ng L_ AC_6

Lo ad_ Ai rCon di ti on in g

L_ AC_7

L oad _AirCo ndi tio ni ng L_AC_ 14

L oad _AirCo ndi tio ni ng

L_ AC_9

Loa d_AirCond iti oni ng L_ AC_10

Lo ad_ Ai rCon di ti on in g

L_AC_ 12

Lo ad_ Ai rCon di ti on in g L_ AC_13

Loa d_Ai rCon di ti oni ng

L_AC_11

Loa d_AirCond iti oni ng L_ AC_8

AAR AAR

HTA

L V2

LV11 LV14

L V5

L V4 LV3

LV6

LV7

LV12 PV13

LV10 LV9

L V8 T_Red

Figure 7 Rural distribution network with air conditioners simulated with EMTP-RV

5.1 Classical temperature control

For this case, the set-point temperature of each

house is 20°C (±1°C) and all the air-conditioners

use the classical temperature control The

permissible power is fixed to 100 kVA This is the rated power of the HV/LV transformer

Fig 10 shows the total power measured at the transformer It shows that there is a 10% overload

0 2 4 6 8 10 12 14 16 18 20 22 24 0

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Times (H)

between 17 and 21H The power of air-conditioner

and the interior temperature of the house at bus 4

are presented in Figs 11 and 12

Figure 8 Exterior temperature

Figure 9 Total power obtained by solar

irradiation for each house

0

20

40

60

80

100

120

Times (H)

P

Q

S

Smax = 100 kVA

Figure 10 Total power (classical control) of the network

0 2 4 6 8 10 12 14 16 18 20 22 24

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Times (H)

Figure 11 Power of air conditioner of the house at bus 4

0 2 4 6 8 10 12 14 16 18 20 22 24 18.5

19 19.5 20 20.5 21 21.5

Times (H)

Figure 12 Interior temperature of the house at bus 4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Times (H)

Va Vb Vc

Figure 13 Three phase voltage at bus 4

(with classical control)

It shows that the interior temperature is maintained at 20°C (±1°C) The thermal comfort is assured for all houses

Fig 13 shows the three phase voltage variation

at bus 4 In light load (0-6H) the voltage is high, and in heavy load (10-22H) the voltage is low The voltage is always maintained between 0.9 and 1.1

pu

5.2 Proposed method

In this case, the adaptive control is applied for all air-conditioners in this network The set-point temperature of each house is 20°C (±1°C)

0 20 40 60 80 100 120

Times (H)

P Q S

Smax = 100 kVA

Figure 14 Total power without load control of the network

0 2 4 6 8 10 12 14 16 18 20 22 24

26

28

30

32

34

36

38

Times (H)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Times (H)

0 2 4 6 8 10 12 14 16 18 20 22 24

0

1

2

3

4

5

6

Times (H)

Figure 15 Power of air conditioner of the house at bus 4

0 2 4 6 8 10 12 14 16 18 20 22 24

18.5

19

19.5

20

20.5

21

21.5

Times (H)

Figure 16 Interior temperature of the house at bus 4

0 2 4 6 8 10 12 14 16 18 20 22 24

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Times (H)

Va

Vb

Vc

Figure 17 Three phase voltage at bus 4

Fig 14 shows the total consumption power of

the network The maximal power is always inferior

to 100 kVA With the help of the proposed method,

the overload in transformer is avoided Fig 15

presents the operation of the air-conditioner at bus

4 The thermal comfort is maintained in this case

(Fig 16), because the maximal interior temperature

is always inferior to 21°C for all houses Fig 17

shows the three phase voltage variation at bus 4 It

shows that the voltage in heavy load is improved in

comparison with the classical control case This

method can be applied to voltage control

With the help of this method, the thermal

comfort is assured if the permissible power is

reduced to 90 kVA (-10%) and the set-point

permissible power is lower than 0.9Smax (90kVA)

the thermal comfort is broken It means that with a

peak load reduction to avoid congestion superior to

10%, the comfort is not maintained with

fixed by DNO, is 0.8 Smax (80 kVA), the maximal temperature is increased to 23.5°C This is equivalent to 20% of load shedding

6 Conclusion

The results of simulation show that the proposed method permits to reduce efficiently the peak consumption while maintaining thermal comfort The suggested method can be applied for the various types of loads (ex: heating) and adapted to the context in the future by taking into account the economic and technical signals from manager and DNO (ex: congestion, dynamic tariff…) The obtained results show that this method can be applied to contribute to ancillary services such as voltage control in distribution networks

The proposed solution is applied to a group of loads or buildings (such as a virtual consumer) in order to reduce the peak consumption (or congestion management) in a large distribution network In order to reduce peak consumption, this method avoids a violent load shedding This control only modifies adaptively the set-point value of temperature for each air conditioner to obtain a desired (permissible) power, fixed by DNO On the one hand, this method avoids a hard optimal calculation with a slow response On the other hand, this solution avoids a load prevision that is sometimes not accurate and very complicated

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