Mitsubishi Heavy Industries Technical Review Vol. 51 No. 2 (June 2014)
9

New Fast Demand Control of
Building HVAC Facilities for Smart Grid

JUNJI MORIKAWA*1

SEIJI KONDO*2

SHINICHI ISOZUMI*3

CHUZO NINAGAWA*4

Real-time, fast demand control is thought to be promising technology to compensate forhighly variable renewable energy sources for the smart grid of the future. Recent HVAC(Heating
Ventilation and Air-Conditioning) facilities for buildings (hereinafter air-conditioning systems) use
sophisticated refrigerant flow control devices and Mitsubishi Heavy Industries, Ltd. (MHI) would
like to propose quick-response (approx. 5 minutes), fast demand control (hereinafter new fast
demand control) to replace existing on-off and coarse slow demand controls.It is, however,cumbersome due to the great complexity in controlling building HVAC systems. As a solution tothis, we considered the use of a numerical formula model for statistical prediction to calculate theamount of electricity need five minutes in the future. Operational testing using a numerical formulamodel successfully proved the effectiveness of new, fast demand control.

|1. Introduction
It is expected that the smart grid of the future will be closely interconnected withphotovoltaic power generation systems or wind turbine generators, which use renewable energy
sources. Such sources are also known for the fact that their output voltage fluctuates, depending
upon momentary climate changes(1)-(3). Although it is possible to compensate for such fluctuations
by using batteries with huge capacities, if future large-scale interconnection of renewable energy
sources is assumed, it is easily conceivable that this will require an inordinate cost. In order tocompensate for highly variable renewable energy sources without depending upon batteries alone,efficient fast demand control is one of the most effective solutions.4-8
In the smart grid field, office building air-conditioning systems are well known as aneffective means to implement demand-side power control. Demand control of building
air-conditioning systems has also been widely studied so far. In addition, there have been a numberof research studies about demand control using a numerical formula model for statistical prediction(AR model).9-12
Contrary to this, research publications for quick (e.g., 5-minute intervals) prediction andcontrol of the power consumed by building air-conditioning systems are scarce. The control
mechanism of many predictive numerical formula models and control methods was based mainlyon power on-off operation, consuming a long average time. Many researchers point out thenecessity of quick-response demand control for future real-time power rate calculation anddynamic demand control.13-19
Office building HVAC systems20 are prevalent in mid-sized buildings in Japan, as well as incountries in Asia, Europe and elsewhere. HVAC systems use a sophisticated refrigerant flow
control device and it is considered possible to embody new, fast demand control by allowing this*1

Air-Conditioner Designing & Engineering Department, Air-Conditioning & Refrigeration Division*2

Manager, Air-Conditioner Designing & Engineering Department, Air-Conditioning & Refrigeration Division*3

Chief Staff Manager, Sales Department, Air-Conditioning & Refrigeration Division*4

Professor, Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University

Mitsubishi Heavy Industries Technical Review Vol. 51 No. 2 (June 2014)10refrigerant flow control device to continuously change its operation.
This paper proposes a mechanism of new fast demand control for building HVAC systems.
An AR model for the power consumed (every five minutes) by building HVAC systems will be
developed from field test data, and prediction and control computation will be carried out throughsimulations executed by cloud computing. It is considered that the new fast demand control
mechanism will be one of the mechanisms to balance supply and demand in real time in the smart
grid of the future including interconnected renewable energy sources.

|2. Demand control of buildings for the smart grid of the future
Figure 1 conceptualizes the new fast demand control of building air-conditioning systems
for the smart grid. The figure contains only one building, but the control of many buildings may
actually be possible. A gateway possessing an ADR (Automated Demand Response) client function
communicates with DRAS (Demand Response Automation Server) to compensate for the
momentary power fluctuations of renewable energy sources.
Our goal is to avoid exclusive dependence upon batteries for compensating for powerfluctuations, that is, to develop equipment which quickly responds pursuant to new fast demand
control.
A typical building HVAC system is composed of several outdoor and a large number ofindoor units. In the cooling cycle of an air conditioner, outdoor units are equipment which allows
refrigerant to condense, and indoor units are equipment which allows refrigerant to vaporize.
Outdoor units are typically installed on the roof, and indoor units are buried in the ceiling of eachroom. Power for the system is supplied to the outdoor units. Using the power supplied, outdoor
units compress refrigerant and distribute it to the indoor units. Typical building air-conditioning
systems have a thermal inertia on the order of 3 minutes. Time-granularity control of 3 minutes orless is, therefore, meaningless. Further taking the resolution of the watt-hour meter’s pulse countinto account, this paper considers it appropriate to use control intervals of 5 minutes.

Figure 1 Structural diagram of building air-conditioning
systems to realize new fast demand control

Figure 2 is a conceptual diagram of the refrigerant circuit in an HVAC system. The outdoor
unit has a built-in inverter-driven compressor, heat exchanger, and controller. Each indoor unit hasa built-in heat-exchanger, supply fan, electronic expansion valve (EEV), and controller. Indoor
units regulate the electronic expansion valve to adjust refrigerant flow to the heat exchanger inconformity to the thermal load in the room.
Each indoor unit periodically transmits the required amount of flow to the outdoor unit. The
outdoor unit periodically puts together these requirements to regulate the total amount of flow byaltering the number of revolutions of the compressor. Thereafter, the outdoor unit distributes thecorresponding amount of flow to each indoor unit. Each indoor unit regulates the opening positionof the electronic expansion valve, using the value of the corresponding flow amount. The gateway
responds to the demand from DRAS for demand control. Moreover, it communicates with each unit
to assign an order of priority to the indoor unit, which regulates the opening position of expansion
valve.

Mitsubishi Heavy Industries Technical Review Vol. 51 No. 2 (June 2014)
11

Figure 2 Structural diagram of HVAC facility refrigerant circuit
control|3. Quick-response AR model
The AR model we propose uses 4 state variables. That is, momentary power consumption
PA5 (k ) , outdoor temperature TO 5 (k ) , temperature set from the weighted average of the nominal
capacities of indoor units TS 5 (k ) and total amount of 5-minute refrigerant flow FR 5 (k ) . The AR
model system equation adopting these state variables is defined in Formula (1).

⎡ A(l )11 A(l )12 A(l )13 A(l )14 ⎤ ⎡ P (k − l ) ⎤ ⎡U (k ) ⎤⎡ P ′ (k )⎤PA5A5⎥ ⎢ A5⎥ L ⎢ (l )
⎢
⎥ ⎢
⎥
(l )
(l )
(l )
U
T
(
k
l
)
−
A
A
A
A
21
22
23
24
⎥
⎢
⎢ * ⎥=⎢ O5⎥ + ⎢ TO 5 (k ) ⎥⎢ (l )
⎥
⎢
(l )
(l )
(l ) ⎥ ⎢T ( k − l ) ⎥⎢U TS 5 (k ) ⎥⎢ * ⎥ l =1 ⎢ A 31 A 32 A 33 A 34 ⎥ ⎢ S 5⎥ ⎢⎥⎢ A(l ) A(l ) A(l ) A(l ) ⎥ ⎢⎣ FR 5 (k − l )⎥⎦ ⎢⎣U FR 5 (k )⎥⎦⎢⎣ * ⎥⎦
41
42
43
44
⎦
⎣

∑

Formula (1)Where k represents the integer value to be counted every five minutes; l , the integer valueof expressed delay; L, AR model order; A(l )11 , A(l )12 , … , A(l ) 44 , elements of the AR coefficientmatrix; and U PA5 (k ) , U TO 5 (k ) , U TS 5 (k ) , U FR 5 (k ) , prediction errors. PA5′ (k ) means thepredicted value of 5 minutes-later power consumption.
First, a unique state variable FR 5 (k ) which indicates the total amount of refrigerant flow isexpressed in Formula (2).
N

FR 5 (k ) =M∑∑ F (t )RnFormula (2)mn =1 m =1

Where n = 1, 2, L , N is the number of indoor units, and t m = t1 , t 2 , …, t M represents
5-minute measuring/sampling time. For example, if M=5, the measuring/sampling time t m occurs
every minute.
Next, 5-minute temperature TS 5 (k ) set from a weighted average calculated using nominal
indoor unit capacities was introduced. TS 5 (k ) is given by the equation of Formula (3).
N

M∑∑ R (t )C T (t )nn =1 m=1
TS 5 (k ) =
N M

m

n Sn

∑∑ R (t )Cnmm

Formula (3)nn =1 m=1

Where Cn is the nominal capacity of an indoor unit. Rn (t m ) stands for a certain indoor
unit n ’s state of operation/stop and is given as in Formula (4).
⎧1 (nth indoor unit is ON at mth sampling)
Rn (t m ) = ⎨⎩0 (nth indoor unit is OFF at mth sampling)

Formula (4)Mitsubishi Heavy Industries Technical Review Vol. 51 No. 2 (June 2014)12|4. Operation test
Table 1 shows a description of the building used in the field tests for the measurement ofdata. The idea of fast demand control is particularly effective for larger buildings, but the building
given here has a size large enough for our research to apply to the evaluation of its effectiveness.
Table 1

Description of Field Test Building

Item
Kind of building
Gross floor area
Structure of building
Number of indoor units existing within the building

Specification
Typical office building
5,000 m2
Reinforced concrete
30 units

Figure 3 shows measurement time-series data of five-minute intervals ( k = 1, 2, L , 144 )from 07:00 to 19:00 on August 2, 2008. Instantaneous power consumption PA5 (k ) was measured
by fitting a watt-hour meter in the outdoor unit and counting output pulses. The 5 minute-interval
outdoor temperature measurements TO 5 (k ) are data obtained from the Meteorological Agency.
The set temperature of each indoor unit was observed and the set weighted-average temperature
TS 5 (k ) was measured. Meanwhile, the opening position of each indoor unit’s electronic expansion
valve was continuously measured and the total amount of refrigerant flow FR 5 (k ) could also be
obtained through measurement. The AR model coefficient matrix was later calculated by anothercomputer. In the actual smart grid of the future, it is believed that data sampling, storage andmemory will be handled using SaaS developed by cloud computing. With the AR model order
L = 2 , the AR coefficient matrix elopements A(l )11 , A(l )12 , … , A (l ) 44 were determined throughcomputer calculation using the Yule-Walker Equation.

(a) 5 minute-interval time-series data of power consumption PA5(k)(b) 5 minute-interval time-series data of outdoor temperature TO5(k)(c) 5 minute-interval time-series data of average setpoint TS5(k)(d) 5 minute-interval time-series data of refrigeration flow index FR5(k)Figure 3 5 Minute-interval time-series measured data for period of 07:00 to 19:00

Figure 4 shows the 5 minute-interval power consumption predicted at the calculation for
07:00 to 19:00. In this figure, measurements are also indicated in an overlapped manner. Calculated
values and measurements are almost identical as a whole. Figure 5 magnifies Figure 4’s
much-power-consuming, just-past-noon time zone for comparison in detail. From Figure 5, it isfound that calculated values less sharply change than measurements. In AR model predictive
calculations, a prediction value can be obtained from the AR model coefficient-based linear
combination of several past values. This is also referred to as a mutual smoothing effect, which
prevents prediction values from changing sharply like measurements. Since the AR model predicts
the next value from several values immediately before it, it is intrinsically difficult for the model topredict an extremely sharp change, if any.

Mitsubishi Heavy Industries Technical Review Vol. 51 No. 2 (June 2014)
13

Figure 4 5 minute-interval time-series datafor period of 07:00 to 19:00 (calculated
value-measurement comparison)

Figure 5 5 minute-interval time-series datafor period of 15:00 to 18:00 (magnified
part of Figure 4)

Figure 6 shows the result of a predictive calculation trial assuming fast demand control. On
the assumption that smart grid demand was responded to between 16:00 and 16:55, 5-minute power
consumption was calculated as the decrement in the total amount of refrigerant flow TR 5 (k ) . It isfound that the effect of control was effectively and promptly reflected as soon as the control period
started. Predictive calculation has yet to be quantitatively evaluated, so we intend to further
improve the accuracy of refrigerant control and predictive calculation.

Figure 6 Predicted value of power consumption
controlled with assumed demand|5. Examination
Table 2 compares an existing demand control of HVAC systems with our new, fast method.
The existing demand control mainly switches the ON/OFF state of operation for control. The new
demand control continuously regulates the amount of refrigerant flow. This allows momentary
power consumption of systems as a whole to be quickly reflected and, as a result, our method
prevents excessive and unnecessary halting of HVAC systems.
Power generation with renewable energy sources, involving large-scale interconnection,
sometimes noticeably fluctuates during a period of 10 minutes or so. This is because of an abrupt,
drastic change in sunshine for solar power generation and in wind speed for wind power
generation. Conventional demand control technology is based on a time frame of 30 minutes and,
therefore, remains unable to achieve timely, e.g., 10-minute, compensation for power control. Our
new fast demand control technology enables the demand side’s accurate and quick response and isalso considered to help realize a real-time power rate system.
Table 2Comparison between Existing and Fine/Fast Demand Control

Prediction time granularity
Time of response to ADR
Control accuracy
Method of control

Existing demand control
1/2 to 1 hourLong
May possibly halt too often
ON-OFF control

Power control mechanism

Thermostatic ON-OFF ratio

Level required for control
Embodiment

Low
Control with integrated CPU

New fast demand control
1 to 5 minutes
Short
Comfort-oriented fine control
Continual feedback control
Adjustment of electronic expansion
valve’s opening position
High
Control with cloud computing

Mitsubishi Heavy Industries Technical Review Vol. 51 No. 2 (June 2014)14|6. Conclusion
This research obtained the following results:(1) A new method of fast demand control for air-conditioning systems was proposed.
(2) An AR model of 5 minute-interval power consumption by air-conditioning systems inan entire building was derived from the field test data.
(3) On the assumption that demand is assumed and demand control is continuously
implemented every 5 minutes, the calculation results verified effective and quick
demand response.
The new fast demand control we are proposing will contribute to a real-time power rate
system, virtual power plants, and other future smart grid initiatives interconnected with large-scale
renewable energy sources.

References
1.
2.
3.

4.

5.

6.

7.
8.
9.

10.

11.

12.

13.
14.

15.
16.

17.

18.
19.

S.Chu, “Investing in our energy future,” in GridWeek talk,
http://www.energy.gov/news2009/documents2009/Secretary_Chu_Gred_Week.pdf, page 11, Sept 2009.
S.Chen, H.Gooi, and M.Wang, “Sizing of energy storage for microgrids,” IEEE Trans. on Smart Grid,
Vol.3, No.1, pp.142-151, 2012.
H. Kanchev, D. Lu, F. Colas, V. Lazarov, and B. Francois, “Energy management and operational planningof a microgrid wiht a PV-based active generator for smart grid applications,” IEEE Trans. on Industrial
Electronics, Vol. 58, No. 10, pp. 4583-4592, 2011.
M. Damm, S. Mahlknecht, C. Grimm, T. Bertenyi, T. Young and C. Wysoudil, “A pertially decentralised
forecast-based demand-side-management approach,” in Proc. IEEE Innovative Smart Grid Technologies,
ISGT Europe 2011, Manchester, U.K., Dec. 2011.
P. MacDougall, C. Warmer, and K. Kok, “Mitigation of wind power fluctuations by intelligent response of
demand and distributed generation,” in Proc. IEEE Innovative Smart Grid Technologies, ISGT Europe
2011, Manchester, U.K., Dec. 2011.
A.Abdollahi, M.Parsa, M.Rashidinejad, and M.Kazem, “Investigation on economic andenvironmental-driven demand response measures incorporating UC,” IEEE Trans. on Smart Grid, Vol.3,
No.1, pp.12-19, 2012.
J.Medina, N.Muller, and I.Roytelman, “Demand response and distribution grid operations: opportunities
and challenges,” IEEE Tran. on Smart Grid, Vol.1, No.2, pp.193-198, 2010
Z.Zhou, and F.Zhao, and J.Wang, ”Agent-based electricity market simulation with demand response fromcommercial buildings,” IEEE Trans. on Smart Grid, Vol.2, No.4, pp.580-588, 2010.
C. Ninagawa and T. Sato, “Remote monitoring integrated state variable for AR model prediction of daily
total building air-conditioning power consumption,” IEEJ Trans. on Electrical and Electronic Engineering,Vol. 5, pp. 611-612, 2010.
C. Ninagawa and T. Sato, “Remote monitoring integrated state variable for AR model prediction of daily
total building air-conditioning power consumption,” IEEJ Trans. on Electrical and Electronic Engineering,Vol. 5, pp. 611-612, 2010.
H. Mori and A. Takahashi, “Hybrid intelligent model of relevant vector machine and regression tree forprobabilistic load forecasting,” in Proc. IEEE Innovative Smart Grid Technologies, ISGT Europe 2011,
Manchester, U.K., Dec. 2011.
N. Amjady, F. Keynia, and H. Zareipour, “Short-term load forecast of microgrids by a new bilevel
prediction stratedy,” in Proc. IEEE Innovative Smart Grid Technologies, ISGT Europe 2011,
Manchester, U.K., Dec. 2011.
J. L. Mathieu, P. N. Price, S. Kiliccote, and M. A. Piette, “Quantifying changes in building electricity use
with application to demand response,” IEEE Trans. on Smart Grid, Vol. 2, No. 3, pp. 507-518, 2011.
S. Paoletti, M. Casini, A. Giannitrapani, A. Facchini, A. Garulli, and A Vicino, “Load forecasting foractive distribution networks,” in Proc. IEEE Innovative Smart Grid Technologies, ISGT Europe 2011,
Manchester, U.K., Dec. 2011.
A. Conejo, J. M. Morales, and L. Baringo, “Real-time demand response model,” IEEE Trans. on Smart
Grid, Vol. 1, No. 3, pp. 236-242, 2010.
P. Nyeng and J. Ostergraard, “Information and communications systems for control-by-price ofdistributed energy resources and flexible demand,” IEEE Trans. on Smart Grid, Vol. 2, No. 2, pp.
334-341, 2011.
Z. Cstvei, J. Ostergaard, and P. Nyeng, “Controlling price-responsive heat pumps for overload eliminationin distribution systems,” in Proc. IEEE Innovative Smart Grid Technologies, ISGT Europe 2011,
Manchester, U.K., Dec. 2011.
T. Jin and M. Mechehoul, “Ordering electricity via Internet and its potentials for smart grid systems,”
IEEE Trans. on Smart Grid, Vol. 1, No. 3, pp. 302-310, 2010.
M.Parvania, J. Fotuhi-Firuzabad, “Demand response scheduling by stochastic SCUC,” IEEE Trans. on
Smart Grid, Vol. 1, No. 1, pp. 89-98, 2010.