Air-Conditioning Control of HEMS and BEMS toward CEMS

Transcription

1 Air-Conditioning Control of HEMS and BEMS toward CEMS Kyosuke Funami Nishi Laboratory Graduate School of Science and Technology, Keio University, Japan 0

2 Table of Contents 1. Introduction 2. KNIVES 3. Comfort Index PMV 4. Air-conditioning Control in HEMS/BEMS 5. Demonstration of BEMS/CEMS 6. Conclusion and Future Works 1

3 Introduction Saving energy is indispensable to solve environmental problems. Global warming by greenhouse gas Exhaustion of fossil fuels Energy consumption in a household and an office sector increases rapidly. Air conditioners account for one quarter of power consumption. Fig.1 Change of energy consumption in Japan Fig.2 Power consumption rate of home appliance 2

4 EMS(Energy Management System) HEMS(Home Energy Management System) Observes electric appliances in a house Controls them by using a demand control terminal or HA HA : Home Automation terminal Problem Perform fine-grained control of the air conditioners BEMS(Building Energy Management System) Monitors total power consumption Controls mainly air conditioners and lighting Problem Maintain comfort of people in the building 3

5 HEMS Purpose Consideration of air-conditioning controls other than ON/OFF control Comparison with power consumption and comfort of three control methods Experiment in real environment BEMS Implement BEMS into the existing building Maintain comfort while energy saving Experiment in real environment Environmental measurement and control KNIVES (Keio university Network oriented Intelligent and Versatile Energy saving System) Comfort Index PMV(Predicted Mean Vote) 4

6 KNIVES Server System Analyzes and saves the information (environment and power) Determines a control command Client System Fig.3 Structure of KNIVES Measures the information (environment and power) Controls electric devices photo MOS relay Sends and receives digital signal I/O port Environmental information : Temperature, Humidity, Illuminance and Carbon dioxide concentration Fig.4 KNIVES terminal 5

7 Comfort Index PMV PMV(Predicted Mean Vote) Evaluated by measuring environment including physical and human elements Physical elements : temperature, humidity, wind speed and MRT(Mean Radiant Temperature) Human elements : amount of clothing and metabolic rate Takes values from -3(cold) to +3(hot) PMV ex. PMV = 0 (0.303e 0.036M p comfortable 0.028) ( M W Ed Es Ere Cre R C) M: metabolic rate(w/ m2 ) W:external work(w/ m2 ) Ed:heat loss by water vapor diffusion through skin(w/ m2 ) Es:rate of evaporative heat loss from skin(w/ m2 ) Ere:rate of evaporative heat loss from respiration(w/ m2 ) Cre: rate of convective heat loss from respiration (W/ m2 ) R:radiative heat loss from the surface of the clothed body(w/ m2 ) C: convective heat loss from the surface of the clothed body (W/ m2 ) 6 (1) PMV value Sensation +3 Hot +2 Warm +1 Slightly warm 0 Comfort -1 Slightly cool -2 Cool -3 Cold

8 Air-Conditioning control in HEMS/BEMS 7

9 Air-conditioning Control(1/2) Control Method ON/OFF Control Photo MOS relay turns on and off a compressor of the air conditioner. Infrared Control The setting temperature is changed by sending infrared signals from infrared transmitter module. The module is connected with digital I/O port of KNIVES terminal. Fig.4 KNIVES terminal Fig.5 Infrared transmitter module Inverter Control The demand power is regulated through the special demand control unit. The power can be regulated into 0, 40, 80% and no control. Fig.6 Demand control unit 8

10 Air-conditioning Control(2/2) Control Algorithm TABLE I Operation by each control method according to PMV value i : time count control method PMV value PMV < < PMV < < PMV < 0.25 ON/OFF Infrared Inverter Power ON / 27 C / No limitation Keeping previous state Keeping previous state 0.25 < PMV Power OFF (temperature + 1) C (temperature) C (temperature - 1) C 80% 40% 0% ex. Infrared control Fig.7 Control Algorithm Temperature : 20 C, PMV : -0.5 Temperature : 23 C, PMV : C 24 C 9

11 Experiments Experimental Environment area 30m 2 height 3.4m volume 102m 3 Experimental Device Air conditioner rated heating capacity(kw) 5.6 (2.5~7.1) rated power consumption(kw) 1.36 manufacturer Daikin Industries, Ltd Measurement Sensor temperature-humidity sensor (temperature, humidity) amenity meter (wind speed) Top(2.9m) Middle(1.5m) Bottom(0.1m) * PMV was calculated by using the central measured value. Fig.8 Setting place of the sensors Air Conditioner Ceiling Light Ventilating Fan Temperature-Humidity Sensor Wind Speed Sensor 10

12 Power Consumption (Wh) ON/OFF(0-1) / Output rate Setting Temperature ( ) PMV Results of Experiments (ON/OFF Control) ON/OFF control Time(min) Time(min) Fig.9 Operation history ON/OFF Inverter Infrared PMV is oscillated extremely. The oscillation can be reduced by narrowing the difference of control thresholds of PMV. Total preparation time for operation is extended. Power consumption of the time is wasted ON/OFF Infrared Inverter Time(min) Fig.10 Comparison of PMV ON/OFF Infrared Inverter Time(min) Time(min) Fig.11 Power consumption 11

13 Power Consumption (Wh) ON/OFF(0-1) / Output rate Setting Temperature ( ) PMV Results of Experiments (Infrared Control) Infrared control Time(min) Time(min) Fig.9 Operation history ON/OFF Inverter Infrared The maximum value of accumulated power consumption has decreased as PMV is close to zero. The switching frequency of setting temperature has decreased gradually ON/OFF Infrared Inverter Time(min) Time(min) Fig.10 Comparison of PMV Time(min) Time(min) Fig.11 Power consumption ON/OFF Infrared Inverter 12

14 Power Consumption (Wh) ON/OFF(0-1) / Output rate Setting Temperature ( ) PMV Results of Experiments (Inverter Control) Inverter control Time(min) Fig.9 Operation history ON/OFF Inverter Infrared The air conditioner is operated in 0.4(40% ) or 0.8(80%). Power consumption is also shifted between 4Wh and 8Wh ON/OFF Infrared Inverter Time(min) Time(min) Fig.10 Comparison of PMV Time(min) Fig.11 Power consumption ON/OFF Infrared Inverter 13

15 Output rate Power Consumption (Wh) ON/OFF(0-1) Power Consumption (Wh) Setting Temperature ( ) Relation between Controls and Power Consumption Time(min) ON/OFF, inverter Control ac_power ON-OFF (a) ON/OFF control Fig.12 Results of controls and power consumption Power consumption is controllable. Infrared Control Power consumption is uncontrollable. It is easy to forecast power consumption in inverter control Time(min) (b) Infrared control ac_power Output Rate Time(min) ac_power Setting Temp (c) Inverter control

16 Evaluation of Power Consumption TABLE II Accumulated power consumption and energy saving rate* Accumulated power consumption(wh) Power saving rate from constant (%) Power saving rate from ON/OFF (%) Constant 24 C ON/OFF Infrared Inverter * The power consumption was calculated by using measured instant power consumptions for 210 minutes except first 30 minutes when PMV is under

17 Temperature ( ) Temperature ( ) Temperature ( ) Evaluation of Comfort Time(min) (a) ON/OFF control top middle bottom Time(min) top middle bottom (b) Infrared control Fig.13 Environmental measurement result top middle bottom Time(min) (c) Inverter control TABLE III Evaluation of indoor temperature Controls ON/OFF Infrared Inverter Temperature difference (t = 30 min) Temperature difference (t = 90 min) Temperature difference (t = 150 min) Temperature of bottom(t = 150min)

18 Comprehensive Evaluation Saving power consumption Controls ON/OFF Infrared Inverter Power consumption controllability TABLE IV Comprehensive Evaluation - Maintaining comfort - Comfort of whole room* - * The temperature change of four corners in the room resembles that of the center. Inverter control is the most effective method. 17

19 Wrap-up of Air-conditioning Control in HEMS/BEMS Wrap-up The infrared control and inverter control can save 19.1% and 30.7% of power consumption compared with ON/OFF control, respectively. The inverter control is effective in maintaining indoor thermal environment. Moreover, power consumption can be controlled and saved. Future works of this section Cooperative control of multi air conditioners should be conducted under the condition of total-power-demand limitation. It is required to improve the control algorithm into fluidly operation. 18

20 Demonstration of BEMS/CEMS 19

21 Construction Environment Site Kurihara City Hall, Kurihara City, Miyagi Prefecture, Japan Summer : max average temperature 29.3 C, min average temperature 19.9 C Winter : max average temperature 1.9 C, min average temperature -7.8 C 3 Floors (1F: 540m 2, 2F: 280m 2, 3F: 501m 2 ), Entrance : 370m 2 Measurement Item Environmental information Temperature, Humidity, Illuminance, CO2 Power information Demand Power Air Handling Unit (AHU) Power Control Device (Air-Conditioner) Hot and Chilled Water Generator - Oil type AHU 4 20

22 Installed KNIVES Terminal KNIVES terminals were installed in the building. Measure power information Measured point : network connection point (demand) : machine room (AHU) Measure demand power Measure environmental information Control air-conditioner devices Control method : ON/OFF control Wireless environmental sensor Temperature-Humidity-Illminunce Sensor 1floor : 5 7 sensors Carbon dioxide Sensor 1floor : 1 sensor Measure environment Control the devices Environmental sensors 21

23 30min_demand_power(kWh) Control Experiment of Air Conditioner in Winter Purpose To observe the influence of the air-conditioner control on indoor environment Premise In the usual case, the control devices are turned off at 17:30. Peak Demand Time : 16:30 17:30 Air-Conditioner Power : 45 kwh Condition The control devices were turned off at 16:30. The indoor temperature was kept over 20 C Time Fig.14 Current change of demand power City_Hall 22

24 30min_demand_power(kWh) Result of Experiment Control time No_Control Control Time Fig.15 A change of demand power TABLE V The evaluation of the control result Air-Conditioner OFF time (min) Demand power of control time (kwh) Oil consumption of Control time (L) Reduced Rate of Power (%) No Control Control % 23

25 Temperature( ) Influence on Environment 25 Control time No_Control min Control hour sunset Time Fig.16 Influence of control on temperature The temperature at the time of control dropped slower than the temperature at the time of no control. A change of the temperature became rapid after sunset while the control devices were turned off. 24

26 Wrap-up Wrap-up of Demonstration of BEMS Existing building was used for installing BEMS. KNIVES was installed into Kurihara City Hall and other concerning facilities for measuring power and environmental information and controlling the devices. As the result of this experiment, 23% of the demand power can be reduced by stopping the air-conditioner for one hour. Enhancing from BEMS to HEMS We build CEMS by connecting two or more BEMS and to carry out electric power trading between BEMS, and it will be shown in the next section. In CEMS, it is indispensable to consider cluster configuration of each building. 25

27 From BEMS to CEMS To build CEMS with 8 buildings. To measure information and control devices in City Hall, Annex, Library and Memorial House. Community Center Divided point Apartment House Shed Divided point Library Memorial House 26

28 City Hall Profile of Buildings The top of demand power in the buildings is used regularly. The number of people is larger than other city buildings. Fixed Holidays : Saturday, Sunday Junior School There is a proper demand tendency. The building has a little cooling equipment. Fixed Holidays : Saturday, Sunday Library The demand power on weekends is larger than one on weekdays. Fixed Holiday : Monday 27

29 Structure of CEMS Laptop etc. A variety of Information Demand Information Network(Wired/Wireless) Connection point of some areas Resource Management Server Sensor Information Control Command Sensor Information Library area Environmental Sensor Air-Conditioner Terminal City Hall area Pulse Sensor Environmental Sensor PC Control Command Air-Conditioner Power Consumption Oil Consumption Terminal Pulse Sensor Network Device Control Command Ventilation fan Keio University Internet 制御端末 Terminal 28

30 Power Demand Control in CEMS START START supply forecasting cycle 1cycle Supply forecasting Cycle (24h) Long-term forecasting Short-term forecasting Power Consumptin Demand Control Cycle (30min) t = 0 [min] 60 CEMS Demand forecasting of next period Dynamic clustering Demand control plan Demand control 29

31 Dynamic Clustering Grouping Matrix X is used to express which building (demand) group belongs to which supply group. X 1,1 (t) X M,1 (t) GMD 1 (t) GMD M (t) = MD 1 (t) MD N (t) X 1,N (t) X M,N (t) Group 1 Building 1 Group 2 Building 2 Building 3 GMD : MD : X : M : N : Total demand of a group Total demand of a building Grouping Matrix Demand Group Number of buildings GMD 1 (t) GMD 2 (t) = MD 1 (t) MD 2 (t) MD 3 (t)

32 Used data Simulation Conditions Real 60 buildings or houses SFC of Keio University (18 th Nov 2011 to 28 th ) This campus has 9 buildings. Fukue port terminal(18 th Nov 2011 to 28 th ) (This BEMS will be explained in the next presentation.) 50 general Households (18 th Nov 2007 to 28 th ) Simulation Parameters Supply forecasting cycle: 24hours Demand control cycle: 30 minutes Demand groups 2 groups Supply rate was fixed; Group 1 : Group2 = 3 : 2 Demand control 5,10,15,20% control was virtually achieved according to the AC power consumption. 31

33 Power Consumption [kwh] Power Consumption [kwh] Simulation Result Difference between prediction and real demand Grp1 Supply Grp2 Supply Grp1 Demand Grp2 Demand One day One hour Prediction Difference [%] Dispersion Group shifting Demand ID 12:30 13:00 13:30 No No No No No No No No Grp1 Supply Grp2 Supply Grp1 DC Grp2 DC Demand Control

34 Improvement of Demand Supply Balancing of AC control Power Consumption [kwh] Balancing rate of days 18th 19th 20th 21th 25th 26th 27th 28th Average of difference [%] Dispersion th is the worst day This is because the supply exceeded to the demand and AC control can not be achieved to the balancing as shown in the graph. 19 th is the best day Grp1 Supply Grp2 Supply Grp1 Forecast Demand Grp2 Forecast Demand Grp1 Real Demand Grp2 Real Demand 33

35 Conclusion Conclusion and Future works The inverter control is effective in maintaining indoor thermal environment. Moreover, power consumption can be controlled and saved. KNIVES was installed into Kurihara City Hall and other concerning facilities for measuring power and environmental information and controlling the devices. Dynamic clustering method is proposed that updates cluster configuration of a building in which the difference between the amount of supply forecasting and demand forecasting becomes the minimum. Future works We would like to calculate grouping matrix considering the environmental amenity of each room or facility. 34

36 Thank you for your kind attention. 35

37 CEMS (Cluster Energy Management System) CEMS Cooperates with various distributed generators and electric devices Coordinates among energy management systems of BEMS or HEMS Optimizes electric and thermal energy usage in CEMS Distributed generators Power-grid Building Home Cluster of buildings is formed by Controllable buildings and power generators where EMS installed Uncontrollable buildings and power generators Energy Information BEMS CEMS HEMS Control Command 36

38 Conclusion Conclusion and Future works The system is introduced into Kurihara City Hall that measures power and environmental information and controls the devices. Then, the existing building is made into BEMS. As the result of this experiment, 23% of the demand power can be reduced by stopping the air-conditioner for 1 hour. Future works Measure exact air-conditioners power. We will evaluate a weighted value of each floor. If the value is determined, we find a relationship between comfort and power consumption. As the result, the schedule of air-conditioners can be determined. It is necessary to build CEMS from two or more buildings, and to carry out power conditioning between buildings. 25

39 Structure of CEMS Real environmental CEMS 38

40 Outside temperature onoff Infrared Inverter 0 39

41 KNIVES 40

42 Structure of experimental system 41

43 Infrared transmitter module 42

44 amount of clothing and metabolic rate Amount of clothing clothing Clo value half-pants, T-shirt, sandal summer-pants, short-sleeved shirt, tie Winter-pants, long-sleeved shirt, high socks, sweater, coat 1.09 metabolic rate activity Met value Sleeping 0.70 Reading 1.00 Working(light)

45 KNIVESML(sample) <knives> </knives> <head> </head> <body> </body> <version name="major">0</version> <version name="minor">1</version> <id>ir_data</id> <device name="ip"> </device> <device name="mac">00:11:0c:04:09:23</device> <owner name="company">keio_university</owner> <message name="sensor_data"> <timestamp name="date"> :37:52</timestamp> <timestamp name="timezone">jst</timestamp> <response name="temperature" type="temp" id="">20.8</response> <response name="humidity" type="hum" id="hum_01">14</response> <response name="illuminance" tyape="illmina" id="illmina_01">447</response> <response name="co2" type="co2" id="co2_01">638</response> <response name="temperature" type="temp_hum" id="82">20.0</response> <response name="humidity" type="temp_hum" id="82">18.1</response> <response name="temperature" type="temp_hum" id="83">19.9</response> <response name="humidity" type="temp_hum" id="83">17.4</response> <response name="temperature" type="temp_hum" id="84">20.8</response> <response name="humidity" type="temp_hum" id="84">19.2</response> <response name="ac_power" type="kw-pulse" id="kw-pulse_01">0.0000</response> <state name="relay" type="relay" id="relay_stt_01">0</state> </message> 44

46 Infrared Signal Code lighting Air-conditioner 45

47 30 分間需要電力 (kwh) 制御の有無での消費電力の違い ( 冬期 ) 制御条件 ピークとなる 16 時半から空調が停止される 17 時半までの 1 時間を制御時間とする 弱制御は人事課の温度低下を考慮し 最小時間での制御 全体制御は人事課は考慮せずに フロアの温度が 20 以上に保たれるよう制御 制御時間 制御なし 弱制御 全体制御 結果 弱制御空調停止の効果以上に 運転開始時の電力増加が大きかったため 制御なしの場合と需要電力の差は出ない 全体制御気温による消費電力の誤差を考慮しても 15% 以上の需要電力削減が可能 制御方法による受電電力の変化 (1/17~19) 制御なし弱制御全体制御 1 時間の合計電力 (kwh) 空調停止時間 ( 分 ) 0 15 (16:30~ 16:45) 55 (16:30~ 17:25) 46

48 F_no 2F_con 3F_no 3F_con :00:00 15:30:00 16:00:00 16:30:00 17:00:00 17:30:00 18:00:00 18:30:00 47