Building Automation System for Grid-Connected

Author’s Accepted Manuscript Building Automation System for Grid-Connected Home to Optimize Energy Consumption and Electricity Bill Rajeev Kumar Chauhan, Kalpana Chauhan www.elsevier.com/locate/jobe

PII: DOI: Reference:

S2352-7102(18)30574-6 https://doi.org/10.1016/j.jobe.2018.10.032 JOBE627

To appear in: Journal of Building Engineering Received date: 11 May 2018 Revised date: 30 October 2018 Accepted date: 31 October 2018 Cite this article as: Rajeev Kumar Chauhan and Kalpana Chauhan, Building Automation System for Grid-Connected Home to Optimize Energy Consumption and Electricity Bill, Journal of Building Engineering, https://doi.org/10.1016/j.jobe.2018.10.032 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Building Automation System for Grid-Connected Home to Optimize Energy Consumption and Electricity Bill Dr. Rajeev Kumar Chauhana*, Dr. Kalpana Chauhanb a

Department of Electronics and Instrumentation Engineering, Galgotias College of Engineering and Technology Greater Noida, Uttar Pradesh-201308, India b Department of Electrical &Electronics Engineering, Galgotias College of Engineering and Technology Greater Noida, Uttar Pradesh-201308, India E-mail: [email protected] E-Mail: [email protected] *Complete contact information for the corresponding author: Dr. Rajeev Kumar Chauhan, T-6, 201 NRI City, Group Housing-1, Greater Noida , Uttar Pradesh-201306, India. Phone: +919411860126

Abstract This paper proposes an algorithm for coordinated control of the distributed generators integrated to a DC microgrid (DCMG), in islanded and grid connected modes of operation. The proposed DCMG connects photovoltaic (PV) panels, energy storage (i.e. battery bank and hybrid vehicle) and public utility access to supply the load of a house. The integration of distributed sources with DCMG depends on their feed in tariff (i.e low feed in tariff high integration priority). Moreover the microgrid electricity price is the function of feed in tariff of a power source, and their power feed in the DCMG. Therefore, DCMG prices are dynamic. The non energy storable appliance (NESA) and energy storable appliance (ESA) control algorithm has been proposed for load controlling. The electricity price is one of the parameter for ESA control algorithm. The ESA control algorithm shifts the operating time of energy storable appliances without affecting the user comfort to optimize the electricity bill with the same amount of energy consumption. The control action of NESA control algorithm depends on the occupant presence and other parameters such as illumination, temperature in the house. So the NESA control algorithm reduces the energy consumption in the house as well as cost of the energy. The SCADA system is used to interface with the smart sensors of the house and to execute the NESA and ESA control algorithms to switch the appliances. The results from a case study of a home in Alinagar (India) is presented in this paper in order to evaluate the energy efficiency of the load control system and to analyses the luminous, and temperature conditions and occupancy obtained through the application of this technology. The environmental and energy performances, together with the degree of users' satisfaction and acceptance of this control system, were analysed for a

day to verify the potentiality and operation of this load control system. The obtained results regarding the potential in savings of energy (31%) and electricity bill (35%) were evaluated taking into account both the monitored annual electric energy consumption (for operation) and the parasitic energy consumption due to the installed devices (sensors and controllers), and were compared with the estimated energy consumption with manual load control. Keyword Demand management, deferrable Load, smart buildings, energy management system, loads scheduling.

1. NOMENCLATURE Pak (t)

Power consumption of kth appliance at time instant t

Ppvk (t)

Power generation of kth PV panel

Peesuk (t)

Power sharing of kth energy storage unit at time instant t

Ppu (t)

Power of Public utility at time instant t

Psk(t)

Power feed by jth power source to DC micro-grid at time instant t

Pd

Building demand

Ck

Energy cost of 1 kWh energy of kth power source

na

Number of appliances in the building

npv

Number of PV panels in the DC micro-grid

neesu

Number of energy storage unit in the DC micro-grid

np

Number of power sources in DC micro-grid

βm

Electricity price of micro-grid

F

Objective function to be minimized

T

Scheduling horizon of the optimization

τh

Optimize time step (1min)

1Indian

1.5¢ (cents)

PEC Rupees

Power Electronics Converter

TS

Temperature sensor

PS

Proximity sensor

LS

Light intensity sensor

LI

Level indicator

1. INTRODUCTION Micro-grid is an innovative control and management architecture at the distribution level, which is helpful in implementing the smart grid techniques at power distribution level. Moreover, microgrid provides a horizontal platform to interconnect the distributed energy sources (DERs) at the load end. Depleting fossil fuels, reducing emission of greenhouse gases, and increasing energy demand factor are responsible for the growing penetration of photovoltaic (PV) generators to power distribution grids [1-3]. The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the micro-grid. Few countries have been adopted the feedin tariff for the grid connected PV systems. This needs the integration of the battery bank (BB) and capacitors with the DERs and loads to improve the quality and reliability of the system. The high energy storage (ES) costs and huge losses in the BB will again enhance the electricity cost in the micro-grid. Minimal utilization of the BB and maximum utilization of the PV can only diminish the overall electricity cost of the consumer. The energy consumption in the residential buildings is playing an important role in the power system (energy generation and distribution system) with the revolution in smart grid technology. A significant amount of energy of the work is consumed in residential buildings. Thus the performance of the power system can be improved by optimizing the energy management in the residential building. The smart micro-grid consists of interconnected distributed energy resources (DER) capable of providing sufficient and continuous energy for a significant portion of internal load demand [4]. A smart micro-grid can work independently or interconnected with the existing power system. The micro-grid has several advantages such as high reliability, low line losses, reduced demand on power network, lower design cost, and better fail

recoveries. The demand side management for energy cost optimization is nothing but it is a type of the smart building including DER (battery bank, photovoltaic thermal storage), that is also called micro-grid. The microgrid may also have the feature of grid connection which allows it to purchase the electricity from the grid during the peak demand or sell the electricity back to the grid during surplus generation of electricity. One way of cost optimization is to use the building automation system which also minimizes the energy consumption. Such an efficient building is presented in [5], in which the user friendly energy saving are the main targets. One method of energy saving has been proposed in [6] by utilizing the information of indoor temperature in different weather conditions, to schedule the AC operation while maintaining the all comforts. In some demand side optimization methods, the user or occupant and his action also affect the energy efficiency [7-8]. Such a study related to occupancy’s behavioral parameter for typical office buildings with different size and weather zones is performed in [9]. The monitoring, controlling and optimization of energy consumption are the basic purpose of a building management system [10]. In [11] a multi-agent system is proposed to make a user interactive and centralized control. The system has the capability to provide an interactive facility agents and central control for the user. There are different management systems for energy cost minimization, in the literature such as, local generation and energy storage based management systems [12], Energy load management [13-14], and multi-agent approach [15]. However some of them are especially based on demand side management scheme [16-17]. Some of them are designed for autonomous DC micro-grid to achieve the desired state of charge of the battery bank and minimize the use of battery energy. In [18], the energy management system for grid connected DC micro-grid of a home is designed with the consideration of real time price of grid. The micro-grid manages its load and exchange (sale or purchase) the energy with grid to minimize the cost of grid energy. In some literature, some types of controllers have been used such as in [19-20] weekly and the daily effect of electricity price variation on the energy storage has

been examined using the fuzzy logic controller (FLC). The designing and study of such controllers for DC microgrid is also done in [21]. In some literatures EMS has been proposed by targeting the according by prices of the grid. As in [22], the EMS for grid connected DC micro-grid of a home is designed with the consideration of fixed price of grid. The micro-grid manages its load and assets to minimize the energy exchange with the grid and maximize the direct use of PV energy. This approach optimizes the power losses in the power source converters and energy storage units during power exchange and improves the system efficiency. In [23-24], the EMS is designed to manage the energy storage unit as per the source ideal characteristic to improve their lifetime and optimize the power losses due to high rate of power exchange with the micro-grid and overheating. Authors found the lack of scheduling of appliances with respect to the electricity price for the residential buildings in the literature. The paper proposes an energy cost optimization by suggesting algorithms for appliance scheduling with an automation system [25] installed in a building. The appliances are categorized as an energy storable appliance (ESA) and non-energy storable appliance (NESA) in order to generate two different scheduling algorithms for both of them. The target is to design an automatic system in order to save electricity in the building. The energy cost is reduced by shifting the load from higher price time to lower price time automatically. The proposed algorithms are also making the building automatic in the sense to switch ON/OFF the appliances according to requirement as well as according to price. The designed system with proposed algorithms is consisting of many sensors to sense different kinds of parameters to switch ON/OFF the appliances. Simulation results show a significant demand curve shifting in the form of lower electricity prices. 2. BUILDING SCADA SYSTEM AND SUSTAINABILITY The sustainability is improved by the building automations as it may influence the consumption from the resources, energy efficiency, and other environmental disturbances and by changing life cycle scheduling during their operating time [26]. So it is beneficial to analyze the performance of

building automation system on the user side and well as grid side.

From the user side the

sustainability means the adjustment of the comfort parameters and their usability [27], while, the cost reduction is the objective of the operators, so that to reduce the life cycle costs. The flexibility is another factor that has to be maintained in an automation system from an operator point of view. DC micro-grid offers variable cost and load curtailment when associated with the automated system [28]. Fig.1 is showing the interaction of the building automation system (SCADA system) with the different types of groups with their affecting issues. It is shown that the SCADA System monitors the parameters (the customer usability, environment disturbances, operator flexibility, and variable cost of energy) and generate the control signal in the form of output parameters (comfort of user, energy efficiency, cost efficiency, and load curtailment). User Comfort

usability Energy efficiency

Variable cost DC Microgrid

SCADA System

Load curtailment

Environment Disturbances

Cost efficiency

Flexibility

Operator

Fig. 1. SCADA system with users, operators, micro-grid, and the environment.

3. SYSTEM CONFIGURATION The DC micro-grid of residential building consists of PV panels, energy storage (battery and hybrid vehicle) and appliances of the building as shown in Fig. 2. The Fig. is showing the automatic building that is designed to analyse the developed algorithms to optimise the electricity bill. The scheduling and operation of the appliances has been tested on such type of building The PV panel1, PV panel-2, battery bank, and hybrid vehicle are interfaced through power electronics converters i.e. PEC-2, PEC-3, PEC-4 and PEC-5 respectively. The micro-grid is also connected to the public

utility (PU) via AC-DC converter (PEC-1). The DERs feed the power to the micro-grid with own power constraints. The building appliances are DC compatible in order to direct connection of the appliances on the DC bus. The wireless sensors are mounted at the different locations of the building to monitor the variable parameters. The temperature sensors installed in the bedroom and living room are TS-1 and TS-2, and connected through the SCADA system, measures the temperature of bedroom and living room and retrieves it at the SCADA control room via remote terminal unit (RTU). While the proximity sensors PS-1, PS-2, PS-3, PS-4, PS-5 and PS-6 installed in the bedroom, study room, living room, bathroom, kitchen and control room via SCADA system, monitor the information of human presence at different locations (rooms) in the building. Similarly, some other smart sensors are also incorporated in the building appliances to monitor the control parameters (state of charge (SOC) of laptop, cell phone battery and temperature of electric geyser and coffee maker etc) of the appliances and send signals to control room. Similarly, the LI-1 and LI-2 installed via SCADA system in kitchen and bathroom measures the water level in the water tank of water purifier and submersible respectively. These sensor inputs to the SCADA system plays an important role to create the diversion of control actions to turn ON/OFF the appliances according to energy price at a particular time instant. The smart switches are also connected to the control room to turn ON/OFF the appliances. The switches can be operated in automatic as well as in manual mode to turn ON/OFF the appliances, based on control action generated by SCADA system and user manual input.

Building PU PEC-1 Microgrid

Gallery

CFL

DC Microgrid

I-3

PS-4

PS-1

LS-1

Exhaust Switch Board LI-1 Gyser Submersible Washing Pump Machine Bath Room LS-4

LED

Bed Room TS-1

I-3

LS-5 CFL PS-5 Mixer Blender

LS-2 LED

CFL Coffee Maker

Sandwich Maker

PEC-4

Fan Modem

Laptop Study Room

Refrigrat or BB

PS-2

Water Purifier

PEC-3

Energy Storage

I-3

Switch Board

CFL

Kitchen

PS-3

PEC-5

LS-3

TS-2 Control Room

PS-6

CFL Switch Board

LS-6 R T U

Switch Board

I-3

PV Panel-2

Hair Dryer

Cell Phone

LI-2

Exhaust

CFL

I-3

PEC-2

Switch Board LS-7

I-2

PV Panel-1

Switch Board

Fan

AC

LED Vacuum Cleaner

TV

Mobile Fan CFL

I-3 AC Living Room

Hybrid Car

Switch Board

24 V DC Bus

Fig. 2. Conceptual layout of grid connected DC micro-grid

3.1

Power Management in DC Microgrid

The DC microgrid consists of two PV panels, battery bank and hybrid vehicle (energy storage) and has unidirectional connection with public utility to supply the load. A high priority based algorithm is used to coordinate the power source to supply the load at high efficiency and low operating cost. The power generation curve of the PV plant (i.e PV panels) mounted on the rooftop of the house is shown in Fig. 3.

6 PV Power

PV Power (kW)

5

4

3

2

1

0 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig. 3 Power generation of PV plant

The public utility is connected via unidirectional power connection with the microgrid, Therefore microgrid operates in both grid connected and isolated mode. The micro-grid is connected to the public utility during the surplus load. I. Grid Connected Mode: In this mode public utility supply the surplus load the microgrid. The priority for power source selection during surplus PV power generation with various scenario can be found in Table 1. The feed in tariff of PV power, public utility, battery bank and hybrid vehicle are ¢ 5.75 per kWh, ¢ 8.64 per kWh, ¢ 6.74 per kWh and ¢ 10.11 per kWh respectively [29]. Therefore the PV panels and hybrid vehicle has highest and least priority respectively to supply the load as mention in Table 1. Hybrid vehicle supplies the load, when the building load is greater than the power generation of PV panels, and power fed by public utility and battery bank with SCADA control as shown in, Fig. 6 during 8:11PM-8:20 PM only. In case of manual control, the hybrid vehicle fed the load during 8:11 PM-11:23PM. While, the public utility feed the power to the microgrid during 8:11PM-10:23AM with SCADA control and 8:11PM-10:23AM as shown in Fig. 4. While the hybrid vehicle supply the load during 9:46PM-9:58PM and 11:07PM-10:10AM with manual control as shown in Fig. 6. The power equation can be expressed as: n pv na Phc (t )   Pak (t )   Ppvk (t )  Pbb (t )  Ppu (t ) k 1 k 1

(1)

where Ppu(t) is the public utility power at time instant t, where Pak (t) is the power consumption of kth appliance at time instant t, na is the number of appliances in the building, npv is the number of PV panels in the DC microgrid, Ppvk (t) is the power generation of kth PV panel, Phc (t) and Pbb (t) are the power sharing of hybrid vehicle and battery bank at time instant t. Table 1: Priority for Power Source during surplus load Power Sources

Building Load

Photovoltaic Plant

Battery bank

Public Utility

Electric Vehicle









n pv na  Ppvk (t )   Pak (t ) k 1 k 1









n pv n pv na  Ppvk (t )  PBB (t )   Pak (t )   Ppvk (t ) k 1 k 1 k 1









n pv n pv na  Ppvk (t )  PBB ( t )  Ppu ( t )   Pak (t )   Ppvk (t )  PBB (t ) k 1 k 1 k 1









n pv na  Ppvk (t )  PBB ( t )  Ppu ( t )   Pak (t ) k 1 k 1

II. Isolated Mode: In this mode public utility acts as an outage power source. Therefore, energy storage units like battery bank and hybrid vehicle are responsible to balance the surplus PV power generation. The charging priority of the battery bank and hybrid vehicle during surplus PV generation with various scenarios can be found in Table 2. Moreover, the battery bank is the second cheapest power source therefore it feeds the surplus load as well, when the PV plant generates a small amount of power or act as an outage power source. The power curve of the battery bank and hybrid vehicle is shown in Fig. 5 and Fig. 6 with manual control and SCADA control. The power equation can be expressed as: n pv na neesu P ( t )   ak  Ppvk (t )   Peesuk (t ) k 1 k 1 k 1

(2)

Table 2: Charging priority for battery bank and electric vehicle during surplus PV power Power Sources

Building Load

Battery bank

Electric Vehicle





n pv na neesu na  Pak (t )   Peesuk (t )   Ppvk (t )   Pak (t ) k 1 k 1 k 1 k 1





n pv na neesu  Ppvk (t )   Pak (t )   Peesuk (t ) k 1 k 1 k 1

3.6 MC SC 3

Power (kW)

2.4

1.8

1.2

0.6

0 0:00

4:00

8:00

12:00

16:00

20:00

23:59

Time (hrs)

Fig. 4 Public utility power curve with manual and SCADA control scheme 2.5 MC SC

2 1.5

Power (kW)

1 0.5 0 -0.5 -1 -1.5 -2 -2.5 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

Fig. 5 Battery bank power curve with manual and SCADA control scheme

23:59

2.5

MC SC

2 1.5

Power (kW)

1 0.5 0 -0.5 -1 -1.5 -2 -2.5 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig. 6 Hybrid vehicle power curve with manual and SCADA control scheme

3.2.

Formulation of Load Scheduling in Building

The residential building microgrid is fed by the PV panels, public utility and energy storage (i.e. battery bank and hybrid vehicle). As per the Indian electricity market survey, the average feed in tariff of PV power is ¢ 5.75 per kWh, while the average tariff for public utility is ¢ 8.64 per kWh for residential consumers. The round trip efficiency of the batteries is 85% [30]. The battery bank is charged by PV power only, therefore its feed in tariff is ¢ 6.74 per kWh, while the hybrid vehicle is charged by PV and fuel generator of the vehicle, therefore its feed in tariff price is ¢ 10.11 per kWh. The average electricity price of the DC microgrid is the function of the power feed by the distributed power source (i.e. PV panels, battery bank, hybrid vehicle and public utility) and their feed in tariff price (¢ /kWh). Moreover, the selection of the power source depends on the priority basis and their power feed vary with their maximum and minimum power constraints. So the power feed by the power sources varies with respect to the time. This is the cause of varying electricity price of the micro-grid varying with respect to the time as shown in Fig. 7. Microgrid electricity price is one of the control parameter for energy storable appliance of the house. The electricity price (βm(t)) of the DC micro-grid at time instant t can be expressed as [28]:

np

m  t  

 Psk  t  Ck k 1 np

(3)

 Psk  t  k 1

where np is the number of power source in DC micro-grid, Psk(t) is the power feed by kth power source to DC micro-grid, Ck is the energy cost of 1 kWh energy for kth power source.

Microgrid electricity price (¢/kWh)

9

8.25

7.5

6.75

6

5.25 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig. 7. Micro-grid electricity prices The objective function is to optimize the electricity bill of the building for both grid connected mode and island mode. This optimization can be achieved by minimization of the following defined objective function: T

F

  t

m

 t   Pd (t )  h

(4)

=1

where F is the objective function to be minimized, T is the scheduling horizon of the optimization, τh is the optimize time step (1min), Pd is the building demand. The optimization gives a solution to reduce the cost of energy by the scheduling of the operating schedule of the energy storable appliance (ESA) load from high electricity price to low electricity price of the microgrid using the energy storable load (ESL) control algorithm. Moreover, the decrement in ‘ON’ time of Non Energy Storable Appliance (NESA) using a Non Energy Storable Load (NESL) control algorithm reduces the energy consumption of NESA.

The DC bus power constraints of micro-grid is as follows np

 k 1

PGj (t ) 

na

P

ak (t )

t  T

(5)

k 1

The above said optimization is achieved by developing the scheduling algorithms for different types of appliances i.e. a demand side management is proposed in the next section of this paper. 4. DEMAND SIDE MANAGEMENT Demand side management (DSM) may be done by load management, improving energy efficiency and /or contribute in energy saving [29]. The residential building users are responsible for a significant amount of the world’s energy need, but the market prices for this energy remains steady. The performance of buildings can be improved by optimizing the use of their energy. The SCADA system including with smart sensors and smart switches are installed to automatically switch ON/OFF the appliances of the residential building. The signals from smart sensors are retrieved at the SCADA control room. The consumer should feed his inputs on an earlier basis, for example budget price and required time instant of operating the appliance to process the particular task. A right or in time feedback from the consumer side improves SCADA performance to fulfill the consumer expectations (budget price and comfort of taking outputs from appliances). 4.1.Control Algorithm for NESA The non energy storage appliances (NESA) such as CFL, fan, television, external modem, air conditioner, etc does not have the property to store the energy for future use. These appliances can not schedule like ESA, but the energy consumption of NESA can be minimized by proper scheduling of ‘ON’ time. Due to manual control and consumer laziness, the NESA appliance may remain ‘ON’ even when the consumer does not want to keep it ‘ON’. The control action of NESA can be the function of some other parameters; for example the control action of the CFL for study room can depend upon the light intensity and the presence of the occupant in the study room. If the natural light intensity in the study room is dull than the set value and the occupant is present in the

room the CFL of the study room will be switched ‘ON’ automatically. On the other hand, if the light intensity in the study room is dull than the desired light intensity and occupant is not present there, the CFL will remain switched ‘OFF in this condition. The CFL will remain switched ‘OFF’, if the light intensity is higher or equal to the desired, no matter of the presence of occupant. The flow chart of demand management for ESA is shown in Fig. 8.

Start

Consumer manual input ( cmi (t )) No

Human detector output ( hdo (t))

Other control parameter set value ( s (t))

Other control parameter set value ( m (t)) If  cmi (t)  1 Yes

If  cmi (t)  1 Yes

If  m (t) δmy (t) Yes

Check Consumer Manual Input (ε) Migrogrid Electricity Price (βm (t)) y th ApplianceSet Price (βay (t))

If βm (t)  βay (t)

No

or ε=1 Appliance Switch 'OFF' Yes Appliance Switch 'ON'

Fig. 9 Flow chart of control algorithm for ESA

The working of the proposed system can be understood by an example of a geyser. The consumer sets his inputs as: the electricity prices ¢ 6.0 per kWh, latest start time 11:00 AM and the water temperature is 40oC for electric geyser. According to consumer inputs the system will automatically turn ‘ON’ the switch of electric geyser by checking the electricity prices of micro-grid. For example, the set price is ¢ 6.0 per kWh (considered), if the price is less than or equal to ¢ 6.0 per kWh and temperature of water in the electric geyser is less than 40oC, then SCADA creates a control action to turn ‘ON’ the geyser. Moreover, if the prices are greater than ¢ 6.0 per kWh and temperature of water in the electric geyser is less than 40oC, and time is equal or greater than the latest start time then the geyser will remain switched ‘ON’. Further, if the prices are greater than ¢

6.0 per kWh and temperature of water in the electric geyser is less than 40oC, and time is higher than the appliances latest start time then the geyser remains turn ‘OFF’. The SCADA system also provides the feature to consumer to override the automated controls. For example, the consumer wants to switch ‘ON’ the geyser regardless the electricity price of micro-grid and latest start time, then he or she will be able to do so. The flow chart of the control algorithm for ESA is shown in Fig. 9. 5. RESULTS AND DISCUSSION In this paper the traditional building is compared with the automation building. In the traditional, the appliances are manually switched ‘ON/OFF’ by the consumer (i.e. There is no feedback for the appliances as per demand side management). Therefore, the deferrable load does not shift their operating time from non sunny hours to sunny hours and the cycle based load operates in the regular cycle mode. While in the automation, building, the appliances are connected to the control room (SCADA system) via smart sensor and actuator. Moreover the SCADA creates the control action to switch ‘ON/OFF’ the appliances while the control action is the function of the electricity prices, user manual input and the feedback of the sensor mounted at the appliances. Therefore, the deferrable load shifts their operating time from non sunny hours to sunny hours and the NESA such as CFL switched ‘OFF’ if illumination is sufficient or occupant is not present is the room. 5.1

Performance analysis of NESA load

The occupant presence is the essential parameter in case of NESA load as there is no significance to keep switching ‘ON’ these appliances without human. The CFL and LEDs are some of the appliances of this category which helps to maintain the illumination level in the building as it is very important to maintain the illumination level inside a building at all time. The illumination level may be different for different room in a same building, for example, the study room and bedroom requires different illumination level in a day.

Table 3. Desired Standard Illumination level in building areas

Natural Light Intensity (Lux)

S.No.

850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0 0:00

Building Area

IES Standards Illumination Level (Lux)

1

Corridor

100

2

Bed Room

50

3

Study Room

150

4

Living Room

50

5

Kitchen

150

6

Bathroom

100

7.

ESS Room

50 Bath Room Bed Room Corridor Kitchen Living Room Study Room Garage

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig.10. Natural light intensity in the rooms of building

The standard illumination level in the different area of the residential building can be found in Table 3 as per illumination engineering society (IES). It can be achieved either by the use of artificial light sources or natural illumination capturing daylight using room window. The value of illumination level at different locations of a building is different, but following a same characteristic in a random day (Fig. 10). The occupant detectors are installed in every room and the detected occupancy at different locations in different time in a day is shown in Fig. 11. The example of kitchen is described here as: The kitchen occupant detector sends ‘1’ if the occupant is present in the kitchen and ‘0’ if the

occupant is not present in the kitchen to the SCADA control (SC) room as shown in subplot ‘a’ of the Fig. 11. Similarly the occupant detectors also send the information about the occupant presence in the room like bedroom, corridor and garage etc. of the building on every minute basis as shown in subplots ‘a-i’ of Fig. 11. 1 (a)

0 1 Occupant presence in rooms

(b)

0 1

Time (minutes)

(c)

0 1 (d)

0 1 (e)

0 1 (f)

0 1 (g)

0 1 (h)

0 1 (i)

0 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig.11. Occupant presence in the rooms a) Kitchen, b)Bed room, c) Living room, d) Study room, e)Bath room, f)Corridore, g) Garage, h)Sleep in bedroom, i) Sleep in living room

The NESA control action depends upon the feedback of the relative parameters. For example the control action of the artificial lighting (CFL, LED bulb) depends on the consumer manual input, natural illumination level, and presence of the occupant in the room. The ‘ON’ and ‘OFF’ state of the CFL in kitchen is shown in the subplots ‘a, b’ of Fig.12 with manual and SCADA control respectively. The consumer manual input for kitchen CFL remains ‘0’ during the 0:01- 4:59hrs, 8:31-10:00hrs, 10:51-12:10hrs, 14:01-16:00hrs, 17:31 -19:00hrs, and 22:01-24:00hrs time intervals and the kitchen CFL remains ‘OFF’ with the manual control scheme while the consumer manual input remains ‘1’ during 5:00-8:30hrs, 10:01-10:50hrs, 12:11-14:00hrs, 16:01-17:30hrs, and 19:0122:00hrs time intervals and kitchen CFL remains ‘ON’ with the manual control scheme as shown in subplot ‘a’ of Fig.12.

The natural light illumination level in the kitchen is greater than the desired illumination level (150 Lux) and the CFL remains ‘OFF’ during 9:39-19:14hrs time interval with SCADA control scheme as shown in subplot ‘b’ of Fig.12 excluding the 9:39-19:14hrs time interval, The natural light in the kitchen remains below than the desired illumination level and the SCADA create the control action for the CFL based on the consumer manual input and occupant detector input. During the 5:00-8:30hrs and 19:01-22:00hrs the occupant detector remains ‘0’ and ‘1’ at different time instants as shown in subplot ‘a’ of Fig.11 but the consumer manual input remains ‘1’ as shown in subplot ‘a’ of Fig.12 so that the CFL remains ‘ON’ with a SCADA control scheme whenever the occupant present in the kitchen as shown in the subplot ‘b’ of Fig.12. Similarly SCADA creates the control action for the other artificial lights placed at the other location of the building. 1

(a)

0 1

(b)

0 1

(c)

Artificial light state

0 1

(d)

0 1

(e)

0 1 (f)

0 1 (g)

0 1

(h)

0 1

(i)

0 1

(j)

0 1

(k)

0 1

(l)

0 (m)

1 0 1 0 0:00

(n) 4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig.12. Artificial light (CFL) ‘ON’ in rooms a,b) Kitchen, c,d)Bed room, e,f) Living room, g,h) Study room, i,j)Bath room, k,l) Corridore, m,n) Garage, with manual and SCADA control respectively.

Table 4 shows the energy consumption of all NESA including artificial lights, TV and fans of the building for manual and SCADA control. The energy saving is varying between 15.49-93.45% with the SCADA control as compared to the manual control. There are approximately 56% energy savings in NESA with SCADA control. Moreover, the energy prices are varying with respect to time, so the cost of energy is the function of operating time or indirectly depends upon the price of electricity. So the energy cost is not decreasing in the same ratio with the energy saving. The energy saving by the LED bulb mounted in the garage is 44.51%, while the saving in the energy cost is 97.97%. The overall saving of energy and cost of energy in NESA is 56.07% and 58.36%, respectively, with SCADA control as compared to the manual control scheme. Table 4 Comparison between manual and SCADA control scheme based on Cost and energy consumption for NES appliances

NESA

Power Rating (Watt)

Appliance ‘ON’ Time (Minutes)

Energy Consumption (kWh)

MC

SC

MC

SC

Energy Saving (%)

Cost of Energy (¢) MC

SC

Energy Cost Saving (%)

CFL-K

12

610

116

122

23.2

80.98

0.86

0.19

77.91

EF-K

25

610

235

254.17

97.92

61.48

1.79

0.65

63.69

CFL-B

12

470

90

94

18

80.85

0.66

0.14

78.79

LED_B

6

470

90

47

9

80.85

0.50

0.12

76

CFL-L

12

630

182

126

36.4

71.11

0.84

0.27

67.86

LED_L

6

630

182

63

18.2

71.11

0.42

0.14

66.67

Fan-L

30

630

492

315

246

21.91

2.1

1.67

20.48

TV-L

30

470

288

235

144

38.72

1.6

0.96

40

CFL-S

12

614

271

122.8

54.2

55.86

0.86

0.42

51.16

LED_S

12

614

271

61.4

27.1

55.86

0.44

0.21

52.27

Fan-S

30

614

469

307

234.5

23.62

2.16

1.62

25

Mo-S

5

1440

469

120

39.08

67.43

0.87

0.27

68.97

CFL-Ba

12

733

125

146.6

25

82.95

0.53

0.11

79.25

EF-Ba

25

733

145

305.42

60.42

80.22

2.19

0.48

78.08

CFL-C

12

779

51

155.8

10.2

93.45

1.11

0.08

92.79

CFL-G

12

71

60

14.2

12

15.49

0.12

0.11

11.08

Fan-G

30

71

32

35.5

16

54.93

0.29

0.12

58.62

EF-G

25

71

32

29.58

13.33

54.93

0.24

0.11

54.17

LED-G

6

1440

799

144

79.9

44.51

1.04

0.02

98.08

*EF-exhaust fan, Mo-modem, K-kitchen, B-bed room, L-living room, S-study room, Ba-bathroom,C-corridor, and Ggarage, PR-power rating, MC- manual control, SC-SCADA contol

5.2.

Performance analysis of CBA load

The air conditioners and refrigerators mainly belong to cycle based appliances (CBA). The smart air-conditioners (ACs) might extend its cycle time slightly to reduce its load on the micro-grid, while not observable to the consumer. Similarly a smart refrigerator could defer it’s defrost cycle until off peak hours, i.e lower prices.

However, thermostat is basically sets and control the

temperature of any equipment, but in advance has a role in energy saving while controlling the temperature [30]. The impact of heating and cooling of thermostat on energy consumption is presented in [31]. By changing the thermostat (desired temperature) settings on the appliances, the ‘ON’ and ‘OFF’ time can be changed. The thermostat setting of the ACs and refrigerator are changed with the electricity prices without affecting the consumer comfort. The ACs can also respond to the occupant presence as in case of NESA. The control action of the ACs depends on the electricity prices and occupant presence in the rooms. The control actions of Air conditions and refrigerators are shown in Fig. 13. Moreover the ‘ON’ and ‘OFF’ time of the ACs are 25 minutes and 45 minutes (cycle-1) is considered respectively, for a set price (¢ 5.73 per kWh is considered for ACs). When the price is higher than the set price, the SCADA system generates a new cycle schedule with 15 minutes and 45 minutes ‘ON’ and ‘OFF’ time (cycle-2) respectively. The consumer has to select ACs cycles (either cycle-1 or cycle-2) manually irrespective of the prices with the manual control scheme. But the ACs cycle automatically changes with respect to the price for the SCADA control scheme.

Status of airconditioners

1

(a)

0 1

(b)

0 1

(c)

0 1 (d) 0 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig.13. Air-conditioners ‘ON’ in rooms a,b) Bedroom, c,d) Living room, with manual and SCADA control respectively. (a)

1000 500 0 1000 500 0 1000 500 0 1000 500 0 100 50 0 100

Power (watt)

(b)

0 0:00

(c)

(d)

(e)

(f)

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig.14 Demand of CBA load a,b) AC bedroom, c,d) AC living room, e,f) Refrigerator with manual and SCADA control scheme

The results of manual and SCADA control scheme are compared when the consumer selects the cycle-1. That’s why the ACs operates with cycle-1 as shown in subplot ‘a’ of Fig.14. The default set prices to ACs in the SCADA system is ¢ 5.73 per kWh. The ACs operates with cycle-1 during the 9:47-18:24hrs time interval because the price remains at ¢ 5.73 per kWh. Excluding 9:47-18:24hrs time interval, the price is higher than the set price so ACs operate with the cycle-2 with SCADA control scheme as shown in subplot ‘b’ of Fig.14. Moreover, SCADA system creates the control action to turn ‘ON’/ ‘OFF’ the ACs based on the occupant detector inputs. The bedroom AC energy consumption and cost of energy are 21.91% and 23.96% with the SCADA control scheme as compared to a manual control scheme as mention in Table 6. Moreover, the living room AC energy consumption and cost of energy are 41.04% and 41.34% with the SCADA control scheme as

compared to a manual control scheme. According to the research, room temperature and door opening of a refrigerator affect its thermostat value and in turn effect the energy consumption [32]. The increase in consumption is around 10%, as compared to without door opening [33]. The refrigerator has thermostat setting 1-8. The ‘ON’ and ‘OFF’ time for all the eight thermostat setting is given in Table 5. The consumer can set the thermostat at one of them at a time with manual control scheme while these settings can be changed automatically with the SCADA control scheme. Table 5: Refrigerator ON/OFF time for thermostat settings Thermostat Setting

ON Time (Minutes)

OFF Time (Minutes)

1

12.5

80

2

15

80

3

17.5

80

4

20

80

5

22.5

80

6

25

80

7

27.5

80

8

30

80

Table 6: Comparison between manual and SCADA control scheme based on cost and energy consumption for cycle based appliances (CBAs)

CBA

Power rating (Watt)

ON Time (Minutes)

Cost of Energy (¢)

Energy Consumption (kWh)

Manual Control

SCADA Control

Manual Control

SCADA Control

Saving (%)

Manual Control

SCADA Control

Saving (%)

AC-B

800

315

246

4.2

3.28

21.91

31.2

23.73

23.75

AC-L

800

490

289

6.53

3.85

41.04

47.82

28.05

41.34

Refrigirator

72

400

285

0.48

0.34

29.17

3.47

2.39

31.12

*AC-B and AC-L: Airconditioner in bed room and living room respectively

5.3.

Performance analysis of non sensitive appliances (ESA) load

There are many appliances such as laptop and geysers etc. in building those can store energy in a different form which may be utilized in the future. These appliances have the energy storage capacity. Moreover, some other devices like washing machine also form a space in this category. The washing machine does not have storable capability, but it can be scheduled at any time without

the consideration of human presence, that’s why it also belongs to ESA category. The list of the ESA mounted in the building is mentioned in Table 7. The power curves of ESA load are shown in Fig.. 15. The SCADA system monitors the state of the energy level which is stored in ESA with the help of different sensors mounted in the appliances.

100 50 0 100 50 0 200 100 0 200 100 0 400 200 0 400 200 0 100 50 0 100 50 0 1000 500 0 1000 500 0 100 50 0 100 50 0 5 2.5 0 5 2.5 0 5 2.5 0 5 2.5 0 0:00

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

Power (Watt)

(f) (g) (h) (i) (j) (k) (l) (m) (n) (o) (p) 4:00

8:00

12:00 Time (hrs)

12:00

16:00

23:59

Fig. 15 ESA demand a,b) laptop, c,d) coffee maker, e,f) water pump, g,h) washing machine, i,j)geyser, k,l)water prifier, m,n) mobile1, o,p) mobile2 with manual and SCADA control scheme respectively Table 7: Comparison between manual and SCADA control scheme based on energy cost for NSA ESA Laptop

Cost of Energy (¢)

ON Time (minutes)

Power rating (Watt)

Manual Control

SCADA Control

Electricity Bill Saving (%)

374

65

2.81

2.51

10.68

Coffee maker

33

135

0.57

0.50

12.28

Water pump

88

350

3.68

2.94

20.11

Washing machine

120

70

1.19

0.81

31.93

Geyser

150

1000

18.92

14.33

24.26

Water purifier

100

70

0.84

0.68

19.05

Mobile-1

150

4

0.08

0.06

25

Mobile-2

150

4

0.08

0.06

25

A comparison of building performance based on demand curve generated with manual and SCADA control scheme for a random day is shown in Fig.16. The red and green curve represents the demand curve of the building with manual and SCADA control scheme respectively. The minimum building demand is 0.025 kW and 0.078 kW for manual and SCADA control scheme respectively. During 9:47-18:24hrs time interval the prices remain at the least level of ¢ 5.73 per kWh. So the building demand remains higher with SCADA control as compared to a manual control scheme most of the time. While 0:01-9:46 hrs and 18:25-24:00hrs, the price remains higher than the ¢ 5.73 per kWh, therefore the building demand remains higher with the manual control scheme as compared to the SCADA control scheme as shown in Fig.16 and it reaches at its highest level of 2.505 kW during 19:01-19:18hrs with the manual control scheme.

2.6 MC SC

2.4 2.2 2

Power (kW)

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0:00

4:00

8:00

12:00 Time (hrs)

16:00

20:00

23:59

Fig.16 Building demand with manual and SCADA control scheme The averages per unit prices are ¢ 6.84 per kWh and ¢ 7.32 per kWh with manual and SCADA control scheme respectively. The saving in energy consumption is 31.14% with the SCADA control scheme as compared to the manual control scheme. Due to higher energy consumption with the SCADA control scheme than the manual control scheme during low price time interval during 9:47-18:24hrs as shown in Fig.16, it helps to increase the saving in electricity bill is 35.72% with the SCADA control scheme as compared to the manual control scheme as mention in Table 8. Table 8: Total energy consumption and cost of energy in building Electricity Bill (¢/kWh)

Energy Consumption (kWh) Manual control 19.78

SCADA Control 13.62

Saving (%)

Manual control

31.14

144.87

SCADA Control 93.12

Saving (%) 35.72

6. CONCLUSIONS The paper has presented a supervisory control and data acquisition (SCADA) system for photovoltaic-battery with grid interconnection home automation. The proposed SCADA system structure sustainably affects the directly influenced groups, such as users, operators, the environment or the power grid. The SCADA system is capable of maximizing user comfort and the use of renewable energy sources while minimizing energy demand and electricity bill (energy costs)

for home in operation. Additionally, it allows the integration of the smart homes to the smart grid. This paper proposes algorithms that are compatible with a SCADA system for automatic control of the building appliances. The SCADA system performs the control action for energy storable appliances (ESA) load is based on the ESA control algorithm and optimize the cost of energy by varying the operating time of ESA load with the same amount of energy. The control of non-energy storable appliances and cycle based appliances is based on the NESA control algorithm. The NESA control algorithms reacts for the occupant presence and physical parameters such as illumination, temperature in the house. The NESA control algorithm reduces the energy consumption in the house as well as cost of the energy. The proposed algorithm reduces the peak demand of the building as compared to the manual control. In other words, the SCADA control approach helps to the utility by varying the building demand during peak and regular hours. The proposed schemes can be used for LVDC micro-grid such as building with renewable energy resources and data centers. REFERENCES [1] A. G. Madureira, J. C Pereira, N. J. Gil, J. A. P. Lopes, G. N. Korres, and N. D. Hatziargyriou, “Advance control and management functionalities for multi-micro-grids,” European Transaction on Electrical Power, vol. 21, no. 2, pp. 1159-1177, Jan. 2010. [2] S. Anand, and B. G. Fernades, “Reduced –order model and stability analysis of low voltage DC micro-grid,” IEEE Transaction on Industrial Electronics, vol. 60, no. 11, pp. 5040-5049, Nov. 2013. [3] R. K. Chauhan, F. M. Gonzalez-Longatt, B. S. Rajpurohit, and S. N. Singh, “DC Microgrid in Residential Building,” DC Distribution Systems and Microgrids, IET, pp. 367-387, Oct, 2018. [4] J. Ekanayake, N. Jenkins, K. Liyanage, J. Wu, and A. Yokoyama, “Smart grid: Technology and applications,” Wiley 2012. [5] S. A. Al-Sanea, M. F. Zedan, “Optimized monthly-fixed thermostat-setting scheme for maximum energy-savings and thermal comfort in air-conditioned spaces,” Applied Energy, vol. 85, no. 5, pp. 326–346, May 2008. [6] T. Peffer, M. Pritoni, A. Meier, C. Aragon, and D. Perry, “How people use thermostats in homes: A review,” Building and Environment, vol. 46, no. 12, pp. 2529-2541, Dec. 2011. [7] T. A. Nguyen, M. Aiello, “Energy intelligent buildings based on user activity: A survey,” Energy and Buildings, vol. 56, pp. 244–257, 2013. [8] K. Chauhan and R. K. Chauhan, “Optimization of Grid Energy using Demand and Source Side Management for DC Microgrid,” Journal of Renewable and Sustainable Energy, vol. 9, no.3, pp. 035101-15, May 2017.

[9] R. Yang, and L. Wang, “Development of multi-agent system for building energy and comfort management based on occupant behaviors,” Energy and Buildings, vol. 56, pp. 1–7, 2013 [10] E. Azar, C. C. Menassa, “A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings,” Energy and Buildings, vol. 55, pp.841–853, Dec. 2012. [11] D. Y. Liu, W. R. Chang, J. Y. Lin, “Performance comparison with effect of door opening on variable and fixed frequency refrigerators/freezers,” Applied Thermal Engineering, vol. 24, no. 14-15, pp. 2281–2292, Oct. 2004. [12] G. P. Henze and R. H. Dodier, “Adaptive optimal control of a grid independent photovoltaic system,” Journal of Solar Energy, vol.125, no. 1, pp. 34-42, Feb. 2003. [13] D. L. Ha, S. Ploix, E. Zama, and M. Jacomino, “Control of energy consumption in home automation by resource constraint scheduling,” in Proc. 15th International Conference on Control Systems and Computer Science, Buccarest, Romania, May 25, 2005. [14] D. L. Ha, S. Ploix, E. Zama and M. Jacomino, “Tabu search for the optimization of household energy consumption,” in Proc. IEEE International Conference on Information Reuse and Integration, Waikoloa Hawaii, USA, pp. 86-92, Sept.16-18, 2006. [15] R. R. Negenborn, “Multi-agent model predictive control with applications to power networks,” PhD thesis, Technische Universiteit Delft, Delft, Netherlands, Dec. 2007. [16] V. S. K. Murthy Balijepalli, Vedanta Pradhan, S. A. Khaparde and R. M. Shereef, “Review of Demand Response under Smart Grid Paradigm,” in Proc. IEEE PES Innovative Smart Grid Technologies, Kollam, Kerla, India, pp. 236-243, Dec. 1-3, 2011. [17] J. W. Moon, S. H. Han, “Thermostat strategies impact on energy consumption in residential buildings,” Energy and Buildings, vol. 43, no. 2-3, pp. 338–346, Feb./Mar. 2011. [18] J. Virotea, and R. N. Silva, “Stochastic models for building energy prediction based on occupant behavior assessment,” Energy and Buildings, vol. 53, pp. 183–193, Oct. 2012. [19] R. K. Chauhan, K. Chauhan, and Josep M Guerrero “Controller design and voltage stability analysis of grid connected DC microgrid,” Journal of Renewable and Sustainable Energy, vol.10, no.3, pp. 035101-10, April 2018. [20] M.H. Athari, and M. M. Ardehali, “Operational performance of energy storage as function of electricity prices for on-grid hybrid renewable energy system by optimized fuzzy logic controller,” Renewable Energy, vol. 85, pp.890-902, Jan. 2016. [21] R. K. Chauhan, B. S. Rajpurohit, R. E. Hebner, S.N. Singh and F.M.G. Longatt, “Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC Micro-grid,” in Proc. IEEE PES Innovative Smart Grid Technologies (ISGT) Asian Conference, Bangkok, pp.1-6, Nov. 04-06, 2015. [22] R. Saidur, H. H. Masjuki, and I. A. Choudhury, “Role of ambient temperature, door opening, thermostat setting position and their combined effect on refrigerator-freezer energy consumption,” Energy Conversion and Management , vol. 43, no. 6, pp. 845–854, Apr. 2002. [23] D. Y. Liu, W. R. Chang, and J. Y. Lin, “Performance comparison with effect of door opening on variable and fixed frequency refrigerators/freezers,” Applied Thermal Engineering, vol. 24, no. 14-15, pp. 2281–2292, Oct. 2004. [24] R. K. Chauhan, and K. Chauhan, "Management of Renewable Energy Sources and Battery Bank for Power Losses Optimization," Smart Power Distribution Systems: Control, Communication, and Optimization, ELSEVIER, pp. 299-320, Oct. 19, 2018. [25] R. K. Chauhan and M. L. Dewal, “Multi Supervisory Control And Data Display,” International Journal of Computer Applications (IJCA), vol. 2, no. 1, pp.1-6, May 2010. [26] R. K. Chauhan, C. Phurailatpam, B. S. Rajpurohit, F. M. Gonzalez-Longatt, and S. N. Singh, “Demand Side Management System for Autonomous DC Micro-grid,” Technology and Economics of Smart Grids and Sustainable Energy, vol.2, no.4, pp.1-11, February 2017.

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

Highlights Building automation system reduced the peak energy demand during peak hours, and electricity bill.



Proposed Scheme reduces the energy consumption without affection the user comfort



In case of Building automation system, the size of the photovoltaic plant and battery bank reduced significantly.



It also decreased the capital cost of the system.