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Energy Management-as-a-Service OverFog Computing Platform

Mohammad Abdullah Al Faruque, Member, IEEE, Korosh Vatanparvar, Student Member, IEEE

Abstract—By introduction of microgrids, energy managementis required to control the power generation and consumptionfor residential, industrial, and commercial domains, e.g. inresidential microgrids and homes. Energy management may alsohelp us to reach zero net energy for the residential domain.Improvement in technology, cost, and feature size have enableddevices everywhere, to be connected and interactive, as it iscalled Internet of Things (IoT). The increasing complexity anddata, due to the growing number of devices like sensors andactuators, requires powerful computing resources which may beprovided by cloud computing. However, scalability has becomethe potential issue in cloud computing. In this paper, fog com-puting is introduced as a novel platform for energy management.The scalability, adaptability, and open source software/hardwarefeatured in the proposed platform enable the user to implementthe energy management with the customized control-as-services,while minimizing the implementation cost and time-to-market.To demonstrate the energy management-as-a-service over fogcomputing platform in different domains, two prototypes of homeenergy management and microgrid-level energy managementhave been implemented and experimented.

Index Terms—Fog Computing, Microgrid, Home Energy Man-agement, Control-as-a-Service, Software-as-a-Service, Network-ing Platform, Internet of Things

I. INTRODUCTION AND RELATED WORK

POWER grids are getting more efficient and smarter as thenew paradigm of microgrid has been introduced. A mi-

crogrid is comprised of distributed generators, energy storage,and loads which may connect to the power grid or operateautonomously [1]–[3]. Hence, an energy management systemis essential to control the power generation and consumption.The microgrid has been shown to improve the reliability,efficiency, and profitability for residential and commercialinstallations [2]. However, in this paper, we focus on theresidential domain for demand-side energy management.

In the year 2013, about 40.7% of the U.S. primary energyconsumption is due to commercial buildings and residentialhomes existing in the power grid [4]. Reducing the energyconsumption of the buildings and homes may help to decreasethis number significantly. For instance, in the U.S. state ofCalifornia, the California Energy Commission (CEC), plannedto reach Zero Net Energy (ZNE) buildings by 2020 [5].Also, the U.S. Department of Energy (DOE) seeks to developtechnologies and techniques to improve the efficiency of thenew and existing residential and commercial homes and build-ings and thereby reduce the national energy consumption [6].

M. A. Al Faruque and K. Vatanparvar are with the Department of Electricaland Computer Engineering, University of California at Irvine, Irvine, CA92697 USA e-mail: (alfaruqu@uci.edu).

Copyright (c) 2012 IEEE. Personal use of this material is permitted.However, permission to use this material for any other purposes must beobtained from the IEEE by sending a request to pubs-permissions@ieee.org.

Hence, the need for energy efficient buildings and homes isgrowing rapidly. Moreover, the advancement in technology,the possibility of integrating low-power and high performanceelectronic devices have enabled us to build advanced embed-ded systems. Also, reduction in the cost and size of deviceslike: sensors, actuators, network adapters, switches, etc., haveprovided us with the opportunity to build sophisticated andlow-cost energy management systems [7]–[12]. Typically, thereduction in energy consumption may be done using periodicenergy cost feedback and remote monitoring the smart devices(demand response) [13], [14]. In this paper, we focus onspecific residential buildings - homes - for managing theirenergy consumption.

In order to implement an energy management system,a platform is required which provides interactivity and in-teroperability between devices and flexibility of operation.In [15], a home energy management has been implementedover a networking platform in order to meet the mentionedrequirements in lower cost, however, the operating domains ofthe system, scalability, heterogeneity, delay-sensitive devices,and controlling cost have not been considered properly.

One of the most important properties to be considered isthe probability of penetrating into the consumer market andaffordability of the platform for an ordinary consumer. Majorrequirements for the architecture which influence this pene-tration and affordability are: 1) interoperability; 2) scalability;3) ease of deployment; 4) open architecture; 5) plug-n-playcapability; and 6) local and remote monitoring [16], [17].Moreover, meeting these requirements in a single packageshould also be cost-effective. Since consumers in residentialbuildings have limited budget and space for deploying theplatform, we are considering them as the case study.

A. Motivational Case Study

Home Energy Management (HEM) as an example forenergy management system has to meet the above-mentionedpolicies and rules. While different hardware, software, com-munication architectures have been proposed and comparedby their power consumption, performance, etc. [14], [18]–[22], the cost of implementing the platform like: comput-ing devices, software stack, communication devices, etc. arestill high enough that hinder the process of deploying itfor ordinary residential users. Moreover, the hardware andsoftware architectures may not be able to handle the growingnumber of sensors and actuators alongside their heterogene-ity. Control4, Honeywell, and various other companies [23]–[26] are providing the customers with HEM platforms whichtransform an existing home to a smart home. These productsimplement various functionalities such as: temperature control,

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efficient lighting, and management of smart devices. All thesefeatures are provided by a single device. Hence, the scalability,adaptability, and cost of the platforms may become an issuefor customers. For instance, the products are strict about thenumber of devices connected to them for management andprocessing. Also, scaling the products for larger homes, maybecome significantly expensive. Furthermore, the users do nothave the option of customizing the services they require andthey have to purchase the whole package as it is.

B. Research Challenges and Concept Overview

In summary, the problem of implementing energy manage-ment platform poses the following major challenges:

1) Performance, interoperability, and interactivity amongheterogeneous devices in energy management platform.

2) Ability to customize the services, adaptability, and scal-ability of the energy management platform for varioustypes of buildings, homes, and applications.

3) Cost of implementing the energy management platform,hardware, and software stack.

C. Our Novel Contributions

To address the above-mentioned challenges, a novel plat-form for energy management system is proposed that employs:

1) Interoperability, scalability, adaptability, and connectiv-ity between smart devices over fog computing platform(see Section II) which consists of:

2) Low-power and low-cost devices for computation, stor-age, and communication.

3) Open source software architecture and hardware infras-tructure (open architecture) built on a fog computingplatform provides us with the ability to scale the plat-form (scalability).

4) The energy management software or control is imple-mented as a service using Devices Profile for WebServices (DPWS) [17], [19], [27] which also used forthe discovery purpose to provide the plug-n-play feature.This Service-Oriented Architecture also abstracts thecommunication and hardware heterogeneity.

As shown in Fig. 1, fog computing brings connectivity andinteroperability to the smart devices. Data transfer with thecloud and need for computation power from the cloud havebeen eliminated in this platform. Energy management has beenimplemented over this platform as a service and two prototypeshave been implemented to demonstrate its features.

Fig. 1. Our Energy Management-as-a-Service Over Fog Computing Platform.The Dashed and Highlighted Parts are Our Novel Contributions.

II. FOG COMPUTING PLATFORM

Platform for an energy management system should have thefeatures and meet the requirements mentioned in Section I inorder to be economical and affordable for ordinary costumers.Internet of Things (IoT) has become the new paradigmfor connecting intelligent and self-configurable devices ina dynamic and global network platform. It overcomes thegap between the physical world and their representation ininformation systems [28]–[33].

Hence, IoT has been seen as a possible platform for moderntechnologies, e.g. energy management system, smart home,and smart grid. The domain where this platform operates isscalable and dependent on the application, e.g. HEM monitorsand controls the devices within a home [28].

Moreover, IoT may comprise of billions of devices thatcan sense, communicate, compute, and actuate. Although,the number of devices is highly dependent on the domainof the IoT operation, e.g. home or microgrid, it is growingrapidly over the years, even within a home. This has changedthe traditional and manual way of controlling towards morecyber-integrated controlling. Also, this huge amount of dataproduced by the IoT will be possibly managed, processed,and analyzed, as part of the managing procedure [34]. Hence,cloud computing as a new way of centralized computing, inte-grates with IoT in order to provide the computing power [23].Moreover, cloud computing provides the customers with in-frastructure, platform, software, and sensor network, as ser-vices [12], [35]–[43]. These services are guaranteed to havethe reliability and performance stated in the agreements, whichare essential for the energy management platform.

However, increasing the number of devices in the IoT,i.e. energy management system, using cloud computing, mayincrease the response time and latency for some delay-sensitivedevices such that it violates the timing requirements [44]–[46].Hence, fog computing has been introduced to alleviate someof the above-mentioned problems [47].Fog computing moves the paradigm of cloud computingfurther to the edge of network. It is a platform which alsomay provide the IoT with the capability of pre-processing thedata while meeting the low latency requirements [29].

As in energy management system, the platform should beadaptive and scalable regardless of the system size. It alsoshould provide the devices with the performance, interoper-ability, and interactivity features in order to help them transferand process the data generated in an adequate response time.The energy management - as a control application - is imple-mented on fog computing platform. The energy managementplatform can be used for any type of buildings and variousdomains of operation, e.g. home or microgrid. The energymanagement may have various purposes like: 1) monitoringand metering the power consumption of each device, e.g. homepower consumption; 2) managing the energy consumptionby controlling the devices efficiently, e.g. intelligent lighting,Electric Vehicle (EV) charger [48], Heating, Ventilation, andAir Conditioning (HVAC) management, etc. The energy man-agement platform is a system of systems. In order to designthe platform, hardware (see Section II-A), software (see Sec-tion II-B), and communication (see Section II-C) architectures

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should be defined and integrated (see Section II-D) properlyfor these systems.

A. Hardware Architecture

The hardware of the energy management platform com-prises of multiple devices, depending on its operating domain,e.g. home or microgrid. These devices may be categorized asfollowing, based on their functionalities:

1) Connecting: these devices provide the connectivityamong the existing and compatible devices. The wires,sockets, and antennas are considered as this category.

2) Gateway: various devices may have different connectiv-ity standards and protocols, e.g. ZigBee, Bluetooth, andEthernet [49]. The gateway devices establish a compat-ible connectivity between multiple devices if required.

3) Sensor: the energy management system needs to mon-itor the environment for timely changes, e.g. weather,light, energy price, etc. The sensors may digitize theanalog signal generated by the environment.

4) Actuator: the energy management system may decideto configure multiple devices according to environmentchanges, in order to optimize a variable, e.g. energy con-sumption. These configurable devices are considered asactuators which may be locally or remotely controlled.

5) Computing: the devices that store, process, and analyzethe data in the system. They may also implement sophis-ticated controllers to configure the actuating devices.

Fig. 2. The Hardware Architecture of the Fog Computing Platform for HEM.

All the devices existing in an energy management platformare considered as connected nodes in the network of the IoT.Moreover, multiple devices or nodes might be grouped as asubsystem in order to perform a single function. In a HEMplatform, multiple smart devices, e.g. HVAC, are available tobe monitored (sensed) and controlled (actuated) in order toreduce the total energy consumption. The essential computingdevice which handles all the monitoring and controlling tasksis called the HEM control panel. The main job of it isto discover and monitor different devices in the platformdynamically, handle different time-flexible load requests, andtrigger and command the devices accordingly based on thealgorithms implemented, in order to optimize a variable. Tomanage the energy consumption of a home, the HEM platformmight need to retrieve information about the environment ofthe home. The physical world condition may be monitoredby multiple sensors (e.g. temperature, humidity, light, etc.)

in different places of the home. Moreover, different types ofsensors may be implemented on a single device (e.g. TelosBmote module). Also, a smart device may have a computingnode, implemented inside of it. This computing device, devicecontrol panel, enables the user or HEM control panel withthe capability of configuring the device or a subsystem (seeFig. 2), in lower level and much more detail than the HEMcontrol panel may be capable of, directly. Fig. 2 shows thehardware architecture used for communication and computa-tion in the fog computing platform. As you see in the figure,the gateway may be eliminated, if the device or subsystem iscapable of communicating with the platform directly.

B. Software Architecture

The computing nodes implement controllers to gather, store,process, and analyze data and manage devices. The opensource and user-configurable routers run a distribution ofLinux available in [50] on a MIPS processor. Using theserouters as computing nodes may help the developer to easilyprogram the controllers, compile, and run them on the router.

The sensor devices, e.g. TelosB mote modules are com-patible with TinyOS [51] which is an open source operatingsystem (available in [52]) designed for low-power wirelesssensors. Therefore, the algorithms implemented for monitor-ing, commanding the sensors and receiving data from themmay be programmed easily and dynamically on the sensors.This flexibility may help the developers to program the sensorsbased on their requirements. In other words, different routingand discovery algorithms for different kinds of sensors (e.g.temperature, humidity, and light) may be implemented indifferent scenarios.

Fig. 3. Demonstration of the Software Architecture Implemented by SOA.

The control panels for each subsystem manage their devicesin its own network through a predefined protocol. However,all the subsystems and devices connected in the main networkneed to follow a unique protocol defined by the HEM controlpanel (e.g. ZigBee). Therefore, the communications betweendevices are built around Devices Profile for Web Services(DPWS) from the Web Services for Devices (WS4D). Also, itrelies on SOAP-over-UDP, SOAP, WSDL, and XML Schema.This protocol stack may be used for sending secure messagesfrom device to device in a heterogeneous platform. Thedevelopers may utilize the WS4D-gSOAP toolkit availablein [53] to implement the services needed for each device.Devices may host various DPWS-compliant services using:

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1) WS-Addressing: provides an addressing mechanism forweb services as well as messages in a transport-neutralmatter.

2) WS-Discovery: provides a way to make the servicesdiscoverable by leveraging a protocol based on IP mul-ticast.

3) WS-MetadataExchange: defines different data typesand the operations to receive a metadata.

4) WS-Transfer: is used to transfer the metadata and isvery similar to HTTP.

5) WS-Eventing: describes a protocol which allows webservices to subscribe to or accept subscriptions for eventnotification messages.

Since the web services are platform-agnostic, using Service-Oriented Architecture (SOA), the platform maintains its flexi-bility of adding new devices, discovering devices and their re-spected services, synchronization between devices, and trans-ferring structured data between them. Also, in HEM platform,the devices or the controllers required by a user may be addedand managed as a service (Control-as-a-Service). Moreover,to establish synchronization capability and interoperabilitybetween devices in the network, e.g. HEM control panel, thedevice may host ’eventing’ web services. In this way, thedevices will be notified of any changes or events related toother devices if they have subscribed to those events. Fig. 3shows how these services are implemented and configured inthe platform in order to provide communication and certainfunctionalities required for the devices.

C. Communication Architecture

The devices in the platform communicate using the existingnetwork adapters and interfaces. For instance, the routers inthe HEM platform may have Wireless, Ethernet, Bluetooth,Universal Serial Bus (USB), etc., according to their specifica-tions. Since a unique protocol (e.g. ZigBee) should be usedfor the main network, these interfaces need to be convertedusing gateway devices for the desired protocol, if necessary.

Fig. 4. Communication Architecture used in the Fog Computing Platform.

The sensor devices, e.g. TelosB mote modules, may use thestandards-based, low-power wireless technology ZigBee tocommunicate with each other. Moreover, the standard usedfor the communication between different nodes may be SmartEnergy Profile (SEP 2.0), which is a standard for IP-basedcontrol for home energy management and it is supportedby these devices, but this profile is not ready yet (standard,protocol stack and hardware). Furthermore, the flexibility of

the ZigBee networks can handle about 65535 devices in anetwork. All the devices are tagged using a unique Identifica-tion (ID) number. These IDs are hard coded into the sensordevices while programming and maybe used for routing andtransferring data in the same network. Also, the services helpthe HEM control panel to discover new devices added viaplug-n-play. The HEM control panel will verify and authorizethe new devices connected to the HEM platform.

To further extend the range that sensor network supports, wemay utilize a two-level hierarchical network. In other words,some of the sensor devices are programmed to only sense,which are called End Devices (ED) and may be placed indifferent corners of a room. Then, a sensor node may beprogrammed to act as an Access Point (AP) and connects to allthe end devices in that room. Also, in a higher level, anothersensor device may act as a Base Station (BS) which connectsto multiple access points in all the rooms. The end devicestransfer the data through their access points and then to thebase station. The number of access points connected to eachbase station or the number of end devices connected to theaccess points are the variables which are adjusted based on thestructure of the building and the requirements. The base stationsends all the data gathered from all end devices to the HEMcontrol panel directly or indirectly (using gateway), at the finalstage. The gateway used here is a Raspberry Pi. The sensornodes may communicate with a Raspberry Pi using serialconnection over ZigBee standard, and then the Raspberry Piconnects to the HEM control panel through Ethernet. However,if the router has the capability of communicating via ZigBeedirectly, or the router had the compatible driver to controlthe sensor module through USB, the Raspberry Pi would beeliminated. Fig. 4 demonstrates the protocols and web servicesused for the communications between devices.

Furthermore, the user may interface with the platform usingthe web pages designed for the control panel of each device.The HEM control panel interface is setup on the main router.However, the interfaces for other devices may be providedby the vendors of the devices. Moreover, AsynchronousJavaScript and XML (AJAX) technique may be utilized toretrieve the information from the devices and view it onthe web page and trigger different functions and servicesimplemented on the control panel in order to process data.Furthermore, connecting the HEM control panel to the Internetenables the user to monitor and control the home locally orremotely through internet.

D. Integration of Architectures

As we have explained in the last three subsections, theflexibility and low cost infrastructure helps us to add anydevice, sensor, actuator, and their respected services. Thefunctions and controllers are implemented as services. Theservices of the devices have to be defined using WS4D to beable to communicate with the routers and each other. On theother hand, if a subsystem designed for an application-specificpurpose, needs to be added to the platform, regardless of itsown protocol and architecture, it needs to be compatible withthe protocol stack used in the HEM platform, or we can makeit compatible by using gateway devices.

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III. PROTOTYPE IMPLEMENTATION

To test the energy management platform, a HEM and amicrogrid-level prototypes have been implemented to demon-strate different domains of operation for energy management.

HEM Prototype: the HEM platform is implemented forone home as shown in Fig. 5. The detailed specifications forthe hardware used are listed in Table I. In the home, multiplesmart devices such as: HVAC, water heater, and EV charger areimplemented. Each device is monitored and controlled by theHEM control panel. Also, the devices have their own controlpanels to monitor their status and set their configurations. Inthis prototype, all the devices are software modeled, however,in the real-world implementation, the control panels of thedevices will be provided by their vendors.

Fig. 5. HEM Prototype Demonstrating the Fog Computing Platform.

The home is being monitored by a network of four sensordevices (TelosB mote modules). The sensor network is definedas a subsystem inside the platform (system of systems). Theysample the temperature, humidity, and lighting inside andoutside of the home. Also, different types of commands to en-able/disable the sensors or set the configurations, e.g. samplingrate for the sensors, may be sent by the sensor control panelto each device by specifying the ID number of that device.

In the HEM platform, a HVAC controller has been imple-mented as a service. Algorithm 1 illustrates the pseudocode formanaging the HVAC in order to reduce its power consumption.The controller receives the temperature set points configuredby the user (Tc, Th), the Demand Response (DR) signal (DR)sent by the transformer, the current room temperature (Tr),and HVAC operation mode (mode) which toggles betweencooler and heater, as inputs. The outputs of this controllerare the adjusted temperature set points and the status ofthe HVAC (status) which toggles between on and off .Initially, the threshold for turning on/off the HVAC is defined.The controller turns off/on the HVAC system based on theoperation mode and the temperature difference between theroom and the user preference. The adjusted HVAC status andthe temperature set points are sent to the HVAC.

TABLE ITHE HARDWARE SPECIFICATIONS OF THE HEM PROTOTYPE.

Product Name NETGEARProduct Number WNR3500LWiFi Performance 300 Mbps

WiFi Band 2.4 GHzWPA/WPA2 PSKSPI NAT Firewall

DoS Attack PreventionProcessor 480 MHz MIPS 74KMemory 128 MB NAND Flash

4x Ethernet1x USB 2.0

Product Name TelosB MoteProduct Number TPR2420CA

Processor 16 bitProgram Memory Flash 48 KB

RAM 10 KBSerial UART

RF 2.4 to 2.4835 GHzUSB

Light 320 to 730 nmIR 320 to 1100 nm

Humidity 0 to 100% RHTemperature 40 to 123.8 C

Product Name Raspberry PieModel B

Processor 700 MHzRAM 512 MB

1x Ethernet2x USB 2.0HDMI

Communication

Gateway

Ports

Router Security

Sensor Communication

Sensors

Algorithm 1: Smart HVAC Control-as-a-Service

Input: Set Points Tc, ThInput: DR Signal DRInput: Room Temperature Tr

Input: HVAC Mode modeOutput: Set Points Tc, ThOutput: HVAC Status status// define threshold for turning on/off the HVAC

1 Threshold = 1// limit set points when DR signal is triggered

2 if DR == true then3 Tc = 794 Th = 65

/* turn on/off the HVAC based on temperature, setpoints, and operation mode */

5 if mode == heater then6 if Th − Tr > Threshold then7 status = on

8 else9 if Tr > Th + Threshold then

10 status = off

11 if mode == cooler then12 if Tr − Tc > Threshold then13 status = on

14 else15 if Tr < Tc − Threshold then16 status = off

17 return status, Tc, Th

EV charger controller has been also implemented as a ser-vice in the HEM platform. Algorithm 2 illustrates a controller

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which schedules the charging time of the battery in order toreduce the electricity cost while meeting the departure time ofthe user. The controller receives user-specified departure time(0 ≤ td ≤ 23), the current time of the day (0 ≤ tc ≤ 23), andthe current battery status (0 ≤ SoC ≤ 100). The output of thecontroller is the adjusted current to charge the EV battery (I).Initially, total battery capacity (Capacity), the start time (tos)and end time (toe) for the off-peak hours, and the maximumcharge rate which the charger is able to provide (maxI ), aredefined. The controller schedules the charging process overthe off-peak hours first. Then, if more energy is required,more charging will be allocated during the on-peak hours. Thecharge rate is assigned based on the current time.

Algorithm 2: Smart EV Charger Control-as-a-Service

Input: Departure Time tdInput: Current Time tcInput: Battery Status SoCOutput: Charge Rate I// define the battery total capacity

1 Capacity = 60KWh// define start and end time for off-peak hours

2 tos = 223 toe = 11// define the maximum charge rate possible

4 maxI = 4KW

// evaluate capacity remaining to charge5 Capacityrem = (100 − SoC)/100 ∗ Capacity// the time interval when there is off-peak hours

6 T imeoff = (min(toe, td)−max(tos , tc))%24// the charge rate during off-peak hours

7 Ioff = min(maxI , Capacityrem/T imeoff )// the charged capacity during off-peak hours

8 Chargedoff = Ioff ∗Durationoff

/* evaluate capacity remaining after chargingduring off-peak hours */

9 Capacityrem = Capacityrem − Chargedoff/* the time interval remaining to charge

subtracting the off-peak hours */10 T imerem = (td − tc − T imeoff )%24

// the charge rate during on-peak hours11 Ion = min(maxI , CapacityRemaining/T imeRemaining)

// deciding the current charge rate based on time12 if tc ∈ Durationoff−peak then13 I = Ioff

14 else15 I = Ion

16 return I

Microgrid-Level Prototype: the energy management plat-form in the microgrid-level comprises three homes connectedto a transformer. A control panel is implemented in thetransformer-level to monitor and manage the power con-sumption of each home. The transformer-level control panelmonitors the load of each home and the current condition ofthe transformer. Based on this information and the controller,the control panel may decide to send commands to the HEMfor each home, in order to reduce their power consumptionby a specific value, which is called DR. The reduction inthe power consumption may prevent over-loading and over-heating the transformer which may improve the efficiency andlongevity. The transformer control panel is implemented inone router and other three homes with their emulated smart

devices are implemented in other six routers (see Fig. 6).

Fig. 6. Microgrid-Level Energy Management Prototype.

In the microgrid-level energy management platform, a trans-former management has been implemented as a service. Al-gorithm 3 illustrates the pseudocode for monitoring the homesconnected to the transformer in order to prevent overloadingthe transformer. The controller receives an array of the homesconnected (H) and the current load on the transformer (loadc),as inputs. The output of the controller is the overloadingstatus of the transformer (status). Initially, the maximum loadin which the transformer can operate efficiently (loadmax)and the maximum load a home should have (homemax) aredefined. The controller checks whether the transformer isoverloaded. In case of overload, a DR signal is sent to eachhome which consumes more than the threshold.

Algorithm 3: Smart Transformer Control-as-a-Service

Input: Homes Connected to the Transformer HInput: Current Transformer Load loadcOutput: Overload Status status// define transformer rating load

1 loadmax = 20KW// define threshold for each home

2 homemax = 4KW// assume no overloading in transformer

3 status = false

// check whether the transformer is overloading4 if loadc > loadmax then5 for h ∈ H do6 if hload > homemax then

/* trigger DR signal for the home withhigher consumption than threshold */

7 Trigger DR(h)

8 status = true

9 return status

IV. EXPERIMENTAL RESULTS ON CASE STUDIES

To demonstrate and experiment the features of the energymanagement over fog computing platform as a service, multi-ple services are implemented and evaluated on the platform(see Section III). The complete working prototypes of ourcase study demonstrations are presented in [54] (for detailedspecifications see Section II).

A. Home Energy Management

In the HEM-level, the services like managing sensor net-work, efficient lighting, smart EV charging, and smart HVAC

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Fig. 7. HEM Control Panel for Monitoring and Controlling Smart Devices.

controlling, are added to the energy management system. Thealgorithms for each of the services are implemented in theHEM fog computing platform (Algorithms 1 and 2). TheHEM control panel views the current devices connected to theplatform. Through HEM platform, the user may turn on/offeach device (see Fig. 7). Fig. 8 shows the user interfacefor managing the sensor network in the home. The usermay check the temperature and humidity in different partsof the home. Using smart EV charging, the EV charger willefficiently decide when and how to charge the EV such that itdoes not violate power consumption restrictions while meetingthe departure time specified by the user (see Fig. 10). TheHVAC consumption is mainly dependent on the temperatureset points adjusted by the user. By monitoring the temperaturein different places of the home and knowing the energyprice, the HVAC controller may efficiently decide on thetemperature set points (see Fig. 9). This process may reducethe power consumption of the HVAC while maintaining thehome temperature within the defined range.

Fig. 8. Sensor Network Control Panel for Managing Sensors’ Configurations.

Fig. 9. HVAC Control Panel for Monitoring and Controlling HVAC Power.

Fig. 10. EV Control Panel for Monitoring and Controlling Battery Charging.

Fig. 11. Transformer Control Panel for Monitoring and Controlling Power.

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B. Microgrid-Level Energy Management

In the microgrid-level energy management, a transformerpowers multiple homes. The services like transformer monitor-ing, and DR may be added to the energy management system.The algorithms for each of the services are implemented in themicrogrid-level fog computing platform (Algorithm 3). Fig. 11shows the user interface for monitoring the power consumptionof each home. The user may define certain power thresholdfor each home. As shown in Fig. 11, the transformer currentcondition and the total load on it may be monitored. Using thisinformation, the transformer will send DR signals to the homewhich have violated the threshold so that they may reduce theirconsumption. Also, the control panel may decide where to getthe power from (see Fig. 11).

Both energy management systems have provided the fea-tures and met the requirements mentioned in Section I byimplementing the cost-effective fog computing platform usingoff-the-shelf instruments listed in Table I. Also, Table IIsummarizes the technologies used to provide these features.

TABLE IISUMMARY OF THE TECHNOLOGIES USED TO IMPLEMENT THE FEATURES

REQUIRED FOR THE ENERGY MANAGEMENT PLATFORM.

Feature Used Technology

InteroperabilityWS Addressing and WS Transfer enable thedevices to communicate with each other.

InteractivityWS Eventing and WS Metadata Exchange enabledevices to synchronize each other periodically.

FlexibilityOpen hardware/software used for implementingany application or adding new devices.

ScalabilityMultiple devices can be connected in a sharednetwork with their own unique IDs.

Ease ofDeployment

Control panel web pages leverage HTML, AJAX,and Java scripting in order to provide user friendlyinterfaces.

OpenArchitecture

Raspberry Pi is used as gateway and Linux basedrouters and Tiny OS based TelosB mote sensorsare used for connecting and computing.

Plug n PlaySOAP over UDP is used for discovery andauthentication of new devices added to network.

Local/RemoteAccess

IP addresses are designated for each device andthey are also connected to the Internet.

HeterogeneityAbstraction

Service Oriented Architecture is used to abstractthe hardware and communication differences.

V. CONCLUSION

We have seen that energy management is essential formicrogrids, homes, and buildings. Hence, in this paper, wehave presented a novel energy management which is im-plemented as a service over fog computing platform. Theimplementation over fog computing platform provides theflexibility, interoperability, connectivity, data privacy, and real-time features required for energy management. Also, opensource software/hardware and the ability to be customizedprovide the user to add the control as a service to the energymanagement platform. Therefore, the implementation cost andtime-to-market will decrease significantly. To demonstrate the

energy management-as-a-service over fog computing platformin different domains, two prototypes of home energy man-agement and microgrid-level energy management have beenimplemented and experimented.

REFERENCES

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Mohammad Al Faruque received the B.Sc. de-gree in computer science and engineering from theBangladesh University of Engineering and Technol-ogy, Dhaka, Bangladesh, and the M.Sc. and Ph.D.degrees in computer science from RWTH AachenTechnical University, Aachen, Germany, and Karl-sruhe Institute of Technology, Karlsruhe, Germany,in 2002, 2004, and 2009, respectively. He waswith Siemens Corporate Research and Technology,Princeton, NJ, USA, as a Research Scientist. He iscurrently with the University of California at Irvine

(UCI), Irvine, CA, USA, where he is a Tenure Track Assistant Professor andwhere he directs the Advanced Integrated Cyber-Physical Systems Laboratory.He is also with the Center for Embedded and Cyber-Physical Systems(CECS), UCI. His current research interests include system-level designof embedded and cyber-physical-systems (CPS), complex software-definedsystems modeling, real-time scheduling algorithms, multicore systems, anddependable CPS systems.

Prof. Al Faruque received the 2015 UCI Chancellors Award for Excellencein Fostering Undergraduate Research, 2015 Hellman Fellow Award, 2015DAC Best Paper Award, 2009 IEEE/ACM William J. McCalla ICCAD BestPaper Award, 2008 HiPEAC Paper Award, and 2005 DAC Best Paper AwardNomination. Prof. Al Faruque is a member of Association for ComputingMachinery.

Korosh Vatanparvar received the B.Sc. degree inelectrical engineering from the Sharif University ofTechnology, Tehran, Iran. He is currently pursuingthe Ph.D. degree from the Advanced IntegratedCyber-Physical Systems Laboratory, Department ofElectrical Engineering and Computer Science, Uni-versity of California at Irvine, Irvine, CA, USA.

His current research interests include cyber-physical energy systems, co-simulation, and mod-eling energy systems. He received 2015 DAC BestPaper Award.