Joule Jotter: An interactive energy meter for metering,monitoring and control

Prabhakar T.V, Nisha Bhaskar,Tejas Pande

Department of Electronic System Engg,Indian Institute of Science, Bangalore

(tvprabs, nisha.b, pandet)@dese.iisc.ernet.in

Chaitanya KulkarniNational Institute of Technology,

Surathkal, Karnatakachaitanya.kulkarni2334@gmail.com

ABSTRACTUtility companies worldwide are adopting Demand Side Man-agement (DSM) methods to cope with unpredictable de-mands. Demand Response (DR) is a common strategy inwhich the utility encourages users to change their demandpatterns dynamically, so as to have short-term reductions inaggregate energy consumption. While users are concernedabout rising costs of electricity bills, utilities are concernedabout shortage of power and the real time-pricing they haveto pay energy generating companies to meet the peak de-mand and supply. In this paper we describe “Joule Jotter”(JJ) a smart energy meter that bridges the gap between util-ity providers and domestic users equipped with such smartmeters. We show the scalability of our protocol integration.Simple algorithms are implemented that work with utilityrequests. Our evaluation results indicate that such energymeters suffer additional time overhead of 1.5s due to SmartEnergy Profile (SEP) 2.0 protocol implementation.

KeywordsDRLC, SEP 2.0, DER, SMS, TOD, Pricing

1. INTRODUCTIONIn India, technology adoption in electricity usage has seen

success in several interesting ways. For example, monthlybills generated by the utility company required transparency.Human error in reading the electricity meter now uses acamera picture to digitally record the reading. This pictureis transmitted to the central database for generating themonthly consumption bills. Pre-paid electricity paymentshave seen the adoption of scratch cards that are availableat convenient outlets such as gas stations, shops and re-tail outlets. While users are concerned about their monthlybills, utility companies have issues to source power by way ofhigher payments to generating companies. Utilities pay forpower based on “real-time” price but only charge “flat rate”

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from domestic users. In the event of peak demand exceed-ing the supply, utilities target home segments by temporar-ily cutting power supply; commonly called “load shedding”.It is not uncommon to have 6 to 8 hours of load sheddingin suburban areas, followed by many hours of low voltage.Thus utilities earn the wrath of domestic consumer who con-stitute the 30% of market segment of power consumption.

Rising costs of electricity bills and lack of empathy by theutilities has prompted home users to look for new solutions.Several brands of “smart energy meters” have appeared inthe markets with the end user in focus. Users are seeing ben-efits because instantaneous energy consumption feedback isnow available. Interestingly, the smart energy meter appearsto have arrived like a boon to utility companies as well. Util-ities are now offering the domestic users with incentives witha view to reduce the gap between peak demand and supply.For example, the user may switch on the electric geyser inthe late evening instead of the regular morning peak hours.Washing machines might have to be operated late afternoonfor “cupboard dry” clothes.

For utility companies, DSM mechanisms and DR pro-grams are required to handle and service short time scaleincreases in peak energy consumption. This requires thatusers have to face the reality of real-time pricing and thusmove away from the traditional “average annual cost” modelbased flat pricing. Electricity consumers have to adjust toprices and create elasticity by altering the demand, partic-ularly over short time frames. For example, on a particularday, between 8PM-9PM the price from was 2.9 cents/kWhand 1.9 cents/kWh between 12 am - 1 am [1]. The Banga-lore Electricity Supply Company (BESCOM) in the state ofKarnataka, India, is perhaps one of the earlier utility com-panies to recognize the need for enabling DSM in its powerdistribution network to manage the peak energy consump-tion.

BESCOM services extend to about 30% of the state’s 60million population and serves 8 districts out of the 30 dis-tricts with 3 zones to administer. The domestic users areoffered flat pricing with 3 or more slabs. One of the tariffcategories from BESCOM is the Time of Day (TOD) pricingfor Low Tension supply users. There can be an increase ordecrease in pricing from the normal price between 06-00 Hrsand 22-00 Hrs. Recent statistics indicate that the month ofJuly 2013 observed a 1000MW shortage between 06-00 and09-00 Hrs [2]. BESCOM has also launched “Happy Hours”for home users in the month of August 2013. The goal ofthis pilot project is to observe if consumers can bring down

their consumption by 10% on average and by 15% duringpeak hours.

It is now apparent that end users have to learn more abouttariffs almost on a daily basis if they have to check theirgrowing monthly bills. BESCOM will soon start collecting“Fuel Cost Adjustment” charge from end users to pass onthe burden of real-time pricing. This will appear as a vari-able component in bills and is essentially short time scalechanges in cost of energy generating fuels. Clearly utilitiesand customers have to work in tandem if peak demand andsupply have to match in the long term.

Figure 1: The big picture: Managing deferrableloads

Fig 1 shows the big picture of a smart grid based homeenergy supply system. Power from the utility is supplied toa home through an energy meter that records the householdconsumption. Assume for the moment that the utility com-pany makes available the pricing profile to the smart meterconnected to the Home Area Network Gateway(HAN). TheHAN is expected to have the knowledge about the possi-ble list of electrical appliances and the time at which theycan be turned on to support the utilities pricing profile. Al-though final decisions are made by humans in the loop, mostsmart energy meters are unable to interface successfully withsmart grid based utilities. In this paper, we build a smartenergy meter called“Joule Jotter”(JJ) that can interface wellbetween domestic user and their utility companies. Apartfrom building the JJ, our contributions in this paper are:(a) SEP 2.0 implementation on the embedded Wi-Fi basedJJ smart energy meter. (b) Seamless integration betweenthe Internet and mobile phone operators (c) Seamless map-ping between utilities and end users. TOD charging, happyhours and other models available from utilities are supportedwithin pricing profiles. These are then used by end users toeffectively manage their electrical appliances.

2. LITERATUREThe importance of smart meters for users and utilities is

brought out in work of [4] where the benefits to users includetransparency in billing and information about energy con-sumption. For utilities it is a way to ensure real-time pric-ing and change in user behavior to reduce the peak demand.Most literature mention that energy consumption feedbackis extremely important since it brings the end user into theloop. For instance, in [5] the authors describe an androidsmartphone application that provides feedback on energy

consumption profile of several home appliances. Load dis-aggregation is required since a single energy meter is used.The value proposition comes because of comparison of en-ergy consumption with peer user homes and consumer or-ganizations. The data obtained from the energy meters isprovided as a feedback to influence user’s behavior. Thework in [6] stresses the need for energy meters with reducedusage barrier. User feedback is provided on a real-time basisat a device level and thus biggest energy guzzlers are easilyidentified. A load disaggregation algorithm from Markus etal [7] propose a disaggregation algorithm called“AppliSense”that breaks down the energy consumption to a device level.Prior to the SEP 2.0, there were several works that proposedmiddleware frameworks. The authors in [8] propose a mid-dleware framework called “Hydra” that facilitates intelligentcommunication through a P2P network. Smart energy plugscalled “Ploggs” were used to gather energy usage statisticsfrom electrical loads. However, the proposed architecturedoes not integrate utility providers and end users. GSMAutomatic Power Meter Reading System (GAPMR), a pre-paid payment based system is proposed in [9]. The num-ber of units consumed is communicated to utility companieswith help of GSM to monitor power theft. Manisa et al [10]have performed a thorough analysis of the load profiles oftypical house-hold appliances and have suggested that whileelectric clothes dryers and water heaters provide the high-est DR opportunity and clothes washers, refrigerators andelectric range ovens offer the least potential. Field trial in50 households by Smartcity Malaga [11] studies the effect ofenergy monitoring. It is found that 42% of the participantshave achieved over 10% consumption reduction, 33% haveseen minimal change (within 10%) and the remaining 25%have increased their consumption more than 10%. As statedearlier, this corresponds to the figures BESCOM is attempt-ing to achieve. PEAKSAVE [12] has implemented a similarsystem using their smart meter named S Plug. It providesboth monitoring and control functionalities. The work innPlug [13], shows that it is possible to reduce peak energydemand for utilities by formulating Demand Side Manage-ment (DSM) strategies and schedule high power consumingappliances. nPlug, a inexpensive smart plug, runs a de-centralized DSM strategy by assessing supply-demand gap.Users are expected to physically key-in their preferences in-dicating the starting and end timings to schedule their ap-pliances. The JJ on the other hand provides users feedbackabout their consumption aligned to the pricing profile of theutility provider. Thus there is a seamless interaction be-tween utility providers, home users, Internet cloud and themobile networks.

3. PROTOCOL INTEGRATION: SEP 2.0WITH INTERNET AND MOBILENETWORKS

Our hardware comprising of the JJ and HAN togetherensure that all protocols are integrated and run in a seam-less manner. The SEP 2.0 standard [3] supports severalresources such as subscription and notification, end deviceand response. The SEP 2.0 supports the RESTful architec-ture. Such an architecture does not differentiate between aclient and server in terms of resource representation, bothin terms of resource exposure and interaction. SEP 2.0 isbuilt around the concept of resources and function sets. Re-

sources can be described to be the properties of a physicalmeter. A set of resources when used together to implementa specific function make a function set. Function sets suchas metering, demand response and load control (DRLC) andpricing provide the seamless communication between utili-ties and home user smart meters. The load control is en-forced using the DRLC protocol using the publish-subscriberesource available under SEP 2.0. Here, the load side JJ isthe DRLC client that runs the subscription server and theHAN is the DRLC server that publishes resources and sendsnotifications. The JJs are also equipped with actuators toenable turning off the loads.

Figure 2: Overview of System Implementation

The SEP 2.0, although available on hardware (for instanceCC2538) platforms, we chose to implement the standard onWi-Fi. The idea is to support scalable architectures rang-ing from single homes to multiuser dwellings. Fig 2 showsthe overall protocol integration in our implemented system.The outermost ring has the communication protocols to enduser and utility provider. The second ring exposes the userto the SEP 2.0 implementation. For example, the controlof loads and metering information is available to the useras a simplified service. These work closely with the pre-paid and post paid payment models to which the user mighthave subscribed. The SEP 2.0 implementation on JJ andHAN is shown in the third ring. Finally, the physical hard-ware is represented in the innermost circle. The HAN im-plements the pricing profile and directly interacts with theutility companies. It also has the SMS gateway capability.Thus the complete system facilitates integration of variousservices and payment models e.g. flat rate, TOD, happyhours, prepaid, postpaid etc. The end user can easily usethese services via SMS, Email. In the event the user is athome, an android application running on the phone connectsto the home network to manage the electrical appliances.

4. JOULE JOTTER: SMART ENERGYMETER

Fig. 3 shows a snapshot of JJ, the smart energy meter.The controller chosen is MSP430F5438A from Texas Instru-ments. It supports a single phase electronic energy metersensor chip ADE7953 from Analog Devices that meters al-most all electrical parameters such as current, voltage, powerfactor, etc. The current input is obtained from a currenttransformer (CT) and the voltage input is obtained from astep down potential transformer (PT). The JJ has a pro-

Figure 3: Joule Jotter

vision for local storage of all metered data. Additionally,the board has support for TCP/IP communication protocolstack over a IEEE 802.11b/g Wi-Fi interface. The systemclock frequency is 25 MHz with support for 16 bit timers.The system has support for SPI, UART and I2C modes. TheADE samples at a rate of 895 kHz. The Wi-Fi chip CC3000operates over the 2.4GHz frequency range with +18dBm ofmaximum transmission power. We measured the currentconsumption of the JJ first during a write operation andthen during a Wi-Fi transmission. We found that it requiresaverage of 60mA and 170mA respectively.

Figure 4: Wash cycle of a washing machine

Fig. 4 shows the load profile for a washing machine. Thesystem is rated for 545 watts. Power consumption data forfour wash cycles were recorded by the JJ on its SD card.When water is pumped into the machine for soaking theclothes and rinsing at the end of washing, a power of 4 wattsis required. A wash cycle and a spin dry cycle require sig-nificantly higher amounts of power. Often, the power drawnexceeds the rated power from the manufacturer; althoughonly for a short interval of time. In the event of a powerfailure after the start of the program, the washing machineis able to “auto-restart” from wherever it stopped. This isjust an example of all the loads for which we are able torecord the power consumption in time.

4.1 Support for SEP 2.0SEP 2.0 is the application level protocol that we have im-

plemented to support all the algorithms. Our implementa-

tion works on top of Wi-Fi and TCP/IP supported CC3000.The data is encapsulated as “XML” packets and transferredover HTTPS. The application protocol was developed to runon the MSP430 microcontroller. The CC3000 is accessed bythe controller through API’s(application programming in-terfaces). The host issues API function calls to CC3000 andthe module responds by performing the appropriate activity.Events are triggered by the CC3000 device as it issues inter-rupts to the host. Command response events are in responseto commands issued by the host and unsolicited events aretriggered asynchronously by the CC3000 device to indicateoccurrence of a system event.

4.1.1 Pricing ProfileSEP provides a very flexible and scalable structure for

pricing profile implementation which the utility serviceprovider issues. Each pricing profile itself is given by Tar-iffProfile. Each TariffProfile will have a list of RateCompo-nents. Each RateComponent specifies the type of user de-pending on the consumption rate. Since the pricing profileis a generic package which can be customized depending onthe resource being metered, for electricity metering applica-tions, RateComponent is measured in terms of kWh. Eachrate component has a list of TimeTarifIntervals. For exam-ple, when a time-of-use tariff with rates varying for eachhour is being used, the list will have a total of 24 TimeTar-iffIntervals (tti). Now, each of these tti can further havea list of ConsumptionTariffIntervals (cti). These again aresplit up depending on the energy consumed. The idea is thatthe price changes (increases) when the energy consumed in-creases. The cti can be used in case the tariff profile requiresit, otherwise, we set the list to hold one object which has aprice associated with it.

4.1.2 Demand Response Load Control(DRLC) Func-tion Set

Figure 5: DRLC with multiple clients

In our implementation of DRLC, each smart appliancewill serve as the DRLC client. The DRLC server is imple-mented on the HAN. The DRLC function set is supportedby the SubscriptionNotification resource. Fig. 5 is a snapshot of our implementation. It shows that soon after thediscovery procedure, the subscription client will first sub-scribe to the server’s metered resource. The subscription

sets a threshold for the power consumed by the load. Onstart of the event, the DRLC client will indicate the same tothe server. If the threshold is exceeded, then the Subscrip-tion server notifies the client. The DRLC server exposesits EndDeviceControl object to the DRLC client indicatingfor the client load to be turned off. This implementationcan be easily extended to multiple smart appliance scenario.The server can set thresholds for each of these appliances orfor the entire system. The server is notified every time thethreshold is exceeded to ensure that the condition is satisfiedat all times. While this implementation is a general proto-col, its elegant use mostly relies on control algorithms andmechanisms that exploit it. For example, client can switchon once the EndDeviceControl replies with a “yes” underconditions such as:(a) automatic increase in threshold, (b)Price tariff changes in time and (c) a time-out occurs.

4.1.3 Metering Function SetSEP’s metering function set requires implementation of

the metering client on the HAN which regularly pulls themetered values from the metering server implemented onthe JJ. The HAN also implements the pricing function setwhich receives the price tariff from the utility provider. Asan example, the profile can be a TOD profile with consump-tion slabs; the prices being higher for a higher consumptionslab. Since the HAN already knows the power utilization ofthe appliances from the metering function, the appropriateprice for that overall consumption can be extracted. Sub-sequently, the DRLC function set can be used to switch offany of the appliances when the power utilization exceeds acertain set threshold on the HAN. This threshold can eitherbe specified by the user or the utility provider.

5. SCENARIOS FOR DEPLOYMENTOur protocol integrated architecture is exploited in sev-

eral ways that demonstrate its scalability. In this section,we present three scenarios for our home energy monitor-ing system. Our implementation stresses on a highly flexi-ble architecture which can be customized depending on userpreferences and requirements. We look mostly at integrat-ing the Internet and mobile network based protocols withthe SEP 2.0 based existing infrastructure. The user is ofparamount importance in our architecture. The completeuser application was built over a smart phone as an androidapp. The basic requirement is that user should be able torespond to the requirements of the house with minimal in-convenience caused either due to physical location or anyother restrictions. In the event the user is at home, directHTTP communication is enabled. All deployment scenariosare capable of running simple algorithms.

5.1 Scenario 1: Joule Jotter with DNS sup-port

In the first scenario, we assumed our user to have de-ployed one or two (a very small number) smart meters athome. The pricing profile along with DRLC runs directlyon one of the JJs and is represented as home server in Fig. 6.The steps shown in Fig. 6 were implemented to control theappliances. To ensure that there is complete protocol inte-gration, we subscribed to an online tool called SMS Globalservice; also shown in Fig.6. This service accepts HTTPconnections from the home server JJ and delivers an SMSto end user. The SMS from the end user is forwarded to a

Figure 6: A protocol integrated architecture

Uniform Resource Locator (URL). This URL is registered tothe server JJ with a free dynamic domain name system (dyn-DNS) service. This scenario supports direct communicationwith the JJ and load control is possible either one-to-one orone-to-few.

5.2 Scenario 2: HAN - JJs in star topologyIn this scenario, we have a HAN that runs the pricing

profile and the SMS gateway service shown in Fig. 6 asthe home server. It is however still possible to use externalSMS service with the HAN registered with the DynDNS.This scenario is envisaged for multi dwelling units and largehomes where several smart meters are required for pluggedloads.

6. EVALUATION AND RESULTS

Algorithm 1 Flat Rate Threshold

TEp← Total energy consumed till nowTh← Threshold energyJJArray ← Array of active appliancesJJTempArray ← Temporary arrayEp← Energy of an appliance in JJArray

Ensure: TEp ≤ Thif TEp > Th then

for i = 0; i < sizeof(JJarray)− 1; i + + doif (TEp− JJArray[i]− > Ep) < Th then

JJTempArray[k] = JJArray[i]k + +

end ifend forif JJTempArray 6= Φ then

sort(JJTempArray wrt energy value)end ifEnergies of two or more devices are added and com-pared with TEpThe group of devices which crosses threshold are sortedinJJTempArraysend the JJTempArray to user and control the appli-ance(s) based on his feedback

end if

To demonstrate that our integrated approach has the nec-essary freedom to adjust the JJ to upcoming business mod-els, we implemented three control algorithms. We now de-scribe them in detail. The user has an interactive role to

play in our implementations and is part of the loop. Theandroid app is built in such a way that the user can checkthe power consumption details in either energy units(kWh)or in terms of money. The algorithms interact with the userby providing options for the load(s) to be controlled.

6.1 Flat Rate Threshold AlgorithmAssume that the utility offers a fixed rate pricing profile

for the first 100 units consumed by the user. The HAN hasthe power profile of each appliance. In this implementa-tion we set a threshold for the average power (including allappliances). Algorithm 1 will compute the total energy con-sumed in real time and ensure the average does not exceedthe set threshold (3 units/ day). In the event of exceedingthe threshold, the system presents the user with a choiceof presently active appliances (single or a set of them) thatcan be turned off to optimally lower the consumption belowthe threshold. The appliance(s) that brings the power con-sumption below the threshold and closest is recommendedto the user. The user selected option is used to control theappliance.

6.2 Happy HoursThe“Happy Hours”algorithm is also utility provider driven

and provides an option to users to reschedule their appli-ances. The algorithm computes the tariff a user incurs inhappy hours compared to time outside these hours. Algo-rithm 2 shows our implementation.

Algorithm 2 Happy Hour Pricing

C[i]← Rate for ith consumption intervaln← Span of device operationh← Happy hour consumption intervalE[k]← Energy consumption in kth interval k ∈ (1, n)JJArray ← Array of active appliancesJJTempArray ← Temporary arrayCwoh←Money spent at current timeCh←Money spent in happy hoursCmin←Min. money for user to consider reschedulingJJTempArray = JJArraysort (JJTempArray wrt Energy)

Cwoh =n,i+n∑

k=1,t=i

E[k] ∗ C[t]

Ch =n,h+n∑

k=1,t=h

E[k] ∗ C[t]

if Ch− Cwoh > Cmin thensend user notification to schedule the appliances

end ifif user rejects then

Repeat the algorithm for next element in JJTempArrayend if

6.3 Pre-Paid AccountsIn this algorithm user can top-up his account for any

amount and the home monitoring system keeps track of hisbalance. Algorithm 3 shows that the pricing profile sup-ported by SEP 2.0 is amenable for this support. Once thetime tariff interval is recognized based on time of the day, theenergy consumed is compared with the consumption slabsfor that time interval and the appropriate price is extracted.

Table 1 shows the results of our SEP 2.0 stack evaluation.

The table shows the average delay with and without the SEP2.0 stack. As can be seen, E-mail takes a 1 sec more withthe protocol integrated stack. With SMS running on theHAN, the system takes a little over 1 s with the integration.The SMS Global solution resulted in an average delay of 48minutes from the user to the home.

Algorithm 3 Pre-Paid Tariff

C[i]← Price for ith consumption intervalS[i]← Start value for ith consumption intervalTEp← Total energy consumed till nowTc← Consumpstion cost of applianceTh← Threshold balanceBUpdate← Updated balance

Ensure: BUpdate > Thfor i=(noOfConsumptionTariffInervals-1);i>=0;i– do

if TEp > S[i] thenS[i + 1] = TEpfor (j = i; j ≥ 0; j −−) do

Temp = Temp + C[j] ∗ (S[j + 1]− S[j])Tc = Temp

end forend if

end forBUpdate = BUpdate− Tcif BUpdate < Th then

Send warning to user phoneend if

Table 1: Latency Measurements (secs)Email(custom)

Email(SEP)

SMS(custom)

SMS(SEP)

JJ to user 5.98 6.9 24.63 26user to JJ – – 13.76 15

7. DISCUSSIONThe protocol integration has resulted in seamless and scal-

able solutions for home and multi dwelling units. Unlike thehardware version of ZigBee SEP 2.0, our software implemen-tation of the stack yielded elegant and simple scenarios. Forexample, our experiments with the SMS Global service, al-though requires to improve the time guarantees, it providedinsights that a smart energy meter is now manageable fromanywhere in the world. The algorithms have shown thatadding variants is easy. For example, the average powerthreshold could be replaced with peak power. The TODcan be replaced with happy hours. The pre-paid algorithmcan be tuned to provide daily alerts for the post-paid sce-nario. The algorithm complexity of the flat rate algorithmis N + NlogN , and that for happy hours is NlogN followedby pre-paid tariff algorithm being n2. Here, N is the num-ber of JJ’s and n is the number of consumption tariff inter-vals(cti’s).

8. CONCLUSIONSIn this paper, we built a smart energy meter called Joule

Jotter from scratch and implemented Smart Energy Pro-file 2.0 in software over Wi-Fi. The integration with mobile

networks and Internet has yielded many deployment optionsand seamless interaction between utilities and the end user.Existing algorithms running on smart meters use the philos-ophy “pay more use more”. The JJ makes a paradigm shiftby aligning the power consumption to utility requests. Webelieve, quite like water consumption, energy consumptionshould be based on global optimization algorithms.

9. ACKNOWLEDGMENTSWe thank ACM SIGCOMM - Community Grant for gen-

erously funding this IoT device, RBBCCPS, IISc for the seedfunding and our partner TUDelft who have stood by us.

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