2014 Power and Energy Systems: Towards Sustainable Energy (PESTSE 2014)

A Novel Design of User

Responsive Smart Meter Integrated Automated

EMS inn SCADA Interfaced Smart Grid

'Mithun Manohar

Student Member, IEEE, Chairman, IEEE Malabar HUB

Dept of Electrical and Electronics Engineering, Vimal Jyothi Engineering College, Chemberi

mithunrnanohar79@gmail.com

Abstract - Electricity supply is rapidly becoming more

complex and variable where energy efficiency rules are

still evolving only to limited end. A near future with

millions of electric vehicles and industrial loads will

dramatically increase the necessity of a demand

modulation system. The best way of practicing demand

management is user responsive load management at the

consumer ends. It will be difficult to alter consumer

behavior in the direction of energy savings unless

consumers are more exposed to market prices than at

present. The proposed system introduces a distributed

smart meter that intelligently notifies the power

consumption rate at various time intervals based on

demand factors and power loss factors. It also notifies

the characteristic behavior of consumption to the

consumer via GSM communication system, and isolates

the supply for various faults and power theft. A new

intelligent TLPF algorithm has been implemented that

confronts all these features and executed using a

SCADA controller.

Key words- Smart meter, GSM,SCADA, DCS,

Power system automation

I. INTRODUCTION The present electricity networks have a technical

hierarchy where energy flows from large, centralized, fully controllable power plants to more or less passive customers at the receiving end of the network. Developing grids to "smarter" will help to eliminate many of the challenges that power systems are currently facing and that will occur with increasing frequency in the future, such as distributed generation, electric vehicles, under-investment in grid infrastructure, and more interest in Smart Grids has skyrocketed in recent years [1].

978-1-4799-3421-8/14/$31.00 ©20 14 IEEE

2Sarin CR B.Tech M.Tech MIEE MRAS 3Prabin James B.Tech M.Tech

4Laly James 2 and 3 Assistant Professor

Prof and HOD Dept of Electrical and Electronics Engineering, Vimal Jyothi Engineering College, Chemberi

crsarin@gmail.com, cpjeee@gmail.com

The proposed system offers a better modeling for the development of an intelligent smart grid system with the help of user acknowledged distributed remote smart meters interfaced in a distributed control structure aided Supervisory Control and Data Acquisition (SCADA) system, thus to avoid many strategies related to power systems, improving the efficiency and eliminating the fractional losses and power theft absolutely [2]. The system may have many applications for optimizing overall energy management within the house hold consumers and industries, manages the load in the grid and prevent power demand peaks with an interface between the utility-controlled smart grid and consumers. The energy management system (EMS) provides this information to user interface and vice versa which can be communicated through a smart monitoring device [3]. Also many advanced systems and control structures have been implemented to make the system safer with an optimum performance cost effective energy conservation scheme [4].

The fractional losses include improper usage of devices, usage of faulted devices can account for 7 -28 % of the average electricity consumption. A large pool of individual products under standby mode integrally together draws a significant share of total electricity use[5]. It could be done if and only if there put into operation that quantify each and every characteristic parameter and value them in the consumer bill.

II. SMART METER AUTOMATED EMS

• Controller. Isolators CBS

Processor

Figure 1: Block Diagram

INTEGRATED

• Display • Alarm

Communication System

The five essential building blocks of any "smart" system described as shown in Figure 1. 1. A sensor system to measure the system

state/power consumption and other parameters [6].

2. Communication infrastructure to transmit data! information back and forth.

3. Distributed Control systems and intelligent programs that analyze information and generate control signals to alter the system state.

4. Actuators, distributed relays and electronic processor controls that effect the desired changes.

5. A smart meter to detect and report faults, economic load dispatch and tariff planning [7][8]. The proposed Smart Meter is an integration of an

acquisition system that monitors the power consumption for a given amount of time, a logic processor to process the data based on the required algorithm and planning, an actuator that consent to active control over consumer appliances or relays or control structures and communication system to interact with consumers or the control structures [9]. Real-time pricing of consumption will be afforded with the customers with the incentive to load shift from peak to off-peak periods (demand response) and to promote energy efficiency measures. Smart meters will automatically identify and infonn the faults, which will enable control structures to achieve necessary proceedings more instantly.

The intelligent smart metering system proposed here not only analyzes the power consumption, but it also consider various parameters like power factor , time period of usage, characteristic parameters over various conditions and time interval making the system more compatible in

the real world operation with most optimistic and cost effective operation [10]. A real-time data management between control center and consumer is made so that at regular time interval information regarding some parameters like

1. Present and Previous Meter Reading. 2. Consumption rate 3. Estimated Bill at End of Month 4. Peak consumption hours are transferred.

A distributed TLPF (Time of the day - load Power Factor) tariff control algorithm determines the tariff based on three main factors, fust user load consumption or apparent power consumption, the time and time period of consumption eg, whether the load is used on peak time or not, if yes how much time and how much load, and finally the power factor of consumption. The smart meter sends all these data to the SCADA system through GSM or internet. The Energy management system analyses all these factors together and analyses and generate a tariff plan for each consumer individually based on their consumption. It frequently sends these information to the consumer, makes user aware of the consumption level, possible tariff so that the consumers can take necessary actions to conserve energy and to control the tariff. Consumer can also take remedial actions by refonning their consumption behavior.

This data is also made available to the consumers through secure web portal where consumers can login to access their account with their login details to know their daily consumption. The consumers will also be able to see graphically exactly how much energy is used, view reports, compare the monthly consumption, thus helping to understand where and when the energy is used the most. Bill can also printed and paid with a credit card online through e-commerce server. Most exciting feature of the system is that it sends SMS to the consumer's mobile phone informing his daily or weekly or monthly energy usage according to the set preferences.

Though up to 30 % of the reactive power consumption is done by house hold customers, it is not taken up in most of the tariff plans but the proposed system. If each and every consumer succeeds to save a defmite sum of energy by reducing losses and well-intended use of device, integrating all from every consumer brings useful adaption of "integrated power additions", totting up saves a large sum of energy [11].

A. Distributed TLPF Tariff Control Algorithm

Let the Pi,tnand Qi,tn represents the real and reactive '" ·th t t' t Th power consumption lor I customer a a [me n' e

reading is taken again after some time interval at a time tn+1 where P (i,tnm+l) or Q (i,tn,"+I) represents the

power consumption of for ith customer during the time interval tn and �1+1. The apparent power consumption of ith customer Step J: Calculate Pi,tn , Qi,nt, €(i,tn)

Where Pi,tnand Qi,tnare given by

P (I,tl) P (I,t2) P (I,t3)

P (n,tl) P (n,t2) P (n,t3)

Q (I,tI) Q (I,t2) Q (I,t3)

Q (n,tI) Q (n,t2) Q (n,t3)

P (I,bt)

P (n,bt)

Q (I,tn)

Q (n,tn)

€i,tn represents the power factor of the load of for ith customer at a time tn.

€ (I,tI) € (l,t2) € (I,t3)

€ (n,tI) € (n,t2) € (n,t3)

€ (I,tn)

€ (n,tn)

Step 2: Calculate power consumption for a definite time period,

II [P (i'�l"'+I)] = [P (i'�l+I)] - [P (i'�l)] II [Q (i,tn>l1+I)] = [Q (i'�l+ 1)] - [Q (i'�l) ] II [€ (i,tn>l1+I)] = ([€ (i'�l+ 1)] + [€ (i'�l)] )/2

Step 3: Sort and group consumers As the number of consumers increases, the data

handling also increases which makes the necessity of advanced data management systems and economic data transition procedure, It may not be a necessity to monitor and analyze the data from consumers of low consumption rate, Thus the total consumers are grouped in to seven segments based on their consumption, They are Group A consumers with least consumptions «1.5 units a day), Group B with lesser consumptions (1.5 -3 units a day), Group C medium level consumptions (3-5 units a day), Group D high consumptions (5-9 units a day), Group E very high level consumptions (9-20 units a day) , Group F large level consumptions (20 - 45 units a day)and Group G very large level consumptions (>30 units a day), Time interval of data collection of a consumer increases as the consumer goes from Group A - G based on their consumption rate, This system helps to

reduce the data band width hence efficient and fast operations, II [P (i,tn>l1+I)] , II [Q (i'�l"'+I)] , gives the real and reactive power consumption of ith consumer for a time period tn to n+1 and II [Q (i,tn,n+I)] gives average power factor for these period. Each customer is assigned with a cost factor based on consumption rate. Haskell implementation is used to sort based on the power consumption based on requested factors. Step 4 : Begin iterative analysis. Let Cmaxbe the total number of consumes, initially Cmax= 0; Then Cmax is iterated for 0 to ith consumer,

For each input item Ci and Ci< Cmaxi=i+ 1 -7 For i = 0 to n: Step 5 : Determine power consumption rate and sort data if ([P (i,tn+l)] <= [P (i,tn) ]) interchange tn and tn+l For given value of i , for each value of n:O-7 n ; k-7k+ l; Step 6 : Determine power factor correction term

Mi, k+1 for 0.99 < II [€ (i,tn,n+I)] ::01

Where Mi,k gives a cost factor that decides the tariff based on the average power factor consumptions for the time period. Step 7 : Implement cost factor for each parameters. Ri= (I�=o ef[P (i, tn, n + 1)] + I�=o ev(i, tn) II [P (i,tn,n + 1)]) Ai= L�=o erf [Q (i,tn,n+I)] + I ;=0 ervf(i, tn) II [Q (i,tn,n + 1 )].

Where ef and erf is the fixed cost factor for an area/group of consumers and ev and erv is the variable cost factor for an area/group of consumers for a particular time interval tn,n+!. Mi= (L�=o - Mi,k+ I�=o - g(i, tn) Mli,k+l) Step 8 : Determine the tariff

Where Mi,k and g is fixed and variable cost factors based on power factor. Total Cost � I �=o (Ri + Ai + M)

The value of cost factors is varied based on amount of consumption, time of consumption like peak time or not or type of load etc. Step 9 : Send the information to the consumers which corrective actions and expected bill of the month. Step 10 : If Si.tn is zero or very for a longer time period, it denotes power outage, fault or power theft. Step 11 : Return initial Conditions of number of customers I and Cmax.

B. Power Theft, Safety and Load Sharing

One of the most positive advantages of the proposed system is the complete elimination of power theft. Some distributed control meters are added which continuously analyze the meter readings from all meters in an area with power supplied, and any mismatch in the data stand for the power fault or power theft and complete analyze over the meter reading of all the meters with proposed algorithm makes the SCADA systems to identify from which point the power is theft is and in a small extend what type of power theft was that. By analyzing the information from meter readings with a well­designed algorithm, the SCADA system can measure loading conditions from each distribution section. Taking into consideration of the power production capacity of various power stations and also the load demand, the designed system could intelligently estimate the mode of proportion of load sharing among the grids with less losses and maximum criteria for meeting the load. It is more effective in peak hours so that intelligent load sharing helps to meet the demand with supply based on the consumption. In addition to the metering, the smart meters integrated with intelligent control system are also interfaced with circuit breakers. In the case of some fault, overload, earth leakage, or lighting, the intelligent controller enables isolating the consumer system from the main. The same system is being used as the distributed control structures, so that in the case of any fault, irregularities, or some improper consumer behavior, or during lighting, the particular or area could be isolated without disturbing the other sectors. When a number of meters in a particular area fails, it reports a power failure, the system alert the electric utility that a power outage has occurred in that area. The Real time monitoring system will monitor instantaneous parameters like voltage, current, frequency of consumers to ensure that fair quality of electricity is delivered to the consumers.

e. Communication Interface

These communication networks can be broadly divided into l. A proprietary network wholly installed for the purpose of meter reading such as RF mesh network.

2. A pre-existing network installed network such as power line communication (PLC), which uses the existing power supply lines to transfer meter data.

Another approach is to use a third party network such a GSM network or PSTN network to read meters at preselected times and frequencies. In this case the meters can be read remotely from the utilities meter reading center at fixed time and frequencies.

DAQ

PROCES

,

r==l l hdJ

G

G

DISTRIBUTED �1YAND DATA ANALYZER !==:::::,J CON11lOL

Figure 3 : Smart meter system

III. EXPERIMENTAL MODELING OF SYSTEM Energy Research wing of Vimal Jyothi

Engineering college has set an experimental modeling of the system, using a current sensor and voltage sensor to measure the power consumption using an Atmega 32 Me. The power consumption, power factor and demand response is measured on various time intervals as per the requirements. A five set of smart meters with experimental load set up -one master meter and four individual meter -representing four individual consumers are used to the set up a model of smart grid. A GSM interface is used for communication of data. The data obtained is analyzed using Intouch software. The experiment is done for an interval of one hour with varying load level. During this complete period, each load is varied irregularly and each load power factor is set from 0.91 to 0.42for experimental calculations. All the meters are interfaced with Intouch and Matlab for the ease of data logging. Data input blocks are added in the program so that tariff cost factor of any parameter can be varied at any time without disturbing the algorithm. All the possible cases of the solutions have been verified.

IV. RESULT AND DISUSIONS

Various results and dats obtained is anaysed to obtain the charcteristics of ech consumer, feasibilty of tariff plans and other parametric behaviours. It was verified that the proposed system provides a user responcive tariff planing based on the consuption rate in least iteration.Each consumer is having different tariff rate based on their consumtion behaviour.

The SCADA inteface for controlling and displaying the tariff based on the various distributer sections, load and power factors are shown in figure 4.

. -- ,..-

Figure 4 : SCADA user interface

The SCADA interface at the control end is facilitated with many features. Each control factor can be varied individually using the interface. It is operated in both automatic mode where the system itself automatically determines the tariffs or in manual mode where the control section is adjusted the tariff rates manually with respect to the varying environment. Once the tariff rate is entered, the will automatically determines the tariff. There are sections where the tariff rates of each and every consumer are defined separately. The separate tariff rate for each and every time interval for real power, ands reactive power is adjusted individually on the sections added in the interface which makes the system convenient.

There are sections to add fixed cost factor irrespective of time for both real and reactive power consumption is facilitated. Time period of the peak load hours may be a variable one which is added in the system using data blocks as shown in figure 5. Individual user selection is done in the main control section itself. Based on the consumption, the load scheduling is done using the interface. Power theft and complete off load condition system also verified using the system.

The characteristics of user or master smartrneter or main control meter can be ploted using the SCADA inteface as shown in figure 5. An example graph on power cator based cost control in SCADA inteface is shown in fugure 6.

. ..

Figure 5: SCADA interface for TLPF

Figure 6 : SCADA user representation

The characteristics behaviors of consumers on various conditions and loads are graphically analyzed. The Figure 7 shows the power consumption of the user l over a period of 60 minutes where real power, reactive power and power factor variations is plotted as the load varies with respect to time. It was noted that for the first fifteen minutes the power consumptions is almost at off load where as for the next fifteen minutes the load is almost in medium range. For the next some time, the power consumption is comparatively large where as for last fifteen minutes; there is peak load consumption. The power factor has been found varying with respect to the load.

I 1000 ! Z

6000

t 5000

i 4000

i 3000 ,

Power Consumption

Tm"[Minul,,J

Figure 7 : Power consumption

Figure 8 shows the real power consumption of four consumers. The fourth consumer has highest

real power consumption; third consumer has lowest real power consumption. Figure 9 shows the reactive power consumption of four consumers.

RealPower{W} -Time

r""�(Mi .... I�J

Figure 8 : Real Power consumption

Reactive Power - Time

-ReKtivePo ...... rCon.u�bonofFj"t

-Re""tivePo_'Consu"",!;onofs..�ond

-ReKtivePoWl!r Consumpbon of Third

.. 6000 ! -ReacljvePo_'Con.umpl"'nofFourt�

j

T,me[Milllllel

Figure 9 : Reactive Power consumption

Figure 10 shows the apparent power consumption of four consumers. The fust consumer has done highest power consumption whereas fourth customer has third consumer has lowest power consumption. The remaining two consumers have shown average consumption.

Figure 11 represents power factor variations of the each consumer with respect to the time. The graphical analysis of all these graphs gives the characteristics power consumption behavior of each consumer. The tariff rates can be fixed based on this behavior. It is to be noted that, on each occasion, the power consumption characteristics ifs of each and every consumer is different from each other completely not only in apparent power consumption but also in real and reactive power consumption.

Apparent Power - Time

-ApPlirentPower COnsumption of First Consumer

-Apparent Power COnsumption of Second COnsumer

8000 -ApparentPower COmumption of Third Consumer

6000

nmf'{Minutrl

Figure 10 : Apparent Power consumption

Power Factor - Time

-Po�r FiKtor fanee of Fint Consumer -PowerFlKtor ranie of Second Consumer

- Power FlKtor ranie of Third Consumer -Power FlKtor ranlle of Fourth Consumer

1 3 5 7 9UUUDwnnBunUHHO_OUOUUUNHn_

Time (Minute)

Figure II : Power factor vs Time

So it is not desirable keep same tariff plan for each and every customer. Using TLPF algorithm, a new tariff structure is produced where each and every consumer has different tariff structure based on their consumption. The figure 12 shows the variation in tariff rates of fust consumer based on the power factor. As the power factor improves, the tariff rates are get reduced and such system will make the customer to use high power factor loads or create a necessity of power factor correction.

Power Factor based Tariff

i " ;

.. -

Figure 12 : Power factor based Tariff

The plot 13 represents the tariff rates of consumer 1 based on conventional fixed tariff algorithm conventional time of the day system and proposed TLPF algorithm. The plot 13 represents the tariff plans of each and every consumer based on TLPF algorithm. The fixed tariff just takes the total consumption of a user over a period and mUltiples with fixed value where as conventional ToFD system facilitate integrated power consumption bases on just power consumption and time. It can be identified that the TLPF has more accurate and favorable variation than other curves. Here along with the true power consumption, power factor and time period of load demand is also taken. Thus it gives complete and detailed information about the consumer. Each and every minute information of the consumption is noted and a perfect tariff system based on these parameters is formed. Such a system will make user to focus on even minute details of the load consumption so that he may be forced to reduce fractional losses

completely. In such a word the proposed method is a perfect system that could eliminate useless load to a great extend thus saving energy.

i !! 30 •

r""�IMinuI�1

Figure 13 : Real Power consumption

The user consumption behaviors of each and every instant are noted and tariff plans are formed for each and every consumer. It is to be pointed that the fourth customer is having highest tariff rate even though consumer has used an average rate of apparent power. It was because the reactive power consumption of the fourth consumer was much larger than others and hence the tariff rate has been increased. Though consumer three has higher true power consumption, the tariff rate was low because the load power factor was much high. It could be noted that rate of increase of tariff rate is very less on off load or light load condition. The rate is gradually increasing in a factional range up to medium range. It has an exponential variation as the load varies to peak load. Thus these make the user aware of regulating the load consumption. For the purpose these data is made available to the customer frequently",-- .�

__

Tariff Rate

Figure 14: Real Power consumption

CONCLUSION. Thus the concept of an intelligent smart energy

meter that has some control features also have been put forth and the SCADA interface is implemented. The proposed meter system identifies the consumption rate at various environments, intelligently calculates tariff rates on that, frequently informs to the consumers to avail them necessary correctable measures. It also identifies the faults and irregularities, power theft and black out so that it's

much easier way of providing a quality output economically and provide a quality improved efficient power system.

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