Multicast Communication for Demand Response Using LTE Technology Essay

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Multicast Communication for Demand Response Using LTE Technology

Contentss

Abstract……………………………………………………………1

1 Introduction

  1. Smart Grid
  2. Demand Response

2 Related Work

2.1 Demand Response Types

3 Multicast based Demand Response theoretical account

4 Future Work

5 Mentions

Abstraction

Smart grid improves the consumer’s ability to supervise and command their electricity usage, potentially leting for cheaper and more efficient energy usage. In this paper, we focus on using 3GPP LTE engineering for multicasting the demand response messages to the smart metres to cut down power ingestion. During peak energy ingestion period, public-service corporation centres can multicast a demand-response bid to a big group of place contraptions ( users ) inquiring them to temporarily turn off or switch to a lower power degree to cut down the power ingestion for which they get some inducements. Multicast communicating is the most promising work to advise the messages to ache grid users. Our plan aims to promote consumers to salvage the energy by utilizing existent clip information and to bring forth smart place contraptions that operate in response to electric public-service corporation rates.

Keywords: Smart Grids, Demand Response, Multicast, LTE, and OMNET++

  1. Introduction
    1. Smart Grid

In response to the worldwide challenges in the power industry, smart grid is progressively recognized as a perfect manner to better the energy efficiency of bring forthing and utilizing electricity in places, concerns, and public establishments. Smart grid agencies, “computerizing” the electric public-service corporation grid. It includes adding bipartisan digital communicating engineering to devices associated with the grid. It is capable of supervising all spheres from coevals workss to consumers’ single contraptions [ 1 ] . Each device on the web can be given detectors to garner informations ( power metres, electromotive force detectors, mistake sensors, etc. ) , plus bipartisan digital communicating between the device in the field and the utility’s web operations centre. A cardinal characteristic of the smart grid is automation engineering that lets the public-service corporation to set and command each single device or 1000000s of devices from a cardinal location. Smart metre is a good illustration of an enabling engineering that makes it possible to pull out value from bipartisan communicating in support of distributed engineerings and consumer engagement. Energy efficiency enabled by smart metre plans depend on the sum of burden consumed.

  1. Demand Response

Demand Response plan is to cut down demand during the high cost peak usage periods or switch that use to other clip. This is done by pass oning and metering engineerings to inform smart devices in the place. When energy demand is high public-service corporation centres track the sum of energy use at that peculiar clip. It besides gives public-service corporation companies the ability to cut down ingestion by pass oning to devices straight in order to forestall system overloads. For the smart grid communicating web, latency is one critical proficient demand. Smart grids measurings must be available within some hold for the grid supervisor to take real-time action on, e.g. , the distribution of the burden [ 2 ] .

A LTE based smart grid as shown in Fig.1. In smart grid, the control centre is considered as Base Station ( BS ) and electric equipment with wireless device as User Equipment ( UE ) . Each UE can pass on with BS by sharing the limited radio bandwidth. These devices send supervising informations sporadically to the local BS straight [ 3 ] .

Figure 1. LTE based smart grid

The smart grid refers to an electricity system that collects real-time informations on supply and demand to do the full grid more efficient and less prone to failure. In this paper, we propose multicast based communicating for demand response plan to advise the users to cut down the power ingestion. We have simulated our work utilizing unfastened beginning OMNET++ tool.

KEYWORDS: Smart Grids, Demand Response, Multicast, LTE, OMNET++

2. Related Work

A growingpopulation and technologycontinues to stop up more and more power-hungry appliances and contraptions into the grid.Power use in a given country fluctuates depending on clip of twenty-four hours and season of the twelvemonth. If temperatures rise and more people turn on their air conditioners, so the country as a whole requires more power. But since power workss can merely set out so much energy, sometimes the juggle act fails. In the yesteryear, the lone manner to turn to this job was merely to spread out the grid and construct more power workss, which elevates electricity costs.

World electricity ingestion in developing states is projected to lift significantly by the twelvemonth 2020. Electricity consumers are non active participants in the operations of electrical webs till now. Research has shown that important benefits may be achieved when consumers participate in such electrical web operations. In these cases consumers can cut down their electrical power use via co-ordinated demand response plans. Consumer interactions are required to accomplish maximal benefits from a demand response plan. Engagement of consumers in electrical web operations has proven its effects. The system discussed in this paper is public-service corporation company sends the multicast messages to users in instance of an DR event and supply them with inducements for take parting in the event to cut down the energy ingestion. In this manner consumers can play an active function and they can schedule their ain electrical energy use profile. This paper presents a multicast system which enables electrical public-service corporations to supply their consumers with a demand response plan.

Federal Energy Regulatory Commission ( FERC ) of the United States provides following definition of demand response [ 4 ] “Changes in electric use by demand-side resources from their normal ingestion forms in response to alterations in the monetary value of electricity over clip, or to incentive payments designed to bring on lower electricity usage at times of high sweeping market monetary values or when system dependability is jeopardized.”

Demand response plans are normally of either monetary value based signal or incentive-based plans [ 5 ] .

1 ) Price-based demand response ( besides called Time-based DR ) :

a ) Real-time pricing ( RTP )

B ) Critical-peak pricing ( CPP )

degree Celsiuss ) Time-of-use ( TOU ) duties.

2 ) Incentive-based demand response:

a ) Direct Load Control ( DLC )

B ) Interruptible/curtail able service ( I/C )

degree Celsiuss ) Demand Bidding/Buy Back

vitamin D ) Emergency Demand Response Program ( EDRP )

vitamin E ) Capacity Market Program ( CAP )

degree Fahrenheit ) Ancillary Service Markets ( A/S )

Price signal based residential demand response is discussed at length in [ 6 ] . Writers of [ 6 ] categorised monetary value signal based demand response into three manners: active, synergistic and trans-active market based. Trans-active market based demand response promises automatic and synergistic residential demand response engagement [ 6 ] . Active market based demand response is discussed in [ 7 ] . Dynamic pricing based demand response is proposed in [ 8 ] . Writers of [ 8 ] considered smart-grid architecture with control units in both public-service corporation and client sides. Power line communicating ( X-10, A-10 ) based place energy direction system is besides proposed in [ 8 ] . Price signal based demand response and control schemes are besides discussed in [ 9 ] . However, monetary value signal based demand response theoretical account fails to vouch the demand response end accomplishment and besides lacks in structural solidarity. Grid Friendly Appliances ( GFA ) can take part in demand response by reacting to frequency fluctuations. However, big geographical distances between generators and GFA tonss can do system instability [ 10 ] . Therefore in this work, residential demand response engagement potency will be analyzed under the consideration of incentive-based demand response theoretical account. In this theoretical account, tonss of several houses can be combined together under an collector company to register in a demand response plan. Aggregator bears the duty to guarantee proper demand response engagement of those houses. Benefit of residential demand response is demonstrated by simulations in [ 11 ] . Collection of big figure of houses is proposed in [ 12 ] . Aggregator Company has to guarantee the contracted sum of demand decrease during a demand response event. Challenges associated with residential demand response are chiefly following: little burden size per house, extremely stochastic nature of residential burden, single monitoring and control operating expense, deficiency of proper smart grid substructure, concern sing comfort misdemeanor etc. Despite these challenges, there is tremendous demand response potency for residential tonss if several houses can be aggregated together. Two cardinal issues must be satisfied to popularise residential demand response engagement. First is to fulfill residents’ comfort for the full demand response event continuance and the 2nd is to maintain the occupants free from the concern of inducement punishment in instance of any demand response threshold misdemeanor.

Traditional grid is non capable of affecting users in the electric web operations. Traveling towards to SG by utilizing DR plans we can better the consumer engagement in the power decrease to do grid into greener web.

3 RESEARCH WORK

The success of smart grid mostly depends on the consumer engagement and involution of divergent web. Furthermore, one can work on the choice and grouping of consumers to direct Demand response message. Besides, we can farther work to do the communicating system more dependable, efficient and sustainable. It should be upgraded to well map with the modern heterogenous networking system.

The construct of smart grid represents the enrichment of the bing power grid with computer science and communicating capablenesss such that it enables the intelligent interaction between all entities in the grid. Therefore, communicating will play a critical function in the following coevals of electric grid. Several communicating engineerings can be used in the smart grid, however, 3GPP Long Term Evolution ( LTE ) plays an of import function.

Demand Response ( DR ) is one of the cardinal functionality of the SG that allows the consumers to cut down the energy ingestion in the peak burden hours or switch that use to off-peak hours. SG Communication need to be more efficient manner to direct the information in such a manner that there will be no hold in directing informations and latency should be low to take existent clip action [ 13 ] . 3GPP LTE engineering is the most promising radio engineering, which can be used to back up the SG communicating [ 14 ] . For DR messages we chiefly concentrate on the hold and support of the figure of devices that can be easy achieved by utilizing LTE engineering [ 15 ] . Table. 1. Shows the communicating demands for DR messages [ 16 ] .

The aim of our research is to direct the multicast messages to the residential users to cut down the power ingestion when the power demand additions for the peculiar clip. We can see the theoretical account diagram in Fig.2. To accomplish this undermentioned undertakings are performed.

  • Choose the user who responds at the maximal degree for energy economy at that peculiar clip in DR plans.
  • Cluster the users in such a manner that maximal chance of reacting to power salvaging messages.
  • Choose the hold tolerant devices.

We are sing the undermentioned properties for grouping the residential users for hold tolerant devices.

  • Power use
  • User response factor

We have made the multicast group based on the power use that who is utilizing the power above the threshold value, and who are reacting ever to DR messages. We make the dynamic multicast group to direct the DR power decrease messages. If the power decrease from the users is non run intoing our DR demand value so we create following group of users to direct the multicast messages.

Application Bandwidth Rotational latency
Demand Response 14Kbps – 100Kbps per node/device 500ms – several proceedingss

Table. 1. Smart Grid Functionalities and Communication Needs

Fig. 2. Smart Grid Communication In Residential Area Network

In the proposed theoretical account we are grouping the users based on the history of the power use and the Demand response engagement index () . Smart metre studies power use sporadically to the public-service corporation by this public-service corporation can hold power use of users merely before the demand response plan and besides public-service corporation can hold the DR engagement history for each user. Based upon these values we calculate the user rank index () for each user as follows.

= ( Phydrazoic acid) /PT+ ( Nhydrazoic acid) /NT( 1 )

 

Where Phosphorushydrazoic acid: Power used by user N

PhosphorusT: Entire power demands to be reduced

Nitrogenhydrazoic acid: Entire Numberss of times user participated in demand response plan.

NitrogenT:Entire figure of recent demand response plans.

( Nhydrazoic acid) /NT( 2 )

We get the user rank index () from ( 1 ) and arrange users from highest to lowest value of user rank index. From the above computation we group the users into different groups and send the DR message to the first group of users. After directing message to users if they cut down the power ingestion as required so we stop directing the multicast messages otherwise once more we send the message for power decrease. Based on the user grouping we proposed the undermentioned algorithm, which is shown in Fig. 3.

We are utilizing OMNeT++ simulation theoretical account. The execution can be done utilizing the SimuLTE and INET model which contains well-tuned theoretical accounts for several wired and wireless networking protocols such as TCP, OSPF, Ethernet, and WLAN.

We have considered Base station as a public-service corporation centre and users as smart metres. For power accommodations public-service corporation centre sends multicast messages in downlink to Smart metres and smart metre sends smart metre readings in uplink. We have created our application SGDR ( SmartGridDemandResponse ) message in the simuLTE theoretical account and sent the multicast messages. We have calculated the hold latency for our messages

Future Work:

  1. Decision

We will work on this job farther, following the suggested solution and continue harmonizing to the flow chart. We have to imitate and look into for the consequences. Graphs will be drawn and compared utilizing these consequences. This strategy will assist us to acquire more than 80 % truth.

The intent of this article is to show the thought of smart grid, mentioning to ache coevals, supply and ingestion between energy providers and consumers. Promoting people to better understand and usage engineerings to assist command energy use throughout the system to win success finally. It shows a way to salvage and cleverly utilize electric energy. Both provider and consumers act and acquire benefits. This helps us to work out the job of overconsumption and power failure due to overload. Furthermore, it makes the smart grid a dependable efficient, secure and optimized in operation in the long tally.

4. Reference

[ 1 ] Peng Cheng, Li Wang, Bin Zhen, and Shihua Wang, “Feasibility Study of Applying LTE to Smart Grid, ” IEEE First International Workshop on Smart Grid Modeling and Simulation ( SGMS ) – at IEEE SmartGridComm 2011

[ 2 ] YuzheXu, Carlo Fischione, “REAL-TIME Scheduling IN LTE FOR SMART GRIDS, ” Proceedings of the fifth International Symposium on Communications, Control and Signal Processing, ISCCSP 2012, Rome, Italy, 2-4 May 2012

[ 3 ] TAN Chong1, YU Kai1, CHEN Shi2, SU Han3, WANG Haifeng2, BU Zhiyong1, “Distributed Channel Allocation Scheme based on LTE System in Smart Grid, ”

[ 4 ] FERC, “Report on demand response and advanced metering, ” [ Online ] . Available: hypertext transfer protocol: //www.ferc.gov/industries/electric/indus-act/demand-response/dem-res-adv-metering.asp. ( Retrieved in Feb 2012 ) .

[ 5 ] J. Berardino and C. Nwankpa, “ Dynamic Load Modeling of an HVAC Chiller for Demand Response Applications, ” Smart Grid Communications ( SmartGridComm ) , 2010 First IEEE International Conference, vol. , ordinal number, pp.108-113, 4-6 Oct. 2010.

[ 6 ] J.C. Fuller, K.P. Schneider, and D. Chassin, “ Analysis of Residential Demand Response and double-auction markets, ” Power and Energy Society General Meeting, 2011 IEEE, vol. , ordinal number, pp.1-7, 24-29 July 2011.

[ 7 ] K.P. Schneider, J.C. Fuller, and D. Chassin, “ Analysis of distribution degree residential demand response, ” Power Systems Conference and Exposition ( PSCE ) , 2011 IEEE/PES, vol. , ordinal number, pp.1-6, 20-23 March 2011.

[ 8 ] A. Cuevas, C. Lastres, . Caffarel, R. Mart nez, and A. Santamar a, Following coevals of Energy Residential Gateways for Demand Response and Dynamic Pricing, ” Consumer Electronics ( ICCE ) , 2011 IEEE International Conference, vol. , ordinal number, pp.543-544, 9-12 Jan. 2011.

[ 9 ] Ying Li and Mark Trayer, “ Automated residential demand response: Algorithmic deductions of pricing theoretical accounts, ” Consumer Electronics ( ICCE ) , 2012 IEEE International Conference, vol. , ordinal number, pp.626-629, 13-16 Jan. 2012.

[ 10 ] S. Tiptipakorn, and Wei-Jen Lee, “ A Residential Consumer-Centered Load Control Strategy in Real-Time Electricity Pricing Environment, ” Power Symposium, 2007. NAPS ’07. 39th North American, vol. , no. , pp.505-510, Sept. 30 2007-Oct. 2 2007.

[ 11 ] Yanyan Li, M.M. Olama, J.J. Nutaro, “ Frequency moving ridges, Grid Friendly Appliances and geographic bounds in a smart grid, ” Innovative Technologies for an Efficient and Reliable Electricity Supply ( CITRES ) , 2010 IEEE Conference, vol. , ordinal number, pp.220-224, 27-29 Sept. 2010.

[ 12 ] C. Roe, S. Meliopoulos, R. Entriken, S. Chhaya, “ Fake demand response of a residential energy direction system, ” Energytech, 2011 IEEE, vol. , ordinal number, pp.1-6, 25-26 May 2011.

[ 13 ] TAN Chong1, YU Kai1, CHEN Shi2, SU Han3, WANG Haifeng2, BU Zhiyong1, “Distributed Channel Allocation Scheme based on LTE System in Smart Grid, ”

[ 14 ] Muhammad Basit Shahab, Asif Hussain, and Muhammad Shoaib, “Smart Grid Traffic Modeling and Scheduling Using 3GPP LTE for Efficient Communication with Reduced RAN Delays” TSP 2013: 263-267

[ 15 ] J. Berardino and C. Nwankpa, “ Dynamic Load Modeling of an HVAC Chiller for Demand Response Applications, ” Smart Grid Communications ( SmartGridComm ) , 2010 First IEEE International Conference, vol. , ordinal number, pp.108-113, 4-6 Oct. 2010.

[ 16 ] Communication Requirements of Smart Grid Technologies, ” US Department of Energy, Oct. 5,2010, hypertext transfer protocol: //www.greendmv.org/reports/Smart_Grid_Communications_Requirements_Report.pdf

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