SMART GRID FUNCTIONALITIES AT DISTRIBUTION LEVEL

International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ SMART GRID FUNCTIONALITIES AT DISTRIBUTION LEVEL P Ajay Sai Kiran1 ,Dr. B. Loveswara Rao2 , 1 Research Scholar, Department of EEE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur,Andhra Pradesh, India 522502 2 Professor, Department of EEE, Koneru Lakshmaiah Education Foundation,Vaddeswaram, Guntur,Andhra Pradesh, India-522502 1 [email protected],2 [email protected] May 22, 2018 Abstract The smart grid is the evolving technology with advancements in Automation, control, IT and IoT systems that enables real-time monitoring and control of power flow from sources of generation to sources of consumption. A set of technologies enable these functionalities and help manage the demand for electricity in a reliable and sustainable manner. Key Words:AMI,SCADA/DMS,Substation automation, distribution automation ,GIS,EV Charging infrastructure,solar roof top PV. 1 I. INTRODUCTION Smart grid is an electricity grid with communication, automation and IT systems that enable real-time monitoring and control of bidirectional power flows and information flows from points of generation to points of consumption at the appliances level.It is a shift 1 International Journal of Pure and Applied Mathematics from centralized generation decentralize generation is happening. The traditional boundaries between generation, transmission, and distribution are fast disappearing and the grid is evolving into an integrated smart grid, a unique solution which integrates all Type of power generation and helps the consumer becomes a producer and consumer (prosumer). Each household will be able to generate and store electricity for its own use or sell it to the grid. Smart grid technologies can empower customers with real-time control and the choice to generate, store and consume electricity at the lowest cost available or sell it to the grid during the surplus generation while ensuring high quality and availability of power. With the help of programs like Demand Response (DR), customers can change their consumption patterns by shifting their consumption from expensive peak hours to cheaper off-peak hours making the power flow more interactive, efficient, more environment and customer friendly. 2 SMART GRID ANALOGY WITH HUMAN BODY Fig1. The analogy to Human Body Key components to make an existing grid smarter is to have two-way communicable sensors to monitor and control power flows in real time and IT systems to process the data captured and issue commands and alerts. The analogy of human body to smart grid system can be explained as the brain will carry data of two-way communication and Nerves will carry frequency and muscle will carry a megawatt 3 KEY COMPONENTS OF SMART GRIDS Fig.2. Key components and functionalities of smart grid 2 Special Issue International Journal of Pure and Applied Mathematics 3.1 Supervisory Control And Data Acquisition System (SCADA): Extra High Voltage (EHV) transmission network (110kV and above) was traditionally smart or intelligent with automation and real-time communication systems integrated for system operations. The load dispatch centers or control centers of EHV systems have Supervisory Control and Data Acquisition (SCADA) and Energy Management System (EMS) which helps monitor and control the power flows in real-time. In order to facilitate the functioning of SCADA / EMS, the EHV network has dedicated communication systems between the control center and all generating stations and EHV Substations. From the control center, the operators can control generation as well as loads at the substations. SCADA OVERVIEW: SCADA collects all the information related to the operation of the grid from various sensors placed in different points and sends those data to master computer and data such collected will be analyzed in real time. 3.2 ENERGY MANAGEMENT SYSTEM (EMS) Energy Management System (EMS) is a tool used by grid operators for monitoring, controlling and to optimize the characteristics of generating/transmission systems. Functions of EMS: • Real-time network analysis and contingency analysis • Study functions like power flow, power factor, security enhancement etc 3 Special Issue International Journal of Pure and Applied Mathematics • Real-time generation functions allows the operator to monitor, analyze and control real-time generation and automatic generation control (AGC) • Economic dispatch directs the dispatcher to set economic base point for a set of units selected. • Reserve monitoring for calculating spinning reserve, operating reserve and regulating reserve • Production costing calculates the current cost of generating power of online units • Load forecasting • Transaction scheduling. Advanced functionalities: • Enhancement in grid reliability • Increased grid capacity • Advanced contingency awareness • Decreased system support cost • • A secure system that meets regulatory requirements EMS works along with a SCADA system and EMS helps the control room operator to manage the transmission System operation efficiently and economically. 3.3 WIDE AREA MONITORING SYSTEM (WAMS): With the deployment of Phasor Measurement Units (PMU), a fast and accurate measurement of grid equipment is possible. Real-time wide area monitoring applications have strict latency requirements in the range of 100 milliseconds to 5 seconds. A fast communication infrastructure is needed for handling the huge amounts of data from PMUs. Smart grid applications are designed to exploit this high throughput real-time measurements. While SCADA data is collected in 1- 5 seconds, PMU data is captured in milliseconds. SCADA data has no timestamps but PMU data is accurate time stamped. While SCADA is like an X-Ray, PMU Data is like an MRI scan of the grid. 3.4 DISTRIBUTION AUTOMATION (DA): Distributed Automation (DA) alludes to different computerized control strategies that upgrade the execution of energy dispersion 4 Special Issue International Journal of Pure and Applied Mathematics organizes by enabling individual gadgets to detect the working states of the lattice around them and influence changes in accordance with enhance the general energy to stream and streamline execution. In the present situation, lattice administrators in concentrated control focuses recognize and break down their energy framework physically and intercede by either remotely enacting gadgets or dispatching an administration expert. DA can be a basic part in blackout counteractive action. The sensors and interchanges related with DA can give early recognition of the gadgets that won’t not work legitimately, consequently enabling the utility to supplant those gadgets previously a through and through disappointment happens. DA is viewed as the center piece of a brilliant lattice, connecting with all other savvy framework applications and making the network more proficient and solid. DA empowers Renewable Energy (RE) by progressively changing appropriation controls to suit fluctuation, control inclining and bi-directional power streams. 3.5 SUBSTATION AUTOMATION: Substation Automation (SA) system enables an electric utility to remotely monitor, control and coordinate the distribution components installed in the substation. SA has been focused on automation functions such as monitoring, controlling, and collecting data inside the substations. SA overcomes the challenges of long service interruptions due to several reasons such as equipment failures, lightning strikes, accidents and natural catastrophes, power disturbances and outages in substations. The main component of SA is digital (or numeric) relays and associated communication systems which can be operated remotely. 3.6 ADVANCED METERING INFRASTRUCTURE (AMI): Advanced Metering Infrastructure (AMI) or Smart Metering comprises of Smart Meters, Data Concentrator Units (DCUs)/gateways /routers/access points, Head End System (HES), Meter Data Management System (MDMS) communicating over bi-directional Wide Area Network (WAN), Neighborhood Area Network (NAN)/Field 5 Special Issue International Journal of Pure and Applied Mathematics Area Network (FAN) and Home Area Network (HAN). Multiple smart meters can connect to a DCU/gateway/router/access point which in turn sends aggregated data to the HES. The smart meter can also directly communicate with the HES using appropriate WAN technologies (for example GPRS sim cards in the smart meters can directly send data to the HES on servers in the control room). The Meter Data Management System (MDMS) collects data from the HES and processes it before sharing with billing system and other IT applications. Appliances such TV, fridge, air conditioners, washing machines, water heaters etc can be part of the Home Area Network (HAN). At the heart of AMI, is the Smart Meter. The key features that make a meter smart are the addition of a communication module capable of two-way Machine to Machine (M2M) communications and a remote connect/disconnect switch. A smart meter is an electronic device that records consumption of electric energy in intervals of an hour or less and communicates that information at least daily back to the utility for monitoring and billing. Smart meters enable two-way communication between the meter and the computers in the utility control center. Smart Meters usually have real-time or near real-time sensors, power outage notification, and power quality monitoring features. 3.7 ELECTRIC VEHICLES: Transportation accounts for 30% of the world energy consumption and nearly 72% of global oil demand. As the oil reserves are declining day-by-day and large emissions of C02 Gases into the environment which causes environmental issues. The total world is now looking forward to accepting new mode of transportation which is free from all the disadvantages that have been discussed in the above statements. Although variety technologies and fuels are being developed, EVs represent one of the most promising technologies. SCOPE OF RESEARCH After discussing many components and functionalities of the smart grid, the area of interest of many researchers will on a particular component. In this paper, the complete focus will be on Electric Vehicles and different charging topologies that have been adopted up to now. 6 Special Issue International Journal of Pure and Applied Mathematics Fig.3.A basic block diagram of a bi-directional EV The above block diagram explains how these EVs are interconnected to grid in charging process. The batteries are initially charged from the grid and depending upon the capacity the battery power will be used for propulsion. Once the energy is utilized and left out of some energy in the battery with the help of Bi-Directional converters the energy left out will be sent back into the grid. Many types of research have been going on how the charging has to be done in an optimized manner so the reliability of the electric vehicle will increase. There are many ways to charge an EVs they are discussed in section-V and VI which deals with the necessity of EVs and different topologies that are adapted and that are ready to be adapted. 4 A NEED OF ELECTRIC VEHICLES Transportation accounts for 30% of the world energy consumption and nearly 7% of global oil demand. As the oil reserves are declining day-by-day and large emissions of C02 Gases into the environment which causes environmental issues. The total world is now looking forward to accepting new mode of transportation which is free from all the disadvantages that have been discussed in the above statements. Although variety technologies and fuels are being developed, EVs represent one of the most promising technologies. EVs have a great scope of innovation and creation of new advancements in the industry which may increase the employment opportunities and make the economy stronger. Ev also has the ability to be an independent distributed energy source for the grid. 7 Special Issue International Journal of Pure and Applied Mathematics 5 BASIC ELECTRIC VEHICLE TECHNOLOGY An EVs uses chemical energy stored in a rechargeable battery and converts it into electrical power the motor which is the source of propulsion of the vehicle. In case of internal combustion engine it uses fuel (petrol/diesel) for propulsion, These batteries are charged via grid when electric vehicles are plugged into it and they can also be charged through regenerative braking. EVs are classified depending upon the topologies they are 1. Battery Electric Vehicles (BEV). 2. Hybrid Electric Vehicles (HEV). 3. Plug-in Electric Vehicles (PHEV). 4. Fuel cell Electric Vehicles(FCEV). The classification of different types of Vehicles with different types of topologies has been discussed comparing different criteria like charging method, tailpipe emission, the power required for propulsion and finally the challenges that are faced in that particular topology. Table.1. Comparison of Various EV technologies. 8 Special Issue International Journal of Pure and Applied Mathematics Fig.4. Schematic diagram of EVs as a distributed energy system. 6 BATTERY TECHNOLOGIES, CHARGING INFRASTRUCTURES AND INTERNATIONAL STANDARDS Batteries are the important component in an EV and constitute approximately 50-60% of the cost of the EV.The battery should be robust and able to support the propulsion of the vehicle with its capacity. 9 Special Issue International Journal of Pure and Applied Mathematics Now a days Lithium Ion Batteries are used in all-electric vehicles. There are different batteries chemistries in the LIB family but the ones popular with most EV manufacturers are: 1. Lithium Iron phosphate oxide or simply LFP for Lithium Ferro Phosphate. 2. Lithium Titanate Oxide(LTO). 7 CHARGING INFRASTRUCTURE. The EV Charging comprises of the following: 1. Electric Supply infrastructure-The components which are required to supply reliable electrical energy for charging of batteries such as Transformers, meters and all the existing grid system. 2. Electric vehicle supply system: It includes many efficient charging methods and other services between EVSE Owners and EV drivers. 3. EVSE Integration with a grid to act as a distributed generating system. There is two basic EV charging methods they are wired charging and wireless charging. Wireless charging is still at early stage, so we consider charging methods in this paper as wired charging.EVs are categorized depending upon the current flow and power capacity at which the battery charges. As batteries require DC to charge up but grid supplies AC .So, there is need of an AC-DC converter in order to convert the required input to battery. Simultaneously, Grid operates in AC nature and there is a need of DC-AC converter in order to utilize the charge which has been left out in the battery. Table.2.Different EVSE types for AC and DC, their voltages and currents. 10 Special Issue International Journal of Pure and Applied Mathematics HDV: Heavy Duty Vehicle; LDV: Light Duty Vehicle Fig.5.Charging Standards for AC and DC infrastructure. 8 OPTIMAL CHARGING METHODS. Charging of battery plays very critical role in EVs as transportation distance depends upon the level of charging and PEHVs has a advanced batteries of storage of 4 to 15 kWh capacity giving the vehicle an electric in range of 15-80 km. Now we will discuss about charging methods that has been adopted without disturbing the existing grid as the charging of vehicles has a huge impact on grid, because load curves cannot estimate how many number of vehicles will be connected to grid at a time. 11 Special Issue International Journal of Pure and Applied Mathematics There are many smart charging strategies in connecting grid-tovehicle and vehicle-to-grid. The charging method will control the charging time and speed of an Vehicle,which have direct impact on the load demand and hence distribution transformer and lines.As,a impact of EVs on the grid there will be a restriction on the penetration of EVs.However studies [12]-[21] have not taken the impact of loading limit The role of an aggregator which coordinates the charging of PEVs to the distribution system. 9 CONCLUSION The functionalities of different components in a smart grid at distribution level has been discussed when there is a two way communication between the consumer and operator the conventional grid will be classified as smart grid .In this paper main concentration has been focused on the Plug-in Electric Vehicle (PEHV) which has more impact on the existing grid when they are connected during charging period. They are charged maximum upto their state of charge(SOC) and there is a chance for these consumers of PEHV to become PROSUMERS by giving back the charge left out with them. Due to this concept of V2G there is a need of Bi-directional converter which will act like an AC-DC & DC-AC converter, When this is interconnected to grid due to power electronic converters there is a chance of Power Quality issues which is another parameter which is seriously to be considered. References [1] C. Liu, K. T. Chau, D. Wu, and S. Gao, Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle, and vehicle-to-grid technologies, Proc. IEEE, vol. 101, no. 11, pp. 24092427, Nov. 2013. [2] M. Yilmaz and P. T. Krein, Review of the impact of vehicle-togrid technologies on distribution systems and utility interfaces, IEEE Trans. Power Electron., vol. 28, no. 12, pp. 56735689, Dec. 2013. 12 Special Issue International Journal of Pure and Applied Mathematics [3] D. Wu, D. C. Aliprantis, and L. Ying, Load scheduling and dispatch for aggregators of plug-in electric vehicles, IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 368376, Mar. 2012. [4] J. Huang, V. Gupta, and Y. F. Huang, Scheduling algorithms for PHEV charging in shared parking lots, in Proc. Amer. Control Conf. (ACC), Montreal, QC, Canada, 2012, pp. 276281. [5] W. Su and M. Y. Chow, Performance evaluation of an EDAbased largescale plug-in hybrid electric vehicle charging algorithm, IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 308315, Mar. 2012. [6] E. Sortomme, M. M. Hindi, S. D. J. MacPherson, and S. S. Venkata, Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses, IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 198205, Mar. 2011. [7] S. Han, S. Han, and K. Sezaki, Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: Case studies for market participation, IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 632641, Dec. 2011. [8] W. Kempton and J. Tomic, Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy, J. Power Sources, vol. 144, no. 1, pp. 280294, 2005. [9] S. Han, S. Han, and K. Sezaki, Development of an optimal vehicleto- grid aggregator for frequency regulation, IEEE Trans. Smart Grid, [10] vol. 1, no. 1, pp. 6572, Jun. 2010. [11] B. Khorramdel, H. Khorramdel, J. Aghaei, A. Heidari, and V. G. Agelidis, Voltage security considerations in optimal operation of BEVs/PHEVs integrated microgrids, IEEE Trans. Smart Grid, vol. 6, no. 4, pp. 15751587, Jul. 2015. [12] S. Rezaee, E. Farjah, and B. Khorramdel, Probabilistic analysis of plug-in electric vehicles impact on electrical grid through homes and parking 13 Special Issue International Journal of Pure and Applied Mathematics [13] lots, IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 10241033,Oct. 2013. [14] B. Jiang and Y. Fei, Decentralized scheduling of PEV on-street parking and charging for smart grid reactive power compensation, in Proc. IEEE PES Innov. Smart Grid Technol. (ISGT), Washington, DC, USA, 2013, pp. 16. [15] Y. He, B. Venkatesh, and L. Guan, Optimal scheduling for charging and discharging of electric vehicles, IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 10951105, Sep. 2012. [16] Z. Tan, P. Yang, and A. Nehorai, An optimal and distributed demand response strategy with electric vehicles in the smart grid, IEEE Trans. [17] Smart Grid, vol. 5, no. 2, pp. 861869, Mar. 2014. [18] K. Clement-Nyns, E. Haesen, and J. Driesen, The impact of vehicleto- grid on the distribution grid, Elect. Power Syst. Res., vol. 81, no. 1, pp. 185192, Jan. 2011. [19] L. Jian, Y. Zheng, X. Xiao, and C. C. Chan, Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid, Appl. Energy, vol. 146, pp. 150 161, May 2015. [20] J. Tan and L. Wang, Integration of plug-in hybrid electric vehicles into residential distribution grid based on two-layer intelligent optimization,IEEE Trans. Smart Grid, vol. 5, no. 4, pp. [21] 17741784, Jul. 2014. [22] L. Jian, X. Zhu, Z. Shao, S. Niu, and C. C. Chan, A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids, Energy Convers.Manag., vol. 78, pp. 508517, Feb.2014. [23] T. Ma and O. A. Mohammed, Optimal charging of plug-in electric vehicles for a car-park infrastructure, IEEE Trans. Ind. Appl., vol. 50, no. 4, pp. 23232330, Jul./Aug. 2014. 14 Special Issue International Journal of Pure and Applied Mathematics [24] A. Mohamed, V. Salehi, T. Ma, and O. Mohammed, Real-time energy management algorithm for plug-in hybrid electric vehicle charging parks involving sustainable energy, IEEE Trans. Sustain. Energy, vol. 5, no. 2,pp. 577586, Apr. 2014. [25] N. Neyestani, M. Y. Damavandi, M. Shafie-Khah, J. Contreras, and J. P. S. Catalo, Allocation of plug-in vehicles parking lots in distribution systems considering network-constrained objectives, IEEE Trans.Power Syst., vol. 30, no. 5, pp. 26432656, Sep. 2015. [26] M. Shafie-khah, N. Neyestani, M. Y. Damavandi, F. A. S. Gil, and J. P. S. Catalo, Economic and technical aspects of plug-in electric vehicles in electricity markets, Renew. Sustain. Energy Rev., vol. 53,pp. 11681177, Jan. 2016. [27] E. Veldman and R. A. Verzijlbergh, Distribution grid impacts of smart electric vehicle charging from different perspectives, IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 333342, Jan. 2015. [28] C. Hutson, G. K. Venayagamoorthy, and K. A. Corzine, Intelligent scheduling of hybrid and electric vehicle storage capacity in a parking lot for profit maximization in grid power transactions, in Proc. IEEE Energy 2030 Conf., Atlanta, GA, USA, 2008, pp. 18. [29] W. Su and M. Y. Chow, Investigating a large-scale PHEV/PEV parking deck in a smart grid environment, in Proc. North Amer. Power Symp. (NAPS), Boston, MA, USA, 2011, pp. 16. [30] S. Rajakaruna, F. Shahnia, and A. Ghosh, Plug In Electric Vehicles in Smart Grids: Charging Strategies. Singapore: Springer, 2015. [31] A. H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid, IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 320 331, Dec. 2010. 15 Special Issue International Journal of Pure and Applied Mathematics [32] Land Transport Authority Singapore, Singapore Land Transport Statistics in Brief. Accessed on Jan. 10, 2015. [Online]. Available: http://www.lta.gov.sg/ [33] K. N. Kumar, B. Sivaneasan, P. H. Cheah, P. L. So, and D. Z. W. Wang,V2G capacity estimation using dynamic EV scheduling, IEEE Trans.Smart Grid, vol. 5, no. 2, pp. 10511060, Mar. 2014. [34] M. Huber, A. Trippe, P. Kuhn, and T. Hamacher, Effects of large scale EV and PV integration on power supply systems in the context of Singapore, in Proc. 3rd IEEE PES Int. Conf. Exhibit. Innov. Smart Grid Technol. (ISGT Europe), Berlin, Germany, 2012, pp. 18. [35] W. Kempton and J. Tomic, Vehicle-to-grid power fundamentals: Calculating capacity and net revenue, J. Power Sources, vol. 144, pp. 268279, Jan. 2005. [32] A. J. Wood and B. F. Wollenberg, Power Generation, Operation, and Control. Hoboken, NJ, USA: Wiley, 1996. [36] D. Singh, R. K. Misra, and D. Singh, Effect of load models in distributed generation planning, IEEE Trans. Power Syst., vol. 22, no. 4, pp. 22042212, Nov. 2007. [37] W. H. Kersting, Distribution System Modeling and Analysis. Boca Raton, FL, USA: CRC Press, 2002. [38] Energy Market Company Price Information. Accessed on Apr. 29, 2015. [Online]. Available: https://www.emcsg.com/ MarketData/PriceInformation [39] S. Shafiee, M. Fotuhi-Firuzabad, and M. Rastegar, Investigating the impacts of plug-in hybrid electric vehicles on power distribution systems, IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 13511360, Sep. 2013. [40] N. G. Omran and S. Filizadeh, Location-based forecasting of vehicular charging load on the distribution system, IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 632641, Mar. 2014. 16 Special Issue International Journal of Pure and Applied Mathematics [41] Energy Information Administration, U.S. Department of Energy. (Jul. 2013). International Energy Outlook 2013 With Projections to 2040. [Online]. Available: https://www.eia.gov/forecasts/ ieo/pdf/0484(2013).pdf. [42] Wang,Yannan,Pradeep Yemula,and Anjan Bose.Decentralized Communication and Control systems for Power System Operation,IEEE Transcations on smart grid,2015. 17 Special Issue