UMD Undergraduate Research Journal

Software Applications for the Integration of Plug-In Hybrid Electric Vehicles within the Smart Grid

Shruti Ramaswamy and Dr. Alireza Khaligh

Abstract

Smart Grid is a new solution to the aging power grid. It is a budding web of controls, automation and intelligent technologies that work together to make the grid greener, secure and reliable. Its innovative dual-direction communication between the grid and the user establishes a system of regular updates to a home that details energy consumption on a real time basis. With an increasing Plug-in Hybrid Electric Vehicle (PHEV) market, consumers have another avenue for involvement with the grid. PHEV charging is a heavy load added to a home. If this extra load is not managed properly, the effects of this pressure may prove detrimental for the consumer and may result in unwanted power outages that may affect an entire residential community. Demand Response programs can help efficiently reduce this added pressure and minimize energy costs. This research explores the idea of developing a software application to manage consumer loads dynamically. The program manages times during the day/night a PHEV can be charged such that the total house load is managed and its cost is minimized. In addition to returning specific times to charge the PHEV, the program also seeks to build a smart home that shifts loads dynamically to save the consumer energy and money. The program divides household loads into three categories - critical, deferrable and interruptible, and returns savings calculated using Real Time Pricing, Time of Use Pricing and Flat Rate Pricing options. The goal is to create a user-friendly environment for the consumer to manage energy consumption reliably, effectively and economically.

Introduction

Figure 1: Benefits of Energy Storage Systems [6]

As the world continues to catch up with changing technology, it leans closer to face its dire need for change in the current power driven market. Rigorous policies backed by environmental laws, increasing consumer concerns and demand have forced utility companies to consider updating the current power grid system from its root. Internally too, utilities are dealing with increasing problems stemming from aging equipment and limited communication between utilities and consumers. A solution, the Smart Grid is built with a vision to "connect everyone to abundant, affordable, clean, efficient, and reliable electric power anytime, anywhere" [1]. To further this idea, US Department of Energy has created the Office of Electricity Delivery and Energy Reliability to provide stronger leadership and serve as the epicenter of policy and technology development activities in the department related to the electric grid.

In order to meet growing demands and actively encourage consumer participation, America's electric system needs to be modernized and expanded by government and industry. Involvement will be accomplished using the grid's two way flow of electricity and information between consumers and utilities. Effective communication will provide consumers with up-to-date information about their energy consumption and money spent using varied price options made available to them. This new autonomy to control and change household load consumption to meet need, unwraps an assortment of Demand Response (DR) programs aimed at giving consumers options to conserve energy and shift loads that will offer them utmost savings.

Table I
Illustrates the household load profile and an illustration of how the various loads will be handled using DR.

Incentive programs and a plethora of price based programs give consumers with PHEVs more options to actively participate in programs such as load shifting that will benefit them directly.

The Smart Grid has the infrastructure to enable the efficient use of the new generation PHEVs. The electrification of the vehicle fleet can radically change our dependence on oil. PHEVs rely on battery as opposed to conventional vehicles that consume fossil fuels. While PHEVs are not pollution free, emissions from PHEVs are far less than conventional cars. 1kWh of energy used in a PHEV releases 0.69kg of carbon dioxide [12] while 1 gallon of gasoline emits 8.8kg of carbon dioxide into the atmosphere [13].

This paper explores the idea of bringing to the consumer a program that will control and give homeowners considerable control over their energy consumption. The purpose of the paper is to build a structure for the basis of the software program from root up. It begins with a brief background on the Smart Grid in "Understanding the Smart Grid." "A Smarter Home" builds a platform for the advantages of the Smart Grid to be used. A smart solution to assist communication for the consumer is proposed in "A Solution." "Preliminary Results" presents results of the basic algorithm employed to utilize demand response. Lastly, "Conclusions" and "Future Work" conclude with results and future work on the progress and expected prospects for the software application.

Understanding the Smart Grid

The vision is for the Smart Grid to be a fully automated power delivery network that supervises and controls every customer and module. Built on a basis of two-way flow of electricity and information, the Smart Grid has an inbuilt system of checks and balances between the consumer and utility companies and all components that duly fall in between the two. Its distributed intelligence coupled with Wi-Fi communications and automated system allows real time pricing options and faultless interfaces among all nodes of the electric network.

Figure 2: Household Load over 24-hour period [10]

Figure 3: Scope of This Technology

Features of the Smart Grid:
Grid Synergy: The Smart Grid is a design built to manage change dynamically. Connection between consumer and utilities will be maintained through secure links at high speed. Consumers will receive real time updates for price and energy and can thus control their energy consumption concurrently rather than having to wait for monthly updates from power companies. Utilities are already investing greatly in Smart Meters and Advanced Metering Infrastructure (AMI) as the first step to secure the prospect of two-way communication between the home and utility company [4].
An Automated System: In addition to contributing to reliable and secure elec- tricity and information, Smart Grids open up an array of possibilities for utilities and consumers. Distributed Generation (DG) at a residential level including mi- cro turbines, solar photovoltaic cells, wind turbines and grid energy storage enable increased bi-directional power flow be- tween power distributors and end-users. A smarter grid will add resiliency to our electric power system and make it better prepared to address emergencies such as severe storms, earthquakes, terrorist at- tacks and blackouts. The interactive nature of the Smart Grid will allow for automatic rerouting of information when the equip- ment fails. This will help minimize outages when they do happen.

Communications Framework: Fiber op- tics, microwave, infrared, power line carriers (PLC), wireless radio carriers such as GSM and CDMA [4], transfer massive amounts of data. Together they make up the network most communication is built on. Wireless communication will enable connections between devices, homes and utilities and information will be sent so all data may be received and managed on a real time basis. By establishing a constant requirement for communication between homes and utilities, security of information can be preserved and constantly improved.

Increased Grid Visibility: A key component of distribution intelligence is outage detection and response. Today, outages are detected based on customer phone calls from an area. Superior automation technology with the help of smart meters will enable grid operators to detect outages as instantly as power is lost. Operators can thus isolate a sector facing a power outage and send technicians to immediately fix the problem area. Another feature of this automation technology allows for newer and well-developed visualization techniques that interpret large amounts of data into information that can be easily understood by the consumer.

Figure 4: PHEV Charging Time Algorithm

Figure 5: Pictorial Representation of Coded Algorithm

Consumer Benefits from the Smart Grid:
Smart Meters provide dynamic information that gives consumers real-time updates on energy consumption and management. Dynamic monitoring of household data gives consumers instant reach to information as opposed to having to wait for monthly statements to determine usage patterns. Customers may now actively participate in three [5] ways. (1) First, customers can reduce their consumption of electricity at peak hours. By reducing their electricity, the drop in demand may be able to ease some pressure off the grid. If this action results in a significant shift in pressure at peak hours, grid operators will notice lesser demand in power that will in turn reduce over all price of power at a peak hour. (2) Secondly, the customer may be able to shift heavy power consuming loads operating at peak hours to off-peak hours. While the same amount of power is demanded off the grid, the consumer may be able to save money by operating his/ her device at hours when the system demand for power is low (3). Thirdly, a customer can alter cost significantly by onsite generation of power. Installation of solar panels and backyard wind turbines can help a customer significantly. A consumer may no longer need to alter his/her energy consumption practices according to peak hours. However, from a utilities perspective, electricity demand patterns will not see significant changes unless an entire residential community adopts onsite generation practices.

Understanding Energy Storage:
Energy storage is defined as the conversion of electrical energy from the power grid into a form that can be stored until used again when converted back into electrical energy [6]. Research in different technologies has made available an array of storage options. While energy storage is a heavily researched subject, breakthroughs made in the field have potential to heavily reduce costs and maintain stability in the power grid. Figure 1 describes the potential benefits of energy storage systems.

A Smarter Home

Building a Smart Home: This section focuses on building a home that has various appliances that can be found in a typical house. The appliances are divided into three distinct load profiles: critical, deferrable and interruptible. Loads that fall under critical loads run irrespective of time of day or peak hours. These are critical to a household and operate at all hours. Loads that fall under deferrable loads run at certain hours in continuity. They can however, be shifted to off-peak hours to reduce cost from functioning during peak hours for the homeowner. Loads that fall under interruptible loads can be run and discontinued without consequence and negligible discomfort to the homeowner.

Figure 6: Load Shifting - Demand Response Class Structure

Figure 7: Load Shifting - Demand Response Algorithm

One may note that the household demands peak load between 12pm- 3pm and between 7pm-10pm. It must also be noted that households pay more money during peak hours than off-peak hours. This sort of pricing option falls under Time of Use (TOU) or Real Time Pricing (RTP).

To keep with varied pricing options, the smart meters/advanced metering infrastructure (AMI) give consumers' constant updates on their load and price. The consumer may shift loads accordingly to off-peak hours to minimize costs on the same. The manual process of monitoring household loads may pose a series of inconveniences for the homeowner as loads may need to be turned on and off manually at all times. Load shifting is an effective method under consumer control. Figure 2 provides two distinct graphs denoting household loads prior to load shifting and an updated profile of the household load with a PHEV that does not increase with the addition of the PHEV. On the contrary, energy is curtailed due to effective load shifting. Furthermore, incentive programs, offered by utilities to actively manage load shifting give consumers enough reason to seek an effective solution. Luckily, the technology market provides a relatively easy solution. Increased knowledge on technology and DR programs can be used to build a system that can be customized as per the requirements of the homeowner. Figure 3 describes the scope of such a system that is built to benefit the consumer in numerous ways.

A Solution

The most unique quality of the Smart Grid is its establishment of the two-way information share of electricity and data. This section highlights how such a connection can be used to ease into a homeowner's life and save him/her money by executing simple shifts performed over a 24-hour period. While the future of the program will be a software application available for smart phones, it is essential to understand the similar underlying algorithm that is be displayed over a different platform to perform the same function.

Table II

Table III

A JAVA Framework: Created as a JAVA project, the framework consists of two subdivisions of classes that act as separate entities.

Figure 4 deals with PHEV charging under three pricing options- RTP, TOU and Flat rate. Divided into two classes, the PHEV Charge Controller class is the main control unit within which various aspects of the class and base class exist. The program is run with consumer input of his/ her choice of pricing option and desired time to start charge. Another input that would be required to calculate this information is the battery capacity that will be extracted from the engine combustion unit of the vehicle itself.

As the program is created using a Chevy Volt battery design, the maximum usable battery capacity is limited at 10.4kWh. It is this 10.4kWh capacity that enables the program to calculate the number of hours left for the battery to attain full charge. The instantaneous result is a price index for the selected hours and a time index that is representative of the number of hours left for the battery to attain full charge. While the controller runs the main program, primary functions to calculate hours, time and price index are handled by a helping class. Figure 5 depicts the algorithm followed in code to compute the best time to charge the PHEV under given specifications.

Figure 8: ComEd Pricing Options and PHEV charge based on scenario

Figure 6 integrates the basic algorithm, as portrayed in Figure 4, into a larger program. The larger program, a super class, deals with all of the basic appliances in a household by segregating appliances into three load profiles; critical, deferrable, and interruptible. Like its first subdivision, this part, too, is divided into classes. One class holds the control that begins the program; three other sub classes inherit methods that are used to compute answers specific to appliances concerned with each subclass.

The code uses maps (consisting of keys with values) to store appliances and their associated loads that are then transferred into a larger map that holds the time of day the appliance and its associated load at that particular time. Here, time is used as the common index of reference to attach appliances with their individual associated cost. These values are then arranged to determine cost of the household over a 24-hour period.

Figure 7 represents the algorithm of the classes covered in code to calculate savings when loads are shifted during times when prices are high which in turn helps to manage loads.

Preliminary Results

The program uses maps to read from three different pricing options namely, RTP, TOU and Flat Rate. After relevant data is accessed from the program, the consumer is prompted to input a time he/ she would like to begin charging. Based on the time, the program calculates the best time index to charge the vehicle in order to save the most money.

Table II [11] contains the assumptions made for portrayal purposes with a time and price index. The graph seen in Figure 8 is representative of the results the consumer will see with estimates on savings on price using each pricing model, thereby giving the consumer a better idea of how much he/she can save if the PHEV is charged at suggested times.

Figure 9: Household loads before any shift

Figure 10: Household Loads after Shifting Load

To demonstrate how the program will be used, the program uses a few appliances per load type. The functions are used on a smaller scale so inconsistencies can be debugged and fixed instantly. Data is divided into maps and stored as described in Table III. The following graphs in Figure 9 and Figure 10, give the reader a visual portrayal of how the code would internally shift loads if the peak load under the base case scenario (no PHEV) were exceeded.

The max peak is a line measured using load calculations from appliances alone. PHEV load is not measured in this account for peak load so as to show how a smart home may manage its appliances economically with little difference caused by a new PHEV load.

Conclusion

This paper studies the integration of technology into consumer life to aid and control residential using advanced and superior technology enabled by Smart Grid developments.

The Smart Grid is a budding web with no beginning or end. Consumer homes, generators and electrical appliances will be connected without bias so energy flow is adequately and securely maintained under all conditions. It ensures a two-way flow of electricity and information between the power plant and the smart meter, which will be installed in every home. The paper helps further the use of the smart meter by using its readings to create a scenario of a typical household. This is created to paint a realistic picture of load shifting and PHEV charging with the primary aim of minimizing cost for the consumer.

Using RTP and TOU rates from ComEd for the Chicago area, the program is built keeping the consumer's needs in mind. The Java framework, using parameters for peak price and load, is able to shift loads dynamically as per consumer convenience to determine significant savings per household.

Future Work

Need for Disaster Management: Today, interruption of electricity due to blackouts can begin a series of botches that can affect communications, signals, security and traffic [14]. In places that are too hot or cold or places that require constant heat or cold suffer greatly and in turn begin another domino effect of failures including losses in infrastructure and personal assets. A smarter grid with automated self-healing features and adaptive technology will strengthen the power grid making it more resistive to natural and man-made attacks. Such a grid will help minimize outages and minimize loses when hit.

The system is built so when an area or sector loses power, it is isolated from the rest of the grid so neighboring areas may function unaffected while the isolated sector is located and power is restored immediately. With the Smart Grid functioning as an interactive web of network and information, each household is built individually yet fully connected to other houses in the community. This way when one house loses power, it is isolated so other houses remain unaffected.

Disaster Management is an important concern that can be tackled using clean and effective programming. Figure 8 depicts a possible algorithm that a program may follow to handle stress and spontaneous failures on the grid. While all of the code is written using JAVA, future work would include working to build a smart phone application so a consumer may be able to control his/her energy consumption at the palm of his/her hand. The technology market is changing quickly and dramatically. Today, a majority of homeowners own smart phones. Hence, a free software application that enables a homeowner to minimize his/her electricity bill and manage energy with his/her smart phone should be made available as soon as possible.

Figure 11: Possible Algorithm for Disaster Management

Exploring Storage Options: Significant research is being made on energy storage integrated with the Smart Grid. Research thus far has external storages functioning as individual units to supply power only when needed and recharged at the earliest convenience. One such energy storage container is a disused PHEV battery that can be installed in a consumer home.

PHEV batteries that have end of life 80% capacity supply power seamlessly and with very little or no struggle. Benefits include: energy arbitrage, ancillary services-regulation, spinning and non-spinning reserve and back-up energy. Energy arbitrage refers to charging batteries at off-peak hours and discharging power to appliances at peak hours to utilize differences in energy prices and minimize cost. Ancillary services are divided into various classes but the most common types include regulation, spinning and non-spinning reserves.

Regulation, the highest quality ancillary services [8], is used to match the frequency and voltage of the grid by unerringly matching dynamic energy demand and supply.

Backup energy refers to using a reusable PHEV battery to supply power in case of an emergency resulting in a blackout. Although PHEV batteries may not be able to supply power to the entire household, the battery could provide enough power to run critical loads such as heating in winter months and home security.

Ethical Considerations

As engineers, we are expected to perform to our highest potential because our work and research may directly or indirectly influence future research in the field. As students, it is harder to understand the direct implications of our work in the real world. We face a constant need to push ourselves to perform to the best of our abilities and work on retrieving data that is accurate and meaningful.

The JAVA program discussed in this paper is developed using an original algorithm. That being said, there is immense room for improvement. Scenarios used to drive a point, are based on educated guesses of an average household that contains basic appliances one may not be able to do without. Thus while the program itself is not ready to be developed into a software application for smart phones, the program does provide a backbone to the algorithm that will be used to accomplish this technological goal.

Acknowledgements

This work has been supported by the U.S. National Science Foundation under Grant number 0852013, which is greatly acknowledged.

References

[1] U.S. Department of Energy. (2003, July). "Grid 2030" A National Vision for Electricity's Second 100 Years [On- line]. Available: http://www.ferc.gov/eventcalendar/ files/20050608125055-grid-2030.pdf

[2] S. Shengnan et al., "Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration," Transmission and Distribution Conf. and Expo., New Orleans, LA, 2010, pp.1-6.

[3] R. Duan and G. Deconinck, "Future electricity market interoperability of a multi-agent model of the Smart Grid," Int. Conf. Networking, Sensing and Control, Chicago, IL, 2010, pp. 625-630.

[4] C. Wei, "A Conceptual Framework for Smart Grid," Power and Energy Engineering Conf., Chengdu, CN, 2010, pp.1-4.

[5] M. Albadi and E. El-saadany, "A summary of demand response in electricity markets," Electric Power Systems Research, vol. 78, no. 11, pp. 1989-1996, Nov. 2008.

[6] A. Mohd et al., "Challenges in integrating distributed Energy storage systems into future smart grid," IEEE Int. Symp. Industrial Electronics, Cambridge, UK, 2008, pp.1627-1632.

[7] B. Kramer et al., "A review of plug-in vehicles and vehicle-to-grid capability," IEEE 34th Ann. Conf. Industrial Electronics, Orlando, FL, 2008, pp.2278-2283.

[8] X. Xi et al., "A Stochastic Dynamic Programming Model for Co-optimization of Distributed Energy Storage," unpublished, doi: 10.1287.

[9] K. Mets et al., "Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging," IEEE Network Operations and Management Symp. Workshops, Osaka, JP, 2010, pp.293-299.

[10] "RELOAD Database Documentation and Evaluation and Use in NEMS," Energy Systems Consulting OnLocation, Inc., Vienna, VA, 2001.

[11] Commonwealth Edison Company. (2010). ComeEd An Exelon Company [Online]. Available: http://www.thewattspot.com/

[12] C. Shiau. (2010, June 18). "Do More Batteries Make A Plug-in Hybrid Better? Implications from Optimal Vehicle Design and Allocation" [Online]. Available: http://eetd-seminars.lbl. gov/sites/eetd-seminars.lbl.gov/files/Shiau-LBL-PHEV-seminar-2010-06-18.pdf

[13] Environment Protection Agency. (2011, Nov.). Greenhouse Gas Emissions from a Typical Passenger Vehicle [Online]. Available: http://epa.gov/otaq/climate/ documents/420f11041.pdf