Power Sensor Applications in a Load Management Network for a Residential Microgrid

Philip Diefenderfer (Student Member IEEE)Electrical Engineering Department

Bucknell UniversityLewisburg, PA USA

philip.diefenderfer@bucknell.edu

Peter Mark Jansson, PhD. (Senior Member IEEE)Electrical Engineering Department

Bucknell UniversityLewisburg, PA USA

peter.jansson@bucknell.edu

Abstract—As we continue to push the limits of technology further, our power grid is struggling to keep up with the technological advances. This project is about the design and implementation of a residential microgrid which is the future of smart grid technology. By definition, a microgrid has generation resources and loads which will need to be balanced using power sensors and load management in order to ensure stable operation. Using Power Line Communications, this project will implement a smart metering infrastructure to allow for remote observation of the sensors and controls. Combining the power sensor networking infrastructure with a PV array and a Natural Gas Generator will give this microgrid smart controls that can respond to both the RTP market and both changing power grid and environmental conditions based on user set parameters and system sensors. Finally, this system will be introduced to an existing home to aid the homeowner in the identification of large drains of power using the power sensors in the home, monitor environmental conditions in the home through environmental sensors, and control both electrical loads and generation resources to lower energy costs and waste while being able utilize excess energy to back feed the power grid. (Abstract)

Index Terms—Microgrid, Smart Metering, Power meters, power sensors, Sensor Network, Power Line Communication, Load Management, Real Time Pricing. (key words)

I. INTRODUCTION

A microgrid is a collection of electricity generators and loads that share a common AC bus that can be isolated from the power grid while remaining operational. A microgrid will have to be able to balance its loads to its generation resources and in order to do that a sensor network to monitor and control both the loads and generators will be required. This project is the design of a residential microgrid that will have generation sources in the form of a natural gas generator and a photovoltaic array and the loads will be the home’s loads. This network of sensors will measure the power consumption of the loads and control both the generators and loads so the generation can match the loads while isolated. While the microgrid is interconnected to the power grid, it will still monitor the power consumption of the loads on the sensor network as well as the power generated internally. This network of power sensors will allow for a home owner to monitor the power usage throughout their home to identify power draws and to remotely control these monitored loads. Overall this microgrid will provide benefits to both the

homeowner and the local utility through its ability to isolate itself from the grid and act as a distributed generator.

II. BACKGROUND

Historically, many projects have been completed to either create a smart metering network or an appliance control network. One of the most common systems that have been implemented is a sensor network connected through a Zigbee (IEEE 802.15.4) wireless network. Some of these projects are implemented as a network of power sensors and while others use the network for controls. All of these systems have a network of Zigbee modules acting as nodes with a base station that then implements the main control system. Most of these then aggregate the data collected by the sensors to the user through the internet [1] [2] [3]. One such project used Zigbee to both collect data from sensors and to send commands to the load management relays [4]. Wi-Fi has also been used in the past for power sensor networks. Wi-Fi is a preferred solution since many locations already have Wi-Fi that the sensors can connect through [5], but the sensors are more difficult to implement requiring a network stack and security to be implemented [6]. This increases the complexity and some may determine the benefits don’t outweigh the increased complexity.

Another common communication infrastructure used by many systems is Power Line Communication (PLC). PLC has been used both to deliver power sensor results and for control of load relays. One such system implemented a smart metering network using PLC to transmit collected data about power usage back to a base station [7]. Another project used a PLC network to control lights and HVAC in a hotel room rather than to record the power usage of connected loads [8]. Some systems use PLC for both power metering and load control. One such system used power meters to record the energy usage from two loads and controlled a set of SSRs to allow for remote operation of the outlets through a PLC interface to a web enabled base station [9].

Deregulation of the power Industry in the United States has led to the creation of a demand response based power market. The prices of power in a demand response market are based on the current usage of power which often corresponds to the time of day. One power metering system previously implemented attempted to leverage the demand response

978-1-4799-2179-9/14/$31.00 ©2014 IEEE

market through measurement of power usage and comparison of this data to historical data from the system’s operation and the power market pricing. Using this, the system was able to make recommendations on when to use appliances to the user. The predication of home energy use at times can allow for better utilization of alternative energy sources through storing the energy generated and releasing it at specific times when energy is in high demand or when desired by an informed user of this system [10]. Another such system was a home energy management system that collected RTP data from the utility and combined it with weather data and energy usage history to predict future usage and make predictions that allowed for future savings [11]. One load management system that leverages the RTP market and current energy usage utilizes a scheduler with previously collected load data to run a scheduler. This scheduler determines when loads should be allowed to run to ensure limit energy usage and costs [12].

III. SYSTEM DESCRIPTION

The goal of this project is build a smart home microgrid complete with a load sensors, load management system and distributed generation. The load management system will inform the user about power consumption by the various loads on the sensor network and allow the user to remotely switch loads on the network. The distributed generation will consist of a photovoltaic (PV) array and a natural gas generator. Power from the PV array will be used when available and the natural gas generator will be dispatched based on electricity pricing, user set parameters, and utility outages. The microgrid will also be connected to the local power utility to allow for load shedding or generator spin up during peak usage hours. This allows for the utility to utilize the load management and generation of the system to eliminate loads at peak usage hours as well as creating revenue for the owner while selling power back to the grid. Finally, the system will be used to reduce the effective capacity factor of PV by providing partial generation during gaps in power generation by the PV during cloud passages and inclement weather.

A. System RequirementsThere are a few requirements and specifications that the

microgrid must meet to be operational. The microgrid will be web enabled in an Internet of Things fashion such that the sensor network can be monitored or controlled remotely over the internet. The microgrid will be a network of nodes where each load connected to the network with its control and monitoring sub-system is a node. Each node will be able to sense and report the current voltage at the node, the current power being used (demand power), and the power dissipated over a period of time. Each node will be able to switch the load connected to it on or off thus connecting or removing the load from the grid respectively. There will be a single command node that will request data from the Load Sensor and Control nodes (LSCNs) and aggregate it for the web server. The command node will also control the switching of the LSCNs to connect or remove loads from the power grid. The microgrid will also be able to interface with the home thermostat to allow for remote operation of the home HVAC

system where the HVAC system can be used to raise or lower the temperature based on the needs of the power grid. In Addition to the LSCNs, the microgrid will control onsite fossil fuel generation and the PV system through its Generation Sensor and Control Nodes (GSCNs). The load management and distributed generation system will be able to control all generation and loads independently to allow for utility interconnection or independence.

B. Communication Network1) Physical Layer Implementation Options

The options considered for the network physical layer and protocols include Wi-Fi, Zigbee, protocol neutral wireless, wired and Power Line Communication (PLC). Of these options Wi-Fi and Zigbee are widely utilized for remote devices but they do have drawbacks. Wi-Fi is no longer being considered due to the increase load that will be placed on the Wi-Fi network and the complexity of implementation of the network stack with microcontrollers. Zigbee is no longer being considered due to possible interference concerns as well as the increased popularity in home appliances may cause increased wireless traffic. Zigbee primarily uses the 2.4GHz frequency band which is shared by many other RF products and Wi-Fi networks. For both of these there remains the issue of wireless signals and poor reception in general causing connectivity issues. A wired connection between the nodes is not a feasible option due to the increase in necessary infrastructure. Finally, protocol neutral wireless has the same drawbacks as Zigbee and Wi-Fi in terms of security and interference. This led to the option to use PLC. Due to their application, all of the nodes will already be connected to the power lines. PLC requires physical access to be on the network increasing the security and reliability of the connection. There is noise on the power lines, but the amount and if any harmonics will be in the band used by the transceivers is not known at this time.

Fig 1. Microgrid sensor network and power architecture

Additionally, use of a generator may create a noisy power signal that PLC cannot operate within. Since the noise level remains unknown until the system tested, the interference may be unacceptable and this is considered below. Also there are not many PLC transceivers available on the market in comparison to Wi-Fi or Zigbee meaning the circuitry will be more complex than simply adding a packaged module. A more in depth analysis of the decision to use PLC can be visualized using the decision matrix in Table 1 which is explained below.Connecting the system through PLC gives a simple wired solution that is shown in Figure 1.

2) Discussion of Decision CriteriaThe criteria that were considered for the network physical

layer include cost, interference, network security, ease of installation, hardware complexity, software complexity, power consumption, and communication speed. The cost column is the materials cost for construction of the physical layer. Wi-Fi and Zigbee would require specialized packaged transceivers that are cost ineffective as compared to a protocol-neutral wireless network. All communication networks will have

interference, but the frequency band that the network communicates on will have an impact on the amount of interference that a system will have to accept in its band. A wired network with shielded wire and twisted pairs will have little interface compared to a protocol-neutral network. The network security category is about the security of the physical layer of the network. This is not considering the internet gateway since all networks will have an internet gateway in some form. The ease of installation deals with the complexity of the installation process which will be simple for all but a wired interface. The hardware complexity is the complexity of the support circuitry that a node will need to connect to the network. The software complexity is the added complexity to the programming that is required by the communication protocol. Wi-Fi and the network stack will add an unacceptable amount of complexity to this section. The power consumption is the amount of power required by the transceivers. Wireless transceivers will use more power than their wired counterparts. Finally speed is the baud rate of the data being carried by the network.

Table 1. Decision Matrix for Sensor Network Physical Layer (U = unacceptable = -1 pts., A = acceptable = 0 pts., S = superior = 1 pts.)

Physical Layer Cost Interference NetworkSecurity

Ease of Installation

Hardware Complexity

SoftwareComplexity

Power Consumption Speed Points

Wired U S S U A A S S 2Wi-Fi U S A S S U U S 1Zigbee A U U S S S A S 2

Protocol -Neutral Wireless S U U S S A A A 1

PLC S U (Unknown) S S A A S A 3

C. Load Sensor and Control Nodes (LSCNs)The LSCNs are the nodes of the load management and

sensor network that connecting the loads to the grid and monitoring their power consumption. The LSCNs are required to track the power used by the load attached to the node through tracking of the voltage, current and the phase between the two. In accordance with local power utility regulations the power metering must be accurate to 2%. Being a part of the load management system, the LSCNs are also required to be able to switch the loads connected to them. Finally the LSCNsneed to be able to communicate on the sensor network to report the power consumption and receive switch commands from the control system residing on the command node.

The LSCNs consist of a power sensor, a relay, a PLC interface, and a microcontroller. The power sensor will be a single phase sensor that measures the voltage and current of the load. Using the voltage and current, the power sensor will calculate the real, reactive, and apparent power being consumed by the load as well as the phase angle and power factor. At this time a power sensor from ST is being considered for this application. The relay for this application will be a relay rated to 20A. The relay will be controlled by the microcontroller and will allow for remote switching of the load. The PLC interface and the microcontroller will then implement a simplistic control system facilitating the communication between the sensor, relay, and the control

system running on the command node. A block diagram can be seen in Figure 2.

Fig 2. LSCN hardware block diagram

D. Generator Sensor and Control Nodes (GSCNs)The GSCNs are the power monitoring nodes situated at

each of the distributed generators. These nodes will report the power generated inside of the system for both the PV array and the natural gas generator. The PV array’s node will only have a single phase power meter since the inverter will automatically handle the islanding. The natural gas generator’s node will monitor the power output from the generator, control the switchgear, and control the generator operation For the power sensors, the generator node will have two single phase power sensors which will monitor the power output on the primary phase and the secondary phase, which out of

phase from the primary by 180°. The switchgear on the natural gas generator node will handle the grid synchronization when the generator is called to spin up while synchronized. A block diagram of how the Nodes will be organized can be seen in Figure 3. The Natural Gas Generator node will include control circuitry and the Inverter will handle the controls for the PV array.

E. Command NodeThe command node is where the control system will reside

making the whole system a smart microgrid. The command node will be responsible for the grid interconnection in both power and data. The command node will also host the web services for the microgrid and act as an aggregate for the power network sensors. It will periodically collect all of the data from the sensors and prepare it for remote viewing by the home-owner. Additionally, it will also be the access point to allow for the home-owner to log in for remote operation. Finally, the command node will monitor the power grid signal to allow for decision making as to if the grid should island itself in the event of an outage or imminent outage.

Fig 3. GSCN hardware block diagram, Note that the PV inverter node will only be included with the node at the PV panels and the Natural Gas node will

only be at the Natural Gas Generator.

The command node will consist of a two single phase power sensors to allow for simultaneous monitoring of the microgrid’s power flow and the grid’s power flow. This will be crucial for automatic connection and disconnection during outage connections. A PLC interface will allow for communication between the LSCNs, the GSCNs, and the command node. The command node will operate the command-response communication protocol where all nodes will listen and respond to the command node only. The internet gateway and web service hosting will allow for

connection to the local power utility and for the user to remotely operate and observe loads on the sensor network. This connection will also allow for the download of the Real Time Pricing data from the utility so the control system can effectively implement the load management system or accept a utility call to reduce load through load shedding or generator spin up. The internet gateway and control system will be implemented on a National Instruments board and accompanying computer for testing which may eventually be replaced by a computer and microcontroller.

IV. DESIGN RATIONALE

The big picture is that this load management network will act using RTP data collected from the electric utility to makedecisions about energy usage based on predicted and currentprices as well as previous usage data. The sensor network will continuously collect data about appliance usage. A sensor network that monitors energy consumption is not new and there are many devices already on the market with this functionality. Additionally, devices that monitor power and allow for remote power control are also available on the market. The difference is that these devices only allow for basic timer functionality or user-controlled remote operation. Also available on the market today are load management devices that can be triggered by the local utility to deactivate large electrical loads during periods of high power demand. These devices are on a very coarse level for large loads, not small appliances which can add up to large electrical loads.

Although it is not discussed in detail, this system will also have a generator that can also be used to generate power when prices are above the cost of generating power. While the generator is operational, the network will monitor power consumption within the load management network and curbpower use to prevent strain to the generator. The generator that can operate synchronously with the local utility within a utility scale load management network and the fine control load management network is what sets this system apart from other similar power monitoring and control sensors available on the market.

The decision to build all the components of the microgrid rather than using off the shelf components is because the off the shelf components do not have the complete functionality or open access that is required for this project. By building custom components, the functionality can be tailored to this project and there will be no question about having open access to the parts. Some off the shelf parts require the use of a proprietary protocol that will be impossible to gain access to for complete control of the device. Additionally, some commercially available parts that could be purchased use networks that are either Wi-Fi or Zigbee. Since Wi-Fi and Zigbee were ruled out, these devices would not be preferable. Finally, there are some options that would allow for open access to the sensors. The issue is that these either do not have the functionality required or do not use PLC to communicate.

V. BENEFITS OF THE MICROGRID

A microgrid with generation resources, load management, and smart metering can pose many advantages in the form of energy and financial savings to both the user and the power grid. First off are the financial benefits to the home owner through the savings through the use of the load management system and the generation. The generation resources can be used to push energy onto the power grid allowing for a profit to be made, especially when the price of electricity is higher than the costs of generating power. Additionally, the load management system can shed load when there is a high demand for energy. Since a microgrid can be independent to the power grid, it can be used to increase the reliability of its electricity. The microgrid can island itself from the grid using the generator for backup power. For the power grid, the microgrid can dispatch its natural gas generator when renewable sources cease to produce energy with a short response time thus stabilizing the overall output of power from the microgrid. Finally the microgrid will be taking part in a demand response market where the load management system can shed load when called on by the local utility. This will allow for a reduction in load on the grid at peak usage times. The generation resources can also reduce the load on the grid as seen by the utility through generation of additional energy when called on by the utility.

VI. COOPERATION WITH UTILITIES

In many power utilities, it is not allowed for a residential generation resource to back-feed the power grid, especially if the generation is fueled by non-renewable resources such as natural gas, petroleum, or propane. Since the conception of this project, cooperation from the local power utility, Citizen’s Electric Co and the local RTO and ISO, PJM, has been sought. After discussion with both parties, a plan has been developed for how such a system can be safely and effectively implemented. At this time the microgrid is going to be implemented at a residential location in an experimental setup to test the validity of system. The generator will only back-feed at specific controlled times when it can be closely monitored by the local utility. This way the local utility can test the effects that the generator has on the local grid using their power monitoring sensors in a controlled setting. PJM is interested in how introducing RTP to residential loads and distributed generation can play a role in the energy market as well as how distributed generation and microgrids will play a role in the evolution of the power grid.

VII. CONCLUSIONS

A residential microgrid with power sensors, a load management network and generation resources will provide many advantages to the home owner in the form of energy and financial savings. This microgrid will consist of a sensor and load management network that will allow for remote observation and control of the electrical loads in the home. The microgrid will internally monitor the energy usage of the loads attached to it and aggregate the data for the user and

allow for remote control through a web server. The sensor network will consist of power sensors in the form of voltage and current sensors and environmental sensors to detect the status of the environment of operation. The control system will then take the data from the power sensors and make decisions as to whether it should shed some load or spin up the generation sources. Additionally it will monitor the power grid to make a decision as to when it should island itself from the power grid. Overall, this microgrid will be a small step forward towards the future of smart grid technologies with distributed generation and a network of microgrids forming the interconnected power grid.

REFERENCES

[1] Z. Zhengwei and Z. Chenyang, "Design of Power Centralized Meter Reading System Based on Zigbee Technology," in Internation Conference on Transportation, Mechanical, and Electrial Engineering,Changchun, China, 2011.

[2] S.-W. Luan, J.-H. Teng, S.-Y. Chan and L.-C. Hwang, "Development of a Smart Power Meter for AMI Based on Zigbee Communication," in International Conference on Power Electronics and Drive Systems,Taipei, 2009.

[3] C.-H. Huang, T.-T. Hsien and G.-J. Jong, "Indoor Power Meter Combined Wireless Sensor Network for Smart Grid Applications," in IEEE International Conference on Information Science and Digital Content Technology, Jeju Island, South Korea, 2012.

[4] D.-M. Han and J.-H. Lim, "Smart Home Energy Management System Using IEEE 802.15.4 and Zigbee," IEEE Transactions of Consumer Electronics, vol. 56, no. 3, pp. 1403-1410, 2010.

[5] Q. Hu and F. Li, "Hardware Design of Smart Home Energy Management System with Dynamic Price Response," IEEE Transactions on Smart Grid, vol. PP, no. 99, pp. 1-10, 2013.

[6] L. Li, X. Hu and W. Zhang, "Design of an ARM-Based Power Meter Having WiFi Wireless Communication Module," in IEEE International Conference on Industrial Electronics and Applications, Xi'an, 2009.

[7] S. Hu and C. Wu, "An Intelligent Hotel Room Controller Based on Power Line Communication," in Internation Conference on Electronics, Communications, and Control, Ningbo, 2011.

[8] S. Khan, R. Islam, D. O. O. Khalifa, J. Omar, A. Hassan and I. Adam, "Communication System for Controlling Smart Appliances Using Power Line Comunication," Information and Communication Tecnologies, vol. 2, pp. 2595-2600, 2006.

[9] C.-H. Lien, H.-C. Chen, Y.-W. Bai and M.-B. Lin, "Power Monitoring and Control for Electric Home Appliances based on Power Line Communication," in IEEE International Instrumentation and Measurement Technology Conference, Victoria, Vancouver Island, Canada, 2008.

[10] A. Barbato, A. Capone, M. Rodolfi and D. Tagliaferri, "Forecasting the Usage of Household Appliances Through Power Meter Sensors forDemand Management in the Smart Grid," in IEEE International Conference on Smart Grid Communications, Brussels, 2011.

[11] C. Kim, K.-D. Moon, T. Pulkkinen and Y.-S. Son, "Home Energy Management System based on Power Line Communication," IEEE Transactions on Consumer Electronics, vol. 56, no. 3, pp. 1380-1386, 2010.

[12] G. T. Constanzo, G. Zhu, M. F. Anjos and G. Savard, "A System Architecture for Autonomous Demand Side Load Management in Smart Buildings," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2157-2165, 2012.