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Demand Response Implementation in a Home Area Network: A Conceptual Hardware Architecture M. Pipattanasomporn, M. Kuzlu and S. Rahman Virginia Tech – Advanced Research Institute, Arlington, VA 22203
Abstract— Demand response (DR) is an important demand-side resource that allows for lower electricity consumption when the system is under stress. This paper presents a DR framework that can be implemented within a home area network, as well as a conceptual hardware architecture for a Home Management System (HMS) and appliance interface units that enable in-home DR implementation. The proposed DR strategy allows for controlling energy-intensive loads taking into consideration both consumers’ comfort and load priority. The HMS acts as the central monitoring and decision-making unit for all energyintensive loads within a home. The appliance interface unit communicates with the HMS while capturing electric power consumption data and performing local load control. Standby electric power consumption of each element in the network is also discussed.
Index Terms—Smart Grid, demand response, smart appliance interface, ZigBee communications and standby power consumption.
I. INTRODUCTION ederal Energy Regulatory Commission (FERC) defines the term “Demand Response (DR)” as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” [1]. FERC also classifies DR activities implemented in the United States into fourteen (14) classifications. These DR activities are either incentive-based programs or time-based variable rate programs, which are further explained in Section II. According to the FERC report that came out in early 2011 [1], the top four DR programs that account for almost 80% of the total U.S. peak load reduction potential are ‘emergency demand response’, ‘interruptible load’, ‘direct load control’ and ‘load as capacity resource’ programs. It is worth mentioning that all four classifications that can provide high peak load reduction potential are incentive-based DR programs, which involve a customer receiving some sort of load control signals, not time-varying price signals. A possible reason could be that these types of DR programs guarantee load reduction, while the time-based rate programs depend greatly on customers’ actions and cannot guarantee the amount of end-use loads reduced. Examples of the currently available incentive-based DR programs that involve a customer
F
M. Pipattanasomporn is with Virginia Tech – Advanced Research Institute, Arlington, VA 22203 USA (e-mail:
[email protected]). M. Kuzlu is with Virginia Tech – Advanced Research Institute, Arlington, VA 22203 USA (e-mail:
[email protected]). S. Rahman is professor and director of Virginia Tech – Advanced Research Institute, Arlington, VA 22203 USA (e-mail:
[email protected]).
receiving a load control signal include PG&E SmartAC program [2], the Energy Smart Thermostat Program by Southern California Edison [3] and the smart thermostat program at San Diego Gas & Electric Company [4] among others. Examples of the time-varying price signal based DR programs that involve a customer receiving price signals include, among others, the PowerCentsDC program [5], and the voluntary time-of-use pilot rate offered by the Sacramento Municipal Utilities District under the PowerChoice label [6]. One objective of this paper is to present a conceptual framework of a demand response (DR) strategy that can be performed at a residential household level once a load control signal is received [7, 8]. This approach provides the customer a choice to decide which appliances to control based on their preference and priority rather than the electric utility making the selection. Our focus is to control the most energy-intensive loads in the household, namely the 240V loads. These loads include central air-conditioning (AC) units, electric water heaters, clothes dryers and electric vehicles (level 2 charging). We do not consider the 240V electric oven as a load control option due to its must-use requirements. Typical power consumption of 240V loads range from 4 kW for a clothes dryer to 9.6 kW for a level-2 EV charging station. The other objective of this paper is to present conceptual architectures for a Home Management System (HMS) and a DR-enabled 240V appliance monitoring and control unit that can allow for DR implementation within a home. There are some commercial off-the-shelf products are available [9, 10, 11, 12, 13, 14] which can monitor and/or control end-use appliances, but they are intended for use with typical 120V appliances, such as TVs, refrigerators, computers, space heaters, floor lamps, etc. By focusing at the conceptual architectures to enable 240V load control, it is expected that this paper will contribute to providing an insight into a possible hardware implementation for large-scale DR applications in a smart grid environment. This paper is organized as follows. Section II describes the classifications of DR activities in the U.S. Section III discusses the proposed DR framework that takes into account customers’ comfort and preference. Section IV presents a conceptual architecture for a HMS while section V presents a DR-enabled 240V appliance monitoring and control unit. Finally, section VI discusses electric power consumption of each system component.
978-1-4577-2159-5/12/$31.00 ©2011 IEEE
2 II. DEMAND RESPONSE CLASSIFICATIONS IN THE U.S. As previously mentioned, FERC classifies DR activities in the U.S. into 14 classifications [1]. These classifications are divided into two groups: A. DR Group 1: Incentive-based DR programs This group comprises demand response activities that a customer’s demand for electricity is reduced according to a load control signal received. There may be some sort of incentive-based payments or contractual arrangements between electricity suppliers and electricity customers. Eight out of the 14 DR classifications fall into this group. These include ‘direct load control’, ‘interruptible load’, ‘load as capacity resource’, ‘spinning reserve’, ‘non-spinning reserve’, ‘emergency demand response’, ‘regulation service’, and ‘demand bidding and buy back’. In these cases, customers refer to residential, commercial, industrial, as well as retail and wholesale customers. The first two DR classifications, i.e. ‘direct load control’, ‘interruptible load’, have long been used by electric utilities. The ‘load as capacity resource’ classification is the demandside resource that can commit to make pre-specified load reduction when system contingencies arise. The ‘spinning reserve’ is the synchronized demand-side resource that are ready to be shed within the first few minutes of an emergency event. The ‘non-spinning reserve’ classification involves the demand-side resource that can be shed after a delay of ten minutes or more. The ‘emergency demand response’ classification is the demand-side resource where load reductions can be performed during an emergency event. The ‘regulation service’ classification is the demand-side resource that can increase or decrease the load in response to real-time signals from the system operator. Lastly, the ‘demand bidding and buy back’ classification involves a program that allows a demand resource in electricity retail and wholesale markets to offer load reduction at a price. Some of these DR activities may require a representation of curtailment service provider [15].
response activity that reduces load during the peaks to reduce transmission charges. III. THE PROPOSED DEMAND RESPONSE FRAMEWORK The proposed DR concept presented below focuses on extending the implementation of DR Group 1 to cover the inhome load management once a load control signal is received from the service provider. The objective is to control 240V energy-intensive appliances. A. Load classification The EPRI’s RELOAD database [16], which is used by the Electricity Module of the National Energy Modeling System (NEMS) [17], classifies residential loads into nine types. These are space cooling, space heating, water heating, cloth drying, cooking, refrigeration, freezer, lighting, others. This paper considers cooking, refrigeration, freezer, lighting and other plug loads as critical loads. These loads will result in noticeable impacts on consumer’s lifestyle if controlled. Therefore, these critical loads will not be controlled by the proposed DR strategy. The other type of loads is known as controllable, interruptible or deferrable loads. The controllable loads are defined as the loads that can be controlled, interrupted or deferred without noticeable impacts on consumer’s lifestyle. B. The scope of the proposed DR framework The proposed strategy aims at performing demand response within a residential house. The strategy is designed to manage the energy-intensive 240V controllable loads, including air conditioning (AC), water heaters, clothes dryers and electric vehicles (EVs). The proposed DR application is initiated with an external load control signal from a service provider who needs capacity (kW) savings for efficient system operation. In the smart grid environment, these external control signals can be sent to all or selected smart meters located within a utility service area. See Fig. 1.
B. DR Group 2: Time-based DR programs This group comprises the remaining six out of the 14 DR classifications. They pertain to the use of different types of time-varying price signals to reduce customers’ demand for electricity. These six DR activities are: ‘critical peak pricing with direct load control’, ‘time-of-use pricing’, ‘critical peak pricing’, ‘real-time pricing’, ‘peak-time rebate’ and ‘system peak response transmission tariff’. The ‘critical peak pricing with direct load control’ is the demand response activity that combines direct load control with a critical peak pricing approach. The ‘time-of-use pricing’ involves electricity tariffs that vary by time periods that are longer than one hour within a day. In the ‘critical peak pricing’, a pre-specified high rate is imposed for a limited number of hours. In the ‘real-time pricing’, the retail price of electricity fluctuates hourly or more often. The ‘peak-time rebate’ allows customers to earn a rebate by reducing energy use during a specified number of hours on critical peak days. The ‘system peak response transmission tariff’ is the demand
Fig. 1. 240V loads controlled by a Home Management System (HMS).
In the proposed DR framework, the Home Management System (HMS) serves as a key component to accomplish the proposed DR control. The HMS can retrieve the external control signal from the smart meter. This external control signal can contain information on how much power demand
3 (kW) is to be shed and for how long - a curtailment of 2kW for 4 hours for example. The HMS processes this request, and devises a strategy to control a pre-selected set of 240V loads in the house based on the homeowner’s preferences. Therefore, a consumer can choose (or preselect) which 240V loads to be turned ON/OFF, when, and for how long. The algorithm to perform DR within a home is discussed below.
TABLE II. THE PROPOSED DR STRATEGY BY LOAD TYPE 240V load Air conditioning (AC)
C. Algorithms to perform demand response The first step before performing DR is to configure the HMS to take into account the load priority and consumer’s preference. This stage includes: (a) setting the load priority for each appliance and (b) preference settings for each appliance. See an example of priority and preference settings in Table I. TABLE I. EXAMPLE OF PRIORITY AND PREFERENCE SETTINGS IN A HOME AC Priority setting Preference setting
2
Water Heater 1
= 100°F
Load Clothes Dryer 4
Electric Vehicle (EV) 3
Finish the job by midnight
Fully charged by 8pm
According to Table I, this home sets the water heater load at the highest priority. This is followed by AC, electric vehicles and clothes dryer. This setting will vary from home to home based on the occupants’ needs, and ambient conditions. In the example above, the preference settings are configured such that the room temperature does not to exceed 81°F, the hot water outlet temperature does not to drop below 100°F, the clothes dryer is required to finish its job by midnight, and the EV must be fully charged by 8pm. Once the load priority and consumer’s preference are set, the HMS will perform DR on all or selected 240V loads once a load control signal is received from an external source. The load control strategy implemented will vary by load type, as summarized in Table II. The DR algorithm is designed such that the HMS will perform DR on the lowest priority loads as necessary to meet the designated demand limit. If the demand reduction from the lowest priority load is not sufficient, the HMS will perform DR on the second lowest priority load on the list. The HMS will continue to perform DR until the requested load curtailment amount is met. Therefore, the number and type of loads to be controlled will depend on the requested load curtailment amount (kW), load priority and preference settings. To take into account the consumer’s preference setting, the HMS will temporarily raise the priority of the load that violates the preset consumer’s preference. For example, if the electric vehicle is being controlled (no charge to EV at the moment) and the HMS foresees that the EV charging will not be able to complete by 8pm, the HMS will temporarily raise the EV priority setting to 1. The EV will then resume its charging status.
DR strategy Load control strategy: During the curtailment period, increase the temperature set point; The AC unit will automatically turn OFF once the room temperature falls below the new set point. The AC unit will turn ON once the room temperature exceeds the set point within the deadband. After the curtailment period, the default set point will be restored. Preference consideration: Changes in the temperature set point must still be within the preference setting limit;
Water heater
Load control strategy: During the curtailment period, turn the water heater OFF. After the curtailment period, the water heater will be turned back ON. Preference consideration: Force the unit ON when the hot water temperature falls below the preset comfort level.
Clothes dryer
Load control strategy: During the curtailment period, turn OFF the heating coil in the clothes dryer, while the tumbler motor keeps on running. After the curtailment period, turn the heating coil back ON. Preference consideration: Turn the heating coil back ON when the HMS detects that (a) the clothes drying job will not finish within the preset duration, or (b) the heating coil’s off time reaches the maximum limit.
Electric vehicle (EV)
Load control strategy: During the curtailment period, stop charging the EV. After the curtailment period, allow the EV to charge. Preference consideration: Resume EV charging when the HMS control center foresees that the EV charging cannot be finished within the preset time.
IV. A CONCEPTUAL ARCHITECTURE OF A HOME MANAGEMENT SYSTEM (HMS) The Home Management System (HMS) comprises a desktop or an embedded system that runs GUI monitoring software applications, as well as a communication module. See Fig. 2.
Fig. 2. Conceptual architecture of the HMS.
The HMS is responsible for (1) receiving a load curtailment signal from a utility through a local gateway, which can be a smart meter; (2) monitoring and recording the electrical consumption data received from the appliance interface unit (to be discussed in the next section); and (3) running DR algorithms and sending control signals to all appliance interface units within a home.
4 The GUI monitoring software is responsible for periodically recording the collected electrical data from the appliance interface unit. The data can be recorded in various fixed time intervals (e.g., 1, 5, 15, 30 and 60 minutes). The communication module in the HMS is connected with the communication module in each appliance interface unit, which is discussed next. V. A CONCEPTUAL ARCHITECTURE OF AN APPLIANCE MONITORING & CONTROL UNIT The implementation of the proposed DR framework calls for an interface unit between the HMS and the 240V load. This interface unit is responsible for monitoring the electricity consumption of the appliance and controlling its status. This paper calls this unit an “appliance interface unit” or an “appliance monitoring and control unit” and are used interchangeably. Fig. 3 depicts the relationship among the smart meter, the HMS, the 240V appliance monitoring and control units, and the 240V loads within a home area network.
• Two-way communications with the HMS: The interface unit must be able to communicate with the HMS within a home area network. This is so that the electrical consumption data collected can be sent to the HMS, and the decision from the HMS can be sent to the interface unit to perform appropriate DR control. Given the above required functionalities, it is envisioned that the 240V appliance interface unit will contain the following three modules: (1) a data capturing and processing module, (2) a control module, and (3) a communications module. This conceptual architecture is illustrated in Fig. 4. Each module is further discussed in more details below.
Fig. 4. Conceptual architecture of the 240V appliance interface unit.
B. The real-time data capturing and processing module
Fig. 3. Relationship among the smart meter, the HMS, the 240V appliance monitoring and control units, and the 240V loads.
A. Functionality requirements of the appliance monitoring and control units It is envisioned that, at the minimum, the 240V appliance monitoring and control unit shall have the following functionalities: • Real-time data capturing and processing: This interface unit must be able to collect and calculate electrical consumption data from the 240V appliance in real time. Electrical consumption data may include voltage, current, apparent power, real power and power factor. • Control: This interface unit must be able to perform different types of control for different types of 240V appliance. Per the DR strategy presented in Table II, the AC load will be controlled by changing its temperature set point. The water heater and electric vehicle loads will be controlled by changing their status to OFF. The clothes dryer load will be controlled by turning OFF the heating coil, but keep the tumbler motor running.
A microcontroller unit (MCU) can be implemented to enable the real-time data capturing and processing functionality of the 240V appliance interface unit. The realtime electrical energy data is measured, including magnitude and phase of voltage and current, as well as electrical frequency. Then, the data are converted to a digital format through the analog to digital converter module of MCU. MCU also performs real-time calculation of complex electrical energy data, such as active power, reactive power, apparent power, and power factor. The MCU is responsible for sending the collected and calculated data to the HMS (through a communication module) in the proposed conceptual architecture. Many MCU-based devices can be used to enable this application [18, 19, 20, 21 and 22]. C. The control module The control module is responsible for switching on/off appliances or adjusting (increase/decrease) the operating set point. While the control module is used to switch on/off type is applicable for electric water heaters, clothes dryers and electric vehicle chargers, it is used to adjust the operating set points in AC units. For the switch on/off application, the control module is simply an electronic relay circuit. The relay is toggled on/off by energizing a coil. It is responsible for connecting or disconnecting its connected load, depending on the control signal received from the HMS. The conceptual architecture of a relay-based control module for controlling 240V loads is shown in Fig. 5, which illustrates two identical SPST (single pole, single throw) relays. It is also possible to use one SPDT (single pole, double throw) relay. In this architecture, the relay circuit receives control signals sent from the HMS through the communication module. As shown, for each relay, a GPIO (general purpose input/output)
5 pin from the communication module is used to control the relay. Many commercial communication modules, including ZigBee-based products provide GPIO pins for such control purposes. The use of GPIO pins from the communication module, instead of using the GPIO pins from the MCU in the data capturing and processing module, provides redundancy to the system. The appliance can still be controlled in the event the MCU-based data capturing and processing module malfunctions. In this case, the appliance control can be handled by the communication module alone. Depending on the appliances’ power consumption, a relay size of up to 30A can be used to control 240V energy-intensive appliances. For the set point adjustment application, the control module needs to have a more complex electronic circuit. An example of such a control module is a smart thermostat, which can be used to adjust the temperature set point of an AC unit for this DR application. A smart thermostat can be controlled by a HMS without additional hardware, provided that the smart thermostat and the MCU-based data capturing and processing module use the same communication protocol. The conceptual architecture of a smart thermostat-based control module for controlling AC loads is shown in Fig. 6.
Communication Module
Load L1
Control Module RC1
RC2
L2
Neutral
L1
L2
Appliance
Fig. 5. Conceptual architecture of the relay-based control module. Home Management System (HMS)
Smart Thermostat
D. Communication module The communication module is responsible for providing a communication path between the HMS and the appliance interface unit. The communication and transmission of data in a smart grid environment can be divided into three key categories, depending on the coverage distance: (1) Wide Area Network (WAN) which enables communications among various devices located within a large area; (2) Neighborhood Area Network (NAN) which enables communications between smart meters and a central collection point in a neighborhood; and (3) Home Area Network (HAN) which enables the connection between the HMS and end-use appliances within a home. The well-known communication technologies within HAN include Bluetooth (IEEE 802.15.1), ZigBee (IEEE 802.15.4), wireless local area network (IEEE 802.11), and power line carrier (PLC). As wireless technologies provide lower installation cost, more rapid deployment, and higher mobility and flexibility than their wired counterparts, wireless technologies are the preferred options in most of the smart grid applications. On the other hand, the wireless communication technologies within NAN and WAN may include WiMAX (IEEE 802.16) and Cellular (2G, 2.5G, 3G and 4G). The data rate and coverage distance of the communication technologies that can be used for DR applications are summarized in Table III. Note that the data rate degrades as the transmission distance increases.
GPIO2
GPIO1
HVAC
Fig. 6. Conceptual architecture of the smart thermostat-based control module. TABLE III. COMPARISON OF DATA RATE AND COVERAGE RANGE OF VARIOUS COMMUNICATION TECHNOLOGIES
Maximum Theoretical Data Rate Technologies for Home Area Network (HAN) Bluetooth 802.15.1 721 kpbs [23-25] ZigBee ZigBee 250 kbps [26-27] ZigBee Pro 250 kbps WLAN 802.11 2 Mbps [28-30] 802.11a 54 Mbps 802.11b 11 Mbps 802.11g 54 Mbps 802.11n 600 Mbps PLC HomePlug 1 14 Mbps [31-37] HomePlug 1.1 85 Mbps HomePlug AV 200 Mbps X10 60 kbps CE Bus 10 kbps LonWorks 1.25 Mbps Technologies for Wide Area Network (WAN) WiMAX 802.16 70 Mbps [25, 38] Cellular 2G 28 kbps [25,39] 2.5G 144 kbps 3G 2 Mbps 4G 100 Mbps Communication Technology
Standard/ Protocol
Typical Coverage Range 1-100 m 10-100 m 1,600 m 100 m 50 m 100 m 100 m 250 m 80-500 m 1-3 km
48 km
2-50 km
6 Since this paper focuses on the HAN, communication technologies for use within HAN are discussed below. These include Bluetooth, Zigbee, WLAN, and PLC. The technologies for NAN/WAN as shown in Table III are for comparison purposes only. Bluetooth is based on the IEEE 802.15.1, which describes a wireless personal area network. It operates on the 2.4–2.4835 GHz unlicensed ISM band and provides a data rate of up to 721 kbps. Bluetooth provides the coverage distance of up to 100 meters. It is typically used for short-range radio frequency (RF)-based connectivity for portable personal devices. Bluetooth has no strong security layer to prevent eavesdropping. Therefore, it cannot satisfy tough security requirements, as compared to other wireless communication standards. Also it provides lower transmission coverage when compared to other wireless communication technologies. ZigBee is a low data rate and low power consumption wireless communication protocol. The ZigBee technology is based on the IEEE 802.15.4 standard. It operates on 868 MHz, 915 MHz and 2.4 GHz unlicensed frequency ranges. It provides a data rate of 20-250 kbps and coverage distance of up to 100 meters. With the same maximum data transmission rate of 250 kbps, ZigBee Pro can provide the transmission coverage of up to 1,600 meters. ZigBee supports various network topologies such as star, tree and mesh topologies. It also offers a robust security layer with 128-bit AES encryption [26, 27]. ZigBee is widely used for building automation, security systems, remote control, remote meter reading and many other smart grid applications. Wireless Local Area Network (WLAN) is a reliable, robust, high-speed wireless computer communication technology that is based on the IEEE 802.11 series of standards. This set of standards is generally known as Wi-Fi. There are a variety of IEEE 802.11 standards, including 802.11, 802.11a, 802.11b, 802.11g and 802.11n. WLAN operates on 2.4 GHz, 3.6 GHz and 5 GHz unlicensed frequency ranges. The IEEE 802.11, 802.11a, 802.11b, 802.11g and 802.11n provide the maximum data rates of up to 2 Mbps, 54 Mbps, 11 Mbps, 54 Mbps and 600 Mbps, respectively [28-30]. For the coverage distance, this set of standards provide the maximum data transmission ranges of up to 100 meters, 100 meters, 50 meters, 100 meters, 100 meters and 250 meters, respectively [28-30]. Power consumption is an important factor for WLAN or WiFi products because of its impact on battery life. Power consumption of WLAN products is mutable according to different architecture, hardware and software designs. However, their power consumption is higher than ZigBee- and Bluetooth-based products. Although WLAN technologies provide reliability and high-speed communications, it is not advantageous for DR applications in HAN due to its high cost and high power consumption in comparison with Bluetooth or ZigBee. Power line communication or power line carrier (PLC) uses the power line infrastructure to transmit data from one device to another. This communication technique injects a high frequency carrier onto the power line and modulates this carrier with the data to be transmitted. It is a cost effective means of data transmission since it uses the existing
infrastructure without rewiring or modifications. The data rate of PLC can be varied as there are various PLC technologies available in the market. In Europe, PLC is restricted to operate in the frequency spectrum ranging from 85 to 150 kHz, while in North America the frequency spectrum goes up to 540 kHz [37]. For an in-home application, PLC devices usually operate between 20 and 200 kHz. Although PLC technologies are cost effective, they may not be suitable for use in many smart grid applications due to possible interferences, noises, distortions and security concerns. VI. POWER CONSUMPTION ANALYSIS The residual power consumption is one of the most important issues when designing systems that run continuously for 24 hours a day. Although the instantaneous power consumption may appear low, the total cumulative energy consumption over a period of a month or a year can be high. Table IV below summarizes the power consumption of both the appliance interface unit – which contains the data capturing and processing module, the control module, and the communication module – and the HMS. TABLE IV. SAMPLE POWER/ENERGY CONSUMPTIONS OF DIFFERENT SYSTEM MODULES
Approx. Power Consum ption Appliance Monitoring and Control Unit Data capturing MCU 30 mW & processing @5V module Control module L1 relay 550 mW @5V L2 relay 550 mW @5V Communication Zigbee/802.1 250 mW module 5.4 Chip @5V (ZigBee) Active Mode Zigbee/802.1 125 uW 5.4 Chip @5V Sleep Mode Module
Sample Device
Home Management System (HMS) Data Notebook 30 monitoring watts module Desktop 150 watts
Operating Duration
Annual energy consumption (kWh/yr)
24 hrs/day
0.263
During operation
N/A
30 seconds each data transmission
intervals
1.096 kWh @ 1min interval or 0.22 kWh @ 5 min interval 262.8
24 hrs/day 1,314
For the data capturing and processing module of the appliance monitoring and control unit, the power consumption as shown in Table IV is based on a system-on-chip (SOC) processor from Texas Instruments (TI). The power consumption of a typical MCU is in general very low (
