nPlug: A Smart Plug for Alleviating Peak Loads

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Tanuja Ganu

IBM Research, India

Deva P. Seetharam

IBM Research, India

Vijay Arya

IBM Research, India

Rajesh Kunnath

Radio Studio, India

Jagabondhu Hazra

IBM Research, India

Saiful A. Husain

Universiti Brunei Darussalam

Liyanage Chandratilake

De Silva

Universiti Brunei Darussalam

Shivkumar

Kalyanaraman

IBM Research, India

ABSTRACT

The Indian electricity sector, despite having the world’s fifth

largest installed capacity, suffers from a 12.9% peaking short-

age. This shortage could be alleviated, if a large number of

deferrable loads, particularly the high powered ones, could

be moved from on-peak to off-peak times. However, con-

ventional DSM strategies may not be suitable for India as

the local conditions usually favor only inexpensive solutions

with minimal dependence on the pre-existing infrastructure.

In this work, we present nPlug, a smart plug that sits be-

tween the wall socket and deferrable loads such as water

heaters, washing machines, and electric vehicles. nPlugs

combine real-time sensing and analytics to infer peak pe-

riods as well as supply-demand imbalance and reschedule

attached appliances in a decentralized manner to alleviate

peaks whenever possible. They do not require any manual

intervention by the end consumer nor any enhancements to

the appliances or existing infrastructure. Some of nPlug’s

capabilities are demonstrated using experiments on a com-

bination of synthetic and real data collected from plug-level

energy monitors. Our results indicate that nPlug can be an

effective and inexpensive technology to address the peaking

shortage.

Categories and Subject Descriptors

B.m [Hardware]: Miscellaneous; E.4 [Data]: Coding And

Information Theory; F.m [Theory of Computation]: Mis-

cellaneous; I.6 [Computing Methodologies]: Simulation

And Modeling

General Terms

Algorithms, Design, Experimentation

Keywords

Smart Plug, Demand Response, Demand Side Management,

Peak Loads, Scheduling, Multiple Access

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e-Energy 2012, May 9-11 2012, Madrid, Spain.

1. INTRODUCTION

As of November 2011, the Indian electricity sector, despite

having the world’s fifth largest installed capacity of 185.5

GW, suffers from a 12.9% peaking shortage [7]. The situa-

tion could worsen with the current trends in population and

income growth, industrialization, and urbanization. Elec-

tricity consumption is expected to increase substantially in

the coming decades as well [10].

Considering that electricity cannot easily be stored in large

scale, peak shortage can be alleviated by increasing supply

or by reducing demand. Supply can be increased through

the use of“peaker”power plants that operate on fast-starting

fuels such as diesel or open-cycle gas/hydro turbines. Peaker

plants operate only during the peak, for a small fraction of

time, so their electricity is inherently expensive. It is es-

timated that if India were to add peakers to the existing

generation portfolio, the average supply cost might increase

by over 35% [19].

Clearly, there is a significant role and potential for demand

side management (DSM) programmes in India. The Gov-

ernment of India, through new Energy Conservation legisla-

tion, is also seeking to implement a host of such programmes

within the country [13]. However, conventional DSM strate-

gies may not be suitable for India as the local conditions

usually favor only inexpensive solutions with minimal de-

pendence on the pre-existing infrastructure [20]. One of the

disadvantages of existing DSM strategies such as direct load

control is their centralized nature which requires communi-

cation between appliance-level monitors and a central con-

troller at the utility. Since monitors require communication

capabilities, it increases their cost. More importantly, this

requires a communication infrastructure between the utility

and appliances which is expensive to deploy. Although cel-

lular communication is inexpensive in India, existing infras-

tructure will need a capacity upgrade to support household

appliances as well. An Internet based solution may not be

widely applicable as only 11.3% of Indian households have

access to Internet [9].

In this paper, we present a decentralized DSM system

based on smart plugs called nPlugs that “sit” between de-

ferrable loads and wall sockets. An nPlug senses line volt-

age and frequency to infer the load level and supply-demand

imbalance in the grid respectively. It processes the sensed

data using simple data mining algorithms to identify the

peak and off-peak periods of the grid. It runs the attached

load(s) during off-peak periods as much as possible without

violating user-specified constraints. To ensure grid and ap-

pliance safety, it avoids scheduling appliances during periods

of supply-demand imbalance. Furthermore each nPlug runs

a decentralized load rescheduling algorithm that contributes

to peak load reduction by distributing the loads over time.

The key contributions of this paper are:

•Design of a low-cost standalone smart plug that can

schedule appliances during off-peak periods. It nei-

ther requires any communication infrastructure nor

any changes to the appliance or grid. It can work with

common deferrable loads such as water heaters, wash-

ing machines, and electric vehicles.

•Design of simple and effective data mining algorithms

to determine peak and off-peak periods as well as sup-

ply demand imbalance, that can run on low-cost mi-

crocontrollers.

•Design of novel decentralized load scheduling algorithms

that contribute to peak load reduction and load-leveling

by spreading the joint load efficiently under varying

grid load conditions.

•Experimental evaluation of above-mentioned algorithms.

The rest of the paper is organized as follows. Section 2

presents the theoretical basis for inferring grid load and

supply-demand imbalance by sensing line voltage and fre-

quency. The details of nPlug hardware design is described in

Section 3. The data mining algorithms used to identify peak

and off-peak periods as well as load scheduling algorithms

used by nPlugs are presented in section 4. Experimental

evaluation of our algorithms is presented in Section 5. Sec-

tion 6 presents related work and finally section 7 concludes

with a discussion about future work.

2. POWER SYSTEMS BACKGROUND

This section uses the power systems theory to explain why

the line voltage and frequency measured in a household can

serve as good indicators of grid load and supply-demand

imbalance respectively.

Figure 1: A two-bus power system

2.1 Inferring grid load from voltage

Figure 1 shows a simple “power system” wherein a load

is connected to a generator using a transmission line. ˜

is the generator voltage, ˜

VRis the load voltage, ˜

ZLN is the

transmission line impedance, and ˜

ZLD is the load impedance

(all quantities are vectors). We will now see how the magni-

tude of load voltage VRdecreases with increasing load. The

current ﬂowing through the line and load, ˜

Iis given by

I=˜

ZLN +˜

ZLD

where ˜

ZLN =ZLN ∠θ=ZLN cosθ +jZLN sinθ

ZLD =ZLD∠φ=ZLDcosφ +jZLD sinφ

Here θis phase angle between reactive and resistive com-

ponents of the line impedance while φis the phase angle

between the load current and voltage. Now the magnitude

of current Iis given by

I=ES

p(ZLN cosθ +ZLDcosφ)2+ (ZLN sinθ +ZLDsinφ)2

Therefore the magnitude of load voltage VRis:

VR=ZLD ×I

=ZLD ×ES

p(ZLN cosθ +ZLDcosφ)2+ (ZLN sinθ +ZLDsinφ)2

(1)

Since the source voltage ESand transmission line impe-

dance ZLN ∠θare generally constant, the load voltage VR

is essentially a function of the magnitude of load impedance

ZLD and the power factor cos φ. To minimize reactive power

consumption, appliances are usually designed to have high

power factor (0.9 to 1). Thus from Eq.(1) we see that the

load voltage VRis dominated by the magnitude of load

impedance ZLD . As the load increases (i.e. impedance

decreases), the load voltage VRdecreases and vice versa.

Therefore as the collective load on the grid increases, the

corresponding voltage drop can be sensed at households. In

section 5, we shall plot the variation in voltage VRmeasured

at a household and show how it drops during peak hours.

Figure 2: Load-frequency control characteristics

2.2 Detecting supply-demand imbalance from

frequency

Conventionally, the grid frequency is regarded as an indi-

cator of imbalance between generation and demand. During

imbalance, the output of each generator is automatically

adjusted to meet the demand. This changes the system fre-

quency according to the load-frequency characteristics of the

generators as shown in Figure 2. The plot shows that when

the generation is higher than Pset (the generation needed to

support a fixed load), the frequency drops. On the other

hand, if it is less than Pset, the frequency shoots up.

Although frequency is a good indicator of imbalance, our

measurements show that it varies continuously and may not

be sufficient to identify grid load accurately. One possible

reason for this is that in anticipation of increased demand,

the generation is ramped up to keep the frequency close

to nominal levels. Even though the power systems theory

explained in this section is well-known, to the best of our

knowledge, none of the existing systems learn the voltage

and frequency patterns to derive load schedules that can

help reduce peak loads.

3. NPLUG HARDWARE DESIGN

Figure 3 shows an initial prototype of nPlug. The hard-

ware design is based on cost-performance trade-offs.

Figure 3: nPlug Prototype

As shown in Figure 4, the hardware modules (drawn in

blue) of nPlug are user controls, grid sensors, relay, real

time clock and power supply.

•User Interface

nPlug is equipped with buttons for entering schedul-

ing preferences and for overriding nPlug’s scheduling

decisions, and a 32-character (16×2) LCD.

•Controller, Memory and Storage

The current design uses a Microchip PIC24FJ128GA010

16 bit microcontroller. This 4MIPS controller has 128KB

of Program Memory, 8KB of RAM and a SPI ﬂash

memory interface. A 4 Mb Flash memory with SPI

Interface is used to store the end user preferences, the

sensing history as well as the outputs from the learning

and scheduling modules

•Voltage Sensing

Voltage sensing is achieved with a resistive divider

(built with 1% tolerance resistors) between phase and

neutral. The divider is sized in such a way that the

dynamic range of microcontroller’s Analog to Digital

Converter (ADC) can cover the input voltage range

(110 V – 350V) 1. Since nPlug is only required to iden-

tify voltage changes due to peak demand, the mea-

surement must be accurate between 185 V and 250

V (≈ ±10% of nominal voltage). Voltage calibration

Figure 4: nPlug: System Overview

scheduler. The following sections explain the functionality

of each component.

4.1 UI Manager

The UI Manager accepts three user-specified constraints:

1.Earliest start time:- the earliest time at which an appli-

ance can be switched on; 2.Latest end time:- the latest time

at which the appliance must finish running; and 3.Dura-

tion:- the duration for which the appliance must be pow-

ered. For example, a residential consumer who leaves for

work at about 8 AM may specify that her insulated water

heater must be run for 30 minutes between 4 AM and 7 AM.

4.2 Data Manager

The data manager works as an interface between the hard-

ware sensors and storage. nPlugs sense the grid at regular

time intervals to measure line voltage and frequency . The

sensed data is preprocessed and saved in the data storage