Smart Grid Simulator –

Retargeting the HDL Simulator Srikanth Jadcherla, Vishwanath Sundararaman, Jyothsna Kakarwada, Swapna Lavanuru

Abstract –The electricity network needs to be able

to connect billions of devices and still operate reliably

just like the Internet of today. Because of growing

environmental concerns, the electric grid needs to be

more flexible, accommodate distributed power

generation from renewable sources and use several

energy-efficiency techniques. A number of

technologies need to be put in place to make the

power grid smarter, notably more automation within

the network and tools to give end users better

information. The changes have to happen in all

aspects from generation, distribution to consumption.

The Advanced Metering Infrastructure (AMI) is an

approach to integrating consumers based upon the

development of open standards. It provides

consumers with the ability to use electricity more

efficiently and provides utilities with the ability to

detect problems on their systems and operate them

more efficiently.

This paper describes a Smart Grid Simulator

methodology that retargets a HDL event driven

simulator to model a flexible, extensible

neighborhood and thereby predicting grid

characteristics like stability, command response and

revenue management. A model of a neighborhood is

built in System Verilog and VMM constrained

random technique is used to create realistic event

profiles for the neighborhood.

I. INTRODUCTION

The idea behind the "smart grid" is to have

devices that plug into electrical sockets and the

appliance plugs into this device. These devices

would communicate and report to the central

authority at what time the appliances were used and

the energy consumption levels. This information is

used to charge for the power consumption based on

peak hour and off peak hour usages. At peak hours

the rates would be higher than the off peak hours.

This would result in generous increases in electric

bills, thus "forcing" consumers to try and save

energy by using as little as possible during peak

hours. The smart grid is the use of digital

technology to modernize the power grid. It

employs innovative products and services

combined with intelligent monitoring, control,

communication, and self-healing technologies to do

the following:

facilitate the connection and operation of

generators of all sizes and technologies

allow consumers to play a part in optimizing the

operation of the system

provide consumers with greater information and

supply choices

significantly reduce the environmental impact of

the electricity supply system

deliver enhanced levels of reliability and security

of supply

II. ADVANCED

METERING INFRASTRUCTURE (AMI)

Advanced Metering Infrastructure (AMI) refer

to systems that measure, collect and analyze energy

usage, from advanced devices such as electricity

meters, gas meters, and/or water meters, through

various communication media on request or on a

pre-defined schedule. This infrastructure includes

hardware, software, communications, customer

associated systems and meter data management

(MDM) software. The network between the

measurement devices and business systems allows

collection and distribution of information to

customers, suppliers, utility companies and service

providers. This enables these businesses to either

participate in, or provide, demand response

solutions, products and services. By providing

information to customers, the system assists a

change in energy usage from their normal

consumption patterns, either in response to changes

in price or as incentives designed to encourage

lower energy usage at times of peak-demand

periods or higher wholesale prices or during

periods of low operational systems reliability.

A. Smart Meters

Smart meters are of advanced metering

infrastructure (AMI) type meters, that provide

communication path from generation plants to

electrical outlets (smart socket) and smart grid

enabled devices. By customer option, such devices

can shut down during times of peak demand.

It is a system which gives information

- On demand reading

- Service disconnection

- Time - of - use

- Load profiling

Remote collection of energy usage data is

made possible with two-way communication

capability. These meters automatically record data

and information and transmit it to some central

location on assigned schedule.

Advanced Metering Infrastructure (AMI) is an

approach to integrate consumers based upon the

development of open standards. It provides

consumers with the ability to use electricity more

efficiently and provide utilities with the ability to

detect problems on their systems and operate them

more efficiently. AMI enables consumer-friendly

efficiency concepts like “Prices to Devices”.

Assuming that energy is priced on what it costs in

near real-time. A smart grid imperative price

signals are relayed to “smart” home controllers or

end consumer devices like washers/dryers and

refrigerators – the home‟s major energy-users. The

devices, in turn, process information based on

consumer‟s wishes and power accordingly.

III. SMART GRID TECHNOLOGY

- OVERVIEW

More than meters and mobility, the smart grid

represents a whole new framework for improved

management of electric generation, transmission,

and distribution. It also presents challenges. As a

complex system, the smart grid requires special

attention to issues of interoperability, security, and

resiliency. A lot of energy is wasted in this era of

consumer electronics. In a modern household as

much as 40% of the electricity usage could stem

from electronic goods and components. This is a

result of unprecedented proliferation of electronics.

Table 1 illustrates how idle mode energy can go

up to 1/3rd the total energy usage, even though idle

mode power is only 5% of the active power.

Many modern gadgets cannot entirely be turned

off; even when not in use, they draw electricity

while they await a signal from a remote control or

wait to record a television program. All devices

must support multiple modes of operation like

active, standby, low power etc to effectively

address energy wastage. In addition to devices

having different modes, there should also be a

communication channel among all the devices in a

home setup. While some of this is already

supported in home devices, we are fast heading

towards standardized states and control signaling

across manufacturers. Specifically, this

communication between devices will require

significant changes to device command and control

communication. First and foremost, it requires

devices to be „connected‟ – the power grid no

longer delivers electricity, it will be used to

monitor and negotiate energy usage dynamically.

TABLE I

POWER CONSUMPTION OF A TV

Fig. 1. Communication amongst the devices and the Home

Controller

For example as shown in Fig. 1, a device such as

a TV or a Blue-Ray player receives a command

(especially related to power management) and this

needs to be appropriately communicated to other

connected devices

A. Smart Functionality

Generically, we are likely to see a „Smart‟

functionality, as illustrated in fig 2 along with the

temporal behavior. Further this architecture has to

support device chaining and voltage/current

monitoring. The connectivity of devices is to

ensure that the communications among devices are

enabled. This would require a great deal of

changes in the architecture of the devices both in

the analog and digital side of device operation and

also in the software side of the devices to facilitate

interaction with the grid.

Fig. 2. Smart functionality around the devices

Thus “Smart” functionality represents a

significant load side component that will be

ubiquitous because of its abilities to communicate

power management needs and conditions.

Extending this concept and tying it back to the

Smart Grid development, there is a natural fit

between Smart devices and Smart Grids.

Smart design allows devices to be controlled

from a power consumption angle and to

communicate their status. Smart Grids need to

monitor loads and trigger generators, (especially

peak supply), „purchase‟ additional power or

negotiate some loads down.

B. Smart Models

The Smart Grid Simulator methodology

retargets HDL event driven simulators to model a

flexible, extensible neighborhood and thereby

predicting grid characteristics like Stability,

command response and revenue management. A

model of a neighborhood is built in System Verilog

and VMM constrained random technique is used to

create realistic event profiles for the neighborhood.

Behavioral, configurable models with smart

functionality are developed for household

electronic components namely Television, Washing

machine, Microwave Oven, Ceiling Fan, Toaster

and Air conditioner. The Smart models can operate

in different modes based on the command received

from the Smart Grid and each mode consumes

different amount of power.

The rated power of the Washing Machine is

around 1 KWh in the normal mode of operation

and is 0.3 KWh in the Low Power mode of

operation. Similar modeling is done for all other

components and is then integrated into a home as

shown in Fig 3.

C. Home Controller

The home controller is the Interface between

the smart grid and the home devices. The home

controller controls the flow of information from the

Grid to the devices like asserting the Smart mode

for all the devices and monitoring the power

consumption and performance of all the devices

and feeding this information back to the grid.

Assuming the „allowed power‟, the maximum

amount of consumable power for a home, a value

set by the grid control authority, the home

controller is capable of turning on or off the

devices to meet this limitation. This turning on and

off of the devices is done in the order of device

priority.

When Smart is asserted from the home controller

the devices operate in a low power mode thereby

reducing the power consumed by the devices. The

home controller also monitors the total power

consumed by all the devices by limiting the number

of devices operating at any time so that the total

power consumed does not exceed the allowed

power set for a home. The intelligent home

controller turns on devices based on the device

priority and hence not exceed the allowed power

set for the home. The device has the option of

overriding the Smart there by choosing to operate

in the normal mode. This can cause the total power

consumed by the home to be more than the allowed

power. This information is fed back to the grid

through an Indicator signal.

Fig. 3. Home Controller and the devices

The home consists of a set of behavioral device

models and the home controller. The model of a

neighborhood is built by instantiating a number of

homes. The rated power for all the devices is

configurable thereby creating a realistic

neighborhood model.

A configurable random System Verilog

testbench is built to simulate the behavior of a

neighborhood over a period of 24 hours.

IV. POWERCONSUMPTION

PROFILE OF A HOME

All the home devices turn on when both the

power on signal at the device level, controlled by

the user and power on signal from the home

controller are asserted. „Power on‟ signal at the

device level is completely controlled by the user,

and hence are randomly generated in the

simulations, according to their use or timing

profiles.

Power on signal from the home controller is

automatically generated by the home controller for

every change in allowed power and current total

power consumed by the home. It is managed such

that the total power consumption is always lower

than the allowed power unless an override signal is

asserted to ignore these limitations.

In the absence of a „smart‟ signal from the grid

the device operation is dependent only on the

allowed power.

When „smart‟ signal is asserted, the devices go to

a low power state (depending on the device level

„override‟ and „respond‟ signals), wherein the

power consumption is very low.

From Fig. 4, the plots of the power consumption

profiles clearly show the variation in the consumed

power with and without „smart‟. The power

consumed reduces in the interval where the „smart‟

is asserted compared to the scenario where „smart‟

is not asserted.

The override signal can be asserted at the home

controller to override „smart‟ and „allowed power‟

limitations, thereby the devices are allowed to

function in the normal mode of operation.

Comparison of the power consumption profiles

with smart and with and without override signal

(Fig. 5), show that the power consumed is higher

when we have an override signal.

Time (10 units = 1hr)

Fig. 4.Power Consumption with and without „smart‟

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wer

(w

atts

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Time (10 units = 1hr)

Fig. 5. Power Consumption with „smart‟, with and without

„override‟

V. EFFECT OF SMART ON THE PERFORMANCE OF A MICROWAVE OVEN

According to the microwave technology, when the

heating process is carried out at a lower power, the

time taken to cook the same amount of food, to the

same extent, is longer.

Fig. 6 shows the operation of the microwave oven.

When a „smart‟ arrives, the oven functions in a low

power mode, wherein its consumption drops to 200

watts from a rated consumption of 1100 watts, but

the time taken to complete the task is much longer.

Time (100 units = 1 hr)

Fig. 6. Effect of „smart‟ on the Power & Time Consumption of a

Microwave Oven

FUTURE WORK

Future work involves development of protocols

for communication between the devices involved,

the controllers and the controlling authority or the

grid. Development of a common interface for all

the devices and controllers is already under

consideration.

The elements of distributed generation, like the

solar PVs, DG sets etc. are to be incorporated into

the present set up as sources of power, such that

their inputs would enable competitive revenue

oriented bidding between the distributed, small

scale generation ends and the central controlling

authority.

Also standardized operating states for all the

devices are under development.

CONCLUSION

The Smart Grid Simulator methodology would

prove to be a very inexpensive technique in

identifying potential ways to manage and reduce

power consumption during the peak hours and

thereby reducing the cost. With appropriate inputs,

this method provides with accurate results without

involving any field trials. With a little refinement in

the methodology, we can open platform for a

competitive, revenue based bidding.

REFERENCES

[1] An Energy Efficient SoC with Dynamic

Voltage Scaling – David Flynn

[2] http://en.wikipedia.org/wiki/Smart_meter

[3] www.oe.energy.gov/Documents

AndMedia/DOE_SG_Book_Single_Pages

(1).pdf

[4] en.wikipedia.org/wiki

/Advanced_Metering_Infrastructure

[5] http://amr.byramlabs.com/overview.php

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