EE5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home

EE5900: Advanced Embedded System For Smart Infrastructure

Single User Smart Home

Smart Gird

A smart grid puts information and communication technology into generation, transmission, distribution and end user, making systems cleaner, safer, and more reliable and efficient.

Smart Home

Smart home technologies are viewed as users end of the Smart Grid.

A smart home or building is equipped with special structured wiring to enable occupants to remotely control or program an array of automated home electronic devices.

Smart home is combined with energy resources at either their lowest prices or highest availability, e.g. taking advantage of high solar panel output.

http://www.yousharez.com/2010/11/20/house-of-dreams-a-smart-house-concept/

Smart Appliances

Smart Appliances Characterized by• Compact OS installed• Remotely controllable• Multiple operating modes

Home Appliance Remote Control

ZigBee Certified Appliances and Home Area Network (HAN)

http://www.zigbee.org/

System

Power flow

Internet Control flow

Dynamic Pricing from Utility Company

Illinois Power Company’s price data

Pricing for one-day ahead time period

Benefit of Smart Home

– Reduce monetary expense

– Reduce peak load

– Maximize renewable energy usage

Smart Home Control Flow

Transition between the Renewable Energy and Power Grid EnergyA transfer switch is an electrical switch that reconnects electric power source from its primary source to a standby source. Switches may be manually or automatically operated.

Smart Scheduling

Demand Side Management

– when to launch a home appliance

– at what frequency

– The variable frequency drive (VFD) is to control the rotational speed of an alternating current (AC) electric motor through controlling the frequency of the electrical power supplied to the motor

– for how long

– use grid energy or renewable energy

– use battery or not

5 cents/kwh 3 cents / kwh

10 kwh

Power Powerr

Time Time1 2 1 2 3

(a) (b)

VFD Impact

5 cents/kwh 3 cents / kwh

cost = 10 kwh * 5 cents/kwh = 50 cents cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents

Uncertainty of Appliance Execution Time

In advanced laundry machine, time to do the laundry depends on the load. How to model it?

Problem Formulation

Given n home appliances, to schedule them for monetary expense minimization considering VFD with considering variations

– Solutions for continuous VFD

– Solutions for discrete VFD

Solutions for continuous VFD

Solutions for discrete VFD

The Procedure of the Our Proposed Scheme

Offline Schedule

A deterministic scheduling with continuous frequency

A deterministic scheduling with discrete frequency

Stochastic Programming for Appliance Variations

Online Schedule for Renewable Energy Variations

The Proposed Scheme Outline

Linear Programming for Deterministic Scheduling with Continuous Frequency

Ibcycy

],...,2[,

],[,,0

minimize:

subject to:

battery theof priceunit

batteryin energy remaining

interval timein theenergy solar

battery tostoredenergy

panelsolar fromenergy

battery fromenergy

interval timein the

appliancean ofn consumptioenergy max

appliancean ofn consumptioenergy total

limitenergy

appliancean by consumedenergy

coston installati

costbattery

energysolar theof priceunit

grid fromenergy theof priceunit

energysolar

grid fromenergy

interval time

Max Load Constraint

Ibcycy

],...,2[,

],[,,0

To avoid tripping out, in every time window we have load constraint

battery fromenergy

grid thefromenergy

energysolar

grid fromenergy

interval time

interval timein the

appliancean ofn comsumptioenergy max

appliancean ofn comsumptioenergy total

limitenergy

appliancean by comsumedenergy

coston installati

costbattery

Appliance Load Constraint

Ibcycy

],...,2[,

],[,,0

Sum up in each time window appliance power consumption is equal to its input total power

battery fromenergy

grid thefromenergy

energysolar

grid fromenergy

interval time

interval timein the

limitenergy

coston installati

costbattery

Appliance Speed Limit and Execution Period Constraint

Ibcycy

],...,2[,

],[,,0

The frequency is upper bounded

TAaPx aa ,,

],[,,0 aaa Aax

Appliance cannot be executed before its starting time or after its deadline

battery fromenergy

grid thefromenergy

energysolar

grid fromenergy

interval time

interval timein the

limitenergy

coston installati

costbattery

Power Resource

Ibcycy

],...,2[,

],[,,0

Power resource can be various

Tyyyx usbAa

battery fromenergy

grid thefromenergy

energysolar

grid fromenergy

interval time

interval timein the

limitenergy

coston installati

costbattery

Solar Energy Distribution Constraint

Ibcycy

],...,2[,

],[,,0

Tezy sss ,

Solar Energy can be directly used by home appliances or stored in the battery

battery fromenergy

grid thefromenergy

energysolar

grid fromenergy

interval time

interval timein the

limitenergy

coston installati

costbattery

Battery Energy Storage Constraint and Charging Cost

Ibcycy

],...,2[,

],[,,0

Solar Energy Storage

],...,2[,111 Tyzzz bsbb ],...,2[,111 Tyzzz bsbb

Battery Charging Cost

Tsc bzb

battery fromenergy

grid thefromenergy

energysolar

grid fromenergy

interval time

interval timein the

limitenergy

coston installati

costbattery

Deterministic Scheduling for Discrete Frequency Flow

Determine Scheduling Appliances Order

Schedule Current Task

Update Upper Bound of Each Time Interval

An appliance

Schedule

Appliances

Not all the appliance(s) processed

All appliance process

Greedy based Deterministic Scheduling for Task i

0 t1 t2 t3 t4

Task i

Cannot handle noninterruptible home appliances

Dynamic Programming based Deterministic Scheduling for Task i For a solution in time window i, energy consumption e and cost c

uniquely characterize its state. For pruning: {e1, c1} will dominate solution {e2, c2}, if e1>= e2 and c1<=

(15, 20) (11, 22)

0 t1 t2

(6, 9) (5, 8)(4, 7)

(5, 7) (4, 6)(3, 5)

(4, 5) (3, 4)(2, 3)

(0,0) (0,0)

(3, 3) (2, 2)(1, 1)

– # of distinct power levels = k

– # time slots = m)( 2kmORuntime :

Dynamic Programming returns optimal solution

Handling Multiple Tasks

According an order of tasks Perform the dynamic programming algorithm on each task

Variation impacts the Scheme

t2 t3 t4

Worst case design

Evaluate Best case can be improved

Best PriceWindow

Cost can be reduced

Ibcycy

],...,2[,

],[,,0

Best Case Design

t1 t2 t3 t4

Variation Aware Design

An adaptation variable β is introduced to utilize the load variation.

t1 t2 t3 t4

maxmin)1( aaTaE

Ibcycy

],...,2[,

],[,,0

Monte Carlo Simulation It takes 5000 different task sets, to

evaluate a β value. Evaluate how many samples do not

violate trip rate requirement. Trip rate = trip out event / total event

Uncertainty Aware Algorithm

Algorithmic Flow

Output: Schedule

Input: Task set with tasks which can be scheduled

up date task load based on β

Generate appliances schedule by solving the LP

Derive current trip rate using Monte Carlo simulation

Current trip rate ≤ Target

Update β

Core 1up date task

load based on β

Generate appliances

schedule by solving the LP

Update β

up date task load based on

Generate appliances

Update β

Generate appliances

Update β

Generate appliances

Update β

YesYesYes

Core 2 Core 3 Core 4

β from 0 to 0.25 β from 0.25 to 0.5 β from 0.5 to 0.75 β from 0.75 to 1

Monte Carlo Simulation takes 5000 samples Latin Hypercube Sampling takes 200 samples

Current S

Latin Hypercube Sampling is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution

Algorithm Improvement

Exercise How to generalize deterministic dynamic programming to an variation

aware dynamic programming?

Online Tuning

Actual renewable energy < Expected– Utilize energy from the power grid

Actual renewable demand > Expected– Save the renewable energy as much as

possible

Actual renewable demand = Expected– Follow the offline schedule

Experimental Setup

The proposed scheme was implemented in C++ and tested on a Pentium Dual Core machine with 2.3 GHz T4500 CPU and 3GB main memory.

500 different task sets are used in the simulation. The number of appliances in each set ranges from 5 to 30, which is the typical number of household appliances [1].

Two sets of the KD200-54 P series PV modules from Inc [2] are taken to construct a solar station for a residential unit which are cost $502.

The battery cost is set to $75 [3] with 845 kW throughput is taken as energy storage.

The lifetime of the PV system is assumed to be 20 years [4]. Electricity pricing data released by Ameren Illinois Power Corporation [5]

[1] M. Pedrasa, T. Spooner, and I.MacGill, “Coordinated scheduling of residential distributed energy resources to optimize smart home energy services,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 134–144,2010.[2] Data Sheet of KD200-54 P series PV modules, available at http://www.kyocerasolar.com/assets/001/5124.pdf.[3] T. Givler and P. Lilienthal, “Using HOMER software, NRELs micropower optimization module, to explore the role of gen-sets in small solar power systems case study: Sri lanka,” Technical Report NREL/TP-710-36774, 2005.[4] Lifespan and Reliability of Solar Panel,available at http://www.solarpanelinfo.com/solarpanels/solar-panel-cost.php.[5] Real-Time Price, available at https://www2.ameren.com.

LP-based Approach vs. Traditional Approach

Energy Cost (cents) Runtime (s)

household appliance household appliance

Cost time

Traditional vs. Continuous VFD vs. Greedy

Household appliance

Only D.P. Can Handle Non Interruptible Task set

Household appliance

Comparison of Worst Case, Best Case Design and Stochastic Design

Energy Cost (cents) Trip Rate (%)

10 seconds

Household appliance Household appliance

Cost Rate

Online vs. Offline

Household appliance

Example of a Task Set

Summary

This project proposes a stochastic energy consumption scheduling algorithm based on the time-varying pricing information released by utility companies ahead of time.

Continuous speed and discrete speed are handled. Simulation results show that the proposed energy consumption

scheduling scheme achieves up to 53% monetary expenses reduction when compared to a nature greedy algorithm.

The results also demonstrate that when compared to a worst case design, the proposed design that considers the stochastic energy consumption patterns achieves up to 24% monetary expenses reduction without violating the target trip rate.

The proposed scheduling algorithm can always generate a monetary expense efficient operation schedule within 10 seconds.

Multiple Users

Pricing at 10:00am is cheap, so how about scheduling everything at that time?

Will not be cheap anymore

Game Theory Based Scheduling

Thanks

EE5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home

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