Multi-Layer Optimization for Load Scheduling to Manage Unreliable Grid Outages in Developing Countries

doi:10.4236/epe.2013.54B201

Paper Menu >>

Journal Menu >>

Energy and Power Engineering, 2013, 5, 1053-1059

doi:10.4236/epe.2013.54B201 Published Online July 2013 (http://www.scirp.org/journal/epe)

Multi-Layer Optimization for Load Scheduling to Manage

Unreliable Grid Outages in Developing Countries

Amit S. Closepet

Spectrum Consultants, Bangalore, India

Email: [email protected]

Received 2013

ABSTRACT

This paper describes the significant cost saving opportunities for consumers in developing countries by the use of com-

putational intelligence and demand-side-management techniques to mitigate the massive use of diesel back-up during

grid outages. Application of load scheduling optimization is investigated during scheduled power outages, for residen-

tial consumer in India. The specific load shifting approaches explored include a day ahead predicted load schedule

which is generated by performing a DSM referring to the forecasted day ahead outage. Whereas in reality the predicted

may not match the actual outage, thus in these cases a fuzzy logic rule base is referred on real time basis to take correc-

tive action & reach the best optimal load schedule possible to attain the lowest cost. The load types modeled include

passive loads and schedulable, i.e. typically heavy loads. It is found that this multi-level DSM schemes show excellent

benefits to the consumer. The maximum diesel savings for the consumer due to load shifting can be approximately

ranging from 45% to as high as 75% for a flat-tariff grid. The study also showed that the actual savings potential de-

pends on the timing of power outage, duration and the specific load characteristics.

Keywords: Residential Demand Side Management; Diesel Mitigation; Real Time Load Shifting; Power Outage

Management; Computational Intelligence

1. Introduction

Demand-side-management (DSM) policies are being

formulated by various stakeholders in India and other

developing countries. These policies are specifically tar-

geted to overcome large energy demand-supply gaps, to

provide inclusive and reliable power for entire popula-

tions. For example, in India, load scheduling has recently

been implemented successfully for the agricultural sector.

As in developed countries, load scheduling is driven by

the utility for peak clipping of demand, load shifting for

energy conservation and/or supporting load growth. In

this work, our aim is to highlight the urgent need for de-

mand-side-management policies to address one of the

major unaddressed challenges for a consumer in a de-

veloping country which is the problem of frequent power

outages. DSM solutions and policies need to be devel-

oped, validated and framed to enable the consumer get

reliable power and reduce his dependence on expensive

diesel back-up systems. The highly stochastic nature of

the power grid (Figure 1) in developing economies with

relentless power cuts forces consumers to rely heavily on

diesel back-up systems for business continuity, risk mi-

tigation and load execution. Major cost investments are

required for both installation and operation diesel

back-up systems. Recent articles [1-4,17] have shown

that, in some cases, cost can run as much as high as 50%

of the customer’s annual power budget. In this study, we

explore the use of demand management techniques that

will enable the consumer mitigate power outages, cut the

dependence on diesel back-up and provide significant

cost savings to the consumer.

Tables 1(a)-(b) in [18] provide power outage data for

several major cities in India and in other developing

economies such as Africa, Philippines, South America

and observe that power outages range from 2 hours to

more than 10 hours a day. Power blackouts typically re-

sult in total loss of power to large parts of the entire city

to large districts. Different scenarios and causes for

planned/unplanned power outages in India are described

in [4,5]. Due to this grid unreliability, the market for

genset and UPS systems is India is worth several billion

dollars and growing at a rapid 20% annual rate. In India,

Figure 1. Stochastic grid in developing countries.

A. S. CLOSEPET

1054

residential consumers sometimes pay large premium

(~3X) over grid power due to use of expensive back-up

systems [4,5]. Service businesses (e.g. photocopier cen-

ters, medical diagnostic labs, service apartments, wed-

ding halls etc.) charge higher rates to the consumer dur-

ing power outages. Home owners association (HOA) in

apartment complexes are faced with large diesel bills due

to a shared gen-set and these additional costs are peri-

odically collected from the consumer. In hospitals, the

continuous power is ensured by using UPS systems. Ac-

cording to the Bureau of Energy Efficiency in India [6],

to deliver a sustained economic growth rate of 8% to 9%

through 2031-32 and to meet life time energy needs of all

citizens, India needs to increase its primary energy sup-

ply by 3 to 4 times and electricity generation capacity

about 6 times. Based on these aforementioned statistics

and the present high inefficiencies in the grid, it is likely

that power outages are here to stay unless DSM policies

are directly targeted at mitigating the large power out-

ages for all consumers.

We explore two specific DSM techniques manage

outages for a residential load in India. Our goals are to

minimize the quantum of loads executed during outage

by load-shifting and deliver costs savings by significantly

reducing the consumption of diesel. Decisions to whether

to execute the load with diesel or to compromise load the

altogether are often encountered for the emerging market

consumer. Specific power outages are announced well in

advance by the utility publicly for each specific locality.

For example, in the city of Chennai in South India, the

local utility has recently announced a 2 hour power out-

age daily for the months of March to June 2012.

In this study, application of load scheduling optimiza-

tion is investigated during scheduled power outages, for

residential consumer in India. The specific load shifting

approaches explored include a day ahead predicted load

schedule which is generated by performing a DSM refer-

ring to the forecasted day ahead outage. Whereas in real-

ity the predicted may not match the actual outage, thus in

these cases a fuzzy logic rule base is referred on real time

basis to take corrective action & reach the best optimal

load schedule possible to attain the lowest cost. The load

types modeled include passive loads and schedulable, i.e.

typically heavy loads. It is found that this multi-level

DSM schemes show excellent benefits to the consumer.

Implementation of a load scheduler can be extremely

difficult for a consumer in a developing country. The

reasons are many including ease of use, availability of

controllable loads etc. In India, consumers typically

switch off their heavy loads during a power outage and

execute them after power is restored. In newer apartment

buildings, the heavy load lines are usually a separate cir-

cuit (e.g. 15 amps) and the apartment back-up generator

simply doesn’t provide power to these lines. All heavy

and shiftable loads are often connected to the 15 amp line.

This technique of casually rescheduling heavy loads re-

sults in unexpected peaks for the utility as soon as power

is restored. While the actual implementation of the load

control and scheduling can be accomplished either by the

utility or the end consumer themselves, the aim of the

DSM policy needs to be consumer-centric. In other

words, the consumer needs to always have the flexibility

of load selection and execution without yield controlling

to the utility.

1.1. Literature Survey

Numerous studies have focused on the impact of unreli-

able grids on consumer cost as seen in the following

works: An in-depth investigation into the impact of pow-

er outages for consumers and businesses in Africa is

performed in [4]. This study also assesses the economic

consequences of the unreliable grids. A report on real

power cost in India [5] reveals that the overall intent of

providing cheap and affordable power to the consumers

in the country is noble, but if the supplies are inadequate

or unreliable, the consumers could actually end up pay-

ing a much higher price. A report from United Nations [6]

provides directions to expand access of modern energy

services at the household level. An application of com-

bined model of extrapolation and correlation techniques

for short term load forecasting of an Indian substation is

presented in [7]. Specific opportunities for DSM in the

Indian scenario are presented in [8]. Low-cost energy

generation using bio- mechanical energy is presented in

[9] and this provides a technology options for both off-

grid users as well as on-grid users who have unreliable

power. The use of casual scheduling loads with time-

varying prices using stochastic dynamic programming is

studied in [11] and its effect on consumer cost are re-

ported.

In [13], a power scheduling protocol for demand re-

sponse in smart grid system is explored which focuses on

limiting the allowable power loads. Algorithmic en-

hancements to a scheduler for residential DSM are pre-

sented in [15].

1.2. Simulation Approach and Analysis

Methodology

The MATLAB methodology to model the demand side

management optimization and scheduling are described

in this section. The MATLAB code is structured in such

a manner that it fetches all the input data from various

excel files, these excel files can be edited for demand, for

the individual load power characteristics, for the load

start time, the load run time, for forecasted outage start

time & outage duration etc., Once these inputs are ready

we can go to the MATLAB GUI to run the code.

A. S. CLOSEPET 1055

Once the code is run for a forecasted outage it results

in a new load schedule for the following day depending

on the outage. Due to the unreliable grid, we have as-

sumed an error in the outage scenario of maximum of 1

hour on either sides of the forecasted outage. Thus to

simulate this unreliable grid we do a real time fuzzy logic

based DSM on the loads by creating an error in the out-

age either in the outage start time or outage end time or

even both. Thus depending on the actual outage the fuzzy

logic rule base is referred for a further correction in the

load schedule to reach to the best optimal cost.

The baseline costs assumed for the grid is 5c/KWhr

(residential) and the baseline diesel costs assumed in the

simulation are 20 c/KWhr. As the power characteristics

of the loads are not constant we have divided the day into

multiple of a 5 minutes chunk, so 24 hours is considered

as 288 chunks, by doing this we can be very accurate in

calculating the effective cost, for a better and simple un-

derstanding we have assumed all the heavy loads consid-

ered in the paper i.e. 3 Geysers, 1 washing machine, 1

dishwasher & a dryer to have flat power characteristics

curves.

1.3. Optimization Equations

The cost minimization equation is as follows: Input pa-

rameters

LPASSIVE(t) Passive Load at time t

LSHIFT,i(t) Shiftable Load i at time t

Total Load ∑(L(t)) = ∑LPASSIVE(t) + ∑LSHIFT ,i(t) for

i=1,n tGGrid available time for a day

tBDiesel usage time in a day CG(tG) Cost per unit

with grid CB(tB) Diesel cost per unit

Total cost per unit at time t C(t)

= CG(tG) + CB(tB)

Total Cost CTOTAL = ∑C(t)L(t) for t = 0,24

COPT = Min (CTOTAL)

Algorithm: - Finding optimal load schedule

The above is done by following the below mentioned

flow chart in Figure 2.

Figure 2 Basic algorithm flow chart

L1NrsT Normal Start Time Of Load (1)

L1NST Earliest Start Time Of Load (1)

L1LST Latest Start Time Of Load (1)

L1RT Run Time Of Load (1)

L1NrETNormal End Time Of Load (1)

L1NrET= L1NrsT+ L1RT

Figure 3. Load schedule constraint.

Figure 3 speaks about how every shiftable load is

normally scheduled & also what are its constraints i.e.

the load cannot be shifted randomly during the day but

has an earliest start limit & also a latest start limit.

Hence any shifting of these loads has to be done be-

tween this time frame.

Figure 4. Expected outage for the following day.

bExST Expected Outage Start Time

bExSTExpected Outage End Time

In Figure 4 we can clearly observe that the outage is

expected to affect the load, thus this load has to be

shifted, Thus the load is scheduled to a new start time

either before the outage or after the outage, this com-

pletely depends on the load constraints & also the run-

time of the load, if the gap is available on both sides of

outage, the algorithm chooses to shift the load before the

outage as it is safer to execute the load beforehand, rather

to risk the execution of the load with the unreliable grid

supply.

This is seen in Figure 5.

Figure 5 Day ahead Load Shift Sc he dule .

A. S. CLOSEPET

1056

L1NeSTNew Expected Load Start Time

L1NeET New Expected Load End Time

L1NeET= L1NeST+ L1RT

Figure 6. Actual Outage.

bAcST Actual Outage Start Time

bAcETActual Outage End Time

In Figure 6 we can observe that the actual outage is

overlapping the new scheduled start time of the load, this

data is obtained from the real time supply sensors.

Now the real time Fuzzy Logic Rule base for this kind

of a scenario where a second shift of the load is required

comes into action.

The rule mentioned below comes into action & the

load is shifted at the bAcET., as shown in Figure 7.

If bAcST ≤L1NeST & L1NeST ≤ bAcET≤ L1NeET =>2nd Shift

Figure 7. Actual Load Schedule.

L1AcST  Actual Load Start Time

Thus the L1AcST= bAcET

All the shiftable loads undergo this exercise to get the

best possible optimal solution to attain the least cost, by

finally reaching COPT = Min (CTOTAL)

2. Results

In this section, key results and benefits from the MAT-

LAB Tool for the 2 level optimization of load-scheduling

of residential loads for diesel mitigationare discussed.

Through the forecast the expected outage can be gen-

erated, thus this expected outage will lead to a first level

demand side management & hence an expected new load

schedule.

Table 1 shows the forecasted diesel savings of various

different outage durations starting at 2 different peak

times in a day. For the case of a 2 hours outage starting at

1pm the expected diesel savings of as high 65.95% can

be seen.

The following Figure 8 explains the aggregate refer-

ence load curve i.e. the original load curve, here we can

see the 2 peaks prominently. The break-up of the load

will be seen in Figure 9.

Next the Figure 10 explains the expected outage lead-

ing to a load shift and finally a new expected load sched-

ule for the next day .

Comparing Figure 11 & Figure 9 we can clearly no-

tice that for an expected outage of 1pm to 3pm the first

level DSM will shift the dishwasher & dryer to a new

time & will schedule them to start at 3pm.

Table 1. Forecasted Diesel Percentages For Various Out-

ages.

Forecasted Diesel Percentage

Savings

Start time/ Outage

duration

hours

7PM

59.80

52.79

54.41 58.60

1PM

65.95

53.52

35.58 29.62

Figure 8. Origin al Day Ah eadLoad C urve.

Figure 9. Individual load schedule.

Figure 10. Expe cted load

schedu le.

A. S. CLOSEPET

1057

shift, this can be observed at an aggregate level in Figure

12 & at an individual level in Figure 13.

Due to the unreliable grid there could be many sce-

narios that are very close to the forecasted outage. Table

2 shows 25 different cases which are very close to the

expected outage.

Here we have assumed a maximum of 1 hour error in

the outage on either sides of the expected outage.

The last column in Table 1 shows the type of shift, i.e.

the green colour shows that those outages are leading to a

real time second shift. The pink colour shows that these

outages are not interfering the first load schedule &

hence need no real time shifting.The Figure 12 shows

the variation in the outage from the expected outage, In

this particular case the actual outage starts at 12pm to

3:30pm.This case involves a real time fuzzy logic based Figure 11. Expected individual load

schedule.

Table 2. Shows 25 different actual cases around a

single

expected 2 hours outage

case.

Table for

Hours

Blackout

SL NO

TYPE OF

CASE

TIME OF

BLACKOUT % SAVINGS

TYPE Of

SHI

ACTUAL

CASE

12PM TO

2PM

75.0

ACTUAL

CASE

12PM TO

2:30PM

70.9

ACTUAL

CASE

12PM TO

3PM

67.8

ACTUAL

CASE

12PM TO

3:30PM

65.4

ACTUAL

CASE

12PM TO

4PM

63.5

ACTUAL

CASE

12:30PM TO

2PM

55.3

ACTUAL

CASE

12:30PM TO

2:30PM

53.8

ACTUAL

CASE

12:30PM TO

3PM

52.8

ACTUAL

CASE

12:30PM TO

3:30PM

52.1

ACTUAL

CASE

12:30PM TO

4PM

51.5

ACTUAL

CASE

1PM TO

2PM

75.0

ACTUAL

CASE

1PM TO

2:30PM

69.6

EXPECTED

CASE

1PM TO

3PM

66.0

ACTUAL

CASE

1PM TO

3:30PM

63.3

ACTUAL

CASE

1PM TO

4PM

61.3

ACTUAL

CASE

1:30PM TO

2PM

75.0

ACTUAL

CASE

1:30PM TO

2:30PM

66.0

ACTUAL

CASE

1:30PM TO

3PM

61.3

ACTUAL

CASE

1:30PM TO

3:30PM

58.4

ACTUAL

CASE

1:30PM TO

4PM

56.5

ACTUAL

CASE

2PM TO

2:30PM

46.7

ACTUAL

CASE

2PM TO

3PM

46.7

ACTUAL

CASE

2PM TO

3:30PM

46.7

ACTUAL

CASE

2PM TO

4PM

46.7

Figure 13. Fuzzy lo gic base d real t ime optimi zed individ ual

load

curve.

Figure 12. Fuzzy logic based r eal tim e optimiz ed

load curve.

A. S. CLOSEPET

1058

Table 3. Shows 25 different actual cases ar ound a single expected 4 hours outage case.

Table for

Hours

Blackout

SL NO

TYPE OF

CASE

TIME OF

BLACKOUT

SAVINGS

TYPE OF

SHIF

ACTUAL

CASE

12PM

TO 4PM 63.51

ACTUAL

CASE

12PM

TO 4:30PM 59.79

ACTUAL

CASE

12PM

TO 5PM 56.63

ACTUAL

CASE

12PM

TO 5:30PM 54.34

ACTUAL

CASE

12PM

TO 6PM 52.22

ACTUAL

CASE

12:30PM

TO 4PM 51.71

ACTUAL

CASE

12:30PM

TO 4: 30PM 48.28

ACTUAL

CASE

12:30PM

TO 5PM 45.02

ACTUAL

CASE

12:30PM

TO 5: 30PM 43.48

ACTUAL

CASE

12:30PM

TO 6PM 41.67

ACTUAL

CASE

1PM

TO 4PM 61.29

ACTUAL

CASE

1PM

TO 4:30PM 57.14

EXPECTED

CASE

1PM

TO 5PM 53.52

ACTUAL

CASE

1PM

TO 5:30PM 51.01

ACTUAL

CASE

1PM

TO 6PM 48.72

ACTUAL

CASE

1:30PM

TO 4PM 56.52

ACTUAL

CASE

1:30PM

TO 4:30PM 51.49

ACTUAL

CASE

1:30PM

TO 5PM 49.10

ACTUAL

CASE

1:30PM

TO 5:30PM 44.44

ACTUAL

CASE

1:30PM

TO 6PM 41.94

ACTUAL

CASE

2PM

TO 4PM 46.67

ACTUAL

CASE

2PM

TO 4:30PM 40.58

ACTUAL

CASE

2PM

TO 5PM 35.90

ACTUAL

CASE

2PM

TO 5:30PM 32.94

ACTUAL

CASE

2PM

TO 6PM 30.43

From Table 2 we can see that this real time shifting of

loads will lead to a diesel savings percentage of 65.4%.

Here we can observe that the real time shift of washing

machine, dryer & dishwasher which was previously

scheduled to start at 12pm, 3pm & 3pm respectively will

now start at 3:30pm.

Similarly Table 3 shows the actual 25 cases that are

possible around an expected outage of 4 hours starting at

1pm. The expected diesel savings from Table 1 is

53.52% where as in reality due to the unreliable grid the

savings percentage can vary from 30.42 % to 63.51%.

3. Conclusions

Use of multi-layer load-shifting techniques to mitigate

power outages in developing countries shows significant

cost savings potential by massive reduction in diesel

consumption by load-scheduling. The maximum diesel

reduction for the consumer due to load shifting during

power outages can be approximately 45% to 75% for a

flat- tariff grid. The study also showed that the actual

savings potential depends on the timing of power outage,

duration and the specific load characteristics. As diesel

prices increase, the economic benefits of load-shifting

are also increase correspondingly. For blackouts of lesser

duration (e.g. 2 hrs) the benefits in saving diesel can be

as much as 75%. For longer blackouts (e.g. 8 hours), the

diesel savings is in the range of 20%-60%. DSM policies

for developing countries should consider specific ap-

proaches to mitigate power outages and provide relief to

customers. Clearly, challenges exist in implementation of

DSM policies since most consumers in India and frugal

markets have outdated appliances that are unintelligent

with a severe need to develop low-cost smart net-

work-controllable solutions as a retrofit.

REFERENCES

[1] Power Annual report 2009-10, CES, Govt. India.

[2] “Energy and the Millennium Development Goals in Af-

rica”, 2006, UN World Summit.

[3] Development Goals in Africa Electricity Supply Moni-

A. S. CLOSEPET 1059

toring Initiative (ESMI), Prayas, India.

[4] V. Foster and J. Steinbuks, “Paying the Price for Unreli-

able Power Supplies” In-House Generation of Electricity

by Firms Africa, 2009

[5] “Real cost of power” 2009, Universal Consulting.

[6] “Energy poverty” World Energy Outlook for UN General

Assembly on Millennium Goals 2010

[7] R. Behera, B. Panigrahi and B. Bhusan, “Hybrid Short

Term Load Forecasting Model of Indian Grid” Energy

Power Engg, 2011

[8] S. Padmanaban and Ashok “Electricity DSM in India

Strategic & Policy Perspective”.

[9] V. Natarajan, A. Baxi, R. Padmanabhan and V. Magesh-

kumar, “Low-Cost Bio Mechanical Energy Generator for

Off-Grid Users in Emerging Markets,” 2011 IEEE

GHTC.

[10] Report, “Action Plan for Energy Efficiency,” 2012, Bu-

reau Energy Efficiency, India.

[11] T. Kim and V. Poor, “Scheduling Power Consumption

with Price Uncertainty,” 2011, IEEE Trans Smart Grid.

[12] Amir-Hamed Mosenian-Rad and A. Leon-Garcia, “Opti-

mal Residential Load Control With Price Prediction in

Real-Time Electricity Pricing Environments,” 2010, IEEE

Trans. Smart Grid.

[13] M. A. A. Pedrasa, T. D. Spooner and I. F. MacGill,

“Scheduling of Demand Side resources Using Binary

Particle Swarm Optimization,” IEEE Transactions Power

System, Vol. 24, No. 3, 2009.

doi:10.1109/TPWRS.2009.2021219

[14] K. Herter, “Residential Implementation of Critical-peak

Pricing of Electricity,” Energy Policy, Vol. 35, 2007.

doi:10.1016/j.enpol.2006.06.019

[15] M. A. A. Pedrasa, Ted D. Spooner and Iain F. MacGill,

“Coordinated Scheduling of Residential Distributed En-

ergy Resources to Optimize Smart Home,” IEEE Trans-

actiond Smart Grid, Vol. 1, 2010.

[16] “Energy Infrastructure in India,” 2011, World Bank

Country Report.

[17] V. Natarajan and A. S. Closepet, “Statistical Analysis of

Cost of Energy Due to Power Outages in Developing

Countries,” 2012, Future Computing Conf Submission,

France.

[18] V. Natarajan and A. S. Closepet, “Demand-Side Ap-

proaches To Manage Electricity Outages In Developing

Countries,” 2012, Energycon, Florence, Italy.