Soft Load Shedding: An Efﬁcient Approach to Manage Electricity

Demand in a Renewable Rich Distribution System

Tayyab Aslam and Naveed Arshad

Department of Computer Science, Lahore University of Managment Sciences, Sector U DHA, Lahore, Pakistan

Keywords:

Demand Side Management, AMI, Clustering.

Abstract:

Matching demand and supply of electricity generation is difﬁcult in a renewable-rich system. This is partly

due to the long-term variability and short-term uncertainty of wind and solar. Utilities use several approaches

to deal with the variations of renewable generation. Some of these include having extra fossil fuel based

peaker plants, managing ﬂexible loads using demand-side management, real-time pricing etc. In this paper,

we present another approach to manage supply variations by introducing semi-ﬂexible loads at the demand

side. These semi-ﬂexible loads are residential loads that cannot be shut down or be moved completely to

another hour but have the possibility to shed a small percentage of their load for a short time. This approach

called soft load shedding is challenging as residential customers have the multitude of energy usage patterns.

In this paper we compare and contrast three soft load shedding techniques and discuss their strengths and

shortcomings in matching the demand with available supply.

1 INTRODUCTION

Renewable sources are taking the center stage in the

generation of electricity. Many countries have plans

to shift to all renewable sources of electricity by 2050

(Connolly et al., 2011; Cosic et al., 2012). While re-

newable sources like solar and wind provide an en-

vironmentally friendly solution to the energy demand

and supply, they also create challenges in the electri-

city distribution system. Figure 1 and 2 shows four

days actual generation and forecasted power genera-

tion of Solar-PV and Wind for Belgium. It is quite

noticeable that the generation is not according to the

forecast. These problems mostly stem from the vari-

ability and uncertainty of renewable sources as varia-

tions in renewable sources output causes a mismatch

between the demand-supply of electricity.

With the current penetration level of renewables,

utilities use different approaches to handle demand

and supply gap. One of the approaches is to have ex-

tra fossil fuel based standby peaker plants. But these

plants are very expensive to run and maintain. Anot-

her approach is real-time pricing where prices of elec-

tricity vary hourly and customers are charged accor-

dingly. But when prices go down real-time pricing

cause a rebound effect which results in another peak

demand. Another technique is demand side manage-

ment (DSM). Many DSM techniques are available in

Figure 1: Solar-PV Power Forcasting for Belgium.

the literature but the ones for residential customers

mostly require direct control over high powered ap-

pliances which is very difﬁcult to implement and may

not be possible legally in some countries. Electricity

storage (Roberts and Sandberg, 2011; Mohd et al.,

2008; Vytelingum et al., 2010) at grid level can also

be used to manage this gap but present storage techno-

logy is expensive and have limits.

Another line of research uses electricity curtail-

ment for managing the variation in supply (Aalami

et al., 2010). In such programs certain ﬁnancial bene-

ﬁts are offered to the customer for reducing their on-

going consumption up to a certain minimum percen-

Aslam, T. and Arshad, N.

Soft Load Shedding: An Efﬁcient Approach to Manage Electricity Demand in a Renewable Rich Distribution System.

DOI: 10.5220/0006774001010107

In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 101-107

ISBN: 978-989-758-292-9

101

Figure 2: Wind Power Forcasting for Belgium.

tage. If participant customer fails to reduce their con-

sumption within the time limit then they get a ﬁnan-

cial plenty. These programs are considered a the last

choice as consumption reduction from the customer

is not guaranteed. Another line of research looks at

varying electricity voltage quilty along with demand-

side management (Craciun et al., 2009). This could

also be used to match supply and demand but com-

promise on electricity quality is often dangerous and

can result in failure of electrical appliances. With in-

creasing renewable penetration we need newer appro-

aches to deal with the problems of renewable integra-

tion into the overall electricity distribution system.

To this end, we are working towards developing a

Deeply Intelligent Demand Side Management System

(DIDS). DIDS integrates the generation and distri-

bution through better forecasting and planning using

various optimization mechanisms. Typically utilities

have ﬂexible and inﬂexible loads. Flexible loads are

industrial, agricultural or other loads that could be ad-

justed within a time window. Inﬂexible loads are lo-

ads that cannot be moved in such a way. We intro-

duce semi-ﬂexible loads that are residential loads that

could not be completely shut down or moved to anot-

her hour but may shed their load by a small percen-

tage.

One of the optimization methods that DIDS uses

is Soft Load Shedding (SLS) of semi-ﬂexible loads.

SLS could be carried out on residential customers

using threshold metering that comes with most AMI

deployments. Of course, SLS could only be carried

out if the customers’ contracts allow for such provi-

sion. For this paper, we assume that such provision

is available in customer contracts. Furthermore, just

like peaker plants are used for only 3-5 of the time,

SLS may only be carried out at times of extraordi-

narily high electricity demand or at the time of low

renewable generation. For example, in Figure 3 a nor-

malized demand and supply situation is presented for

a 24 hours period. The demand is under the supply

Figure 3: Normalize Demand and Generation for a Rene-

wable Rich Electricity System.

throughout the day except for a short period at 18:00-

19:00 hours. This particular time requires some form

of load shifting or load shedding.

Although semi-ﬂexible residential loads are smal-

ler loads, their myriad number adds up to a large share

in the distribution system and could be used together

to shed a certain percentage of the demand. For ex-

ample, the utility distribution company we work with

has a total of 4.1 million customers. Out of which 3.5

million customers are residential customers using up

to around 53% of the total available electricity. One of

the goals of this paper is to develop a scalable techni-

que for SLS. This is important because most of the

techniques we study in the literature are not scalable

beyond a certain number of customers. Moreover, the

SLS approach is a proactive approach like Demand

Side Management (DSM) and not a reactive approach

like Demand Response (DR).

To keep the demand under the supply, the idea be-

hind SLS is that residential customers mostly use at

least a handful of electric devices and household ap-

pliances. Shutting down a limited number of them

may not affect customers’ quality of life, but overall

this step may help to keep the demand under the avai-

lable renewable supply. The decision of which appli-

ance to shut down to reduce the energy usage is left

to the customer through only assigning an allocation

that is based on historical load proﬁles.

Managing the demand for large industrial and

commercial customers has been a thoroughly resear-

ched area in DSM. However, as we will see later in the

paper SLS cannot be applied to residential customers

in a straightforward manner using traditional DSM

techniques. Soft load shedding of semi-ﬂexible lo-

ads requires an insight of customers’ electricity usage

patterns to save energy.

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102

2 APPROACH

DIDS manages the energy balance through the fore-

casts of energy demand and supply for a 24 hours pe-

riod. Hourly generation of renewable energy, in parti-

cular, wind and solar could be predicted with a mean

absolute error (MAE) of as low as 2.5% in some re-

gions for a 24-hour forecast (Miettinen and Holttinen,

2017). Similarly, the day ahead forecasts of demand

at utility-scale has considerably low MAE at around

2% as well(Ghalehkhondabi et al., 2017).

DIDS compares the forecasted demand with the

available forecasted supply. If the demand exceeds

the generation for any set of hours, DIDS employ va-

rious mechanisms to keep the demand under the sup-

ply. Some of these methods include moving ﬂexi-

ble loads to another hour where supply is adequately

available, using backup renewable generation sour-

ces such as stored hydro, or biomass, etc. The de-

tails of these methods will be presented at another

venue. However, if all these methods fall short of

meeting the demand for a particular hour, the systems

resorts of SLS. To develop an SLS schedule, the tra-

ditional techniques of load shedding cannot be app-

lied as these techniques are mostly designed for utility

scale(Laghari et al., 2013). Moreover, an important

goal of SLS is to minimize customer inconvenience

which requires insight into the energy usage behavior

of individual customers before being able to shed any

load.

To get an insight into the energy consumption

behavior, we utilize AMI data to build proﬁles for

each customer using historical usage data at hourly

intervals. This historical data combined with tem-

perature forecast provides the forecasted individual

energy proﬁle of each customer. Similar proﬁles

have been studied by other colleagues as well(Iyengar

et al., 2016). However, since energy usage proﬁles at

ﬁner levels have variabilities across days and seasons,

in DIDS we have used the PARX method of ﬁnding

customer energy demand proﬁles (Ardakanian et al.,

2014). The PARX method requires up to three days of

historical data that is used in conjunction with tempe-

rature readings to forecast day ahead individual custo-

mers’ demand proﬁles. Weekdays and weekends are

forecasted separately in the PARX method. For the

hour(s) where the demand exceeds supply SLS provi-

des a way to assign an allocation of energy for each

customer based on their forecasted usage. In the sub-

sequent section, we discuss three ways of carrying out

SLS.

3 SLS METHODS

To discuss the SLS methods, we use data set of hourly

consumption from a city in Sweden. This dataset con-

sists of 1436 customers. We simulate a 20% shortfall

of energy supply for a single hour i.e. 18:00-19:00.

Thus the goal in the following SLS methods is to re-

duce the demand by 20% to keep it under the avai-

lable supply. To deal with any changes in forecasted

demand or generation, in a real distribution system

the amount of shedding may be slightly higher than

20%. However, for simplicity sake, we assume that

shedding of 20% is enough to keep the demand under

the supply.

Figure 5 shows the 24 hours forecasted usage pro-

ﬁles of a subset of customers on a Saturday. Some

customers have a pretty constant usage of electricity

while for some the usage vary quite a lot during the

day. To keep usage in perspective, we plot the fore-

casted usage of the SLS hour with the total forecasted

usage of the day. This analysis is depicted in Figure 6.

Except for a few outliers, this ﬁgure shows two trend

lines. The ﬁrst line shows a linear behavior which

means that most customers’ total usage of energy is

proportional to their usage in a particular hour. The

second trend line is a horizontal line that shows cus-

tomers whose energy usage is concentrated mostly in

a particular hour. As the data is on a Saturday, these

could be customers who may spend their day outside

and used energy only at a certain time during the day.

3.1 Equal Allocation / Equal Reduction

The ﬁrst SLS method is Equal Allocation, that is

equally distributing the available energy to the custo-

mers. Thus if the available energy during the hour is

around 2772 units each of the 1436 dwellings will get

1.93 KWH for the hour. Apparently, such allocation

seems fair, but on a deeper look, this allocation may

create the following problem. The low-end customers

whose requirement is less than the 1.93 KWH are gi-

ven more electricity as using this method 570 units

are allocated beyond the need of such customers. On

the other hand, 730 customers are the ones on which

SLS will be carried out.

Thus, for the low-end customer, if the electricity

is not utilized the utility will face a loss of earnings

and for the high-end customers, the utility will face

an opportunity loss. Thus equal allocation will not get

the true beneﬁts of SLS as we will not get the energy

savings needed for this particular hour.

A similar method is an Equal Reduction where we

calculate the shortfall which is the difference between

demand and supply. The shortfall number is equally

Soft Load Shedding: An Efﬁcient Approach to Manage Electricity Demand in a Renewable Rich Distribution System

103

Figure 4: DIDS.

divided across the customer base. For example, if

the shortfall is 693 KWH, each member of the com-

munity will face a reduction of 0.47 KWH each to

make up the demand and supply equation. This met-

hod again seems fair, but on a deeper look it may be

a good solution for a high-end customer, but for 77

low-end customers decreasing 0.47 KWH may mean

shutting down the complete supply for the SLS hour.

Thus this method favors the high-end customers while

deals with the low-end customers unfairly.

3.2 Percentage Reduction

In this method instead of allocating/reducing load ba-

sed on KWH, a percentage of the total forecasted load

is reduced from allocation. This means that if the

shortfall is 20%, each customer will get a 20% re-

duction in their allocation of electricity based on their

forecasted demand. Again this method may seem fair

but it favors the high-end customers as 20% reduction

for a lower end customer may mean not be able to

operate a critical appliance during that hour.

3.3 Clustering Based Incremental

Reduction

The Clustering Based Incremental Reduction (CBIR)

method uses the customer proﬁling performed

through PARX method as described previously.

Instead of just looking at the hour where the SLS is

to be carried out it takes into account the total energy

used by a customer in the whole 24 hours forecasted

period. We group this data using K-means clustering

with a threshold of 0.2, and K=3.

We show the results of clustering in Table 1. Cu-

stomers are put in three clusters called low usage,

medium usage, and high usage. The low usage cu-

stomers are around 815 and their average demand in

1.41 KWH with a total share of 33.4%. The medium

usage customers are 489 and their average usage is

2.9 KWH with a total share of 40.9%. The high-end

usage customers are 132 and their average usage is

6.72 with a total share of 25.6%.

The total predicted demand for 1436 houses is

3465 KWH for the SLS hour i.e. 18:00-19:00. With

20% less supply we have to cap the demand at 2772

KWH.

In CBIR our ﬁrst goal is not to carry out SLS

on customers whose usage is below a given thres-

hold. This exemption is to ensure fairness to custo-

mers who are already frugal in their energy usage. Ta-

ble 2 shows customers whose energy consumption is

less than 1 KWH for the SLS hours. Around 221 cu-

stomers are in the low usage cluster but a small num-

ber are in medium usage cluster as well. Other than

fairness, carrying out SLS on these customers yields

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104

a b

c d

Figure 5: Sample predicted demand proﬁles of cutomers.

Table 1: Clustering Result: Demand for SLS Hour.

Cluster Customers Demand (KWH) Demand % Avg Demand

Heigh 132 889 25.6 % 6.72

Medium 489 1420 40.9 % 2.9

Low 815 1156 33.4 % 1.41

Table 2: SLS Analysis with 1 KWH Threshold.

Cluster Cutomers Customers under 1 KWH SLS (KWH)

Heigh 132 0 0

Medium 489 7 0.9

Low 815 221 24.66

only 25.56 kWh in energy savings. Thus in CBIR, we

do not carry out any reduction to these selected low

usage customers.

Beyond these customers, we apply the SLS to all

other customers in the following manner: As shown

in Table 3, the low usage customers will get 19.5%

percent reduced allocation in their expected usage for

saving around 201 units. Similarly, for the medium

usage customers, the allocation is reduced by 21% ex-

cept for the seven customers who are exempted from

SLS. This reduction saves around 297.2 KWH from

this category. Finally, the high-end customers are al-

located 22% reduction allocated saving around 195.6

KWH from this cluster.

In total, CBIR manages to reduce the energy usage

by 694.42 KWH while ensuring that the allocation is

carried out as fairly as possible.

4 DISCUSSION

The implementation of AMI with threshold metering

allows SLS on residential customers. In this paper,

Soft Load Shedding: An Efﬁcient Approach to Manage Electricity Demand in a Renewable Rich Distribution System

105

Table 3: SLS percentage for clusters.

Cluster Customers SLS % Demand (KWH) SLS (KWH)

H with SLS 132 22 % 889 195.6

M with SLS 475 21 % 1415.6 297.2

M w/o SLS 7 0 % 4.5 0

L with SLS 594 19.5 % 1032.7 201.4

L w/o SLS 221 0 % 123.3 0

Total 1436 - 3465 694.42

Figure 6: Data for forecasted demand of 1436 customers.

SLS Hour vs. 24-hrs Demand.

we have merely scratched the surface of evaluating

methods in carrying out SLS.

In CBIR we limit the number of clusters to three.

This number is only a demonstration, and in reality,

the number of clusters may vary according to the

need. Similarly, we ﬁxed the threshold to 1 KWH

while in a real distribution system this may vary from

feeder to feeder, area to area and may also have a dy-

namic value. Stemming from this the percentage re-

duction for each cluster also requires further investi-

gation. For this paper, we choose an arbitrary percen-

tage while in reality, it will depend on the actual gap

between demand and supply and the customer con-

tracts. The actual implementation of SLS is also an

important question. How will the customers get the

information of the assigned allocation? What to do if

the customer does not follow the allocation and use

more energy? Should we shut down the customer’s

connection for a brief period which seems harsh or

we should have enough energy buffer in the system to

allow usage beyond her SLS allocation?

Finally, SLS is a technique that we will need in

future to reduce costs associated with the variability

and uncertainty of renewables. The negative energy

prices in Germany and US in recent months due to

overproduction from renewables requires us to resort

to SLS-like methods to manage the growth of renewa-

ble generation in a controlled manner.

5 RELATED WORK

Managing load shedding has been an area of interest

in the power engineering community. However, so

far this interest is mostly focused on utility-scale load

shedding at feeder levels. Recently some work has

been carried out that takes us towards large-scale SLS.

Chandan et al. described a DR control from the uti-

lity that maximizes the user convenience(?). Howe-

ver, this approach requires deep insight into the appli-

ance level usage of the customers while in our appro-

ach we only utilize the meter data from the last few

days to develop SLS schedule. While their technique

may be more beneﬁcial for the customer convenience,

it requires a lot of data at the appliance level from

the customers which may not be possible for all cu-

stomers at utility scale. Moreover, their technique is

more of a DR technique while ours is more of a DSM

one.

Bashir et al. have proposed Direct Load Control

(DLC) system that can enforce several user-deﬁned

low-power states. It directly controls the devices of

the house to manage the load. (Bashir et al., 2015).

While this may be a good solution, the DLC method

is only applicable when all major appliances have this

control capability. Secondly, for millions of custo-

mers, this means managing tens of millions of devi-

ces. Such control may not be possible without incur-

ring a huge extra cost. Chandan et al. provided the de-

signing for demand response event by analyzing cu-

stomers data of smart meter and weather. (Chandan

et al., 2014)

On customer proﬁling, Ardakanian et al. propo-

sed customer proﬁling using autoregression based on

data from three days, separately for weekdays and

weekends(Ardakanian et al., 2014). Srinivasan et al.

grouped data from a utility into eight consumption

patterns(Iyengar et al., 2016). Albert and Rajagopal

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

106

proposed a technique to similar group customers for

forecasting their energy proﬁles (Albert and Rajago-

pal, 2013).

Queuing-Based energy consumption management

system for residential smart gird is proposed by Yi Liu

et al. (Liu et al., 2016). Mainly there are two types

of demands, essential and ﬂexible demands. Flexible

demands includes delay-sensitive and delay-tolerant

loads. These loads can be controled by residential

smart grid directly and scheduled accordingly. Which

means they need extra resources for controlling these

appliances, for millions of customer it’s not a feasible

solution.

6 CONCLUSION AND FUTURE

WORK

We have introduced SLS to overcome the issues with

conventional DR and DSM techniques and to manage

the variability factor of renewable energy resources.

The main idea behind SLS is to manage the variabi-

lity factor of renewable resources while keeping fair-

ness and customer inconvenience in view. Our future

work involves ﬁnding the most suitable balance be-

tween forcing and requesting the customers to shed

their energy usage at the time of need. Moreover,

since SLS depends on the forecasting of individual

customers’ energy proﬁles, we will be working to im-

prove this forecasting. While consumer level forecas-

ting techniques claim to have 80% accuracy, our goal

is to use contextual information from customers to im-

prove forecasting further. Smart grid testbed (Tushar

et al., 2016) could be use to test our solution and will

help us to imporve our technique.

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