A Novel Demand Side Management (DSM) Technique for Electric Grids

with High Renewable Energy Mix using Hierarchical Clustering of Loads

Muhammad Arbab Arshad, Syed Hasnain and Naveed Arshad

Department of Computer science, Lahore University of Management Sciences, Sector U, DHA, Lahore, Pakistan

Keywords: Demand Side Management, Hierarchical Clustering, Quota Allocation, Machine Learning.

Abstract:

Shortfall can occur at irregular times in an electric grid that has high a concentration of intermittent renewable

energy sources. Many methods are being studied, proposed and used to change the demand in order to match

the supply with the most common being Load Curtailment. New DSM techniques have evolved as a result

of advancements in AMI technologies. The goal is to minimize the difference between supply and demand

at the time of shortfall. Our proposed algorithm selects consumers and limits their energy consumption by

proﬁling the commercial sites based on their historical consumption behaviour. Then, to save the required

amount of energy, the sites with peak consumption levels with respect to their own daily usage are targeted.

Thus, it harnesses the maximum potential of electricity deduction from a site while minimizing its effects on

the residents.

1 INTRODUCTION

Due to global warming and other environmental prob-

lems associated with the fossil fuels, the whole world

is trying to shift to renewable energy sources to pro-

duce electricity. For example,by 2050 the generation

from wind and solar energy sources will be 2400 GW

and 2700 GW respectively, which will contribute 60%

to the mix of renewable energy (Zou et al., 2017).

Also, recently the policies have been introduced to

encourage installation of Solar PV systems among

residential consumers (McKenna et al., 2018). Un-

like conventional power sources, renewable energy

sources are intermittent. The production of electricity

from such renewable sources is dependent on factors

that we cannot control, such as sunlight, intensity and

speed of wind. Increasing popularity and integration

of renewable energy might be a good step so solve

environmental issues, but it is making generation less

controllable. Due to this problem, the integration of

these sources becomes difﬁcult.

The nature of demand from renewable energy sources

requires new methods from demand side manage-

ment. One (old) method, as shown in ﬁgure 1, to

overcome the issue is to store the extra energy at the

time of extra generation and then use it at the time of

need; called Electrical Energy Storage (EES). (Chen

et al., 2009) However, this method is very expensive,

for example it cost $410/KWh for storage in Li-ion

Figure 1: EES in peak shaving and load leveling.

batteries (Obi et al., 2017). Different approaches are

being used by utilities to achieve this goal and such

approaches can be broadly classiﬁed into Direct Load

control or indirect load control. Direct load control is

the administered control of appliances by utility in or-

der to shed load at certain times. While indirect load

Arshad, M., Hasnain, S. and Arshad, N.

A Novel Demand Side Management (DSM) Technique for Electric Grids with High Renewable Energy Mix using Hierarchical Clustering of Loads.

DOI: 10.5220/0007721501370142

In Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2019), pages 137-142

ISBN: 978-989-758-373-5

137

control involves techniques like real time pricing or

incentivizing consumers to reduce their electricity us-

age (Allcott, 2009).

Figure 2: Different Clusters (Dendogram). Hierarchical re-

lationship is shown within different sites based on their elec-

tricity consumption behaviour.

section − 2 represents various effective ap-

proaches proposed in the literature for the DSM.

Then, section −3 explains our proposed methodology

after mentioning the gaps in literature while also ex-

plains its signiﬁcance and mathematical formulation.

At the end, section−4 and section −5 expands on the

results and possible improvements in future research

respectively.

2 RELATED WORK

Load Curtailment has previously been done on re-

gional/feeder level due to underdeveloped metering

infrastructure. However, with the development of

smart electric grids and advance metering infrastruc-

ture (AMI) , it is now possible to target individ-

ual consumers.One such approach, called soft load

shedding, is presented by (Aslam and Arshad, 2018).

Their approach makes use of clustering the consumers

based upon the amount of electricity foretasted and

limiting their usage through Cluster-based Incremen-

tal Reduction (CBIR) algorithm. Also, (Bashir et al.,

2015) have proposed Direct Load Control (DLC) sys-

tem that administers the devices of the site to limit

the overall consumption. However, for this technique,

every major device should be controllable by utility.

Additionally, due to the lack of authority over control

of these devices for consumers, many consumers may

not want to enroll into the program. Another problem

is the complexity of the system. For millions of con-

sumers, this method can get very complex. (Stenner

et al., 2017) have shown that customer distrust can re-

duce willingness to participate in Direct load Control

(DLC) programs. Utilities have to develop the trust

among consumers, which is a problem in itself.

(Erdinc et al., 2018) have proposed an incentive

model to attract more users to the DLC program. But

this method was only developed for Heating, venti-

lation, and air conditioning (HVAC) systems. Again,

this method requires utility companies to set the tem-

perature level of HVAC systems of the consumers,

and thus is impractical and also expensive(as ex-

plained earlier). (Xia et al., 2017) have also proposed

an incentive-based model to increase the effectiveness

of DSM programs. (Chrysikou et al., 2015) state that

people start losing interest in these kinds of programs

with time and hence, are no longer effective. In 2005,

Rocky Mountain Power -a company based in Utah,

USA, evaluated the use of their differential pricing-

based tariffs. They found that in opt-out schemes, up

to 98% of participating consumers chose to leave the

program after the mandatory period had been com-

pleted (Holyhead et al., 2015)

(Chandan et al., 2014) described a demand re-

sponse control by the utility that maximizes the users

convenience. However, this approach requires data

at the granularity of each appliance in site. (Hus-

sain et al., 2015) present a review of several demand

response techniques with a view on pricing signals,

appliance scheduling, optimization and their beneﬁts.

(Lu et al., 2018) proposed an artiﬁcial intelligence

based dynamic pricing demand response algorithm.

(Allcott, 2009) presents different schemes of real time

pricing in electricity markets.

The complexity and cost associated with DLC sys-

tems makes it difﬁcult to be deployed on large scale

while dynamic pricing has its own drawback. Our

proposed algorithm is not only inexpensive and eas-

ily deployable but also it eliminates the uncertainty

in consumer behaviour (as would have been observed

through peak pricing) by limiting their consumption

levels in advance.

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138

Figure 3: Same cluster Commercial sites.

Figure 4: Different cluster Commercial sites.

3 METHODOLOGY

The ultimate goal is to allocate quota to users to limit

their electricity usage. It can be thought of as a hy-

brid of indirect and direct load control programs, as

the utility has the control over maximum limit of elec-

tricity usage, while consumers have the choice of how

they want to use their allocated quota. After sites with

similar consumption behaviours are clustered, then at

the time of shortfall the cluster with maximum contri-

butions to the grid load is selected and each site within

it is allocated a quota.

3.1 Assumptions

The ﬁrst assumption is that the shortfall is given or in

other words the demand and supply for the next day is

known in advance (without forecasting).Here it is im-

portant to note that the commercial sites have a pre-

dictable usage routine which could be predicted using

various Time Series forecasting techniques. In case

the sites do not show a trend and have a lot of vari-

ance in its usage behavior then our proposed strategy

is not applicable and we might need to devise a ﬁx

quota allocation for those outliers. Secondly, the con-

sumption behaviors of people are assumed to remain

A Novel Demand Side Management (DSM) Technique for Electric Grids with High Renewable Energy Mix using Hierarchical Clustering of

Loads

139

same throughout selected time frame.

3.2 Data Acquisition And Synthesis

The data used to apply the technique consisted of

100 commercial/ industrial sites with 5-minutes en-

ergy usage granularity obtained from the Greenbut-

ton. The data consisted of different attributes but the

consumption over time of all the sites was compiled.

After compilation, to simulate the shortfall present at

a point in time, some attributes of the data were also

synthesized with a deﬁnite scheme. At each point, the

probability of a shortfall was considered 16%. If the

shortfall happens then the value of shortfall will be

under 20% of the cumulative demand of all sites at

that time.

3.3 Clustering And Algorithm

Development

The primary motivation of the work is to be able to

allocate a fair amount of quota to each site by exploit-

ing similar trends in consumption patterns of differ-

ent sites. To make different clusters machine learn-

ing technique of hierarchical clustering was used. R

was used to synthesize data and apply clustering using

HClust package. After clustering, the cumulative us-

age of each cluster was calculated. Different numbers

of sites were accompanied in each cluster which re-

ﬂected the consumption patterns similarity in a more

granular way. However, this presented challenge in

cluster selection for soft-load shedding since now, the

cumulative consumption of a cluster does not reﬂects

that the sites in that cluster are consuming electricity

at their peak. To overcome this issue, the values were

normalized so that we should be able to check which

cluster contributes the most with respect to its own

consumption behavior and irrespective of other peo-

ple’s behavior. After this a Global Deduction Rate

(GDR) is deﬁned which implies how much of the

power will be deducted from the usage value of a par-

ticular consumer (as represented in ﬁgure 5).

The selection of site within the cluster is considered

to be random. After this a point is selected where the

shortfall is present and the above mentioned steps are

performed until the shortfall is met. In every next al-

location, previous sites are white-listed and a round

robin method is used to traverse through the clus-

ters.We have used Hierarchical clustering and its re-

spective algorithm is represented in Algorithm 1.

Algorithm 1: Hierarchical clustering.

1: procedure SIMPLE HAC(d1, ..., dN)

2: for n ← 1 to N do

3: for i ← 1 to N do

4: C[n][i] ← SIM(dn, di)

5: I[n]  keeps track of active clusters

6: A ← []  Clusters as sequence of merges

7: for k ← 1 to N − 1 do

8: i, m ← argmax

<i,m>:i6=mAI[i]=1AI[m]=1

C[i][m]

9: A.APPEND (< i, m >)  Store merge

10: for j ← 1 to N do

11: C[i][ j] ← SIM(i, m, j)

12: C[ j][i] ← SIM(i, m, j)

13: I[m] ← 0  Deactivate cluster

14: return A

Figure 5: Flow Chart of Algorithm.

4 RESULTS AND DISCUSSION

4.1 Case Study

Based on the similar trends in their usage patterns

sites were divided into ﬁfteen clusters. The division

of clusters and how much they differ from each other

according to previously mentioned criteria can be

observed in ﬁgure 2.

Radial phylogenetic tree in ﬁgure 2 shows that each

site is represented at its leaf and the arrangement

describes the similarity between their usages. The

SMARTGREENS 2019 - 8th International Conference on Smart Cities and Green ICT Systems

140

Figure 6: User consumption Vs allocated quotta at shortfall.

credibility of this technique can be illustrated if we

draw the normalized value of their usage across a

day and observe the difference between the usage

trends of sites in a single cluster vs. sites in different

clusters. The three sites in ﬁgure 3 represent a single

cluster. It can be seen how they show load peaks at

almost the same time, show increase/decrease in con-

sumption within same time period and mostly show

a single peak along a day. In contrast, if we look at

ﬁgure 4 then there is no clear relation between usage

pattern of these three sites which have been selected

randomly (site number 10, 20 and 30 belonging to

cluster 4, 5 and 6 respectively). Each site has its

own peak time, different trend for increase/decrease

in consumption, and shows multiple peaks differing

from each other.

The algorithm presented in the paper provides an

end to end solution for allocating quota to each

site. Let’s look into the scenario where a shortfall is

present with GDR equal to 25%. The results from

simulations and the allotted quota to each consumer

is displayed in ﬁgure 6

4.2 Communication with Smart Meters

The proposed algorithm requires the forecast of elec-

tricity twenty-four hours in advance. Based on the

forecasted values of generation and consumption of

electricity, the algorithm is triggered. Then, based on

those ﬁgures, recommendations will be made by the

system to limit the consumption of electricity for par-

ticular users. Having the values in advance enables us

to inform the users prior to the demand response ac-

tions. This notiﬁcation system can possibly add to the

convenience of the users, leading to voluntary actions

by them to limit the electricity consumption. Also,

currently the granularity level is just one hour but the

planning can be made better by increasing the granu-

larity to 15 minutes level. For the prediction of next

ﬁfteen minutes, mean bias error (MBE) of just 1.3%

could be achieved (Raza et al., 2016).It is advised to

use the spinning reserves as the buffer to accommo-

date the user’s energy demand. It will ensure the sta-

bility of the grid in case of a wrong forecast circum-

stances.

5 CONCLUSION AND

LIMITATIONS

The presented algorithm exploits the existing func-

tionality of advance metering infrastructure to carry

DSM, thus reducing the cost and making the solu-

tion feasible. In future we plan to improve the algo-

rithm by making the value of GDR dynamic instead

of ﬁxed. Also, the forecasting modules will be imple-

mented with the algorithm to make an end to end so-

lution. The system relies on the forecasted values and

does not takes into account the validity of such fore-

casts. So, the scenarios where the demand becomes

lower than the generation or vice verse needs to be

accommodated. Also, to understand the consumer’s

perspective we plan to conduct an on-site experiment

in near future.

A Novel Demand Side Management (DSM) Technique for Electric Grids with High Renewable Energy Mix using Hierarchical Clustering of

Loads

141

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