Towards Improving Resilience of Smart Urban Electricity Networks by

Interactively Assessing Potential Microgrids

Eng Tseng Lau

, Kok Keong Chai

, Yue Chen

and Alexandr Vasenev

School of Electronic Engineering and Computer Science, Queen Mary University of London,

Mile End Road, E1 4NS, London, U.K.

Faculty of Engineering, Mathematics and Computer Science, University of Twente, Drienerlolaan 5,

Keywords:

Critical Infrastructure, Grid Architecture, Grid Planning, Grid Resilience, Smart Grid.

Abstract:

When a city adds a renewable generation to improve its carbon footprint, this step towards a greener city

can be a step towards a smarter city. Strategical positioning of new urban electricity components makes the

city more resilient to electricity outages. Money and resilience are two conﬂicting goals in this case. In case

of blackouts, renewable generation, other than distributed combustion generations, can serve critical demand

to essential city nodes, such as hospitals, water puriﬁcation facilities, and police stations. Not the last, the

city level stakeholders might be interested in envisioning monetary saving related to introducing a renewable.

To provide decision makers with resilience and monetary information, it is needed to analyze the impact of

introducing the renewable into the grid. This paper introduces a novel tool suitable for this purpose and

reports on the validation efforts. The outcomes indicate that predicted outcomes of two alternative points of

introducing renewables into the grid can be analyzed with the help of the tool and ultimately be meaningfully

compared.

1 INTRODUCTION

Integration of a renewable into the grid is an entangled

task that concerns multiple domains. One might con-

sider the renewable energy-related landscape (Barjis,

2009) and have the overall aim to reduce greenhouse

gas emission (Zubelzu et al., 2015). For instance, to

ﬁnd a suitable location for a biogas plant, one should

account for distances from the site to the biomass

sources is needed (Dugan and McGranaghan, 2011).

In case of solar urban planning, an important concern

is the interplay between the urban form and solar en-

ergy inputs (Amado and Poggi, 2014). Importantly,

planners should consider how the grid can behave

in case of undesirable conditions (see e.g. (Bennett,

2007; Jung et al., 2016)).

Additionally, there is a need to account for grid

resilience – the ability of the grid to withstand a

failure in an efﬁcient manner. Speciﬁcally, it con-

cerns supplying electricity to critical infrastructures

(e.g., hospitals) during blackouts, as well as the ability

to quickly restore normal operation state. (Bollinger,

2015). Threat analysis related to non-adversarial

and intentional threats (e.g., (Vasenev and Mon-

toya Morales, 2016; Vasenev et al., 2016a)) can high-

light which components may deserve particular atten-

tion. Distributed Generations (DGs) can also be used

to compensate for the discontinuity of electricity pro-

duced by renewables. However, optimizing the cost

of dispatches of DG units is needed to ensure that this

task performed efﬁciently.

To account how the city can beneﬁt from intro-

ducing a new generation, stakeholders might consider

both monetary and resilience aspects. Such decisions

might be located in the context of larger considera-

tions on improving efﬁciency of fault and attack mit-

igation measures, threat ranking, the energy cost and

resilience analysis, and the impact on different critical

infrastructures.

Even though numbers of tools exist to model grids

(as described in Section 2), they lack important fea-

tures to enable an interactive resilience analysis, such

as user interfaces and resilience calculations mod-

ules. To enable city-level stakeholders to account

for resilience, an Overall Grid Modelling (OGM)

was developed to account for introducing renewables

into low voltage (LV) and mid voltage (MV) grid

nodes. The methodology, policy and the development

352

Lau, E., Chai, K., Chen, Y. and Vasenev, A.

Towards Improving Resilience of Smart Urban Electricity Networks by Interactively Assessing Potential Microgrids.

DOI: 10.5220/0006377803520359

In Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2017), pages 352-359

ISBN: 978-989-758-241-7

governing the OGM is available in the documenta-

tions (IRENE, 2016a; IRENE, 2016b). The decision

makers are able to manipulate/control the tool and va-

rieties of resilience coefﬁcient metric and cost analy-

sis across the grid are illustrated whenever a grid com-

ponent modiﬁcation is applied.

This paper reports initial validation efforts related

to the features and functionalities of the tool in terms

of its practicability and efﬁciency. The tool is at the

center of an interactive approach where information

is given to decision makers, who make their choice

which is the best option in terms of grid planning, and

also to evaluate how the introduction of a renewable

increases grid resilience and also account for possible

monetary savings.

The overall organisation structure of the paper

is as follows: Section 2 reviews the state-of-the-

art modelling tools to ensure that the OGM tool is

aligned with standard core functionalities of the ex-

isting tools. Section 3 presents the methodology of

OGM tool usages. Section 4 reports the methodology

of workshop organized that validates the functionali-

ties of the OGM tool. Finally, Sections 5 and 6 dis-

cusses and concludes.

2 STATE-OF-THE-ART

MODELLING TOOLS

In this section, a state-of-the-art modelling and con-

trolling smart grid tools are reviewed. The function-

ality of the smart grid tools and the OGM tool are

cross-related for the functionalities in order to iden-

tify the desired functionality of the advanced smart

grid modelling tools.

DNV GL, the international certiﬁcation body has

developed a microgrid mathematical optimization

tool (DNV GL, 2016) to evaluate the full integration

of distributed generations, electrical, thermal stor-

ages, new innovative technological updates, building

automation and customers behavioural usages. The

simulation is holistic-based and aims at maximizing

the economic value and reliability of electrical sys-

tem and power. The whole model simulates the day-

ahead energy prices, demand forecasts, weather fore-

casts, dynamic performance of the buildings, stor-

age, and distributed generation, and management of

the controllable resources (CHP, storage, and Demand

Response (DR)) that optimize the energy economics

during the day. The optimization problem is formu-

lated through the Mixed Integer Linear Programming

(MILP) approach. The optimization tool is also capa-

ble of shifting its operational module from optimizing

energy economics to maximizing the uninterruptable

and critical load that can be served from available re-

sources during the outage period.

The Massachusetts Institute of Technology (MIT)

has built a laboratory-scale microgrid based on the

earlier model developed from computer simulation

studies (Stauffer, 2012). The project focuses on a

small-scale power system that combines the energy

generation and storage devices to serve local cus-

tomers at low level grid. The Masdar Institute cor-

porates with MIT by concentrating on developing an

analytical-based weighted multi-objective optimiza-

tion within the Microgrid (Stauffer, 2012). The an-

alytical methods analyses the two factors (system

conﬁguration and operation planning) simultaneously

that determines the costs and emissions. The method

generates a set of optimal planning/designs and op-

erating strategies that minimizes costs and emissions

simultaneously.

Siemens PTI provides a consulting, software and

training program to optimize system networks for

generation, transmission and distribution and power

plants for smart grids (Siemens PTI, 2016). The con-

sulting services offer expertise in power system stud-

ies. This includes the system dynamics and threat

analysis, energy markets and regulation, control sys-

tems, power quality, and steady-state and dynamic

system evaluations.

Etap grid has developed a Microgrid Master Con-

troller software (Etap Grid, 2015). The software con-

troller is capable of predicting and forecasting energy

generations and loads. The controller also integrates

and automatically control (automated load shedding

and generation) of microgrid elements, such as PVs,

energy storages, back-up generations, wind, gas tur-

bines, CHP, fuel cells, and demand management. The

software automatically manages and optimizes the

load during grid-connected or islanded grid opera-

tions. The software aims to lower the total cost of

ownership by reducing the average cost of electricity

from the national electricity price.

2.1 Summary of Smart Grid Modelling

Tool Functionalities

Table 1 summarizes important features of the men-

tioned modelling tools. Importantly, most of them

do not provide user interfaces between the software

and users. This can hamper their use in an interactive

manner. Besides, having user interface enables inter-

actions with less experienced users. Also, only the

DNV GL tool accounts for critical loads. As some

urban-level loads in times of blackouts can be more

critical than others, this functionality is particularly

relevant for resilience tools. The same applies for re-

Towards Improving Resilience of Smart Urban Electricity Networks by Interactively Assessing Potential Microgrids

353

silience analysis.

The OGM tool, as described next, particularly fo-

cus on these aspects. Through the mathematical op-

timization module implemented, the important fea-

tures such as the simulation of outage, islanding oper-

ation, cost and resilience analysis are performed. The

users are able to manipulate/control the tool and to

calculate changes in the resilience coefﬁcient when-

ever a new case/scenario is applied (i.e., adding or

remove a local generator). The tool does not only

supports the simulation of electricity continuity plan-

ning (adding/removing alternate generation sources)

from the technical perspective, but also ensures the

cost concerned through the interventions for beneﬁts

of business planning (International Electrotechnical

Commision (IEC), 2014).

Table 1: Summary of microgrids modelling tools in com-

parison with the OGM tool.

Tool DNV GL MIT Masdar Siemen Etap OGM

Institute PTI Grid tool

Functionality

Mathematical

optimization

User interface ready

Demand forecasts

Generation and

storage modelling

Account for critical

loads

Support of

threat ranking

Islanding operation

Scenario/case studies

Outage/contingency

simulations

Cost analysis

Emission analysis

Resilience analysis

Reliability analysis

Power ﬂow analysis

3 THE OGM TOOL

The network topology tree (or the system architec-

ture) is loaded into the GUI and as shown in Fig. 1,

where the architecture included a number of city grid

components. The distribution of grid components as

in Fig. 1 is presented in Table 2.

The OGM tool incorporates a graphical-based

user interface (GUI) (see Table 1). The GUI is to

facilitate continuous interactions with the tool that is

user-friendly, easily controllable and manipulated.

As the tool is aimed for decision makers (Munic-

ipal authority planner, DNO, Developers, Critical In-

frastructure Operator, Business and Citizen Represen-

tative) with various technical/conceptual background,

the tool aims to be easily-interpretable for fellow de-

cision makers, without incorporating complex power-

ﬂow model and analysis. The components can be in-

troduced/removed/moved within the grid.

The tool simulates outage consequences using

the input of known outage scenario (winter/summer;

Figure 1: The baseline system architecture of the OGM tool.

Table 2: Number of distributed generators, energy storages,

types of consumer proﬁles and their populations included.

Node Number of generators Number of

Proﬁles included Populations

no. Non-renewable Renewable energy storage

1 2 2 1 Households 15000

2 3 2 0 Ofﬁces 2

3 4 0 1 Hospitals 2

4 2 0 2 Outpatient clinics 5

5 2 1 2 Supermarkets 5

6 2 0 2 Warehouses 5

7 0 0 0 - -

8 0 0 0 - -

9 0 0 0 - -

10 1 0 2 - -

11 0 1 2 - -

12 0 1 0 - -

13 0 1 0 - -

14 0 0 0 - -

start/stop time) Critical loads are known, as well as

speciﬁcs of generation proﬁles. Then, computations

modules will process the outage scenario. Output

results will demonstrate the monetary savings and

resilience indicator through the component changes.

The decision makers will select most suitable alterna-

tive for grid outage mitigations and repeat the simula-

tion if needed.

This tool assumes that hardware solutions to is-

land a microgrid (de-attach and re-attach it to the

main grid) can be located at the point of coupling

nodes (transformers). Thus, each node with a crit-

ical load might strive to be self-sustaining: balance

the (critical) supply and demand. A node can be ei-

ther connected or disconnected completely from the

main grid. Thus, we have only one connection to the

main grid for each single nodes in the tool. We do

not account for mesh networks. The tool particularly

focuses on threats that lead to outages: (1) those re-

sulting in the disconnection of a node from the main

grid; and (2) outage of a component (e.g., a DG as an

electricity generation element).

Support for threat ranking is another distinctive

feature of the OGM tool. Taking as input threat

analysis methodologies, such as the need to envi-

SMARTGREENS 2017 - 6th International Conference on Smart Cities and Green ICT Systems

354

sion which grid component approaches should be

paid particular attention to. Such analysis can be re-

lated to non-adversarial threats (e.g., (Vasenev and

Montoya Morales, 2016; Vasenev et al., 2016a)) as

well as threats related to intentional disruptive actions

(e.g., (Le et al., 2016; Vasenev et al., 2016b)). This

provides opportunities to enter threat characteristics

to calculate relative value of threat event frequencies.

Analysts, equipped with this information may focus

on grid components where this value is higher of a

predeﬁned threshold.

Concerning technical implementation details, the

GUI of the OGM is developed using IntelliJ IDEA,

the Java IDE software. For the numerical optimiza-

tion algorithm, the dual-simplex algorithm is applied

for such Linear Programming problem of the grid op-

timization. The lp solve 5.5.2.3 (lp solve, 2015) is

applied as the library ﬁle for Java that is called to per-

form the optimization algorithm for the OGM tool.

The conﬁguration as deﬁned in Fig. 1 is simulated.

The tool calculates two indicators – resilience co-

efﬁcients and monetary costs (with or without sav-

ings) – to inform users how the grid would operate

during a blackout. The resilience coefﬁcient in this

paper is computed based on the extents in which the

amount of energy demand within consumers are met

when there is an outage in the grid (Bollinger, 2015).

The resilient coefﬁcient is determined as the mean

fraction of the demand served for the outage node di-

vided by the overall demand to be served. The re-

silience coefﬁcient in this case is therefore the frac-

tion of demand served at dth consumer (P

d,t

) divided

by the total demand D (P

D,t

) in the contingency state

at time t:

α(t) =

d,t

D,t

. (1)

A grid is robust and resilient when the com-

puted resilient coefﬁcient is high, or is maintained

throughout the outage period. The cost savings are

determined based on the difference in between the

business-as-usual operation of the traditional grid

(without capability of islanding, and also without im-

plementation of DGs, energy system storages and re-

newables), and the alternative operation mode, when

DGs, energy storage systems and renewables are acti-

vated.

Fig. 2 shows the example of resilience coefﬁ-

cient and monetary costs calculated for the grid de-

scribed in Fig. 1, where the top panel presents the

plot of monetary savings in relation to the business-

as-usual and the optimised grid planning, and the bot-

tom panel that illustrates the distribution of resilience

coefﬁcient. Negative monetary savings indicate addi-

tional costs, whereas positive savings indicate the cost

Figure 2: Resilience coefﬁcient and monetary costs calcu-

lated for the grid: top panel – plot of monetary savings in

relation to the business-as-usual and the optimised solution;

bottom panel – the distribution of resilience coefﬁcient.

saved through the grid planning improvement. The

resilience coefﬁcient would be between 0 – 1 (the re-

silience coefﬁcient is computed as zero at a particular

time interval when no outage occurs) because of the

fraction of demand served over the overall demand

during an outage event.

4 VALIDATING THE TOOL

4.1 Methodology

The gaming workshop with students was conducted

to validate the applicability of the OGM tool as a sup-

port tool for improving the resilience of the urban

electrical grids. In the beginning of the workshop,

mini-lectures on smart grids were delivered to intro-

duce students to major ideas of smart grids, as well

as the current issues and challenges. The OGM tool

was demonstrated to students to clarify the idea how

modelling tools can be used to improve the resilience

of the overall grid.

During the gaming session, exercise handouts

were given to six PhD students. Students formed

two groups (Group A & B). Within each group stu-

Towards Improving Resilience of Smart Urban Electricity Networks by Interactively Assessing Potential Microgrids

355

dents represented stakeholders (City planner, DNO,

and Citizen & Business Representative). These stake-

holder roles correspond to professionals who might

beneﬁt from using the OGM tool. These profession-

als need to collaborative decide how to introduce new

components or modifying the existing components to

improve resilience of the grid. The system architec-

ture as illustrated in Fig. 1 and Table 2 was used as

the baseline conﬁguration, where the amount of re-

newable sources are low. In addition to the descrip-

tion of the grid architecture, students were briefed on

the changes that the grid context might undertake. It

was suggested that the city grows, hence the popula-

tions within the city are increased, and towards the de-

carbonization plan. Speciﬁcally, amount of city com-

ponents would be as follows: Households = 25000;

Ofﬁces = 3; Hospitals = 3; Outpatient clinics = 5; Su-

permarkets = 5; Warehouses = 6.

After providing the information, students were

asked to discuss what grid updates might be intro-

duced to ensure that a city can withstand a blackout

with less negative impact. The aim of this exercise

is to investigate how the manipulation of the OGM

tool can guide the fellow professionals to improve the

resilience of a complex urban grid, in the context of

collaborative decision making in the situation of un-

certainty.

Two different outage scenarios (4 and 8 hours)

were chosen to examine the resilience of the city in

sustaining both the shorter or longer outages. The

outage in every single node is also examined, be-

cause it is intended to examine the outage effects on

the changes of the supply towards the demand pro-

ﬁle across individual consumer and the overall de-

mand, as well as the changes in the monetary sav-

ings and resilient coefﬁcient in the grid level city as

shown in Table 3. The ‘economic-islanding’ capabil-

ity during the normal grid operation is enabled that

employs DGs, renewable sources and energy storage

systems to provide power at times of high electric-

ity price, rather than drawing the electricity from the

main grid (IRENE, 2016b).

Questionnaires were disseminated to fellow stu-

dents at the end of the workshop.

4.2 Results

In order to access the effectiveness of the collabora-

tive decisions as made by Groups A & B, normal and

failure of grid operations are simulated for each node,

and also the entire microgrid level. Failures occur

when there is a line-disconnection between the mi-

crogrid and main grid level, and also the line discon-

nection within the microgrid nodes. When there is a

Table 3: Type of grid operations and the indicators applied.

Grid operation Economic islanding Indicators

capability Resilient Cost

coefﬁcient saving

Normal

Outage 4 hours

for single node

Outage 8 hours

for single node

Outage 4 hours

for complete grid outage

Outage 8 hours

for complete grid outage

Figure 3: The modiﬁcation of the grid architecture as pro-

posed by Group A.

line-disconnection due to a failure event, the island-

ing capability is activated to ensure uninterrupted op-

eration during a utility system outage with N-1 com-

pliance (IRENE, 2016b). Decisions placed and the

performance of the implemented decisions by each

groups are compared with the baseline case in terms

of resilience coefﬁcients and monetary savings.

The decisions are simulated using the OGM tool

and the timeline for the simulation is allowed for 24

hours. The grid with various operating conditions are

simulated for the baseline case, Groups A & B.

Group A after some discussions proposed to up-

date the base scenario (Fig. 1) as shown in Fig. 3. The

updates were:

i. Move solar PV from Node 2 to Node 7;

ii. Remove one non-renewable generation in Node 2;

iii. Remove one non-renewable generation and add

one energy storage in Node 3;

iv. Add one non-renewable generation in Node 6;

v. Remove solar PV and add one non-renewable

generation in Node 1;

vi. Add one non-renewable generator and one energy

storage in Node 7.

The collaborative decisions as proposed by

Group B, using the base conﬁguration of Fig. 1 were

SMARTGREENS 2017 - 6th International Conference on Smart Cities and Green ICT Systems

356

Figure 4: The modiﬁcation of the grid architecture as pro-

posed by Group B.

presented in Fig. 4, which were:

i. Add two non-renewable generations in Nodes 7 &

ii. Add one solar PV in Nodes 7 & 8;

iii. Add one small-scale wind turbine in Nodes 7 & 8;

iv. Add one energy storage in Nodes 7 & 8.

4.2.1 Case 1 – Normal operation

In this case, assuming no failure occurs, the normal

mode of operation is applied. The cost savings and

resilience coefﬁcient achieved for baseline, Groups A

& B are shown in Table 4.

Table 4: Cost savings and resilience coefﬁcient for normal

operations.

Baseline Group A Group B

Cost savings (£) 1865.39 2112.27 2136.36

Resilience coefﬁcient 0 0 0

Based on Table 4, the collaborative decisions pro-

posed by Group B achieve higher amount of cost

savings than Group A, and also higher than the

Baseline scenario. Hence the decision by Group B

achieves higher amount of cost savings, particularly

for ‘economic-islanding’ normal mode of grid opera-

tions. The resilience coefﬁcients are all zeros. This

is because the grid resilience is not considered dur-

ing the normal mode of operation (without any out-

age events). The simulation excludes the addition of

installation and maintenance costs of individual gen-

erators.

4.2.2 Case 2 – Four Hours of Outage Duration

In this case, it is assumed that an outage within the

microgrid or the entire grid occurs at 0900 for the du-

ration of four hours. The ‘economic-islanding’ capa-

bility is disabled in the case of outage events. Table 5

shows the result of the simulation using the baseline

scenario, Group A & B. Negative sign indicates that

additional costs are introduced (no monetary savings

are achieved). Overall Group A’s collaborative de-

cision promotes highest amount of cost savings than

Group B, and also the baseline case. In all cases crit-

ical loads were served during the outage events. The

computed resilience coefﬁcients are identical.

Table 5: Case 2 – cost savings and resilience coefﬁcient for

outage operations.

Outage Node

Cost savings (£) Resilient coefﬁcient

Baseline Group A Group B Baseline Group A Group B

Node 1 208.47 138.98 208.47 0.21 0.21 0.21

Node 2 -94.79 -29.06 -94.79 0.218 0.218 0.218

Node 3 198.33 368.82 198.33 0.242 0.242 0.242

Node 4 211.16 259.30 211.16 0.131 0.131 0.131

Node 5 206.19 125.25 206.19 0.109 0.109 0.109

Node 6 321.80 205.25 321.80 0.007 0.007 0.007

Grid outage 1286.65 1559.54 1558.27 1 1 1

Total savings

2337.81 2628.08 2609.43 – – –

(£)

4.2.3 Case 3 – Eight Hours of Outage Duration

In the ﬁnal case, it is assumed that an outage within

the microgrid or the entire grid occur at 0900 with

prolonged outage duration of eight hours compared

to Case – 2. The ‘economic-islanding’ capability is

also disabled. Each outage node disconnections is

evaluated. Table 6 shows the result of the simula-

tion using the baseline scenario, Group A & B. Sim-

ilarly as in the previous case, Negative sign indicates

additional costs are introduced (no cost savings are

achieved). ‘Invalid’ indicates that monetary savings

are not calculated as the proportions of the demand at

the particular node during the outage is not met. Over-

all Group B’s collaborative decision promotes highest

amount of cost savings. The installation of a new en-

ergy storage system and also the removal of one of

the non-renewable generation in Node 3 proposed by

Group A results in insufﬁciency of energy supply to

match the fraction of demand to be served for hospital

loads during the outage in Node 3. The low resilient

coefﬁcient as computed in Node 3 suggests the failed

portion of demand (0.252 − 0.15 = 0.105) served in

Node 3 during the outage.

Towards Improving Resilience of Smart Urban Electricity Networks by Interactively Assessing Potential Microgrids

357

Table 6: Case 3 – cost savings and resilience coefﬁcient for

outage operations.

Outage Node

Cost savings (£) Resilient coefﬁcient

Baseline Group A Group B Baseline Group A Group B

Node 1 310.83 136.55 310.83 0.219 0.219 0.219

Node 2 -189.66 -159.42 -189.66 0.208 0.208 0.208

Node 3 272.31 Invalid 272.31 0.252 0.12 0.252

Node 4 225.49 116.06 225.49 0.132 0.132 0.132

Node 5 234.59 -12.35 234.59 0.106 0.106 0.106

Node 6 546.84 267.97 546.84 0.064 0.064 0.064

Grid outage 1817.43 1850.51 2118.89 1 1 1

Total savings

3217.83 – 3519.29 – – –

(£)

5 DISCUSSION

Overall, the gaming exercise was successfully con-

ducted with pros and cons of the grid component al-

terations within the collaborative decisions made by

two groups, in comparison with the baseline case. Ad-

ditionally, the gaming workshop also noted the exten-

sive collaboration within students in successfully in-

creasing the resilience of the electricity network that

is prone to outage events.

The feedback questionnaire is shown in Table 7.

Based on Table 7, the outcomes of the gaming ses-

sion showed that the tasks related to grid update (in-

cluding the introduction of renewables and changes in

the consumption) could be effectively performed in an

understandable manner. Results can be compared and

a better alternative (with respect to some criteria) can

be selected.

Participants indicated that the tool can be

used even without having advanced domain-speciﬁc

knowledge. Five participants also agreed that the

OGM tool is practicable for evaluation of urban elec-

tricity network. Positive scores were obtained about

the practicability of the demand management, con-

trolled generations, islanded operations, critical loads,

disconnected and uninterruptible loads in the OGM

tool.

However, one of the participant outlined the dif-

ﬁculty in understanding the given scenario and de-

manded more relevant data in order to provide better

decisions, rather than the overall grid outlook. Sev-

eral participants also pointed out the need for addi-

tional amount of data given (particularly to advanced

knowledge of decision makers) in order to provide a

clearer indication of grid component modiﬁcations.

Still, some additional explanations are needed before

using the tool through the lectures. For instance, the

participant indicated that the resilience coefﬁcient ap-

plied is not completely understandable, as well as

some advanced functionalities of the OGM tool were

not clear.

Table 7: Questionnaire results.

Question Rating scale

(1 – Very negative, 7 – Very positive)

1 2 3 4 5 6 7

Number of respondents

Q1. Knowledge on Smart Grids 0 0 1 4 1 0 0

Q2a. Practicability of demand management capability 0 0 0 2 2 1 1

Q2b. Practicability of controlled generations 0 0 0 1 2 3 0

Q2c. Practicability of islanded operation during outage 0 0 0 0 2 1 2

Q2d. Practicability of disconnected load during outage 0 0 0 1 2 3 0

Q2e. Practicability of critical loads 0 0 0 0 1 2 3

Q2f. Practicability of uninterruptible loads 0 0 0 2 1 2 1

Q3a. Effectiveness of OGM tool in addressing outage 0 0 0 1 2 1 2

Q3b. Effectiveness of threat ranking support 0 0 2 0 3 1 0

Q4a. Speed of OGM tool to run/re-run a simulation 0 0 0 1 2 0 3

Q4b. Speed of OGM tool to construct/re-construct 0 0 0 1 1 1 3

grid components

Q5a. Level of knowledge required in using the tool 1 0 2 1 0 1 1

Q5b. Level of easiness in using the tool 1 0 0 1 1 0 3

Q6. Reason for rating as 5 or above in Q5. –There are many components and

the users must have knowledge

in understanding them.

–The GUI of the toolset is

easy to use.

–The scenario is complex, and

more data is preferred to make

decision, instead of just having

an overview.

There are many components in the

tool which will require background

knowledge.

It would be more useful to indicate

electricity ﬂow direction.

It is very convenient to add/delete

a component in the tool.

–Functionality of the component

is not quite clear.

–Types of power supplies are not

illustrated accurately.

–The tool is useful for designing

the city development.

–Key parameter can be provided

graphically.

–Panel to support add/drag icons.

–More data to make decisions.

–More description of components.

–Specify the different kind of

threat and the type of hazardous

disconnection.

–Distance or distribution of grid

planning is not fully presented.

–Capacity of generators should be

provided.

–Costs and distance analysis

should be considered in the tool.

Q7a. Understandable of resilient coefﬁcient metric 0 0 2 2 2 0 0

Q7b. Understandable of threat ranking 0 1 1 2 1 1 0

Q8a. Practicability of resilient coefﬁcient metric 0 0 0 2 3 1 0

Q8b. Practicability of threat ranking 0 0 0 2 2 2 0

Q8c. Practicability of grid evaluation 0 0 1 1 1 1 2

Q9. Speed in providing analysis 0 0 0 0 1 2 3

Q10. Usefulness in addressing outage 0 0 0 1 0 4 1

Q11a. Usefulness of the tool as a collaborative 0 0 0 1 2 2 0

decision support system

Q11b. Usefulness of the tool in establishing 0 0 0 1 2 3 0

collaborative frameworks among stakeholders

6 CONCLUSION

This paper presents an approach to improve resilience

of smart urban electricity networks by using a deci-

sion support tool to assess the potential microgrids.

The OGM tool is developed that allow fellow deci-

sion makers to manipulate/control the tool in examin-

ing the resilience coefﬁcient metric and the potential

monetary savings across the grid are illustrated when-

ever a grid component modiﬁcation is applied.

Overall, the obtained perspectives of the OGM

tool from decision makers (simulated by students)

through the workshop are indeed useful not only to

improve the usability of the OGM tool, but also to

SMARTGREENS 2017 - 6th International Conference on Smart Cities and Green ICT Systems

358

improve the overall understanding of decision mak-

ers. Different cases and solutions were presented

that showed the trade-off in between the resilient co-

efﬁcient and monetary savings (e.g. one may wish

to increase the resilience of electricity network but

may result in extra investments). The tool would be

needed to make the point that all those complex as-

pects should be considered by minimizing such trade-

offs.

In summary, the idea and logic of using the tool

for grid planning are well-received. Based on the

feedback obtained, the tool can be further improved

by providing more descriptions of the grid scenario,

data information such as the capacity of generations

and demands, better navigation of adding/removing

components, and a clearer description of the OGM

tool.

The tree representation of the grid architecture is

a ﬁrst step in the OGM tool. As the grid architecture

does not account for mesh networks, moving to the

meshed grid is the future work.

REFERENCES

Amado, M. and Poggi, F. (2014). Solar urban planning:

a parametric approach. Energy Procedia, 48:1539–

1548.

Barjis, J. (2009). Collaborative, participative and interac-

tive enterprise modeling. In Enterprise Information

Systems, Proceedings of the 11th International Con-

ference ICEIS 2009, Milan, Italy, May 6 – 10 2009,

pages 651–662. Springer Berlin Heidelberg.

Bennett, B. (2007). Understanding, assessing, and respond-

ing to terrorism: Protecting critical infrastructure and

personnel, volume 4 of 10. John Wiley & Sons.

Bollinger, L. A. (2015). Fostering climate resilient

electricity infrastructure. [Online]. Available:

http://repository.tudelft.nl/islandora/object/

uuid:d45aea59-a449-46ad-ace1-

3254529c05f4/datastream/OBJ/download. [accessed

06.12.16].

DNV GL (2016). Microgrid optimizer - a holistic oper-

ational simulation tool to maximize economic value

or electrical power reliability. [Online]. Available:

http://production.presstogo.com/ﬁleroot7/gallery/

DNVGL/ﬁles/original/

3a1dd794f6ff46b9a279175c15af0f11.pdf. [accessed

05.12.16].

Dugan, R. and McGranaghan, M. (2011). Sim city. IEEE

Power and Magazine, 9(5):74–81.

Etap Grid (2015). Power technologies

international. [Online]. Available:

http://etap.com/Documents/Download [accessed

19.01.17].

International Electrotechnical Commision (IEC) (2014).

White paper - microgrids for disaster preparedness

and recovery with electricity continuity and systems.

Technical report, IEC WP Microgrids, Switzerland.

IRENE (2016a). D2.2 – root causes identiﬁcation and soci-

etal impact analysis. Technical report.

IRENE (2016b). D3.1 – system architecture design, supply

demand model and simulation. Technical report.

Jung, O., Bessler, S., Ceccarelli, A., Zoppi, T., Vasenev,

A., Montoya, L., Clarke, T., and Chappell, K. (2016).

Towards a collaborative framework to improve urban

grid resilience. In Proceedings of 2016 IEEE Inter-

national Energy Conference (ENERGYCON), 4 – 8

April, pages 1–6. IEEE.

Le, A., Chen, Y., Chai, K. K., Vasenev, A., and Mon-

toya Morales, A. L. (2016). Assessing loss event fre-

quencies of smart grid cyber threats: Encoding ﬂex-

ibility into fair using bayesian network approach. In

Proceedings of the 1st EAI International Conference

on Smart Grid Inspired Future, 19 – 20 May 2016,

Liverpool, United Kingdom, pages 43–51. Springer

Verlag.

lp solve (2015). Introduction to lp solve 5.5.2.5. [Online].

Available: http://lpsolve.sourceforge.net/5.5/. [ac-

cessed 19.10.16].

Siemens PTI (2016). Power technolo-

gies international. [Online]. Available:

http://w3.siemens.com/smartgrid/global/en/products-

systems-solutions/software-solutions/planning-

data-management-software/PTI/Pages/Power-

Technologies-International-(PTI).aspx. [accessed

19.01.17].

Stauffer, N. (2012). The microgrid - a small-scale ﬂexi-

ble, reliable source of energy. [Online]. Available:

http://energy.mit.edu/news/the-microgrid/. [accessed

19.01.17].

Vasenev, A. and Montoya Morales, A. L. (2016). Analysing

non-malicious threats to urban smart grids by interre-

lating threats and threat taxonomies. In Proceedings

of 2016 IEEE International Smart Cities Conference

(ISC2), 12 – 15 September 2016, Trento, Italy, pages

1–4. IEEE.

Vasenev, A., Montoya Morales, A. L., and Ceccarelli, A.

(2016a). A hazus-based method for assessing robust-

ness of electricity supply to critical smart grid con-

sumers during ﬂood events. In Proceedings of the 11th

International Conference on Availability, Reliability

and Security, ARES 2016, 31 August – 02 September

2016, Salzburg, Austria., pages 223–228. IEEE.

Vasenev, A., Montoya Morales, A. L., Ceccarelli, A., Le,

A., and Ionita, D. (2016b). Threat navigator: group-

ing and ranking malicious external threats to current

and future urban smart grids. In Proceedings of the

1st EAI International Conference on Smart Grid In-

spired Future, 19 – 20 May 2016, Liverpool, United

Kingdom, pages 184–192. Springer Verlag.

Zubelzu, S., Alvarez, R., and Hernandez, A. (2015).

Methodology to calculate the carbon footprint of

household land use in the urban planning stage. Land

Use Policy, 48:223–235.

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