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Analyzing Mission Impact of Military Installations Microgrid for Resilience

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This article develops a method to model, analyze, and design military microgrids with the objective to improve their resilience in the face of disconnections from the larger electrical grid. Military microgrids provide power to installation and base facilities to enable base mission objective accomplishments that are related to national security. Previous research, tools, and methods for microgrid design and assessment do not adequately address resilience in terms of accomplishing mission objectives and instead primarily focus on economic outcomes. This article proposes a novel metric to quantify microgrid resilience in terms of its ability to minimize the impact of power disruption on missions supported by the microgrid. The metric is used in a novel design method to ensure an islanded military microgrid can continue operations while disconnected for a two-week duration. Our model examines the ability to continue mission operations subject to various microgrid disruptions as well as equipment reliability.
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systems
Article
Analyzing Mission Impact of Military Installations Microgrid
for Resilience
Christopher J. Peterson 1, Douglas L. Van Bossuyt 1,* , Ronald E. Giachetti 1and Giovanna Oriti 2


Citation: Peterson, C.J.; Van Bossuyt,
D.L.; Giachetti, R.E.; Oriti, G.
Analyzing Mission Impact of Military
Installations Microgrid for Resilience.
Systems 2021,9, 69. https://doi.org/
10.3390/systems9030069
Academic Editor: Vladimír Bureš
Received: 5 August 2021
Accepted: 10 September 2021
Published: 15 September 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Systems Engineering, Naval Postgraduate School, Monterey, CA 93943, USA;
christopher.j.peterson54.civ@us.navy.mil (C.J.P.); regiache@nps.edu (R.E.G.)
2
Department of Electrical Engineering, Naval Postgraduate School, Monterey, CA 93943, USA; goriti@nps.edu
*Correspondence: douglas.vanbossuyt@nps.edu
Abstract:
This article develops a method to model, analyze, and design military microgrids with
the objective to improve their resilience in the face of disconnections from the larger electrical grid.
Military microgrids provide power to installation and base facilities to enable base mission objective
accomplishments that are related to national security. Previous research, tools, and methods for
microgrid design and assessment do not adequately address resilience in terms of accomplishing
mission objectives and instead primarily focus on economic outcomes. This article proposes a
novel metric to quantify microgrid resilience in terms of its ability to minimize the impact of power
disruption on missions supported by the microgrid. The metric is used in a novel design method to
ensure an islanded military microgrid can continue operations while disconnected for a two-week
duration. Our model examines the ability to continue mission operations subject to various microgrid
disruptions as well as equipment reliability.
Keywords: microgrid; resilience; system architecture; risk analysis; defense; mission impact
1. Introduction
The US Department of Defense (DoD) is increasingly interested in improving energy
security and increasing resilience at its installations and base facilities [
1
]. The DoD is
concerned with the loss of power from the electric utility, whether due to natural weather
events or intentional attack, which can disrupt the base’s operations. Microgrids are
systems that can increase the resilience of military facilities to provide power during
interruptions by providing multiple redundant local power sources and infrastructure
independent of the larger electric utility. Whenever the base becomes disconnected from
the grid, the microgrid can operate in what is called “island mode” and continue to provide
power to critical electrical loads [2,3].
Analyzing microgrid resilience remains an ongoing topic of interest. Existing resilience
analysis techniques primarily focus on economic analyses [
4
6
]. While cost is important
to the military, a focus on cost often ignores the mission impact of power loss to national
security functions the military microgrids support. Further, it is very difficult to monetarily
quantify national security [
7
,
8
]. Base resilience is best achieved by minimizing the mission
impact of the loss of utility power.
This paper contributes to the literature on DoD facility resilience a modeling and
analysis method, and an associated resilience metric, to support the design of microgrids
to minimize mission impact in the face of power disruptions. We approach the microgrid
resilience issue from a systems engineering perspective because many command-level
decisions on funding allocations and assessment of base and force readiness are made
from this perspective [
9
,
10
]. We define overall resilience of the microgrid as the expected
electrical disruption mission impact (EEDMI). Our approach differs from extant work that
classifies loads as either critical or non-critical or using what is essentially a binary scale.
We assign a mission impact to each load, which is derived from the Mission Depdencency
Systems 2021,9, 69. https://doi.org/10.3390/systems9030069 https://www.mdpi.com/journal/systems
Systems 2021,9, 69 2 of 19
Index (MDI) [
11
,
12
], allowing for a prioritization among all the critical loads when trying to
minimize mission impact. Using this approach, microgrid resilience is tied to continuation
of installation operations rather than only purely economic or power requirements.
2. Background and Literature Review
Microgrids consist of connected loads and energy generation sources (e.g., diesel
generators, photo voltaics (PVs), etc.) with a variety of potential control systems and
operating philosophies that often include energy storage systems (ESS) (e.g., chemical
batteries, thermal storage, etc.) [
13
]. Many microgrids can either be connected to a larger
power grid or operate in “island-mode” temporarily disconnected from the larger power
grid [14].
Military microgrids are deployed for various reasons such as to increase electrical
power security to meet mission requirements, reduce energy life cycle costs, increase
utilization of renewable energy resources, and provide a supply of electrical power to
remote areas [
3
]. Although deliberate attacks on microgrids are not typical [
15
], military
microgrids can be a more attractive and likely target due to the importance of their mission
and national security value.
Threats to a military base’s energy security result from a variety of sources, including
disruption of power from the utility grid, reliability of components on the base, damage
to the grid due to accidents or natural disasters, and deliberate attack (both physical
and cyber) [
16
,
17
]. Navy guidance prescribes each Navy installation to consider the
likelihood and impact of each of the various threats to energy security [
10
]. While it is
desirable to protect against all possibilities, balancing the costs of security measures against
the likelihood and impacts of potential threats must be undertaken [
16
,
18
]. A holistic
approach based on risk and consequences of all threats is necessary to effectively design
the system [10,18,19].
A variety of efforts are currently underway to better understand and improve re-
silience of both civilian and military microgrids. Much existing research on optimizing
microgrid design focuses on cost objectives, where minimizing life cycle cost is a prior-
ity [
4
6
,
20
]. When load shedding is factored in, a cost is assigned to the unmet load [
21
25
].
However, scant guidance is available on how to assign a monetary value to lost load when
the load’s intrinsic value is national security [
7
,
8
]. Other research assigns the amount of
load shedding allowable as a constraint [
22
,
24
]. Microgrid resilience research has been
primarily focused on a variety of contexts that are not directly applicable to military micro-
grids or do not take into account some of the nuances that make military microgrids distinct
from civilian microgrids. For instance, many focus on civilian microgrid infrastructure
where deliberate attack is not taken into account and often instead focuses on adverse
weather events [
26
28
] or on recovery actions that may not be practical during a deliberate
attack [
29
]. Other research does not define critical loads in a manner that is applicable to
the military [30].
Guidance documents for energy security of military microgrids attempt to optimize
microgrid design through maximizing the reliability of meeting critical loads given a fixed
investment or by targeting a specific reliability value and minimizing a life cycle cost
objective function with reliability as a constraint [
9
,
31
,
32
]. However, cost-based resilience
analysis in practice often focuses on reducing operating costs during normal operating
conditions (e.g., peak shaving, etc.) rather than on improving resilience [
7
,
8
]. Research
into deliberate attacks on large electric grids and how to design said grids to defend
against such attacks has been conducted [
23
]. Methods to support military microgrid
design decisions based on cost, stakeholder needs, uncertainty, and other factors have been
developed [
33
,
34
]. Within most of the above efforts, a variety of design parameters are
used, such as system response to disruptions, component selection, economics, control
system schemas, etc.
DoD guidance and policy have focused primarily on some form of maintaining
critical mission functions in defining microgrid resilience. In the military context, multiple
Systems 2021,9, 69 3 of 19
definitions of resilience exist and often contain some or all of the following elements: (1) the
ability of the system to continue to function despite a disturbance, (2) the ability of the
system to maintain vital functions despite a major disruption, and (3) the ability of the
system to minimize the duration and impact of a disruption or set of disruptions [
35
].
DoD Instruction 4170.11 defines energy resilience as “the ability to prepare for and recover
from energy disruptions that impact mission assurance on military installations” and
critical energy requirements as “critical mission operations on military installations or
facilities that require a continuous supply of energy in the event of an energy disruption or
emergency” [
36
]. Within 10 U.S. Code §
·
101(e)(6), energy resilience is stated as “the ability
to avoid, prepare for, minimize, adapt to, and recover from anticipated and unanticipated
energy disruptions in order to ensure energy availability and reliability sufficient to provide
for mission assurance and readiness, including task critical assets and other mission
essential operations related to readiness, and to execute or rapidly reestablish mission
essential requirements” [
37
]. This is similar to resilience definitions and assessments
outside of the military context [
38
40
]. Recent resilience analysis work within a military
systems context has focused on breaking resilience up into multiple phases of a system
design and operation process with associated terminologies to suit each phase of a system’s
life cycle [
41
,
42
] and across multiple methods of increasing resilience in a system [
43
].
Further, there have been arguments made to use resilience in analysis of alternatives for
military systems [
44
] and from a mission engineering perspective [
45
]. Our proposed
measure is similar to the value-oriented measure described by Ferris [
46
,
47
], except we
provide a means to define value as mission impact, whereas Ferris presented a general
framework.
DoD doctrine prescribes installation resilience using a days-of-autonomy metric.
Naval facilities are required to have seven days of autonomy as driven by UFC 3–540-01,
which dictates the amount of onsite fuel storage for backup generators [
48
]. The Army
requires 14 days of power and water for critical missions [
49
]. For the Marines, the metric
is prescribed as the ability for installations to “stay mission operable off of the grid for at
least 14 days” [
50
]. In our professional experience, DoD resilience doctrine is interpreted
by base energy managers as requiring one plausible method of providing electrical energy
to specific critical missions for the mandated days of autonomy (e.g., 7 days, 14 days, etc.).
The days of autonomy is selected by each service branch based on how long it is expected
to be able to replicate specific critical missions performed at a base with a power outage at
other bases not impacted by a power outage. This paper defines resilience of a military
microgrid as the ability of the microgrid to maximize performance of critical missions
powered by the microgrid against the entire set of potential disruptions, considering both
the likelihood and impact of each disruption. This resilience definition aligns with the
military’s needs for performing its missions in the face of all potential adverse events.
For the purposes of the research presented in this article, we propose that resilience of
a military microgrid be defined as the ability of the microgrid to maximize functionality
of critical missions powered by the microgrid in the event of a disruption. Maximizing
resilience means the microgrid provides the maximum functionality against the entire set of
potential disruptions, considering both the likelihood and impact of each disruption. This
definition of resilience most closely aligns with the overall functional requirement of mili-
tary microgrids. The proposed definition also closely aligns with the resilience objectives
for military facilities within the doctrine and guidance documents reviewed above.
2.1. Understanding the Value of Resilience for Military Microgrids
In many civilian microgrid cases, the value of resilience can be defined in terms of real
dollars [
51
] such as in industrial applications where the loss of production or material in
process due to a power loss can be determined [
52
]. For military microgrids, the “product”
is national defense, which does not have an easily defined value [
7
]. No standard for
defining the value of resilience exists within the DoD [
8
]. Some methods proposed to
quantify the value of resilience to the military by equating it to the cost of providing the
Systems 2021,9, 69 4 of 19
resilience such as the cost of backup generators [
51
]. Other methods to define a value for
resilience include using the cost to relocate the mission or buy services to complete the
mission [
53
] and calculating a monetary customer damage function (CDF) based on the
duration of outage [52].
Instead of approaching the value of resilience of military microgrids from a purely
monetary perspective, some have approached the issue by assigning a mission dependency
index (MDI) to facilities that captures the relative criticality of a facility [
11
,
12
]. Several
deficiencies have been identified in MDI, including: (1) inconsistencies in application,
(2) time dependency of corrective actions, and (3) the MDI scoring equation [
54
]. Further,
MDI does not take into account that the Navy’s Resilient Energy Program Office (REPO)
has recently shifted to using the Energy Security Assessment Tool (ESAT), a spreadsheet
model that aids in identifying and prioritizing gaps according to the Navy’s “3 pillars
of energy security”, namely efficiency, resilience, and reliability [
55
]. However in our
professional experience, ESAT can suffer from the same issues as MDI. While the Navy’s
REPO has shifted to ESA for some applications, we observe MDI still being used broadly
across the DoD. In spite of the identified deficiencies of MDI, it is the most accepted and
widely used method of quantifying the criticality and importance of a particular facility
from a resilience perspective.
2.2. Microgrid Resilience Analysis Techniques Built upon This Research
Several researchers have developed extensions to the research presented in this article
based on Peterson’s masters thesis [
56
] and upon an early draft manuscript of this article.
The research presented in this article is fundamental to others’ works. Kain et al. [
57
]
present a method to analyze specific local external threats to a military microgrid such as
wildfires, truck bombs, airplane crashes, etc. and relies on the method developed in this
article to prioritize improving resilience of the microgrid by adding additional storage and
generation at individual critical loads to form self-isolating nanogrids. Herster-Dudley
built upon this work to investigate how human factors can delay recovery times for military
microgirds that have suffered damage [
58
]. Hildebrand developed cost models based on
the research presented in this article [
59
]. Beaton used the research presented here to
develop an analysis tool to determine if distributed energy storage would result in higher
resilience in specific scenarios of interest to military installations [
60
]. Bolen et al. [
61
]
integrated the research presented in this article with research from several other authors to
provide decision-makers with several perspectives on military microgrid resilience. This
article provides the fundamental underpinnings of many others’ works and is novel.
3. Methodology
This section presents a novel modeling and analysis method to analyze the mission im-
pact of power disruptions on a military installation using the proposed expected electrical
disruption mission impact (EEDMI) metric. The method can optimize microgrid archi-
tectures, thus maximizing mission achievement. The method does not take into account
issues such as phase imbalances, power factor issues, energy flow direction, and related
issues, because we are interested in the higher-level engineering trades necessary for mini-
mizing mission impact. This idealization of the problem space is typical of other high-level
architectural microgrid methods [62].
3.1. Step 1: Define Mission and Associated Load for Each Facility
The model links power usage to mission, and the first step is to identify the mission
each facility contributes to, the load associated with conducting the mission, and the impact
any loss of power would cause to mission accomplishment. We define mission impact
MI
as a measure of the base commander’s preference for completion of a particular mission.
Bases conduct many missions—e.g., a base might provide in-service engineering to the
fleet, logistics support, and test and evaluation of new weapons. In order to quantify
MI
,
an energy manager can either use existing MDI scores (our recommendation) or develop
Systems 2021,9, 69 5 of 19
their own method of quantifying the importance of each facility’s mission to the base and
to national security. In general, MI is attempting to answer the questions:
1. What is the importance of the operation a facility supports?
2. Does disruption cause further loss?
3. Can the mission be delayed, moved to other facilities, or achieved by other means?
4. To what degree can the mission continue without the power source?
5.
Is the mission impacted by other resources (e.g., water, fuel, etc.) other than power
loss that might cause discontinuation?
We advocate using MDI to quantify
MI
. However, if MDI scores are not available
for all facilities on a DoD base, a subjective
MI
score could be developed similarly to the
concept of utility in decision analysis [
63
]. Due to the current methods of quantifying the
importance of specific loads to national defense (MDI and ESAT),
MI
is currently a unitless
measure and the range of
MI
is arbitrary and based on the method of quantification.
For instance, MDI is a 0–100 scale as implemented in DoD; a different scale could be used,
such as 0–500, if an energy manager were to develop their own process for producing
MI
.
However, in the future, a new national defense quantification method may be developed
with an associated unit. We adopt the 0–100 MDI scale for MI.
3.2. Step 2: Generate the Set of Failure Scenarios
The systems engineer then generates the set of failure scenarios
S
that could disrupt
the power supply and estimates the probability of each occurring over the course of
a year, here denoted as
Pr(S=s)
for each specific scenario
s
. Events for equipment
failure should use historical data if available. This step should also consider the impacts
of other systems and factors upon which the microgrid is reliant such as fuel delivery
disruption, spare parts unavailability, etc. Existing sources of data and quantification
processes for predictable failure scenarios originating outside of a site boundary can be
used for DoD bases such as weather events, earthquakes, and other natural and man-made
hazards [
64
,
65
]. However, deliberate attacks are currently challenging to postulate and
predict in a way that is applicable to all DoD bases. Instead, an energy manager must
evaluate the specific circumstances of a specific DoD base, relevant current and anticipated
future threat postures, and other relevant conditional information to construct plausible
attack scenarios. For instance, a DoD base located on an isolated island with no civilian
population likely does not need to consider a vehicle-based improvised explosive device,
but a DoD base with direct road access to population centers likely does. It should be noted
that not all militaries take the same approach as the DoD when locating bases and when
determining the level of integration a base will have with the surrounding community.
For that matter, DoD bases do not have a one-size-fits-all approach to how a base is located
and how it is connected to or protected against the surrounding community. Thus, it is
important that practitioners carefully assess each individual military base to determine
potential failure scenarios of interest.
We recommend that the following failure scenarios, in order, should comprise the
minimum set of scenarios analyzed. Additional scenarios (e.g., forest fire, improvised
explosive device, tsunami, plane crash, etc.) should be chosen based on the specific
circumstances of the base. Variations of the minimum set of scenarios may be justified if
PV is a major generation source or if other time-of-year-related factors may significantly
influence microgrid operations (e.g., higher failure rate of ESS and diesel generators in
winter or summer due to extreme temperatures).
1.
Island mode over mission duration (e.g., 7 days, 14 days, etc.) with no additional
failures (baseline scenario);
2. Island mode over mission duration with random failures of hardware components;
3. Island mode over mission duration with no access to diesel fuel resupply;
4.
Island mode over mission duration with random failures of hardware and no access
to diesel fuel resupply;
Systems 2021,9, 69 6 of 19
5.
Additional scenarios of specific interest to a base such as malicious and human-caused
events (e.g., physical attack, cyber attack, plane crash, etc.), and natural disaster events
of interest to a base (e.g., tornado, flood, wildfire, etc.).
3.3. Step 3: Determine the Recovery Time
The systems engineer then determines the recovery time of each component in the
microgrid. This can be a probability distribution to account for the variation in repair
times, and is generally scenario-dependent. For instance, the repair times during a natural
disaster will likely be higher than those due to equipment failure due to widespread impact
of a natural disaster in the area and availability of resources to perform repairs.
3.4. Step 5: Simulate the Microgrid System
The systems engineer then simulates the microgrid system using a model developed
with information collected in the above steps and initial systems engineering analysis
effort. The modeling effort is described in detail in Section 4.1 below. The model generates
the impact of each event in terms mission impact (
MI
). The model determines the load
shedding and behavior of the system and facilities and then calculates
MI
using the
function mapped in the previous step. Since load shedding and subsequent
MI
varies
depending on conditions, demand, and starting state of the system, we advocate using a
Monte Carlo simulation for each scenario; however, other simulation methods can be used
if desired. The mean
MI
over all iterations of the Monte Carlo simulation,
Ms
, quantifies
the expected mission impact over that specific scenario s.
3.5. Step 6: Calculate the Total Mission Impact
The systems engineer then calculates the total impact of disruption events over all
considered failure scenarios, defined here as expected electrical disruption mission impact
(
EEDMI
). This quantifies the resilience of the system against all expected threats and
disruptions. We calculate the mission impact for a single scenario as the
MI
per unit time
(T) of the entire duration scenario,
Ms=
T
t=1
MIs,t(1)
We calculate EED M I as,
EEDMI =
sS
Pr(S=s)Ms(2)
Assuming MDI has been used for
MI
,
EEDMI
is unitless. Otherwise,
EEDMI
may
have units based on what quantification was chosen in Step 1 above. The
EEDMI
serves
the purpose of comparing different microgrid architectures for a base with the lowest
EEDMI
signifying the microgrid architecture that best supports the base’s missions. When
using MDI or other quantifiable measures,
MI
allows for a ordinal ranking of all the
microgrid architectures, which is what we use it for.
3.6. Step 7: Analyze Results
Finally, the systems engineer inspects the results to uncover the main drivers of
mission impact and evaluates possible changes to the system configuration, specifications,
or operation to minimize those effects. Inspection of the contribution of each scenario
to
EEDMI
informs the systems engineer which scenarios or probabilities contribute the
most. The systems engineer can iterate the analysis for different microgrid architectures as
required, generating the EEDMI for alternate designs for comparison.
Systems 2021,9, 69 7 of 19
4. Illustrative Military Microgrid Analysis and Results
This section demonstrates how a system engineer could apply the proposed model
and method to analyze the resilience and mission impact for a microgrid on a DoD base.
Figure 1presents the microgrid baseline architecture. The baseline microgrid consists of
two electrical feeders denoted as BUS1 and BUS2 connected to each other, with the utility
grid connection at BUS1. The microgrid distribution system is typical of office buildings
and local distribution systems found on military bases.
Figure 1. Baseline Example Microgrid System One-Line Diagram.
The baesline microgrid includes two power-generation sources of two diesel genera-
tors, each with 300 kW capacity, that are operated together, which is a typical operating
mode at some bases, and one 3000 m
2
PV array operating at 18% efficiency. The microgrid
has an ESS in the form of a battery system for energy storage with capacity of 3000 kWh
with a charge/discharge rate of 300 KW/h, which stores power to balance load and demand.
The PV and ESS sizing of the baseline microgrid is typical of demonstration renewable
energy projects found on some bases. The PV and ESS can help to supplement the diesel
Systems 2021,9, 69 8 of 19
generators in island mode and provides a small level of redundancy. The installation has
5000 gallons of fuel storage split evenly between the two diesel generators sufficient for one
week operation. Table 1summarizes the facilities, power demand when emergencies occur
(the critical loads), and
MI
on a per hour basis derived from MDI if the loads represented
by the facilities are lost.
Table 1. Summary of Facilities within Baseline Microgrid Model. Adapted from [66].
Load Facility Type Floor Area ( f t 2)Avg CL (kW) Max CL (kW) MI
EP1 Small Office 5500 2.8 5.8 10
EP3 Small Office 5500 2.8 5.8 15
EP4 Medium
Office 53,628 32.3 71 61
EP5 Large Office 498,588 267 523 93
EP6 Warehouse 52,045 10.9 31 74
Total 315.8 636.6 253
The control logic seeks to maximize resilience of the system against an unpredictable
long-term outage of utility power. For this reason, the ESS is maintained in a fully charged
state when connected to utility power with no attempt to reduce costs through peak shaving
or other means. When in island mode, if demand exceeds generator capacity, then the
additional energy needed is drawn from the battery. The PV energy charges the battery
when excess PV energy is available and otherwise supplements the diesel generators and
ESS. The microgrid only sheds loads after demand exceeds both the generator capacity and
battery storage capacity.
Failure rates of the components in the microgrid come from publicly available data
sources [
66
70
]. Reliability block diagrams were developed to calculate the overall failure
rate for each power line link within the microgrid, including between the PV array, the ESS,
the diesel generators, the loads, and other microgrid components.
4.1. Modeling Approach
The model determines the power flow within the microgrid, the pertinent states of the
equipment within the microgrid such as the ESS charge state, PV power being generated,
and state of the components that make up the microgrid. The model records demand not
met and required load shedding and calculates the resulting impact to mission. Power
demanded at each facility and PV power generated are stochastic and time-dependent.
The simulation model is implemented via a set of MATLAB scripts, which are available
in [
56
]. The active power balance equation
Plo ad =Pgenerated +Pbattery
is used to model
the microgrid’s power system in steady state, which can change hourly [
71
]. Of note,
this model can be used for either AC, DC, or hybrid microgrid analysis with any power
conversion losses included in the model through efficiency of each distributed resource the
power balance equations.
The simulation model uses the normal hourly load data demanded at each facility,
as was discussed in Section 4and shown in Table 1, which is derived from [
66
] for this
case study. The data source contains ten years of hourly load data. During island mode
operation, the model modifies the demand profiles to only power critical loads and sheds
non-critical loads. We assume that the base has determined for each facility what loads
must be served during the island mode instances and have a control system to switch
off non-critical loads. The model calculates the amount of PV power generated using
solar incidence received at the ground, PV area, and efficiency using solar incidence data
from the National Solar Radiation Data Base [
72
]. The model can optionally be set to run
over a randomly selected two-week window in a specific year of solar data or run over a
specific two-week window, which can illuminate potential microgrid architecture issues
related to PV sizing during winter months if PV is a significant contribution to generation
capacity.
Figure 2
shows an example of the output from the simulation model during a
Systems 2021,9, 69 9 of 19
two-week island mode operation with a 72 h generator failure event. Figure 3shows an
example output of a 72 h loss of the PV array and the connection between the BUS1 and
BUS2 feeders.
Figure 2.
ESS Storage and Generator Fuel Storage in 72-H Generator Failure Scenario. Note that the
Generators are Slaved to One Another and Provide Identical Output.
Figure 3.
ESS Storage during 72-H Loss of the PV Array and the Connection Between the BUS1
BUS2 Feeders.
The simulation implements failures or attacks using the operational state of each of the
components within the system; i.e., either operational or not operational. The operational
state of each connection to the feeders (e.g., BUS1, BUS2) between nodes within the
microgrid may be user-defined as an input to the simulation. This allows the user to
define a scenario of interest for the simulation, such as failure of multiple elements for a set
amount of time. The model performs Monte Carlo simulations to calculate the overall risk
and effects given the stochastic nature of load and PV generation. The user can vary the
failure rates and repair times to capture the sensitivity of the grid resilience to different
factors. This informs the systems engineer by allowing her to evaluate different types
of equipment and sparing and repair strategies to be used when operating the system
to maximize its resilience. Table 2presents the summary of inputs and outputs in the
simulation model.
Systems 2021,9, 69 10 of 19
Table 2. Simulation Model Inputs and Outputs.
Input Output
Generator size Power flow
Generator fuel storage, resupply probability and timing Generator fuel level
ESS storage and maximum output ESS state of charge
PV array area and efficiency Mission impact
Map of load shed to mission impact
Hourly facility loads
Functional state of each component (optional)
Failed component and recovery time (optional)
Solar incidence
4.2. Scenario Investigation
The subsequent subsections investigate specific failure scenarios and iterate on the
microgrid design as deemed necessary. The baseline scenario where all microgrid equip-
ment is functioning properly and no events beyond the initial grid disconnect occur is
first investigated. Then random equipment failures are added to the baseline scenario
which provides motivation for a redesign of the microgrid. Next, the case of missed diesel
resupply is investigated. Then several additional scenarios of interest (cyber attack, plane
crash, weather event, etc.) are investigated to determine if further redesign of the microgrid
is necessary. A practitioner must carefully analyze their specific installation’s scenarios
of interest.
4.3. Baseline Scenario
The baseline microgrid architecture operating in island mode due to electrical grid
disruption over a 14 day duration with no random failures and successful diesel resupply
shows that the microgrid successfully completes its mission over 1000 Monte Carlo runs
across a year of solar data. The results of the runs indicate the baseline microgrid archi-
tecture is sufficient to sustain all critical loads in this scenario—an
EEDMI
of zero results
from this scenario.
4.4. Baseline Scenario with Random Failures
Random failures are now introduced into the baseline scenario where each component
in the microgrid is provided an annual probability of failure and mean time to repair
(MTTR). Each failure mode is run on 1000 Monte Carlo simulations to calculate
Ms
, and then
the mean and standard deviation (SD) of each
Ms
for each failure mode is calculated.
Table 3
shows the input values for probability of failure (used in later
EEDMI
calculations)
and MTTR, and the output of the mean and standard deviation of
Ms
for each failure mode.
The failure data come from publicly available data sources [6670].
Systems 2021,9, 69 11 of 19
Table 3. Ms
Due to Equipment Failures with Probability of Failure on a Yearly Basis, and with
MTTR (Hrs).
Failures of Connections Between Feeders, and Loads and Equipment
Node 1 Node 2 Pr(Failure) MTTR Mean MsSD Ms
BUS1 BUS2 3.980 ×1035 7.1 89.2
BUS1 EP1 2.230 ×1035 1680 0
BUS1 EP5 2.592 ×1036 15,624 0
BUS1 EP6 2.955 ×1035 12,432 0
BUS1 GEN1 1.795 ×1036 4181.6 3210.2
BUS1 GEN2 1.795 ×1036 4243.5 3218.5
BUS2 ESS 1.141 ×10219 820.2 546.4
BUS2 EP3 2.773 ×1035 2520 0
BUS2 EP4 3.860 ×1035 10,248 0
BUS2 PV 1.163 ×10219 0 0
Failures of Power Generation and Storage Equipment
Component Pr(Failure) MTTR Mean MsSD Ms
GEN1 3.763 ×10218 4181.6 3210.2
GEN2 3.763 ×10218 4243.5 3218.5
ESS 7.592 ×10284 820.2 546.4
PV 1.533 ×10324 0 0
Failures of Multiple Equipment and Links
Component(s) Pr(Failure) MTTR Mean MsSD Ms
GEN1 & GEN2 7.526 ×10236 26,382.3 4732.6
GEN1 & PV 5.768 ×10542 13,031.8 1203.8
GEN1 & ESS 2.856 ×103102 8009.1 3231.6
ESS & PV 7.745 ×102108 3316.9 259.9
The results shown in Table 3indicate that in the baseline microgrid architecture
the PV array is not needed to reduce
Ms
, which is expected because the PV is sized as a
demonstration renewable energy project that is under-sized to support situations where one
of the two diesel generators fail. However, the ESS is important to support the microgrid
because the two diesel generators cannot meet peak critical loads and must rely on the ESS
providing power during those times.
The
EEDMI
can be calculated for the above set of failure scenarios using Equation (2).
In the above set of failure scenarios, the total
EEDMI
is 2797.4. This can serve as a useful
baseline reference when examining what changes could be made to an existing microgrid
architecture. Improvements in resilience will decrease EEDMI.
A systems engineer may want to use these results as motivation to adjust PV and
ESS sizing to better account for failures that may occur while the microgrid is islanded
such as a diesel generator failing. For instance, increasing ESS and PV size to a 10,000
m
2
PV array and a 10,000 kWh ESS with a 1000 kW max charge/discharge rate virtually
eliminates load shedding events when one of the two diesel generators is offline and results
in 1.5 Mean
Ms
and SD
Ms
33.9. This indicates that only in very rare circumstances, when
there are multiple back-to-back days with very low solar energy, will a load be shed in this
scenario. Throughout the rest of the illustrative analysis, the above PV and ESS sizing is
used. Re-running the simulations performed above produces an
EEDMI
of 407.8, which is
an order of magnitude less than what the
EEDMI
was prior to resizing the PV and ESS.
Such a significant reduction in
EEDMI
indicates that the PV and ESS resizing significantly
improved resilience of the microgrid.
We observed that many of the events in the Monte Carlo simulation are binary such as
equipment being in one of two states: operational or failure. Consequently, the summary
statistics do not always convey the full picture of what may occur. For instance, in just
under half of the simulations, no load shedding occurs when the ESS is lost. Similarly, loss
Systems 2021,9, 69 12 of 19
of a single generator results in no load shedding in two thirds of the simulations. Loss of
the PV array never results in load shedding. In many cases where the SD is 0, the critical
loads that lose power are always the same based on the microgrid control logic.
4.5. No Diesel Resupply Scenarios
The next set of scenarios examines what happens when the diesel generator fuel
bunkers are not resupplied at the expected interval. A missed diesel resupply can occur
for a wide variety of reasons, and DoD microgrids that are overly reliant on regular diesel
delivery during a prolonged island mode period may have excessive load shed events.
The microgrid architecture is sized to require refuel of the diesel generators every seven
days. Because of the redesign of the microgrid to incorporate significantly more PV and
ESS capacity, the Mean
Ms
is 115.1 and the SD
Ms
is 695.9. This indicates that in most
scenarios, the PV and ESS are sufficient to support all critical loads. The next step is to
re-run the no-diesel scenario including random equipment failure as was done previously,
which has been omitted here.
4.6. Additional Scenarios of Specific Interest
This section explores the mission impact and response of the system against natural
disasters, malicious events, and human-caused events. The intent is to explore the resilience
of the microgrid against atypical failures, extended failures of portions of the system due to
catastrophic failures, and failure of multiple portions of the system due to common cause
failures, natural disasters, or deliberate attack, all of which are of particular relevance to
the DoD.
4.6.1. Malicious and Human-Caused Events
We separate malicious and human-caused events from natural disasters to highlight
the specific issue of assigning probability of occurrence to malicious and human-caused
scenarios. For instance, assessing the likelihood of an adversary conducting a cyberattack,
an attack on a portion of the microgrid, and other malicious attacks is challenging. Likewise,
assessing human-caused events such as military plane crashes is equally challenging.
In order to understand the contribution that malicious events and human-caused events
make to
EEDMI
, we propose assigning a probability on an annual basis of 1
×
10
2
to these
scenarios in the absence of reliable data. For instance, while many attempts at quantifying
cyberattack probability exist in the literature, we suggest that military microgrids are
potentially a bigger target for cyberattack versus their civilian counterparts, including
zero-day exploits that are difficult to predict.
A cyberattack could lead to the failure of the system to switch to only supplying
critical loads on the loads supplied by the BUS2 feeder in the event of the failure of the
connection between the BUS1 and BUS2 feeders. If the switch to critical loads within the
system only occurs when disconnected from utility power and operating in island mode,
noncritical loads could continue to receive power. The DoD facility may be unaware of
the failure of the connection between the BUS1 and BUS2 feeders due to the ability of the
ESS and PV array to continue to meet normal loads. Under all investigated durations of no
non-critical load shedding on feeder BUS2 and with the connection between the feeders
disconnected, mean and SD
Ms
remained 0, meaning that the system is very resilient to this
type of cyberattack failure. Other cyberattacks targeting other portions of the microgrid
may be warranted based on specific microgrid architectures and is omitted here. It is
important for a cybersecurity threat assessment to be conducted regularly on microgrid
infrastructure to determine if specific components have become more vulnerable to attack
and then re-run a cyberattack scenario to determine if Mshas significantly changed.
The scenario of a large portion of the microgrid being destroyed by an airplane
crash is next investigated. Many bases are co-located or in close proximity to airfields.
In this scenario, we assume that the grid connection is severed, both diesel generators
are destroyed, and the link between the BUS1 and BUS2 feeders is severed for 72 h until
Systems 2021,9, 69 13 of 19
firefighting and disaster response operations have concluded so that utility crews can
access and re-energize the BUS2 feeder. In this scenario, mean
Ms
is 22,523.3 with an SD of
10,320.9. This indicates that an airplane crash could cause significant critical load shedding
in the first 72 hours to EP1, EP5, and EP6. After the connection between the BUS1 and BUS2
feeders is re-energized, the three critical loads are picked back up by the the PV and ESS.
The scenario of a deliberate attack on the distribution system that supplies the three
facilities with the highest
MI
is next examined. In this scenario, the transformers for EP4,
EP5, and EP6, which are located above ground, are destroyed while at the same time the
utility power grid in the region is bombed, destroying several high voltage transmission
towers feeding area substations. While in this scenario, the base has spare transformers
on-site and crews ready to dispatch to replace the destroyed transformers, it is assumed that
it takes 96 hours to restore power to the EP4, EP5, and EP6 loads due to a delay in securing
the base and ensuring utility crews will not be targets of a second attack and conduct the
repair work. Power from the utility grid is not restored for 14 days due to the off-base
destroyed transmission towers. Under this attack, a mean
Ms
of 21,888 and SD of 0 results.
Many additional malicious and human-caused events likely exist for military micro-
grids. Location-specific and threat posture-specific scenarios should be postulated by base
energy managers and then analyzed in the same manner as was done above.
4.6.2. Natural Disasters
We now focus on natural disasters. Natural disasters are generally well-understood,
and the probability of occurrence of a wide range of natural disasters has been determined
for locations throughout the globe. The specific natural disasters relevant to a particular
military microgrid must be selected by the base energy manager. Several representative
scenarios are examined below.
A scenario where a wildfire enters a base is next investigated. In this scenario, the wild-
fire destroys the grid connection, the ESS, and one diesel generator. While in the past,
wildfire season was contained to the summer and fall months, recent fires in the American
west point toward year-round fire season. Thus, the Monte Carlo simulation continues
to run across an entire year rather than only one period of the year. The resulting mean
Ms
is 5922.7 and SD is 16.9. Future
EEDMI
calculations of this scenario use a probability
of 5
×
10
2
/year, which is based on a generic estimated wildfire frequency in California.
Practitioners should adjust the probability based on their specific base’s location and local
wildfire patterns.
A scenario where a debris flow going through a base following a wildfire or atmo-
spheric river event is next investigated. Burn scars and atmospheric river events can
produce large debris flows, which can destroy infrastructure downstream. This has oc-
curred with unfortunate regularity in the American west in the last few decades. In this
scenario, we assume that one diesel generator, the grid connection, and the connection
between the BUS2 feeder and the PV array are destroyed. This results in a mean
Ms
of 21,805.6 and SD of 1582.4. A probability of occurrence of 2
×
10
1
/year is used in
future calculations.
4.7. Total Impact to Mission
After all potential events and scenarios have been investigated and
Ms
and probability
data have been tabulated for each,
EEDMI
is calculated from Equation (2). The
EEDMI
is
5283.9 for the scenarios discussed above. The contribution to
EEDMI
of the equipment
failure scenarios is 407.8, which is an order of magnitude lower than the contribution of
the additional scenarios of specific interest. This indicates that a base energy manager
should investigate potential upgrades or modifications to the microgrid architecture to
better protect against the additional scenarios of specific interest.
Systems 2021,9, 69 14 of 19
4.8. System Design Impacts on Resilience
The following analysis investigates the effect that different design choices not directly
related to resilience improvements may have on microgrid resilience. The first alternative
explored is the control strategy of the ESS charge. We initially assumed that maintaining the
ESS in the fully charged state would maximize resilience of the system. The investigation
tests the possible consequence of using the ESS to perform peak shaving to maximize the
financial benefits of the microgrid. In our professional experience, a common request of
base energy managers is to use an ESS to conduct peak load shaving even though it is rarely
justifiable to fund ESS construction based on a net present value calculation of peak energy
shaving versus cost of ESS. To maintain resilience against a loss of utility power, the ESS
is maintained at no less than 50 percent charge. A design selection using the minimum
50 percent charge results in no impact on resilience. No load shedding occurs during the
simulation of 1000 iterations of a two-week loss of utility power.
Investigations of generator fuel supply, ESS capacity, PV sizing, and related microgrid
design changes, is presented in [
56
]. In most scenarios, the diesel fuel supply could
be reduced by about one third before degrading the microgrid’s ability to function and
meet all critical loads. However, several of the additional scenarios of specific interest
are significantly negatively impacted by the proposed design changes. We recommend
that base energy managers analyze any proposed design changes from the perspective of
resilience prior to implementing said changes. What initially looks like a cost savings may
have significant deleterious effects on microgrid resilience.
4.9. Summary of Findings
The above example revealed the initial military microgrid design was not adequate to
meet the objective of operating for two weeks in island mode. Refinement of the microgrid
design through iterative design changes and simulation resulted in a system that was
validated to meet the two-week island mode operation requirement with a high likelihood.
The final system design resulted in no mission impact against the design goal of continued
operations for a two-week duration following the loss of utility power and minimal mission
impact due to equipment failures concurrent with the grid disconnect event.
The method investigates how equipment and infrastructure reliability impact overall
system resilience in the face of loss of utility power. We applied the design method
to a set of scenarios involving equipment failure that are expected to most commonly
occur to determine the mission impact of equipment failure events. The results revealed
which potential equipment failures resulted in the highest mission impact. Results from
equipment failure analysis indicated that the ESS and PV array required up-sizing to
better protect against said failures. Design changes to protect against both the equipment
failure scenarios and the the additional scenarios of specific interest could include installing
redundant ESS and PV in several locations across the microgrid (likely co-located with the
critical loads) and are worth exploring further. The reasons are that the ESS is essential for
the temporary storage of energy in order to balance the demand and supply for power
during different times of the day and that the PV array is essential for supplying energy if
the diesel generators are unavailable.
When considering events with a low likelihood but potentially high impact on
EEDMI
,
the method helps to identify loss of a generator, or combined loss of any power generation
source with other failures, that results in the greatest
EEDMI
before up-rating the PV array
and ESS. This indicated that additional generation redundancy or up-rating the PV array
and ESS (which was pursued) would be an option worth pursuing. Details from the simu-
lation model also provided information on the time dependence of failure and resilience of
the microgrid specifically in scenarios where PV is relied upon for all generation capacity.
Deliberate attacks can also result in significant impacts, and a scenario of an attack on
microgrid components of key facilities, which could be carried out without many resources,
can lead to a significant mission impact. Hardening the microgrid components to reduce
the likelihood and attractiveness of these targets, or adding redundancy, would benefit
Systems 2021,9, 69 15 of 19
the expected mission impact. Natural disasters also indicate the potential for significant
mission impact due to the destruction of microgrid components. This further indicates
that hardening said components, adding redundancy, and/or distributing generation
and ESS resources across the microgrid may lead to improvements in mission impact in
these scenarios.
5. Discussion and Future Work
The paper presents a systems engineering modeling and analysis method to design
military microgrids resilience in the face of disruptions and equipment failures. The method
focuses on minimizing mission impact due to threats to energy security and can be applied
in the early design phase of a microgrid when only architectural data are available. Existing
microgrids may also benefit from application of the method to assess their resilience and
target modification and plans of actions to address any deficiency uncovered.
The method depends on the identification of scenarios, a reasonable approach for
low probability, high impact events, and malicious and human-caused events. Scenario
generation should not just identify the extreme cases but also more common and routine
issues such as equipment failures, degradation of equipment, accidents, and weather events.
The probability of certain high impact scenarios, such as deliberate attack, is very difficult to
quantify and will likely vary over the lifetime of the system as the threat environment and
enemy tactics change. The set of initiating events for some of the high impact scenarios can
be relatively common events such as vehicular accidents or tree branches causing damage
to distribution equipment. Focusing on one, possibly low-probability scenario, that has
suddenly gained attention due to a recent high-profile event or potential threat discovery
can result in a lack of attention to more common threats and divert resources away from
contingencies or design changes that could benefit resilience against more common threats.
Conversely, ignoring additional scenarios of specific interest may result in a worse mission
impact to a microgrid in the future.
A decision-maker can use the proposed method to map power loss to mission impact.
The assumption is reasonable for many military bases where facilities and their power
requirements can be traced to particular missions. A limitation of the example in this article
was the assumption of a constant value to
MI
over time for each facility for each hour of
load shed.
MI
due to load shedding in a real microgrid application is likely more complex.
For instance, load shedding that occurs in the middle of the night will likely have a lower
MI
than that during the day when the facility is in use. As another example, load shedding
at a facility where the critical loads are air conditioners may see no
MI
until the load is lost
for several hours.
This article advises allocating resources towards reducing the
EEDMI
and developing
potential actions and plans to address events that could impact the mission given both
the impact and probability of threats. For instance, power transmission via temporary
transmission paths or spare equipment maintained onsite can decrease the recovery time of
power and subsequent mission impact. Generation via locally procured small generators
or use of temporary facilities could also lessen the potential mission impact in some of
the scenarios.
Given the difficulty in defining the probability of certain events, such as deliberate
attack, additional analysis and assessment of possible initiation events is warranted. This
article identified several potential scenarios that could result in a high mission impact, such
as direct attacks of microgrid components that cut power to facilities with a significant mis-
sion impact. While we proposed using 2
×
10
1
/year occurrence of such events, additional
analysis to quantify the probability of attacks that would cause a high mission impact may
improve the results of the method developed in this article to be more realistic and identify
potential mitigation strategies. An investigation to explore possible contingency actions to
include such scenarios and the effects those action would have on mission impact may also
provide useful guidance and examples to facility managers.
Systems 2021,9, 69 16 of 19
We advocate using MDI to quantify
MI
for specific loads in a military microgrid
because MDI is an indication of a load’s importance to national defense. Other potential
quantification methods such as ESAT are available, although none is as widely used in the
military and across the federal government as MDI. Several limitations and drawbacks of
MDI have been identified in the literature [
54
]. We suggest that a new method of quanti-
fying a specific load’s contribution to national defense be developed to better represent
the mission impact of losing a specific load for a specific amount of time during a specific
portion of a scenario (e.g., 14 day grid outage). Such an undertaking will be a significant
effort involving all federal agencies involved in national defense.
Linking cost to resilience has intentionally been avoided. Current policies generally tie
microgrid upgrades and improvements to efficiency metrics rather than resilience metrics.
In the future, it may be useful to quantify and understand the cost of increased resilience.
At present, it is most important to develop and highlight the usefulness of a method of
quantifying military microgrid resilience.
The method presented here is not appropriate for comparing between bases to priori-
tize investment of limited resources.
EEDMI
is base- and scenario-specific. Future work
may investigate how to normalize
EEDMI
or similar across multiple bases in a region or
globally to better understand the regional or global impact of local events at bases. Future
work may investigate a mission engineering approach to prioritizing investment across
multiple military bases to support the larger national security mission.
While the illustrative military microgrid analysis demonstrated investigation of spe-
cific scenarios of interest to military bases, it did not seek to optimize the microgrid
design. Instead, the microgrid design was improved to better support mission critical
loads. The process outlined above could be optimized using a linear optimization model
or similar.
This work did not investigate the potential benefits and drawbacks of different micro-
grid types such as an AC microgrid versus a DC microgrid or a hybrid microgrid. Each
type of microgrid uses different major components with different reliability values that
can impact the
EEDMI
. Future work may investigate different microgrid types and how
specific types can be more or less resilient for specific facilities based on local conditions.
The case study in this article focuses on a microgrid topology that is not particularly
resilient due to the single interconnect between BUS 1 and BUS 2. This topology is similar
to that found on many small and medium-sized bases. Some large bases have implemented
ring topologies where high-voltage transmission lines circle the base with multiple substa-
tions present that are able to feed many distribution lines and can be rapidly reconfigured
to route electricity around damaged portions of the microgrid. While a ring topology
appears to have superior prerformance to hub-and-spoke and other topologies, this article
has not pursued that line of inquiry. Future work could analyze if ring topologies are in
fact more resilient to the types of failure scenarios that military microgrids can be expected
to encounter.
6. Conclusions
This article presented a systems engineering modeling and analysis method to analyze
the resilience of military microgrids in terms of the impact on mission achievement. We
focus on resilience in the context of a military microgrid that must continue operations
for a desired duration (such as two weeks) following disconnection from the utility grid.
The model differs from existing approaches to analyzing resilience that either seek to
minimize total cost, maximize renewable energy sources, or treat all electrical load as
interchangable. Instead, we link electrical demand to mission accomplishment and then
seek to minimize the mission impact due to disruptions caused by loss of utility power,
equipment failures, and malicious attacks. The expected electrical disruption mission
impact (
EEDMI
) metric introduced in this article quantifies the system resilience in terms
of the impact to the mission performed by a facility when it loses power. The model is not
intended to predict actual failures but to provide information on failures and how they
Systems 2021,9, 69 17 of 19
would impact the mission accomplishment at the military base so that a decision maker can
make design and investment decisions on how to best address potential resilience issues.
An example using a representative microgrid similar to currently deployed military
microgrids demonstrated how the method can be applied to assess different microgrid
designs and configurations in terms of resilience. The example serves as a template for
microgrid designers to follow in assessing their own microgrids.
Author Contributions:
Conceptualization, C.J.P. and D.L.V.B.; methodology, C.J.P.; software, C.J.P.;
validation, C.J.P.; formal analysis, C.J.P.; investigation, C.J.P. and D.L.V.B.; resources, G.O. and
D.L.V.B.; data curation, C.J.P.; writing—original draft preparation, C.J.P. and D.L.V.B.; writing—
review and editing, D.L.V.B., R.E.G. and G.O.; visualization, C.J.P. and D.L.V.B.; supervision, D.L.V.B.
and R.E.G.; project administration, G.O. and D.L.V.B.; funding acquisition, G.O. and D.L.V.B. All
authors have read and agreed to the published version of the manuscript.
Funding:
Naval Facilities Engineering and Expeditionary Warfare Center Command (NAVFAC
EXWC) Navy Shore Energy Technology Transition and Integration (NSETTI) Program.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Source data and code are available upon request from the authors.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript;
or in the decision to publish the results.
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