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Military bases perform important national security missions. In order to perform these missions, specific electrical energy loads must have continuous, uninterrupted power even during terrorist attacks, adversary action, natural disasters, and other threats of specific interest to the military. While many global military bases have established microgrids that can maintain base operations and power critical loads during grid disconnect events where outside power is unavailable, many potential threats can cause microgrids to fail and shed critical loads. Nanogrids are of specific interest because they have the potential to protect individual critical loads in the event of microgrid failure. We present a systems engineering methodology that analyzes potential nanogrid configurations to understand which configurations may improve energy resilience and by how much for critical loads from a national security perspective. This then allows targeted deployment of nanogrids within existing microgrid infrastructures. A case study of a small military base with an existing microgrid is presented to demonstrate the potential of the methodology to help base energy managers understand which options are preferable and justify implementing nanogrids to improve energy resilience.
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applied
sciences
Article
Investigation of Nanogrids for Improved Navy Installation
Energy Resilience
Alissa Kain, Douglas L. Van Bossuyt * and Anthony Pollman


Citation: Kain, A.; Van Bossuyt, D.L.;
Pollman, A. Investigation of
Nanogrids for Improved Navy
Installation Energy Resilience. Appl.
Sci. 2021,11, 4298. https://doi.org/
10.3390/app11094298
Academic Editor: Charis S.
Demoulias
Received: 31 March 2021
Accepted: 5 May 2021
Published: 10 May 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2020 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Systems Engineering, Naval Postgraduate School, Monterey, CA 93940, USA;
alissa.kain@nps.edu (A.K.); agpollma@nps.edu (A.P.)
*Correspondence: douglas.vanbossuyt@nps.edu
Abstract:
Military bases perform important national security missions. In order to perform these
missions, specific electrical energy loads must have continuous, uninterrupted power even during
terrorist attacks, adversary action, natural disasters, and other threats of specific interest to the military.
While many global military bases have established microgrids that can maintain base operations
and power critical loads during grid disconnect events where outside power is unavailable, many
potential threats can cause microgrids to fail and shed critical loads. Nanogrids are of specific interest
because they have the potential to protect individual critical loads in the event of microgrid failure.
We present a systems engineering methodology that analyzes potential nanogrid configurations to
understand which configurations may improve energy resilience and by how much for critical loads
from a national security perspective. This then allows targeted deployment of nanogrids within
existing microgrid infrastructures. A case study of a small military base with an existing microgrid is
presented to demonstrate the potential of the methodology to help base energy managers understand
which options are preferable and justify implementing nanogrids to improve energy resilience.
Keywords: nanogrid; microgrid; resilience; systems engineering
1. Introduction
Many militaries such as the United States Department of Defense (
DOD
) and the US
Navy (
USN
) heavily rely on uninterrupted electrical power to execute national security
missions. With approximately 800
DOD
military installations around the world, the supply
of electricity is paramount in order to maintain operations [
1
]. The supply of power to
loads that support national security missions (critical loads) must be maintained [
2
]. In the
civilian sector, uninterrupted power is important for human safety and survival in extreme
weather conditions, and losing power in other conditions can have significant economic
impacts (e.g., losing in-process product, losing production over the duration of the outage,
production re-start costs, etc.).
Recent events have exemplified the need for a more localized energy generation
and storage system to reduce vulnerabilities such as centralized generators failing due to
extreme cold weather or transmission lines going offline due to wildfires. For instance, in
February 2021 the state of Texas experienced a near catastrophic failure to its power grid
due to severe cold weather. Rolling blackouts issued by the Electric Reliability Counsel of
Texas (
ERCOT
) was deemed vital to prevent a worst case scenario from occurring where
“demand for power overwhelms the supply of power generation available on the grid,
causing equipment to catch fire, substations to blow, power lines to go down” [
3
]. California
has seen public safety power shutoffs to millions of electrical customers in recent years
due to fire weather events where transmission lines can be turned off in order to prevent
potential wildfires [4].
Already, many
DOD
and Department of the Navy (
DoN
) bases, and other global mili-
tary bases have microgrids to allow for base-level generation, storage, and consumption of
Appl. Sci. 2021,11, 4298. https://doi.org/10.3390/app11094298 https://www.mdpi.com/journal/applsci
Appl. Sci. 2021,11, 4298 2 of 30
energy during events that cause a base to be disconnected from the larger grid which im-
proves base energy resilience [
2
]. However, events that occur within a base perimeter (e.g.,
airplane crash, adversary action, stored munitions explosion, fire, etc.) can disrupt, disable,
or destroy a base microgrid which can cause critical loads necessary to support national
security missions to be un-served even when grid power is available [
5
]. One potential
avenue to improve energy availability to critical loads on military bases is nanogrids where
highly localized generation, storage, and consumption of electricity is implemented to
allow for individual critical loads to continue to be served even when microgrids go down.
Efficiency, reliability, and resilience (sometimes called resiliency in certain
DoN
source
documents) are the three pillars of energy security defined by
DoN
and Naval Facilities
Engineering Command (
NAVFAC
) issuances [
6
]. The desire to increase energy security
ushered in a new age of smart grid infrastructure for
DOD
installations and installations
of other militaries with a focus on developing base microgrids.
DOD
installations ashore
are defined as secure land locations where the maintaining and projecting of war-fighting
strength is critical for executing national security missions to preserve the United States
of America’s national defense [
7
]. Microgrids have been used in many
DOD
installations
to improve resilience and reliability [
8
]. Resilience, defined by the
NAVFAC
as “the
ability to respond, adapt, and recover from a disturbance” is the focal pillar of energy
security for this research [
6
]. In general,
DOD
microgrids aim for either 7 or 14 days of
grid independence where only local generation and storage resources can support critical
loads [
8
]. However, current
DOD
and
DoN
guidance (including
NAVFAC
guidance) has
not addressed potential disruptions occurring to microgrid infrastructure. In addition to
the three pillars of energy security,
DOD
has mandated that 25% of all power used on bases
be renewably generated by 2025 which is often achieved by some combination of solar and
wind power [9].
Microgrids are considered one of the key building blocks of current and future ashore
and military power infrastructure. Hallmarks of microgrids include local energy generation
and storage resources that are connected to loads within a clear microgrid system boundary
such as an installation perimeter [
10
]. Many military microgrids also have the ability to
connect and disconnect from the larger grid as needed [
10
]. Nanogrids take the concept
of local energy generation, storage, and consumption one step smaller by drawing the
system boundary around only a few or even just one load and associated generation and
storage equipment. Some nanogrid implementations (and of most interest to militaries)
are connected to microgrids via one or more switches that allow a nanogrid to share its
generation and storage capabilities with the larger microgrid during normal operations,
and disconnect from the microgrid to preserve the ability to serve local loads during a
microgrid disruption.
DoN
and other navies have experience operating shipboard power systems that are
similar in scope and goal to nanogrids in the form of zonal distribution systems that localize
generation and consumption of power aboard a ship. One of the goals of zonal distribution
is providing uninterrupted power to critical loads and/or zones in a ship so that the
ship’s mission may continue even after a disruptive event (e.g., equipment failure, battle
damage and compartment flooding, cyber-attacks, etc.) has occurred. The surface Navy
has implemented zonal distribution systems where Direct Current (
DC
) zonal shipboard
electrical systems are configured on ships with the sole purpose of ship survivability which
is a similar concept to resilience in shore-side electrical systems. System survivability of
zonal distribution focuses on detection and isolation of disturbances which is done by
reconfiguration management [
11
]. Because of the success of zonal distribution,
DoN
and
DOD are both interested in implementing the concept ashore using nanogrids.
Specific Contributions
This paper contributes a systems engineering method to analyze existing military
installation (e.g., a
DoN
ashore installation, an Army base, etc.) microgrid infrastructure
from the perspective of improving energy resilience for critical loads specifically using
Appl. Sci. 2021,11, 4298 3 of 30
nanogrid technology. The method helps to elucidate potential benefits of targeted nanogrid
deployments to critical loads at military installations which are important to national secu-
rity. Additional benefits may be realized by implementing nanogrids such as improving
installation energy independence among others.
2. Background and Related Research
This section provides background on several topics that are needed to understand the
contribution of this paper. Further, related research is discussed and gaps are identified
which this paper aims to fill.
2.1. Military Microgrids
Many military bases and other critical facilities have or are in the process of imple-
menting microgrids, at least in part in an attempt to provide reliable energy to critical
loads. Microgrids on many military bases generally perform six key functions for the
attached critical loads including: (1) stepping down high voltage power received from the
main utility grid to intermediate and/or low voltage, (2) distribute energy to critical and
non-critical loads, and connect other electrical hardware, (3) generate energy locally (e.g.,
diesel gensets, Photovoltaic (
PV
) arrays, wind turbines, etc.), (4) store energy (e.g., battery
banks, pump-storage hydro, etc.), (5) control the flow of energy, the generation, the storage,
and the loads throughout the microgrid using one or more controllers to automatically
operate electrical equipment, (6) step up and down voltage (transformers) and convert
energy from AC to DC or vice versa (inverters and converters) [
12
,
13
]. The main compo-
nents of a microgrid often include diesel generators and other fossil fuel generators;
PV
,
wind turbines, and other renewable energy sources; energy storage systems such as battery
banks; Points of Common Coupling (
PCC
) where the microgrid connects to the utility grid
(generally with a switch to allow for operation of the microgrid while disconnected from
the utility); one or more control systems (often involving Supervisory Control and Data
Acquisition (
SCADA
) systems); and switches, converters, inverters, relays, transformers,
power lines (above ground, underground, etc.), and other related hardware.
An established
PCC
and other requisite hardware (grid-forming generation and/or
storage, generation, local controller(s), etc.) provides one of the primary benefits of mi-
crogrids for military bases and other critical facilities. The
PCC
provides the ability to
disconnect from the main utility grid, called “island-mode” [
14
], and continue uninter-
rupted service of critical loads. In island-mode, loads critical to a military base’s primary
mission functions (the critical loads) are still provided power from local generation and
storage sources [
14
]. Issuances and instructions from organizations within militaries such
as the
NAVFAC
generally state that in order for military bases to perform their missions,
energy security of critical loads must be ensured regardless of the state of the grid beyond
the
PCC
and outside of the microgrid system boundary [
8
]. One way of identifying critical
loads in the United States military is the Mission Assurance and Continuity of Operations
plan for
DOD
installations which instructs that the essential buildings needed to conduct
national security missions based on criteria identified by the individual branches of the
military (e.g,
DoN
, etc.) and the installation mission must continue to receive power even
during utility disruptions (e.g., external power to a base is cut) [8].
Military microgrids have found several benefits in use including integrating smart
grid technologies, reducing peak load and losses by enhancing integration of Distributed
Energy Resourcess (
DER
s) (often including diesel gensets, wind turbines, micro hydro
generation, fuel cells,
PV
, and other generation sources), localizing power quality and
reliability for end-user satisfaction, and supporting the utility grid by managing sensitive
loads and variability of
DER
s [
10
,
15
]. Currently military microgrids are allowing the
interconnection of loads and
DER
that can replace duplicitive and expensive-to-maintain
small Uninterrupted Power Supply (
UPS
) and stand-alone backup generators [
10
]. The
flexible architecture of microgrids eases the employment of
DER
in conjunction with
controllable loads and storage devices [15,16].
Appl. Sci. 2021,11, 4298 4 of 30
2.2. Nanogrids
Nanogrids are generally much smaller than microgrids. While an average military mi-
crogrid may serve several dozens or hundreds of loads and operate in the 5–500 megawatt
range, nanogrids often serve on the order of one to five loads and a few kilowatts up to 5–10
megawatts. Nanogrids generally are technologically simpler than microgrids because they
only serve a single building or a few loads [
17
]. The smaller, simpler design of nanogrids
usually allows power production to occur much closer to the point of consumption versus
grid infrastructure and even military microgrids which sometimes can encompass many
hundreds of square kilometers of service territory. Generation occurring at the point of use
significantly reduces the potential negative effects of transmission and distribution lines
such as loss of efficiency, single point of failure, etc. [
18
]. Certain nanogrid configurations
(discussed later) where the nanogrid is connected to a microgrid or grid via its own
PCC
can be very fault-tolerant where they are able to successfully transition to island-mode and
continue serving critical loads until microgrid and/or grid power is restored. Many of the
same benefits found with the implementation of military microgrids are expected to be
realized with the deployment of nanogrids on military bases.
It is important to note common technical characteristics of nanogrids. Nanogrid
voltage levels are often lower than microgrids and often are in the 100–500 volt range for
both AC and DC nanogrids. Power generation and consumption is often much smaller in a
nanogrid than a microgrid with ranges from 1 watt for the smallest nanogrids powering
extremely small loads up to 1 megawatt; however, the upper limit can vary and is defined
by the entity implementing the nanogrid [
19
]. Typical nanogrid loads are on the scale of
a single appliance or computer up to a building [
17
]. Nanogrids can sometimes be used
to improve electrical efficiency by 5–13% in some residential applications versus other
options [
19
]. A common nanogrid system architecture uses
DC
because using
DC
can
be more efficient due to many nanogrid power sources producing
DC
power and many
storage systems (Energy Storage System (
ESS
)) using
DC
power which results in fewer
inverters and converters [
18
,
19
]. However, while
DC
nanogrids can increase efficiency,
they also come with the consequence of needing enhanced protection against short circuit
line faults and ground faults [
18
]. The mitigation of faults is done with arching-type circuit
breakers or more advance tactics that involve special mechanical circuit breakers that open
against fault currents by forcing currents to zero by external means to extinguish arcs [
20
].
2.3. Energy Resilience
The focus of this paper is on improving energy resilience of military bases, and specifi-
cally for critical loads, through the implementation of nanogrids. In order to understand
the amount of improvement in resilience a proposed nanogrid may have over existing
microgrid infrastructure, a working definition of resilience in this context and a quantitative
means of measuring resilience must be defined.
Rather than adopting a civilian-focused energy resilience definition and quantification,
it is important to first understand the value of and difference in resilience from a military
perspective. Civilian grid systems often focus on defining resilience in terms of real
dollars lost when energy supply does not meet demand and a facility stops production
of something that is easily monetized (e.g., steel, automobiles, computer chips, processed
food, vaccines, etc.) [
21
]. Conversely, military energy systems such as nanogrids and
microgrids produce something less tangible: national security [
22
]. National security is
intangible and has no easily defined value [
22
,
23
]. The cost to national security due to lack
of energy resilience becomes subjective and theoretical [24].
There are a variety of definitions of energy resilience within military communities
although they all focus on several commonalities: preparing for an event, riding out the
event, stabilizing after the event, and recovering from the event in order to continue to sup-
port mission essential operations and maintain readiness [
5
,
25
]. The military definitions of
energy resilience generally align with civilian definitions although the military definitions
always tie back to the mission of national security [
5
,
26
]. Thus, this paper adopts the
Appl. Sci. 2021,11, 4298 5 of 30
definition of energy resilience from a military perspective as encompassing the ability of
an energy system to support critical loads before an event, during an event, immediately
after an event, and in the recovery from an event back to a normal operating state.
There have been several attempts to quantify energy resilience for military purposes
from a financial perspective. For instance, a cost benefit analysis of stand-alone diesel
generators attached to critical loads on installations was performed to calculate a Customer
Damage Function (
CDF
) which is representative of the cost of interruption as a function of
the duration of an outage [
21
,
27
]. However, in most situations it is very difficult to quantify
national security in a dollar amount. Instead, within the United States military, the value
of resilience is sometimes defined using the Mission Dependency Index (
MDI
) where
MDI
captures the relative criticality of various infrastructure on a base with respect to the mission
of the tenant organizations on a base on a 0–100 scale with 100 being absolutely critical [
28
].
In contrast, some researchers have criticized the use of
MDI
when directly ranking criticality
of loads to their overall role in national security missions. These researchers claim existence
of inaccuracies in addressing time dependency of corrective actions, and misrepresentation
of mission interdependence and intradependence in the
MDI
equation [
29
]. Recently the
DoN
has begun using Resilient Energy Program Office (
REPO
) (an attempt to address
the shortcomings of
MDI
with similar objectives and using aspects of Energy Security
Assessment Tool (
ESAT
)) as a replacement in some of the roles that
MDI
has been previously
employed [
30
]. However, many organizations across the United States federal government
retain
MDI
and some issues with
REPO
are currently being identified. Thus, this paper
adopts MDI as the base measure of a unit of energy resilience.
In order to use
MDI
to quantify energy resilience for the military, a method of quantify-
ing resilience over time is needed. Several exist or are in development [
5
,
22
,
26
,
31
,
32
]. This
paper adopts the approach proposed by Peterson et al. [
22
] where the Expected Electrical
Distribution Mission Impact (
EEDMI
) quantifies the resilience of an energy system versus
all expected initiating events, threats, disruptions, etc. In Peterson et al.’s approach,
MDI
is
used to understand the value of each critical load to national security where it provides the
input to Mission Impact (
MI
) on a per unit time (T) basis.
MI
is the impact to a mission on
a per unit time basis for if a specific electrical load is not served. A single scenario (specific
initiating event, threat, etc.),
Ms
, is defined by the
MI
per unit time (T) that is not served
electricity throughout the duration of the scenario [22],
Ms=
T
t=1
MIt(1)
During normal operations and with no electrical interruptions,
Ms
is zero. During
a scenario where
MDI
= 50, the unit of time is hours, and power is not delivered for 2 h,
Ms=
100. In scenarios where not enough power is available to serve all loads, load
shedding occurs. The details of how load shedding occurs (e.g., which loads are shed first,
rotating blackouts, etc.) is dependent upon behavior of the energy system and controller(s).
In such situations where load shedding occurs,
Ms>
0 which indicates an impact to
national security and thus warrants further investigation.
The aggregate of all scenarios (
S
) calculated in Equation
(1)
is
EEDMI
which includes
the probability (
Pr
) of the specific threat or initiating event occurring over the course of
a year:
EEDMI =
seS
Pr(S=s)Ms(2)
The total
EEDMI
value for a specific electrical system configuration is used as a way
to compare between different potential electrical system architectures from the perspective
of energy resilience. A lower
EEDMI
value is more desirable because it means that the
electrical system is less susceptible to initiating events and threats disrupting power to
critical loads [
26
]. The process of developing
EEDMI
is the same whether analyzing a very
small nanogrid or a very large microgrid on a military base.
Appl. Sci. 2021,11, 4298 6 of 30
2.4. Mission Threats
Threats to the uninterrupted delivery of energy to critical loads on a military base are
called a variety of terms such as initiating events, mission threats or threats, disruptions,
etc. While there are well-understood and well-quantified initiating events available for
external threats (events that occur outside a facility) and internal threats (events that occur
inside a facility) [
33
], many threats of specific interest to the military are unique to military
installations and not included in existing resources. For instance, a nation-state adversary
conducting a coordinated cyber-attack and physical attack against a variety of electrical
infrastructure both internal and external to a facility is generally not considered in most
existing initiating event handbooks. In the Methodology section of this paper we propose
a minimum list of potential initiating events of interest to the military.
2.5. Nanogrid System Design
A variety of nanogrid system designs are proposed in the literature and have seen
limited implementation in the real world. Different nanogrid designs can serve differ-
ent purposes and will have different impacts on the energy resilience of an electrical
system. From a systems engineering perspective, Giachetti et al. advances six criteria
for understanding electrical systems used to power military loads: (1) System Purpose
(2) Stakeholders (3) System Boundaries (4) Functional Requirements (5) System Architecture
(6) Operating Modes [2].
In this paper, the nanogrid system’s purpose is to improve resilience, transmission
efficiency, and ease of integration of renewable resource and energy storage. Though
nanogrid resilience has not been validated, our analyses (detailed in subsequent sections)
indicate that nanogrids do improve resilience over baseline microgrid infrastructure in
many situations.
Military nanogrid stakeholders include base commanders, tenant commands, local
energy companies, microgrid providers and contractors, and maintenance and funding
organizations. Higher authorities that base commands report to (e.g., The Pentagon) are
also impacted by nanogrids.
We view the physical and functional boundaries of a microgrid from a holistic perspec-
tive. The system includes physical equipment, processes, software, and people who sustain-
ing operational effectiveness (e.g., maintenance, operations, supply chain, etc.). Nanogrid
system boundaries differ from microgrid system boundaries in that nanogrids are much
smaller than microgrids. However, individual nanogrids can be part of a larger microgrid
from the perspective of a Systems of Systems (
SoS
). By considering several nanogrids
as part of a
SoS
, individual nanogrids can be placed on various critical loads within a
microgrid to work together to improve the microgrid’s energy resilience. Nanogrids
generally have external inputs from maintenance organizations, fuel providers, operator
organization, a microgrid, and the external grid.
The primary functional requirements from a systems engineering perspective of
nanogrids are to generate, distribute, control distribution, and store energy. Where lower
level functions under generate energy include generate electrical energy and adjust energy
production. Distribute energy includes transmit energy, control energy flow, and convert
energy. Controlling a nanogrid includes measure nanogrid state, process measurements for
control decisions, and send control signals. Energy storage provides the benefit of stability
of the nanogrid system when energy demand exceeds energy generation capacity. This
includes store energy, release energy, and adjust energy flow.
System Architecture for a nanogrid differs from a microgrid due to its complexity and
potential configurations. We suggest specific system architecture details of any individual
nanogrid be determined after an analysis of alternative existing nanogrid architectures is
conducted in order to identify which type(s) of nanogrid architecture(s) best benefit the
energy resilience of any specific military installation.
Nanogrid operating modes include four primary modes of operation: microgrid-
connected, transition-to-island, island, and re-connection. Microgrid-connected mode
Appl. Sci. 2021,11, 4298 7 of 30
establishes normal parallel operations with the microgrid and (assuming a microgrid-
to-grid
PCC
) utility grid while all distributed resources in the grid operate within IEEE
standards and information is exchanged with the nanogrid controller. Transition-to-island
mode represents the nanogrid’s transient state of transition between being connected to
the microgrid and being fully islanded. The nanogrid must have sufficient energy storage
available in addition to the ability to stabilize voltage and frequency (in the case of an AC or
AC/DC nanogrid) for successful transition. A major concern of this mode is the dampening
of transients in the nanogrid to avoid tripping protective devices [
2
]. Island mode is when
the nanogrid is operating independent of any outside energy sources, and loads are
solely supported by
DER
and
ESS
with the responsibility to maintain set frequency and
voltage parameters [
2
]. Re-connection mode is the transition period where the nanogrid
is reconnected to the microgrid. Before synchronization can occur, the frequency, voltage,
and phase angle between the two must be within acceptable parameters in order for the
nanogrid and microgrid to resume unified operations [2].
2.6. Nanogrid Architectural Configurations from a Resilience Perspective
A number of major architectural configurations have been proposed in the literature
and some have been implemented on a limited basis. This section discusses some of
the considerations of nanogrid architectural configurations from a resilience perspective
and from the perspective of other important military requirements such as efficiency and
renewable energy.
The conflict is that many authors design nanogrid infrastructure differently: with
centralized and decentralized control systems, the sole use of DC power, or a hybrid of
DC-AC\AC-DC power conversions. Currently no actionable guidance exits on potential
nanogrid configurations that may improve resilience of critical loads to outages on military
installations. However, commercial solutions that stem from the demand for electrical
power for space applications have led to similar refinement of existing technologies for
nanogrid-like solutions. Similar to nanogrids, these new technologies address power con-
version from PV arrays with management, regulation, and monitoring of electrical demand.
Though these power systems are not specifically called nanogrids, their basic elements are
similar: energy storage, power conversion, power management and distribution, and use
by spacecraft systems [
34
]. Further, the details of critical loads vary from installation to
installation; thus, there is no one size fits all solution.
Research has investigated the benefits and drawbacks of implementing both AC and
DC nanogrids which highlights the difference in cost between DC nanogrids (high up-front
costs) and AC nanogrids (low up-front costs) [
18
]. Though we explicitly do not consider
cost within our research, it is important to note the added cost to augment existing military
microgrid infrastructure with DC nanogrids. Some research has recently been focused on
understanding the cost of increasing energy resilience from a military perspective [
26
,
35
].
While some military energy organizations are primarily driven by cost, we expect that cost
will soon be balanced with energy resilience to better align with the high-level desire to
assure that important national security missions can continue in spite of disruptions to
grid and microgrid infrastructure.
Safety is another concern with military requirements and therefore protection concerns
arise when choosing DC nanogrid architecture. Short circuit line and ground faults are
more common at output terminals for
DC
nanogrid architecture then Alternating Cur-
rent (
AC
) nanogrid [
18
]. Examples of this are seen in Okinawa, Japan with experimentation
done by researchers to stabilize
DC
power on nanogrids with three sets of bidirectional
DC
to
DC
converters (used in current and voltage regulated mode) to maintain a constant bus
voltage [
17
]. In addition, mitigation of these potential failure modes can occur with the
use of arcing-type circuit breakers or more advanced strategies [
18
]. Control strategies and
control system design can have a large impact on a variety of important nanogrid require-
ments. Nanogrid architectures using either centralized or decentralized control provide
different solutions to optimize power production and consumption to better match a load’s
Appl. Sci. 2021,11, 4298 8 of 30
supply and load curves, and reduce the negative effects of intermittency [
18
]. Centralized
control at the microgrid level (controlling the microgrid plus any constituent nanogrids)
enables a cohesive control strategy of system dynamics but provides a potential single
point of failure. Though cohesion is important, militaries are generally more concerned
about reliability and resilience. In addition, when a microgrid is under stress, the purpose
of the nanogrid is to independently disconnect from the microgrid at its
PCC
and operate
as an independent system in island mode. Therefore, centralized control strategies are
undesirable for most military applications and decentralized nanogrid control systems that
can react to threats to the uninterrupted delivery of power to critical loads is important for
energy security and improves resilience. However, decentralized control of nanogrids can
inhibit overall microgrid reliability due to there being many more potential independent
failures of distributed nanogrid controllers over time [18,36].
A benefit of a
DC
nanogrid is the commonality of
PV
array and
ESS
output power
generally being
DC
power [
18
]. This commonality of a
DC
based nanogrid would allow
for a smoother and efficient transition of power amongst renewable energy sources to
battery storage. Though an
AC
based nanogrid will save money on initial cost upfront with
no necessary retrofitting, added efficiency benefits of a
DC
based nanogrid will outweigh
initial capital required criteria and benefit the military’s mission-focused requirements.
2.7. Related Research
Existing research into and deployment of nanogrid technologies to date have gen-
erally not directly focused on the ability of nanogrids to support critical loads from the
perspective of energy resilience and especially for military base applications. As far as
we are aware, no one has proposed using nanogrids from the perspective of improving
resilience of critical loads that support national security. The majority of current nanogrid
research focuses on conceptual nanogrid design and defining nanogrid infrastructure. Only
a few publications have reported on nanogrids successfully implemented in real-world
conditions. For instance, a nanogrid was implemented in a housing community in Oki-
nawa, Japan where it was found that a decentralized DC-DC solution was beneficial [
17
].
However, the authors noted that additional research is needed to explore limitations of
decentralization and the development of higher level intelligent exchange strategies that
enhance efficiency [17].
Enhancing efficiency of nanogrids (an important aspect of energy security for mili-
taries) occupies a significant portion of the existing literature where identified challenges
include protocols, demand-side management, security, and the self-control of the overall
system [
37
]. One promising area of research is fully DC-based nanogrids where higher
efficiency, better power quality, and better stability is achieved versus other options and
may contribute significantly to energy security as a result [18].
As mentioned previously, a similar concept to DC nanogrids is zonal distribution
(sometimes referred to as zonal shipboard power) which is implemented on a growing
number of vessels in many Surface Navies. Zonal distribution uses DC power architec-
ture in order to avoid some of the issues associated with AC power architecture such
as generator sets working at fixed speed that limit fuel efficiency, reactive power flow
and power quality problems, bulky conventional transformers, and challenges associated
with supporting pulsed electric loads which will become increasingly common on Navy
ships [38].
In addition, current research in nanogrids fails to support a mission-focused objective
for
DOD
shore installations and instead supports a commercial cost effective mission with
an ease of integration to existing infrastructure. Current research suggested the pursuit of
future work in resilience of smart load configurations, networks and connections, and fast
response to disasters [
18
,
39
]. This segues into the uniqueness of our research where we
propose a methodological approach to enhance resilience of mission critical loads to create
greater energy security that in turn supports the mission of national security.
Appl. Sci. 2021,11, 4298 9 of 30
3. Methodology
This section introduces a systems engineering method to investigate if nanogrids
can increase the resilience of critical loads on military installations and lower the mission
impact from potential threats. Figure 1illustrates the method.
Figure 1.
Overview of Methodology. This methodology is created for base energy managers to follow,
starting with the collection of system information and ending with final design recommendations.
Appl. Sci. 2021,11, 4298 10 of 30
3.1. Step 1: Collect System Information
The first step in the proposed methodology is to collect system information that is
necessary for subsequent steps. The requisite information is generally readily available to
base energy managers and others involved with energy systems at military installations.
3.1.1. Step 1.1: Develop Nanogrid System Information
Basic information about potential nanogrids must first be developed following Gi-
achetti et al.’s six criteria for understanding electrical systems that power military loads
including: (1) System Purpose (2) Stakeholders (3) System Boundaries (4) Functional Re-
quirements (5) System Architecture (6) Operating Modes [
2
]. This information is useful
to ensure accurate and explicit communications between all stakeholders (e.g., base en-
ergy managers, tenant commands, critical load owners, etc.) and helps to elucidate the
architectural process to find weaknesses and validate checkpoints within the grid devel-
opment [
40
]. We advocate that this information is captured in a Model Based Systems
Engineering (
MBSE
) tool such as Papyrus, Magic Draw, Innoslate, etc. which helps with
later analysis.
As part of developing the information for Giachetti et al.’s six criteria, stakeholder
needs are elicited. The stakeholder needs are then developed into requirements which
will be used later in a decision matrix to help judge different nanogrid configurations for
suitability. Potential requirements include but are not limited to efficiency, reliability, ease
of integration, and communication cohesion.
3.1.2. Step 1.2: Collect Solar, Critical Load, and Current Microgrid Data
Next, site-specific information is collected which will help a base energy manager
to understand the feasibility of implementing nanogrids on specific critical loads. Data
collected include load profile data, solar irradiance data, existing energy generation and
storage systems including support infrastructure (e.g., fuel storage tanks, etc.), existing
microgrid power infrastructure (e.g., transmission lines, switches, protection equipment,
transformers, feeder lines, PCC, etc.), and other related information.
Data such as site-specific historical solar irradiance information is used in later steps
to better size
PV
generation assets for nanogrids. Load profile data is used to understand
the average and peak loads, and how much energy is needed over the course of a potential
outage scenario. We recommend that load data be collected over at least the course of
one year and indeed these data are often available for many years from existing
SCADA
systems.
3.1.3. Step 1.3: Define Critical Loads and MDI Scores
The next step is to define the importance of various loads in relation to national security.
We suggest using
MDI
[
28
] which is a unitless measure that can be tied to the importance
of a specific load to national security in spite of the imperfections in the measure [
29
]. As
other better measures become available, they can be substituted for
MDI
. These scores
become the
MI
used in Peterson et al.’s method [
22
] which is implemented later in this
methodology.
It is important to note that all loads within this case study are considered critical.
Further, the responsibility of assigning an
MDI
value lies with the base energy manager
and installation leadership. When a base energy manager or installation leadership deem
that a load has no or very low
MDI
, it can likely be ignored as being unimportant to national
security. We do not recommend a specific cutoff value for low
MDI
scores; however, in
some situations where there are many loads and challenges exist identifying true critical
loads, a pre-established cutoff value may be justified. For instance, the loads associated
with a recreational facility are likely not critical to national security and thus will have a
very low
MDI
score while the loads associated with a radar system may be very critical to
national security and then are expected to have a very high
MDI
score. The loads that are
deemed critical can then be carried forward through the rest of the methodology.
Appl. Sci. 2021,11, 4298 11 of 30
3.1.4. Step 1.4: Size Potential DER Configurations for Specific Critical Loads
Next a preliminary high-level
DER
sizing study is performed for each critical load with
the assumption that each critical load will be placed on its own nanogrid. This includes
sizing both energy generation and storage (e.g., how large of a diesel generator? How large
of a
PV
array? How much diesel storage? How large of a battery storage system? etc.) to
meet the maximum outage duration a military base is mandated to be able to sustain (e.g.,
7 days, 14 days, etc.) which often indicates how long a mission must continue at a specific
military base before a different base elsewhere that is not impacted by the initiating event
or realized threat can start performing the mission.
We suggest picking a time period with very low solar irradiance and very high load
if such a period exists for a specific site location and a specific load profile. For instance,
in northern hemisphere locations, the December-January period often combines very low
solar irradiance due to short, cloudy days and higher load profiles due to systems needing
more energy to function in cold, dark conditions. However, it is possible that summer
conditions in very hot climates may have high enough load profiles due to cooling needs
(i.e., chillers and air conditioners to keep electronics within operating temperatures, etc.)
that even with more solar irradiance to generate more
PV
energy, this time period is the
most extreme load versus generation scenario.
3.2. Step 2: Develop and Down-Select Nanogrid Architectures
In this step, potential nanogrid architectures are developed and down-selected based
on site-specific and stakeholder-specific conditions.
3.2.1. Step 2.1: Evaluate Potential Nanogrid Architectures
As discussed above, there are a variety of nanogrid architectures available. In this step,
evaluation of potential nanogrid architectures to serve critical loads at a specific military
base is performed. We suggest using a Pugh matrix approach although a variety of other
Analysis of Alternatives (
AoA
) methods are available to the practitioner. The Pugh matrix
criteria map to the requirements developed in Step 1.1. The Pugh matrix is a type of tool
that uses a scoring system of plus and minus to determine if the base energy manager’s
intuition of a baseline grid structure is the best decision. An example Pugh matrix is
provided in Section 4.2.
3.2.2. Step 2.2: Develop One-Line Nanogrid and Microgrid Designs
Once the
AoA
is conducted a high-level one-line diagram of the microgrid and pro-
posed nanogrids can be developed. All critical loads, existing
DER
, other existing power
infrastructure (e.g., power lines, transformers, etc.), and proposed nanogrids should be
included. This one-line diagram serves as the basis for further analysis within this method.
Note that at this point, we are interested in including all potential nanogrids as a down-
select step to choose which nanogrids should be implemented occurs later.
3.2.3. Step 2.3: Develop Nanogrid Operational Concept View
In order to understand how various threats may impact the operation of a microgrid
and associated nanogrids, we advocate developing an operational concept view that is
commonly used throughout many military organizations [
41
]. The operational concept
view combines information such as geographic location of energy infrastructure and
critical loads, the one-line diagram of the microgrid and associated nanogrids, locations
of perimeter fences and entry control points, and other site-specific information into one
figure that allows for better communication with stakeholders and permits more rapid
human analysis of potential threats. An example operational concept view is provided in
the Case Study (Section 4).
Appl. Sci. 2021,11, 4298 12 of 30
3.3. Step 3: Analyze Threats to Base Energy Security
The method now proceeds to analyze potential threats to base energy security. These
threats comprise the initiating events that are used in subsequent methodology steps.
3.3.1. Step 3.1: Reference Baseline Threats
An analysis of threats is important for the base energy manager to ensure that the
microgrid and associated nanogrids are resilient against a variety of potential threats.
Currently
DOD
and
NAVFAC
consider island mode as a primary threat from an energy
resilience standpoint. Island mode, where the base is cutoff from outside electrical energy
for 14 days, serves as the baseline threat scenario in subsequent steps of this methodology.
In addition to island mode, we advocate for inclusion a variety of additional internal and
external threats of specific interest to military installations that are subsequently discussed.
While we believe this is a reasonable minimum list of threats to consider, specific locations
and specific global and regional threat protection postures may require additional threats
to be included in subsequent methodology steps where they serve as initiating events.
The following internal threats are the minimum set that we recommend a base energy
manager use:
LOSS OF GE NER ATO R(S): This threat is the loss of one or more generators simulta-
neously. Simultaneous failure (a common cause event) can happen due to contaminated
diesel fuel being delivered to all generator fuel bunkers or incorrect maintenance being
performed on all generators, for instance.
LOSS OF
PV
ARRAY(S): Similar to the loss of generators threat,
PV
array maintenance
could have been mismanaged or there could be a design flaw across all installed
PV
panels
or controllers. Other ways a loss of
PV
array can occur is dirt or avian organic material on
the PV modules, shading, or incorrect incidence angle among others [42].
MAINTENANCE PERSONNEL INSIDER THREAT: The
DOD
defines insider threat as
“the threat that an insider will use her or their authorized access, wittingly or unwittingly, to
do harm to the security of the United States” [
43
]. All militaries face a similar insider threat
that can compromise national security mission success. Two incidents of insider threats
at United States military bases include include Fort Hood in 2009 where an lone Army
officer shot and killed 13 people and in 2013 when an Navy contractor shot and killed 12
civilian employees [
43
]. It is understood that military installations are not a perfectly safe
sanctuary and therefore it must be taken into consideration that, for instance, damaging
PV
arrays or transformers with a gun, or generators by draining engine oil would be a
potential occurrence [
43
]. This type of deliberate attack is likely to occur against critical
loads that are most valuable to mission success.
The following external threats are the minimum set that we recommend a base energy
manager use:
NATURAL DISASTERS: Site-specific natural disasters must be included in potential
external threats. Such threats could include flooding, earthquake, tsunami, tornadoes, etc.
We specifically focus on fire and severe storms in this article, and provide more detail below
on the fire threat. Similar detail can be developed for each potential natural disaster based
on site-specific considerations.
A 2013 study showed that the Northern California and Monterey Peninsula (where
the military base we work at is located) has an estimated 31–40% chance that given a single
source of ignition, a fire will grow to 100 plus acres in size [44]. This is the second highest
ranking category for fire growth within the United States. Given the severity and likelihood
of this natural disaster or ignition occurring by an attacker not only in the American west
but globally anywhere that a wild-land urban interface exists [
45
], we advocate this threat
be included. Indeed, the 2020 fire year saw three major fires within 30 miles of the military
bases in the greater Monterey Peninsula area [46].
EXPLOSION FROM TERRORISTS: Since 1997 the Joint Chiefs of Staff implemented
the Combating Terrorism Readiness Initiative Fund which has allocated approximately
$80 million to U.S. and overseas installations for Anti-Terrorism Force Protection improve-
Appl. Sci. 2021,11, 4298 13 of 30
ments [
47
]. Today, the probability of an explosion by a vehicle-born IED or other means of
explosives still exists. A heighten intent of disrupting military operations and comprising
national security makes military installations more favorable to this type of external event
in comparison to civilian grid facilities. Terrorist attacks such as Beirut in 1983, 11 Septem-
ber 2001, and most recently in 2019 a vehicle borne IED outside the U.S. Embassy in eastern
Kabul have changed the paradigm of force protection on military installations [48,49].
FUEL DEL IVE RY DISRUPTION WITH LOW PRO BABIL ITY O F RESUPP LY: Disruption
of Fuel delivery may happen to any military installation. For instance, supply chain
contracts and limited refining capacity within the United States limit the
DOD
’s fuel supply
domestically with reliance on extensive supply chains from foreign suppliers [
50
]. A
single natural disaster not directly impacting a base can cause delay in fuel supply to
base installations, which is why this type of external event is considered as a more likely
occurrence for military installations. Other potential disruptions to fuel delivery such as
protests blocking shipments, adversary destruction of fuel tankers, and other scenarios are
of particular concern to military installations. Many military installations are heavily reliant
on fuel delivery to power generators that are expected to function in order to support
critical loads during an island mode situation.
The above internal and external threats are the minimal list of threats that we believe
base energy managers should use to assess their microgrid and proposed nanogrid architec-
tures. We take this opportunity to vigorously remind the practitioner that location-specific
threats either from a military installation itself or from natural or human-made phenomena
locally or regionally must also be considered. For example, a military installation such as 29
Palms in the Mojave Desert has a higher probability of a rocket launched from the Mojave
Spaceport crashing into microgrid infrastructure than Naval Support Activity Monterey
which is far from any rocket launch trajectories. Another example is Camp Santiago in
Puerto Rico and Camp Lejeune in North Carolina where both installations have a higher
probability of a hurricane destroying microgrid and grid infrastructure due to the frequency
of hurricanes in their locations versus North American west coast facilities.
3.3.2. Step 3.2: Determine Percent Occurrence of Threat per Year
After a final set of threats are determined, the probability of occurrence of these events
initiating within any given year must be determined. In this methodology, probability
of occurrence is the likelihood over a given time frame (often one year) that a threat
will be realized. While natural disasters such as fires and floods have well-established
probabilities of occurrence in the literature, it can be challenging to ascertain the probability
of occurrence of human-caused threats such as insider threats or terrorist attacks [51].
While it maybe desirable for some military installations to examine rare threats, we
recommend that a threat only be carried forward for further evaluation if the probability
of occurrence on a yearly basis is greater than two percent (
Pr(S=s)
0.02). If the
potential threat is below this threshold, then we suggest it can be disregarded based on our
professional experience. Existing sources of data and quantification process for probability
of occurrence for failure scenarios are challenging due to to information available for
military installations. The base energy manager’s responsibility is to postulate probability
of occurrence for specific threats on the location of their base [
22
]. A variety of useful
resources to develop probability of occurrence of threats exists [
52
,
53
]. It is important to
note the limitations on historical data due to climate change. Increasing climate changed
has caused considerable changes in weather patterns and poses risk to military installations.
For instance, in 2019 the Pentagon reported that a total of seventy-nine military installations
were at risk of flooding due to rising sea levels induced by climate change. Norfolk, one
of the Navy’s largest bases, is seeing the effects already. The main road to Norfolk Naval
Station floods a few times a month with predictions that it will continue to flood more
frequently up to two hours per day in the upcoming years [54,55].
Appl. Sci. 2021,11, 4298 14 of 30
3.3.3. Step 3.3: Develop Microgrid and Nanogrid Threat Scenarios
The operation concept view previously derived is beneficial when maturing microgrid
and nanogrid threat scenarios. After a threat has been realized (e.g., a fire), the damage
that threat inflicts on the microgrid and constituent nanogrid(s) must be catalogued for
subsequent analysis. In most of the threats we advocated above that base energy managers
evaluate, the location of the critical load and where the threat initiates can be used to deter-
mine what nanogrid(s) and microgrid components are impacted, and what
DER
are still
operational when operating in island mode. We conservatively assume that when a threat
is realized, the microgrid disconnects from the utility grid at the
PCC
. We assert this is a
conservative and realistic assumption. Several tables in the Case Study (Section 4) demon-
strate a tabulation of equipment expected to be functional or offline/disabled/destroyed
in a variety of threat scenarios.
3.4. Step 4: Calculate Resilience
After sufficient information and models are constructed in the above steps, it is now
possible to calculate the energy resilience of various nanogrid configurations to various
threats. This will allow a comparison between nanogrid architectures, and against the
baseline microgrid architecture in a future step.
3.4.1. Step 4.1: Simulate Nanogrid Systems
Using the equations developed by Peterson et al. [
22
], a simulation can be developed
to examine various nanogrid configurations. We modified an existing MATLAB simulation
tool [
32
] to develop the Case Study (Section 4). Other implementations of Peterson et al.
have been developed [5].
The simulation implements a simple power flow analysis that matches generation and
storage with loads to identify periods of time where insufficient capacity is available to
support critical loads. Input parameters include sizing information for
PV
area, battery
capacity and power output, generator power output, amount of fuel storage available,
solar irradiance data, load data, and initial conditions of all electrical hardware. Output
parameters include load shedding information (which load(s) is/are shed, and for how
long) among others. The power flow analysis takes into account the charge state (modeling
charge and discharge rates, capacity, etc.) of the
ESS
, the amount of fuel available for
generators and when fuel resupply may occur, load profiles, load shedding strategies
(which loads to shed first when insufficient power is available to support all loads), and
other considerations. The output of the simulation is then used along with individual
MI
scores of the critical loads to calculate Msas shown in Equation (1).
We advocate using a one hour time step in the simulations which is sufficient for the
high level systems engineering analysis of energy resilience conducted in our methodology.
Practitioners can choose to develop higher fidelity models based on their individual
situations although we have observed in our professional practice that most military
installation energy managers can make decisions using data with one hour time steps.
While it is possible to simulate across one or multiple calendar years using a Monte
Carlo approach or similar (as Peterson et al. did [
22
]) to develop a broader picture of load
shedding under different load and solar irradiance conditions (e.g., diurnal temperature
and solar irradiance swings, seasonal swings, etc.), we recommend that one particular time
period with the highest loads and lowest solar irradience is specifically chosen. This greatly
reduces computation time and also focuses the analysis on the worst case scenario of high
load and low availability of solar power. The reason for this focus is that threats from
adversaries are likely to choose the time of greatest electrical infrastructure vulnerability to
attack. This is unique to military energy resilience and microgrids versus civilian energy
systems that generally do not have nation-state or terrorist adversaries looking to cause
damage to national security.
Appl. Sci. 2021,11, 4298 15 of 30
3.4.2. Step 4.2: Check for Unacceptable Load Shed
Once results for all threats have been developed, the results must be examined to
determine if any particular threat scenarios have unacceptably high load shed hours. While
we use
Ms
to understand the overall contribution of a particular threat scenario to not
completing national security missions, it is important to look at the raw load shed data to
verify any particular load shed events of interest are not being obscured. If such a situation
is found, we advise returning to Step 1.3 to re-examine the
MI
score (likely directly derived
from
MDI
) to verify that information is correct. Occasionally, the
MI
may need to be
manually adjusted to reflect intangible factors that were not captured in the
MDI
analysis
or similar load-specific national security importance measure. Unacceptable load shed
events may also indicate a need for an up-sized nanogrid design for a specific load – in this
case, return to Step 1.4. If individual load shed events are all within acceptable parameters,
then continue forward.
3.4.3. Step 4.3: Calculate Mission Impact Across All Threats
Next, mission impact across all threats is calculated using the
EEDMI
metric devel-
oped in Equation
(2)
. This involves using the probability of occurrence for each threat
developed in Step 3.2 and the results from Step 4.1. The
EEDMI
for each potential nanogrid
architecture can be compared against the baseline microgrid EEDMI score to determine if
specific architectures are better or worse than the baseline microgrid which is performed in
the next step.
EEDMI
is a measure of resilience of electrical energy to threats so that critical
loads at a military base can perform important national security missions.
3.5. Step 5: Analyze Results
Now
EEDMI
scores are analyzed to validate if adding nanogrids to an existing micro-
grid improves resilience. Three outcomes may occur comparing EEDMI scores including:
EEDMI =
0: The most ideal scenario from a military energy resilience perspective is
when a specific system architecture returns a result of
EEDMI =
0. This means that for all
postulated threats, no load shedding occurs and there is no impact to the national security
mission.
EEDMIProposed System <EEDMIBaseline
: In this case, the proposed nanogrid system
design is superior to the baseline microgrid because the proposed design’s
EEDMI
is less
than the baseline microgrid.
EEDMIProposed System EED M IBasel ine
: If the
EEDMI
of the proposed nanogrid sys-
tem design is greater than or equal to the baseline, it is worse than the baseline design with
respect to energy resilience. Such an outcome may indicate undersized generation and
storage capacity within the nanogrid(s). In this case, it may be worthwhile to double-check
the sizing carried out in Step 1.4 and the designs produced in Step 2.2.
The proposed nanogrid designs that produce
EEDMI
values below the baseline
nanogrid can then be rank-ordered to understand which designs may be preferential to
others. While the
EEDMI
metric is useful in understanding which designs are more re-
silient and are more likely to protect critical loads from threats to energy security that could
impact national security missions, resilience is only one of several important requirements
that military installations often use when focusing on improving energy infrastructure.
3.6. Step 6: Produce Final Design Recommendations
Now that a list of potential nanogrid design options has been produced and ordered
based on resilience, we turn our attention to producing final design recommendations that
a base energy manager can use to promote upgrading existing microgrid infrastructure to
include nanogrids. Two questions must be addressed to guide the final recommendations:
ARE STAKEHOLDER REQUIREMENTS MET? It is important to ensure the requirements
collected in Step 1 are validated as being met. While the methodology presented above
develops an understanding of energy resilience, other important requirements must be
verified to still be met. For instance, a proposed design may only include diesel generators
Appl. Sci. 2021,11, 4298 16 of 30
with no other generation sources but a requirement for a certain percentage of renewable
generation may be present [
9
]. In such a case, this would indicate that particular nanogrid
system design is inappropriate for further consideration without extreme extenuating
circumstances to violate the renewable energy requirement. If no proposed designs remain
after validating other requirements are met, then the base energy manager should return to
Step 1 and carefully re-evaluate the requirements before verifying that no other proposed
designs exist which would meet requirements.
ISFUNDING AVAI LAB LE? While a proposed nanogrid design may meet all require-
ments, there may not be funding available to fully implement the proposed design on
the existing microgrid infrastructure. In this case, a base energy manager may choose to
prioritize which critical loads receive a nanogrid first based upon available funds and cost
of installing and commissioning the nanogrid(s). Depending upon the particulars of how
military budgets are allocated for projects such as base energy infrastructure upgrades,
it may be possible to fund a nanogrid for a critical load that is less critical than another
critical load because of the specific configuration of the two different nanogrids. In other
words, if there is money available to do a certain type of energy project (i.e., renewable
energy projects [
9
], etc.) and one proposed nanogrid design meets the criteria for that
funding source while another does not, then the nanogrid design that meets the funding
source requirements should be pursued. When other funding becomes available, then
other nanogrid designs can be pursued for other critical loads. The complexities of funding
for military energy projects is largely unique to militaries and is not as pronounced in most
civilian sectors.
4. Case Study
This section demonstrates how a practitioner such as a military base energy manager
may use the proposed methodology by introducing a fictionalized version of the Northern
California-located Naval Support Activity Monterey base where the Naval Postgraduate
School is located. The case study uses a generic base structure and focuses on location and
threat-specific initiating events. A potential nanogrid configuration is compared against an
existing fictionalized generic microgrid design to demonstrate the potential usefulness of
the method.
4.1. Step 1: Collect System Information
4.1.1. Step 1.1: Develop Nanogrid System Information
First, Giachetti et al.’s six criteria for understanding electrical systems from a systems
engineering perspective are collected. The system purpose is to deliver reliable power
to multiple critical loads on the Naval Support Activity Monterey military base. The
stakeholders include base energy manager,
DOD
employees, high ranking officials within
the base chain of command,
NAVFAC
, and the Department of Energy (
DOE
). Figure 2
provides context to the base energy infrastructure from a systems engineering perspective
where the system boundary is indicated by the dashed red line. Four external inputs to
the internal system are the external grid, fuel provider, maintenance organization, and
operator organization. This step in the systems engineering process aids in defining
system boundaries to examine external interactions that may effect the internal system
environment. Labeled in blue are what material or resource is provided by each external
input.
Appl. Sci. 2021,11, 4298 17 of 30
Figure 2.
Nanogrid Context Diagram [
2
]. This diagram is useful in the context of the systems
engineering process to identify external interactions with the system and identify system boundaries.
The requirements analysis is illustrated in Figure 3where top level functional re-
quirements include: generate energy, distribute energy, control distribution, and store
energy.
Figure 3.
Microgrid and Nanogrid Functional Decomposition [
2
]. The functional decomposition
represents the high-level functions that the system must perform to meet system requirements.
Figure 4outlines the operating modes for the microgrid and potential nanogrids
illustrating the process of planned/unplanned disconnection and returning to a complete
re-connection to the utility grid.
Appl. Sci. 2021,11, 4298 18 of 30
Figure 4.
Microgrid and Nanogrid Operating Modes [
2
]. The figure shows how a microgrid or
nanogrids can move between different modes of operation.
4.1.2. Step 1.2: Collect Solar, Critical Load, and, Current Microgrid Data
Critical load energy consumption data is obtained from the
DOE
using hourly load
data referencing 70% of the buildings within the United States used for building energy
studies [
56
]. Historical solar incidence data is collected from 1991 to 2010 for climate
conditions in Monterey, California [
57
]. We specifically use solar data from the year 2000
for illustrative purposes. Load data analysis indicates that the highest amount of electricity
use occurs from January to March while simultaneously having the worst
PV
power output.
This worst case scenario of high power demand and low solar output power is the time of
year used for the rest of this Case Study. This ensures that energy resilience is evaluated in
the worst conditions when adversaries are more likely to attempt hostile actions. Table 1
shows the summary of the critical loads within the existing microgrid. For the purposes
of the case study, these values are derived from only taking into consideration the critical
electricity needed to maintain mission operations at a military base such as Naval Support
Activity Monterey which is assumed to be 50% of interior lighting and 67% of interior
equipment [
32
]. This eliminates the use of auxiliary services of water heaters, fans, and
electric cooling and heating systems among others that provide comfort service rather
than mission-critical needs [
56
]. This critical load assumption is then used to calculate the
hourly load (kW). The hourly load data is compiled to during the selected time of year to
determine the maximum and average critical load values for each type of facility.
Table 1.
Building Load Data. This data is derived from the
DOE
Commercial Reference Building
dataset hourly load data [
56
]. This data is used to extract the average maximum load and average
normal load for associate facility types.
Load Facility Type Average Maximum
Load (kW)
Average Normal
Load (kW)
1 Small Office 5.8 2.8
2 Small Office 5.8 2.8
3 Medium Office 71.3 32.3
4 Large Office 524.9 267.5
5 Warehouse 31 10.9
4.1.3. Step 1.3: Define Critical Loads and MDI Scores
Table 2represents the scaling of loads developed from the
NAVFAC
, United States
Coast Guard (
USCG
) Office of Civil Engineering, and National Aeronautics and Space
Administration (
NASA
)’s
MDI
and modified to fit our design [
58
]. All the loads simulated
within our design are deemed critical and therefore a relative scale was developed.
Appl. Sci. 2021,11, 4298 19 of 30
Table 2.
MDI Scores for Individual Loads. Information from
USCG
Office of Civil Engineering,
and
NASA
are used to calculate the case study
MDI
scores. In the case study, all
MDI
scores are
represented on a 0-100 scale and all loads shown are considered critical.
MDI Score Load Load Type
14 1 Small Office
14 2 Small Office
53 3 Medium Office
84 4 Large Office
89 5 Warehouse
4.1.4. Step 1.4: Size Potential DER Configurations for Specific Critical Loads
It is apparent from Table 1that appropriate sizing of
DER
s is needed to accommodate
the various critical loads. Each proposed nanogrid within the microgrid will have an
associated
DER
that favors the average normal load and withstands the maximum critical
load. Table 3lists the summary of each nanogrid configuration.
Table 3.
Nanogrid
DER
Sizing Data. Sizing of generator power output is derived from specification
sheets from generator datasheets [
59
62
]. Fuel storage for each generator is calculated for a 7 day
period based on fuel consumption rate. PV array size is determined by simulating PV power output
from solar irradiance data based on each nanogrid relying solely on PV generation and ESS storage.
Load 1 and 2 3 4 5 Microgrid
Mission Impact 14 53 84 89 -
Maximum Critical Load (kW) 5.8 71.3 524.9 31 -
GEN Power Output (kW) 20 80 300 60 300
GEN Fuel Storage (gal) 1385 2147 3696 1512 8740
Battery Capacity (kW*hr) 384 5384 30,769 538 37,075
Battery Output (kW) 40 300 1000 70 1410
PV Array Area (m2)50 500 5360 160 6070
Generator power output values and fuel consumption are derived from the specifi-
cation sheets from companies such as Cummins and GENERAC [
59
62
]. Fuel storage for
each generator is calculated from fuel consumption in gallons per hour and provisioned for
7 days with expected refuel to endure the mission duration of 14 days (this is a common
tactic to reduce stored on-site fuel at both civilian and military facilities [
63
]).
PV
array
area is determined by simulating
PV
power output from the solar irradiance data based
on each nanogrid solely relying on
PV
generation and
ESS
storage. However, many bases
have limited land available for large
PV
installations. While not widely implemented yet
at military bases, civilian facilities have begun placing solar canopies over parking lots
both to increase solar generation area and also to shade cars from the sun. Thus, the total
available area for
PV
often may not meet the goal of supporting critical loads entirely off of
PV
and
ESS
. In this case study, we assume approximately 6000 m
2
is available for
PV
. This
is close to reaching the goal of both the diesel generator and the
PV
and
ESS
independently
supporting the critical load on each nanogrid.
Iterations are done on each nanogrid until the generation capacity is able to accom-
modate the maximum and average critical loads with no load shedding. The simulation
model of the nanogrid uses hourly time steps in order to determine the mission impact,
load shedding, and system response for each scenario through deterministic methods [
32
].
Load shedding in our simulation is a vital factor in determining the constraints of the
system and facility behavior. Energy demands not met result in required load shedding
which influences mission impact [
22
]. Load shedding can impact the critical loads which
can in turn impact national security [
64
]. Ensuring nanogrid
DER
s are sized appropriately
ensures we are meeting the active power balance equation
Plo ad =Pgenerated +Pbattery
at a
steady state with all load demands being met [22].
Appl. Sci. 2021,11, 4298 20 of 30
4.2. Step 2: Develop and Down-Select Nanogrid Architectures
4.2.1. Step 2.1: Evaluate Potential Nanogrid Architectures
An analysis of potential nanogrid infrastructure is performed in order to determine
the most beneficial system architectures for Naval Support Activity Monterey including
the benefits and drawbacks of
DC
and
AC
based nanogrids, centralized and decentralized
control systems, and other important metrics. The primary viable nanogrid structures are:
(1) DC-based nanogrid with centralized control, (2) AC-based nanogrid with centralized
control, (3) AC-based nanogrid with decentralized control, and (4) DC-based nanogrid
with decentralized control. A Pugh Matrix is provided in Table 4that shows the different
potential nanogrid structures and their benefits and drawbacks.
Table 4.
Pugh Matrix for Nanogrid Design. The baseline (DATUM) design of the DC-based cen-
tralized controller nanogrid is compared against the alternatives. The +, -, and 0 symbols indicate
“better,” “worse,” and “no difference” respectively for each criteria for each of the three alternatives to
the DATUM. The criteria are summed for each alternative and the DATUM; this provides a ranking
which a base energy manager can use to inform a final decision (the “revised ranking”). In the case
study, while the ranking indicates the AC-Based/Centralized Controller is most preferred, the base
energy manager has decided to overrule the ranking and instead has indicated preference for the
DC/Decentralized Controller due to survivability concerns.
Criteria
Alternatives
DC/Centralized
Controller
AC
Based/Centralized
Controller
AC/Decentralized
Controller
DC/Decentralized
Controller
Ease of Integration D+ + 0
Efficiency A- - 0
Initial Capital Required T+ + 0
Reliability U0 + +
Safety/Grounding M+ + 0
Communication Cohesion 0 - -
SUM: - 2 2 0
Ranking - 2 1 3
Revised Ranking -321
Keep YES NO NO NO
The analysis in Figure 4indicates that an
AC
nanogrid would be better than a
DC
nanogrid with centralized control. However, the need for energy security and survivability
is paramount to military installations. This outweighs the other existing criteria. Therefore,
a
DC
nanogrid with a decentralized controller is chosen as the best design solution for
the research objective and stakeholder concerns. This design optimizes power balance by
independently controlling each load, DER, and ESS [65].
4.2.2. Step 2.2: Develop One-line Nanogrid and Microgrid Designs
Figure 5represents the microgrid and proposed nanogrids for the Naval Support
Activity Monterey case study.
Appl. Sci. 2021,11, 4298 21 of 30
Figure 5.
Microgrid and Nanogrid Design. This one-line diagram represents the overview of the
chosen nanogrid architecture highlighting the amount of loads, DERs, and controller architecture.
4.2.3. Step 2.3: Develop Nanogrid Operational Concept View
Next an operational concept view of the baseline (microgrid only) and proposed
nanogrids for the fictionalized version of the Northern California-located Naval Support
Activity Monterey base which is shown in Figure 6. The illustration highlights the infras-
tructure similarities and differences while also illustrating key generic elements of
DOD
installations (e.g., base perimeter which is often fenced, entry control points, utility grid
connection (PCC), etc.
4.3. Step 3: Analyze Threats to Base Energy Security
4.3.1. Step 3.1: Reference Baseline Threats
In this case study, we use the baseline threats outlined in Step 3.1 in the Methodology
(Section 3) above. We also include cyber attack because the fictionalized case study assumes
that Naval Support Activity Monterey has extensive
SCADA
systems and automation
(many military bases have not yet upgraded to
SCADA
systems and automated or remote-
operated controls) which can be vulnerable to cyber attack [
66
68
], severe storms due to the
installation’s proximity to the coastline, and plane crash due to proximity of runways and
flight paths to critical loads. As opposed to civilian microgrids and nanogrids, militaries
are often specifically concerned with plane crashes due to a variety of causes (training
accident, intentional attack, equipment malfunction, etc.), and with the potential for much
worse outcomes due to munitions and fuel loads [
69
]. We omit additional threats beyond
these for brevity. However, a full analysis of a real military installation should ensure any
additional location-specific threats are accounted for. The following threats are analyzed
in the case study: loss of all generators, loss of all
PV
, insider threat, fire, explosion, fuel
delivery disruption, cyber attack, severe storms, and plane crash.
Appl. Sci. 2021,11, 4298 22 of 30
Figure 6.
Operational Concept View of Naval Support Activity Monterey. Baseline microgrid
is represented in the top diagram while the proposed microgrid augmented with nanogrids is
represented in the bottom diagram.
4.3.2. Step 3.2: Determine Percent Occurrence of Threat per Year
Table 5outlines the probability of occurrence of postulated threats. The probabilities
for these threats are developed from historical weather data, familiarization with the
location and threat dependency, and related research [
22
,
52
,
53
]. Because all applicable
threats have a probability that is greater than or equal to two percent, the case study
continues forward with all of the threats in Table 5.
Table 5.
Probability of Occurrence for Postulated Threats. The threats and probabilities are derived
from a number of sources including natural disasters (e.g., fire, flood, fire, seismic, high winds,
tornado, hurricane, etc.) and human-caused events [
52
,
53
]. It is important to note the limitations on
historical data due to climate change. Increasing climate changed has caused considerable changes in
weather patterns and poses risk to military installations.
Threats Probability of Occurrence/Year
Fire 0.05
Explosion 0.04
Insider Threat 0.10
Fuel Delivery Disruption 0.02
Loss of Equipment 0.10
Cyber-Attack 0.10
Severe Storm 0.02
Plane Crash 0.02
4.3.3. Step 3.3: Develop Microgrid and Nanogrid Threat Scenarios
Tables 6and 7matrices illustrate the scenarios that are connected to each threat in
order to determine nanogrid resilience compared to current microgrid resilience. Each “X”
in the tables is an indication of a specific piece of equipment being unavailable, offline, or
destroyed within the specific threat scenario.
Appl. Sci. 2021,11, 4298 23 of 30
Table 6.
Microgrid Threat Scenarios. The microgrid components that are offline, unavailable, or
destroyed in each threat scenario are represented by an “X.”
Microgrid
Threats
PV Array
Battery
GEN1
GEN2
Load 1
Load 2
Load 3
Load 4
Load 5
Internal
Loss of all Generators X X
Loss of all PV Arrays X
Insider Threat Sabotage
X X X
External
Fire X X X X
Explosion X X X X
Fuel Delivery
Disruption X X
Cyber-Attack X X
Severe Storm X X X X X X X X X
Plane Crash X X X X X
Table 7.
Nanogrid Threat Scenarios. The nanogrid components that are offline, unavailable, or
destroyed in each threat scenario are represented by an “X.”
Nanogrid
Threats
PV1,2
Bat t1,2
GE N1,2
PV3
Bat t3
GE N3
PV4
Bat t4
GE N4
PV5
Bat t5
GE N5
Internal
Loss of all Generators X X X X
Loss of all PV Arrays X X X X
Insider Threat Sabotage X X X X
External
Fire X X X X X X
Explosion X X X X X X
Fuel Delivery Disruption X X X X
Cyber-Attack X X X X X X X X
Severe Storm X X X X X X X X X X X X
Plane Crash X X X
Figure 7illustrates the operational concept view for a threat of a fire. This illustrates
that a fire can effect more than just the loads compromised and is dependent on the situation.
The fire threat scenario assumes that the spread of the fire was able to be stopped on the
north-east side of base and did not impact any of the power lines to the rest of the base.
4.4. Step 4: Calculate Resilience
4.4.1. Step 4.1: Simulate Nanogrid Systems
The threat scenarios are then simulated in MATLAB. Referring back to Section 2.3
(Energy Resilience) all associated equations (Equations
(1)
and
(2)
) are embedded within
the MATLAB code. While we used MATLAB, the simulations can be developed in almost
any programming language desired.
Appl. Sci. 2021,11, 4298 24 of 30
Figure 7.
Threat of Fire Operational Concept View. The large shaded red circle indicates area
destroyed by fire. In the microgrid scenario (top), the fire destroys the warehouse and large office
building (Critical Loads 4 and 5), and both diesel generators. In the nanogrid scenario, the same
buildings and associated nanogrids are destroyed.
4.4.2. Step 4.2: Check for Unacceptable Load Shed
The number of hours load shed for each threat scenario results in no risk to national
security. All scenarios simulated within the proposed nanogrid only load shed the applica-
ble loads that are directly impacted by the threat (e.g., the load is destroyed) and keeps the
remaining critical loads online throughout the 14 day mission. The insider threat scenario
shows the microgrid shedding all five critical loads for approximately 200 h of a 336 h
mission. This places national security at risk. However, the proposed nanogrid architecture
is able to maintain all critical loads not directly impacted in spite of the insider threat.
4.4.3. Step 4.3: Calculate Mission Impact Across All Threats
Table 8shows the resulting mission impacts (Ms).
We use Equation
(3)
to calculate the
EEDMI
of the proposed nanogrid to compare
resilience against the existing microgrid. Table 9shows the resulting
EEDMI
for the existing
microgrid and proposed nanogrid.
EEDMIt=EEDMIload1+EEDMIload2+EED M Il oad3+EEDMIload4+EED M Iload5(3)
Appl. Sci. 2021,11, 4298 25 of 30
Table 8.
Mission Impact Simulation Results. The Mission Impact (
Ms
) values for the nanogrid and
microgrid architectures are shown for each threat. The bold values indicate the lower mission impact
(less impactful, better) outcome. In all cases, the nanogrid either performs the same or better than the
microgrid architecture.
Mission Impact (Ms)
Load 1
Load 2
Load 3
Load 4
Load 5
Nanogrid
Microgrid
Internal
Loss of all Generators 0 0 0 0 0 0 0
Loss of all PV Arrays 0 0 0 0 0 0 0
Insider Threat
Sabotage 0 0 6201 16,884 0 23,085 50,800
External
Fire 0 0 0 28,224 29,904 58,128 58,128
Explosion 4704 0 17,808 0 0 22,512 30,589
Fuel Delivery
Disruption 0000000
Cyber-Attack 4704 4704 17,808 10,080 29,904 67,200 67,200
Severe Storm 4704 4704 17,808 28,224 29,904 85,344 85,344
Plane Crash 4704 0 0 0 0 4704 85,344
Table 9.
EEDMI Simulation Results. These values are resilience measurements for each threat
scenario and are compared between the existing microgrid and the potential nanogrid configuration.
Bold values indicate lower (preferred) outcomes.
EEDMI
Load 1
Load 2
Load 3
Load 4
Load 5
Nanogrid
Microgrid
Internal
Loss of all Generators 0 0 0 0 0 0 0
Loss of all PV Arrays 0 0 0 0 0 0 0
Insider Threat
Sabotage 0 0 620 1688 0 2309 5080
External
Fire 0 0 0 1411 1495 2906 2906
Explosion 188 0 712 0 0 900 1224
Fuel Delivery
Disruption 0000000
Cyber-Attack 470 470 1781 1008 2990 6720 6720
Severe Storm 94 94 356 564 598 1707 1707
Plane Crash 94 0 0 0 0 94 1707
Total EEDMI 14,636 19,344
Overall the simulation results show that nanogrids improve
EEDMI
scores for a threat
of an explosion, an insider threat, and plane crash, where both nanogrids and the baseline
microgrid see similar resilience for all remaining threats. It is important to note that because
of appropriate
DER
scaling for both existing microgrid and proposed nanogrid infrastruc-
tures, the loss of all diesel generators, the loss of all
PV
generation, and a disruption in fuel
delivery do not impact resilience. However, if diesel generators, and
PV
and
ESS
were not
appropriately sized to provide redundant generating capacity, this would not be the case.
Having redundant generation capacity is realistic because many military bases already
have redundant generation capacity already installed–in some cases redundant generation
capacity can be an order of magnitude higher than average base loads.
Appl. Sci. 2021,11, 4298 26 of 30
4.5. Step 5: Analyze Results
Based on the results of Step 4, the case study moves forward and does not need
iteration at this point. The proposed nanogrids are as or more resilient than the existing
microgrid. Because of the
SCADA
system deployed at the military installation, and location
of runways for military aircraft, cyber attacks and plane crashes can impact both the
microgrid and proposed nanogrids. It is important for the base energy manager and
stakeholders to stay one step ahead and test all vulnerable
SCADA
systems within any
base energy system, and continually upgrade cyber security.
4.6. Step 6: Produce Final Design Recommendations
ARE STAKEHOLDER REQUIREMENTS MET? Final recommendations for the case study
conclude that stakeholders from the base command, tenant commands, local energy com-
panies, microgrid provider, maintenance personnel, and chain of command all have their
requirements met with the proposed nanogrid design. The nanogrid design validates top
level functional requirements of: generate power, distribute power, control power, and
store energy.
ISFUNDING AVAI LAB LE ? Improving resilience by implementing nanogrids on critical
loads within the current base microgrid structure is determined to be effective for a majority
of the threat scenarios. We make the assumption that funding is available to implement the
nanogrids rapidly and that no selection of specific nanogrids based on available funding
needs to occur. Note that we explicitly did not conduct cost analysis within the proposed
methodology. However, base energy managers often conduct cost studies when choosing
between different potential energy system upgrades. The case study does not mention if
funding is available because this is often encountered in the acquisition process of program
upgrades and is beyond the scope of this research. This research focuses on how to improve
military installation energy resilience.
5. Discussion and Future Work
This paper presents a methodology that can be used to determine if supplementing
existing microgrids on military bases with nanogrids may improve energy resilience.
Through proper sizing of generation and storage resources, it is possible for nanogrids to
eliminate or greatly reduce many of the energy disruptions to critical loads that can impact
national security on military bases. We believe that the methodology will aid base energy
managers in identifying which critical loads will benefit from implementing nanogrids
for specific threats that are of concern to individual military bases. While we advocate for
some threats to be analyzed in all cases, many threats are not universal across all bases.
Thus, we assert that there is not universal guidance on which loads should or should not
be outfitted with nanogrids.
The methodology is specifically meant as a high-level systems engineering analysis
of existing microgrid infrastructure and proposed nanogrid infrastructure. The time step
on the simulations in the case study is 1 h. This large time step and the simple power
flow model ignore real concerns such as transients that can trip protection equipment;
the ability of specific generation and storage equipment, and power electronics hardware
to be grid-forming; and other issues. If implementing nanogrids at a specific military
base proves promising based on the results produced by the methodology, then detailed
electrical engineering analysis should be conducted to verify no other concerns exist.
Developing threat probabilities can be challenging especially for human-caused threats
and even more so for situations where adversaries are involved. Producing detailed
methods of estimating such probabilities are beyond the scope of this work. Natural
disasters and other nature-caused threats have significant historical data available to
estimate their probabilities of occurrence. However, climate change and other related
factors may begin to call into question using historical data to estimate future probabilities
of occurrence of natural threats.
Appl. Sci. 2021,11, 4298 27 of 30
We specifically do not address the cost of increasing energy resilience through the
implementation of nanogrids. Other work has investigated the costs associated with
increasing resilience and has attempted to produce energy resilience versus cost curves for
decision-makers to understand how much resilience may be gained with a specific level of
investment [35]. A potential future expansion of this work is to include cost analysis.
After recent events in Texas [
3
], we believe renewed focus will be placed on upgrading
military microgrids to be more self-sufficient and not rely upon fossil fuels or regular fossil
fuel delivery. Furthermore, price shocks in regional and global oil markets due to issues
such as the closing of key sea lanes to tanker traffic have occurred in the past and are
starting to occur as of writing this article due to things such as war and most recently a
stuck cargo ship blocking a canal [
70
] can significantly impact military budgets. Removing
highly variable diesel fuel bills from base energy manager budgets could be a significant
stabilizing force on year-to-year management of energy infrastructure.
We use
MDI
scoring to understand the relative importance of critical loads to national
defense. However,
MDI
has several significant flaws and other recently proposed metrics
have their own issues. Future work needs to develop a new method of understanding how
each load on a military base impacts national security. One potential avenue to explore is
mission engineering where tying specific mission objectives to specific loads under specific
force protection postures may help to produce a better measure.
6. Conclusions
We present a systems engineering methodology that is useful for conducting high
level energy resilience analysis of military base energy infrastructure to determine if
implementing nanogrids within an existing microgrid helps to protect electrical loads
critical to national security from power outages. The methodology takes into account
threats that are of specific interest to the military such as adversary actions and terrorist
attacks. Rather than focusing on reducing energy cost during nominal conditions (as most
civilian energy analyses attempt to achieve), our method focuses on improving resilience
to threats.
A case study is presented of a fictionalized Naval Support Activity Monterey military
base where the Naval Postgraduate School is located. Several threats of interest to all
military bases and of specific interest to military bases located on coastal California are
analyzed against a proposed microgrid with nanogrid augmentation using the method.
This case study demonstrates how the methodology can help a base energy manager to
select an appropriate nanogrid and show the feasibility of the nanogrid in improving
energy resilience of specific critical loads from a national security perspective as compared
to existing microgrid infrastructure.
Author Contributions:
Conceptualization, A.K., D.L.V.B., and A.P.; methodology, A.K., D.L.V.B., and
A.P.; software, A.K.; validation, A.K., D.L.V.B., and A.P.; formal analysis, A.K., D.L.V.B., and A.P.;
investigation, A.K., D.L.V.B., and A.P.; resources, D.L.V.B. and A.P.; data curation, D.L.V.B. and A.P.;
original draft preparation, A.K.; writing—review and editing, A.K., D.L.V.B., and A.P.; visualization,
A.K.; supervision, D.L.V.B. and A.P.; project administration, D.L.V.B.; funding acquisition, D.L.V.B.
and A.P. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments:
This research is partially supported by the Naval Postgraduate School. Any
opinions or findings of this work are the responsibility of the authors, and do not necessarily reflect
the views of the sponsors or collaborators. Approved for Public Release; distribution is unlimited.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Vine, D. Where in the World Is the U.S. Military? Available online: https://www.politico.com/magazine/story/2015/06/us-
military-bases-around-the-world-119321.html (accessed on 25 August 2020).
2.
Giachetti, R.; Van Bossuyt, D.; Parker, G.; Peterson, C. Systems Engineering Issues in Microgrids for Military Installations. Incose
Int. Symp. 2020, 1–16. [CrossRef]
Appl. Sci. 2021,11, 4298 28 of 30
3.
Douglas, E. ERCOT: Texas was “seconds and minutes” away from catastrophic months-long blackouts. Texas Tribune, 18 February
2021. Available online: https://www.texastribune.org/2021/02/18/texas-power-outages- ercot (accessed on 1 March 2021).
4.
Goss, M.; Swain, D.L.; Abatzoglou, J.T.; Sarhadi, A.; Kolden, C.A.; Williams, A.P.; Diffenbaugh, N.S. Climate change is increasing
the likelihood of extreme autumn wildfire conditions across California. Environ. Res. Lett. 2020,15, 094016. [CrossRef]
5.
Beaton, D. Testing Whether Distributed Energy Storage Results in Greater Resilience of Microgrids. Master’s Thesis, Naval
Postgraduate School, Monterey, CA, USA, 2021.
6.
Naval Facilities Engineering Command. NAVFAC P-602 Instruction: 3 Pillars of Energy Security; Naval Facilities Engineering
Command: Washington Navy Yard, DC, USA, 2017.
7.
Conger, J. An Overview of the DOD Installations Enterprise. Herit. Found.
2019
, 47–59. Available online: https://www.heritage.
org/military-strength-topical- essays/2019-essays/overview-the-dod-installations-enterprise (accessed on 20 November 2020).
8. Savena, M. P-601 Microgrid Design Guide; Naval Facilities Engineering Command: Washington Navy Yard, DC, USA, 2016.
9.
Department of Defense. Title 10 Armed Forces; United States Government Publishing Office: Washington , DC, USA, 2011;
Volume 17, pp. 1759–1763.
10. Ton, D.; Smith, M. The U.S. Department of Energy’s Microgrid Initiative. Electr. J. 2012,25, 84–94. [CrossRef]
11.
Baran, M.; Mahajan, N. System Reconfiguration on Shipboard DC Zonal Electrical System. IEEE Electr. Ship Technol. Symp.
2005
,
14, 86–92.
12. Díaz-González, F.; Sumper, A.; Gomis-Bellmunt, O. Energy Storage in Power Systems; John Wiley & Sons: Chichester, UK, 2016.
13. Hatziargyriou, N. Microgrids: Architectures and Control; John Wiley & Sons: Athens, Greece, 2014.
14.
Guerrero, J.; Chandorkar, M.; Lee, T.; Chiang Loh, P. Advanced Control Architectures for Intelligent Microgrids-Part 1:
Decentralized and Hierarchical Control. IEEE Trans. Ind. Electron. 2013,60, 1254–1262. [CrossRef]
15.
Jiayi, H.; Chuanwen, J.; Rong, X. A review on distributed energy resources and Microgrid. Renew. Sustain. Energy Rev.
2007
,12,
2472–2483. [CrossRef]
16.
Hirsch, A.; Parag, Y.; Guerrero, J. Microgrids: A review of technologies, key drivers, and outstanding issues. Renew. Sustain.
Energy Rev. 2018,90, 402–411. [CrossRef]
17.
Werth, A.; Kitamura, N.; Tanaka, K. Conceptual Study for Open Energy Systems: Distributed Energy Network Using Intercon-
nected DC Nanogrids. IEEE Trans. Smart Grid. 2015,6, 1621–1630. [CrossRef]
18.
Burmester, D.; Rayudu, R.; Seah, W.; Akinyele, D. A review of nanogrid topologies and technologies. Renew. Sustain. Energy Rev.
2017,67, 760–775. [CrossRef]
19.
Nordman, B.; Christensen, K. Local power distribution with nanogrids. In Proceedings of the 2013 International Green
Computing Conference Proceedings, Arlington, VA, USA, 27–29 June 2013.[CrossRef]
20.
Airoli, P.; Kondratiev, I.; Dougal, R.A. Controlled power sequencing for fault protection in DC nanogrids. In Proceedings of the
2011 International Conference on Clean Electrical Power (ICCEP 2011), Ischia, Italy 14–16 June 2011.[CrossRef]
21.
Rickerson, W.; Gillis, J.; Bulkeley, M. The Value of Resilience for Distributed Energy Resources: An Overview of Current Analytical
Practices; National Association of Regulatory Utility Commissioners: Washington, DC, USA, 2019.
22.
Peterson, C.; Van Bossuyt, D.; Giachetti, R.; Oriti, G. Analyzing Mission Impact of Military Installations Microgrid for Resilience.
J. Syst. Eng. 2021, In Review.
23.
Rusco, F.; Lepore, B.J. DOD Renewable Energy Projects: Improved Guidance Needed for Analyzing and Documenting Costs and Benefits;
United States Government Accountability Office: Washington, DC, USA, 2016.
24.
Hathaway, T. Microgrid Knowledge: Putting a Dollar Value on Energy Resiliency for US Military. Microgrid Knowledge, 1 July
2019. Available online: https://microgridknowledge.com/value-energy-resiliency-military/ (accessed on 28 February 2021) .
25.
Cornell Law School Legal Information Institute. 10 U.S. Code 101 Definitions. Available online: https://www.law.cornell.edu/
uscode/text/10/101#e_6 (accessed on 22 February 2021).
26. Giachetti, R.; Van Bossuyt, D.L.; Oriti, G.; Anderson, W. Resilience and Cost Tradespace for Microgrids on Islands. IEEE Syst. J.
2021, In Review.
27.
Giraldez, J.; Booth, S.; Anderson, K.; Massey, K. Valuing Energy Security: Customer Damage Function Methodology and Case Studies at
DOD Installations; National Renewable Energy Laboratory: Golden, CO, USA, 2012.
28.
Smith, C.W. Mission Dependency Index of Air Force Built Infrastructure: Knowledge Discovery with Machine Learning. Master’s
Thesis, Department of the Air Force Air University, Montgomery, AL, USA, 2016.
29.
Kujawski, E.; Miller, G. The Mission Dependency Index: Fallacies and Misuses. Incose Int. Symp.
2009
,19, 1565–1580. [CrossRef]
30.
Call, S. Navy’s Energy Mission Integration Group (EMIG) Overview, 2019. Available online: https://www.energy.gov/sites/
prod/files/2019/05/f62/17-fupwg-spring-2019-call.pdf (accessed on 20 January 2021).
31.
Herster-Dudley, M.R. Building Resilience Within DoD Microgrids by Considering Human Factors in Recovery Procedures.
Master’s Thesis, Naval Postgraduate School, Monterey, CA, USA, 2021.
32.
Peterson, C. Systems Architecture Design and Validation Methods for Microgrid Systems. Master’s Thesis, Naval Postgraduate
School, Monterey, CA, USA, 2019.
33.
International Atomic Energy Agency. Defining Initiating Events for Purposes of Probabilistic Safety Assessment; International Atomic
Energy Agency: Vienna, Austria, 1993.
34. Hyder, A.K.; Wiley, R.L. Spacecraft Power Technologies: Chapter 1; Imperial College Press: London, UK, 2000.
Appl. Sci. 2021,11, 4298 29 of 30
35.
Hildebrand, J. Estimating the Life Cycle Cost of Microgrid Resilience. Master’s Thesis, Naval Postgraduate School, Monterey,
CA, USA, 2020.
36.
Schonbergerschonberger, J.; Duke, R.; Round, S.D. DC-Bus Signal.: A Distrib. Control Strategy A Hybrid Renew. Nanogrid. IEEE
Trans. Ind. Electron. 2006, 1453–1460. [CrossRef]
37.
Antonysamy, S.; Murugasan, S.; Simon, E. Future Nano-grid technologies and its implementation challenges for Smart Cities.
IOP Conf. Ser. Mater. Sci. Eng. 2020,955, 012002. [CrossRef]
38.
Jin, Z.; Sulligoi, G.; Cuzner, R.; Meng, L.; Vasquez, J.; Guerrero, J. Next-Generation Shipboard DC Power System: Introducing
Smart Grid and DC Micgrogrid Technologies into Maritime Electrical Networks. IEEE Electrif. Mag. 2016,4, 45–57. [CrossRef]
39. Pilehvar, M.; Shadmand, M.; Mirafzal, B. Analysis of Smart Loads in Nanogrids. IEEE Access 2018,7, 548–562. [CrossRef]
40.
Shevchenko, J. An Introduction to Model-Based Systems Engineering (MBSE), 2020. Available online: https://insights.sei.cmu.
edu/sei_blog/2020/12/an-introduction-to-model-based-systems- engineering-mbse.html (accessed on 15 January 2021).
41.
Ertaul, L.; Hao, J. Enterprise security planning with department of defense architecture framework (DODAF). In Proceedings of
the International Conference on Security and Management (SAM), Citeseer, Las Vegas, NV, USA, 12–15 July 2011; p. 1.
42.
Derevyanko, V. Array Losses, General Considerations. Available online: https://www.pvsyst.com/help/index.html?array_
losses.htm (accessed on 5 January 2021).
43.
Thornberry, M.; Smith, A. Insider Threats: DOD Should Improve Information Sharing and Oversight to Protect U.S. Installations;
Technical Report; U.S. Government Accountability Office: Washington, DC, USA, 2015.
44.
Nelson, K. Fire Danger Forecast: Map of Large Fire Probability. Available online: https://www.usgs.gov/ecosystems/lcsp/fire-
danger-forecast/map-large-fire-probability (accessed on 2 February 2021).
45.
Litzenberg, E. America Wildland–Urban Interface Burning: A Modern Application of A Historic Success. Master’s Thesis, Naval
Postgraduate School, Monterey, CA, USA, 2020.
46. Monterey County Weekly Staff. 2020 Wildfires in Monterey County; Monterey County Weekly: Seaside, CA, USA, 2020.
47.
Skelton, I. Combating Terrorism: Action Taken but Considerable Risks Remain for Overseas; U.S. Government Accountability Office:
Washington, DC, USA, 2000.
48.
Rempfer, K. Paratrooper Killed by Vehicle-Borne IED Has Been Identified. 2019. Available online: https://www.armytimes.com/
news/your-army/2019/09/06/paratrooper-killed-by-vehicle- borne-ied-has-been-identified/ (accessed on 6 January 2021).
49. CNN Editorial Research. Beirut Marine Barracks Bombing Fast Facts; CNN Editorial Research: Atlanta, GA, USA, 2020.
50.
Andrews, A. Department of Defense Fuel Spending, Supply, Acquisition, and Policy; Technical Report; U.S. Government Accountability
Office: Washington, DC, USA, 2009.
51.
Papakonstantinou, N.; Van Bossuyt, D.; Linnosmaa, J.; Hale, B.; O’Halloran, B. A Zero Trust Hybrid Security and Safety Risk
Analysis Method. Am. Soc. Mech. Eng. 2021. [CrossRef]
52.
Sancaktar, S.; Ng, C. Risk Assessment of Operational Events: External Events; United States Nuclear Regulatory Commission:
Washington, DC, USA, 2017.
53.
Wielenberg, A.; Alzbutas, R.; Apostol, M.; Bareith, A.; Brac, P.; Burgazzi, L.; Cazzoli, E.; Cizelj, L.; Hage, M.; Hashimoto, K.; et al.
Methodology for Selecting Initiating Events and Hazards for Consideration in an Extended PSA; Technical Report; ASAMPSA E and
EURATOM: Brussels, Belgium, 2016.
54.
Goldman, D. The U.S. Military Is Terrified of Climate Change. It’s Done More Damage than Iranian Missiles. NBC News, 2020.
Available online: https://www.nbcnews.com/think/opinion/u-s-military-terrified- climate-change-it-s-done- more-ncna124
0484 (accessed on 22 March 2021).
55.
Conger, J. Prioritizing Climate & Security in DoD. In Proceedings of the NPS Climate & Security Speaker Series, Monterey, CA,
USA, 7 April 2021.
56.
Office of Energy Efficiency and Renewable Energy. DOE Commercial Reference Building; Office of Energy Efficiency and Renewable
Energy: Washington, DC, USA, 2012.
57.
National Renewable Energy Laboratory. National Solar Raidation Data Base 1991–2010; National Renewable Energy Laboratory:
Golden, CO, USA, 2019.
58.
Antelman, A.; Dempsey, J.; Brodt, B. Mission Dependency Index: A Metric for Determining Infrastructure Criticality. In
Infrastructure Reporting and Asset Management: Best Practices and Opportunities; Amekudzi, A., McNeil, S., Eds.; American Society
of Civil Engineers: Reston, WV, USA, 2008; Chapter 19, pp. 141–146. [CrossRef]
59. Cummins. Quiet Connect Series-RS13A, RS17A, RS20A, & RS20AC; Cummins: North Columbus, IN, USA, 2015.
60.
GENERAC Power Systems Inc. Standby Generators Liquid-Cooled Gaseous Engine (80kW); GENERAC Power Systems Inc.: Waukesha
County, WI, USA, 2017.
61.
GENERAC Power Systems Inc. Industrial Diesel Generator Set: EPA Certified Stationary Emergency; GENERAC Power Systems Inc.:
Waukesha County, WI, USA, 2016.
62.
GENERAC Power Systems Inc. Protector Series: Standby Generators Liquid-Cooled Gaseous Engine (60kW); GENERAC Power
Systems Inc.: Waukesha County, WI, USA, 2017.
63.
Unified Facilities Criteria. Engine-Driven Generator Systems for Prime and Standby Power Applications; Technical Report; U.S.
Department of Defense: Washington, DC, USA, 2019.
64.
Sanchez-Mateos, J. Reliability-Constrained Microgrid Design. Master’s Thesis, KTH School of Electrical Engineering, Stockholm,
Sweden, 2016.
Appl. Sci. 2021,11, 4298 30 of 30
65.
Bryan, J.; Duke, R.; Round, S. Decentralized generator scheduling in a nanogrid using DC bus signaling. In Proceedings of the
IEEE Power Engineering Society General Meeting, Denver, CO, USA, 6–10 June 2004.[CrossRef]
66.
Stouffer, K.; Pillitteri, V.; Lightman, S.; Abrams, M.; Hahn, A. Guide to Industrial Control Systems (ICS) Security; NIST Special
Publications: Gaithersburg, MD, USA, 2015.
67. Campbell, R. Electric Grid Cybersecurity; Technical Report; Congressional Research Service: Washington, DC, USA, 2018.
68.
Lee, R.; Assante, M.; Conway, T. Analysis of the Cyber Attack on the Ukrainian Power Grid; Technical Report; SANS Industrial
Control Systems and Electricity Information Sharing and Analysis Center, E-ISSAC: Washington, DC, USA, 18 March 2016.
69.
The Associated Press. Hydraulic Failure Caused F-16 Crash into California Warehouse; Air Force Times: Vienna, VA, USA, 23 April
2020.
70.
Lee, J. What a Long Suez Canal Closure Means for the Oil Price. Bloomberg, 2021. Available online: https://www.bloomberg.
com/opinion/articles/2021-03-25/suez-canal- blocked-what-a- lengthy-closure-will-mean-for-the-oil- price (accessed on 17 April
2021).
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... Single load microgrids for critical loads may provide increased energy resilience, but the resources associated with designing and building single load microgrids customized for specific loads is substantial, especially if they incorporate renewable power generation. Previous research recommends that critical loads and potential microgrid solutions are modeled and simulated during design and prior to implementation [6][7][8]. The number of critical loads across DoD installations varies but designing customized single load microgrids for each critical load may not be worth the time or cost. ...
... Previous research demonstrated increased energy resilience by augmenting critical loads with their own dedicated microgrids [7]. This provides a second layer of redundant power beyond the installation microgrid for specific critical loads on the DoD installation. ...
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... Several recent publications focus on systems engineering approaches to improve energy resilience for national security installations [2][3][4][5]. However, most exigent work assumes a constant operational environment with predictable loads. ...
... There are many ways to measure and define electrical grid resilience such as: the total degradation of service after an event; the time spent taking recovery actions; or the rate at which service is recovered [46]. A variety of tools and methods have been developed in the literature to study microgrid or energy resilience [2][3][4]10,[47][48][49][50]. For instance, the Energyresilience Analysis (ERA) Tool [10] assesses energy resilience by comparing life-cycle costs related to system availability and reliability to assist decision makers with choosing alternative energy designs [10]. ...
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... Hildebrand [79] and similarly Bolen et al. [80] applied net present value to ascertain the life-cycle cost of energy resilient solutions. A cost-based approach, however, proves difficult when assigning a monetary value to mission assurance and (to a greater extent) national security [36,81]. ...
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