The document discusses modeling and simulation (M&S) for smart grid infrastructure. It notes that M&S is being used to study the complex interactions between power systems and communication networks in a smart grid context. Several challenges of smart grid simulation are outlined, including combining power system and communication network simulation and different time models used. Solutions discussed include co-simulation, where separate power system and communication network simulators are connected, and integrated simulation, where components are simulated together within one environment. Government agencies and laboratories leading M&S research are also outlined.

1. IN THIS REPORT:
Energy systems in the United States are experiencing a fundamental transition of historic
proportion. Smart grid technologies have been developed and continue to evolve through
research and development. The deployment of these technologies is now impacting how our
energy systems function, particularly in regard to the power grid, and implies an essential re-
engineering of the technical infrastructure to enable new and legacy energy systems to work
together efficiently. The interconnectedness of infrastructure systems, both physical and cyber,
coupled with factors including climate change, population shifts, and aging infrastructure
amplify the risk to infrastructure and community resilience. Managing and operating the new
smart grid will require intelligent digital models of electrical networks that enable the simulation
and analysis of electrical flows in response to changing demand and intermittent power
generation. Modeling and simulation (M&S) tools are vital to understand the vulnerabilities of
both the individual infrastructures and the entire interconnected system of infrastructures, as well
as being able to analyze and predict their performance. In this paper, we will trace the evolution
of M&S, the challenges and solutions associated with the implementation of M&S projects, the
types of power system and ICT models and simulations that are geared for the utility industry,
and information on the tools and resources that are available for the varying applications inherent
in the smart grid infrastructure We will also outline much of the work that is being conducted in
this space, particularly in the laboratory and university realms, as well as a few case studies on
utility collaborations. We will conclude with insights into future trends as well as with
recommendations and opportunities.
KEY FINDINGS:
• M&S has been a key technology area for many decades. However, we are now able to
capitalize on the evolution of computing equipment and rapid advances in digital
computing, display and networking technologies to use models and simulations
differently and more efficiently, particularly in the areas of advanced distributed and
interoperable simulations. Nonetheless, the M&S of interdependencies between critical
infrastructure elements is a relatively new and very important field of study.
• Infrastructure modeling tools enable users to integrate geographic information systems,
engineering design, high-resolution photogrammetric and laser-scanning data into a 3-D
environment to create digital models of urban environments, including buildings and
energy, water, transportation and communications infrastructure networks. The technical
advances that have been achieved have opened the door to a much broader market,
including small to medium municipalities.
• Although power system or communication network simulators are being used extensively
in both domains, it is the combined simulation of the power system and communication
network that has recently attracted more attention.
• Smart grids promise to facilitate the integration of highly variable and widely distributed
resources such as solar and wind power. Likewise, new types of loads, such as plug-in

2. electric vehicles and their associated vehicle-to-grid potential, will offer opportunities.
However, these technologies will require the use of advanced control techniques for
which M&S can be an enabler.
• M&S reduce CAPEX and OPEX risks.
• M&S enable the utility to evaluate the benefits of optimizing their distribution system
voltage for the purposes of energy efficiency, Volt/VAR optimization, and voltage
stabilization.
• M& S make it possible to significantly improve system performance while diminishing
the need to build expensive hardware for test purposes.
INTRODUCTION:
In general, modeling and simulation refers to the process of representing an entity and its
behavior. The Department of Defense has defined modeling as a physical, mathematical, or
otherwise logical representation of a system, entity, phenomenon, or process, and simulation as a
method for testing and implementing a model over time.
As incongruous as it may seem, the use of modeling and simulation techniques is both old and
new. M&S actually developed its roots in the military and defense domains of the early 1940’s,
and it continues to be considered as an integral part of systems engineering to conduct events and
experiments which influence requirements and training for military systems. The evolution of
computer-based models and simulations began to grow and expand as computing equipment
became more advanced. Since the 1970’s, our generation has experienced the privilege of
watching mainframes being replaced by mini-computers, and in the 1980’s, the development of
workstations and PC’s, and in the 1990’s, improved networking capabilities which provided the
ability to connect different computers running different simulations. In our current age, we can
even perform computations on I-pads, smart phones and watches! So even though M&S has been
a key technology area for many decades, we are now able to capitalize on the rapid advances in
digital computer, display and networking technologies to use models and simulations differently
and more efficiently, particularly in the areas of advanced distributed and interoperable
simulations.
As life is today, other application domains are rapidly exceeding the military’s use of M&S,
particularly in the fields of medicine, transportation, and infrastructure. M&S efforts involve
setting up a coherent synthetic environment that allows for integration of simulated systems in
the early analysis phase, and eventually, to a virtual test environment of the final system. This is
due, in large part, to the ability to allow free configuration of environment parameters found in
the operational application field of the final product. New systems and designs can be subjected
to “if-then” analyses of different alternatives before costly physical prototypes are built and/or
before integrations or upgrades are implemented in the field. In addition to being cost effective
and risk-aversive, simulations can be conducted faster than real time.

3. M&S OVERVIEW:
Be it through direct connectivity or geospatial proximity, critical infrastructure systems interact.
These interactions often create complex relationships, dependencies, and interdependencies that
cross infrastructure boundaries, and these effects continue to grow as information technology
pushes interconnectivity. The implementation and integration of smart technology into the legacy
grid has created the growing need to analyze the variables of influence that cross multiple sectors
to lessen or eliminate negative consequences due to unforeseen secondary effects. M&S efforts
in the utility industry are aimed at studying the interaction and interoperability of the following
variables of influence:
• Advanced metering infrastructure (AMI)
• Meter data management (MDM)
• Distribution management system (DMS)
• Geographic information system (GIS)
• Outage management systems (OMSs)
• Intelligent electronics devices (IEDs)
• Wide-area measurement systems (WAMS)
• Energy management systems (EMSs)
• Demand-side management (DSM)
• Demand response (DR)
• Field Area Networks (FAN)
• Neighborhood Area Networks (NAN)
• Home Area Networks (HAN)
• Building Area Networks (BAN)
• Industrial Area Networks (IAN)
• Distributed renewable energy sources (DRES)
• Electric Vehicles (EVs)
• Distributed generation (DG)
• Energy Storage (ES)
• Electricity Markets (EMs)
Add to the above list the challenges of the intermittent nature of renewable energy sources, such
as solar and/or wind, and the additional load on the power grid caused by the electrification of
vehicles needing power from the grid for battery charging and the location where the charging
will take place (which can be anywhere). The rise of distributed energy sources placed
throughout the grid requires bi-directional power flows, something for which the traditional grid
was not designed to handle. Demand and supply must be in balance in the power grid. Therefore,
large shares of renewable energy require stand-by controllable generation or the presence of
storage to cope with sudden changes in power output. It comes as no surprise that M&S
modalities are required for the careful design and management of these energy variables.
Wow, what a task! Indeed, the modeling and simulation of infrastructure interdependencies is a
gigantic effort in terms of development resources such as infrastructure expertise with M&S,

4. data accessibility, and financial resources. For these reasons, U.S. government agencies, industry
organizations, universities and laboratories have taken the lead and are currently doing most of
the research in this space. Participants in this area include:
• Department of Homeland Security (DHS) – The DHS is home to the National
Infrastructure Simulation and Analysis Center (NISAC). The NISAC program is
sponsored by the DHS Information Analysis and Infrastructure Protection Directorate,
and is a core partnership of Los Alamos and Sandia National Laboratories. NISAC
integrates the modeling and simulation expertise of both laboratories to address the
nation’s potential vulnerabilities and the consequence of disruption among our critical
infrastructures.
• Department of Energy (DOE) – The Visualization and Modeling Working Group
(VMWG) sponsored by the DOE’s Office of Electricity Delivery and Energy Reliability
activates in response to national energy emergencies to provide data, analyses, and
visualization tools as was done for Hurricanes Katrina and Rita. Their technical expertise
is combined with modeling, GIS, data libraries on past energy disruptions, and other tools
to conduct in-depth analysis. It was formed in September 2003 to improve the ability of
DOE to perform quick turn-around analyses during energy emergencies, and is comprised
of energy experts from several DOE offices and energy infrastructure and modeling
experts from various DOE national laboratories.
• Argonne National Laboratory (ANL) – ANL, the largest national laboratory by size and
scope in the Midwest, is a non-profit research laboratory operated by the University of
Chicago for the DOE. ANL’s Risk and Infrastructure Science Center (RISC) plays an
important role in the nation’s infrastructure assurance efforts, developing methods,
research programs and technologies to understand interdependencies and build
resilience among infrastructures, and to serve as a resource for emergency response,
transportation security, and crisis management in U.S. communities. Argonne is also the
home to DOE’s new Electric Vehicle (EV) Smart Grid Interoperability Center. The
Center plays a key role in supporting global harmonization of standards and technology
for the EV-grid interface, as well as charging interoperability to ensure future electric
vehicles and charging stations worldwide work together seamlessly.
• National Renewable Energy Laboratory (NREL) – NREL is the only federal laboratory
dedicated to research, development, commercialization, and deployment of renewable
energy and energy efficiency technologies. Their recently opened Energy Systems
Integration Facility (ESIF) addresses challenges like integrating advanced solar inverters
onto the grid, operating micro grids that can keep providing power when the main power
grid is down, supplying the power needs for the Army’s forward operating bases, and
improving automotive fuel cells and advanced battery technologies. The ESIF is the
nation’s first research facility that can conduct megawatt-scale research development, and
demonstration (RD&D) of the components and strategies needed to safely integrate clean
energy technologies into the electrical grid and utility operations at the speed and scale
required to meet national goals. ESIF is also home to Peregrine—the largest high
performance computer (HPC) in the world exclusively dedicated to advancing renewable

5. energy and energy efficiency technologies. Peregrine’s unique capabilities can be
virtually linked to other grid integration research facilities throughout the country and the
world. NREL has also worked with the DOE’s Office of Energy Efficiency and
Renewable Energy (EERE) to develop a holistic approach to grid integration through the
Integrated Network Test Bed for Energy Grid Research and Technology Experimentation
(INTEGRATE) project which focuses on characterization of distributed energy
technologies at the ESIF, development of open-source interoperability standards, and
cross-technology demonstrations showing how EERE technologies can work holistically
to provide services to the grid.
• Electric Power Research Institute (EPRI) – EPRI has developed and released a
methodology that can be used with existing software and planning tools to determine
ways to reinforce distribution systems that face increased deployment of distributed
energy resources (DER), such as rooftop solar, micro grids, and energy storage. The
methodology applies analysis techniques that can capture the voltage, circuit protection,
and thermal impacts resulting from the growing deployment of distributed resources.
EPRI is also collaborating with utilities and other partners to develop high penetration
solar future scenarios in the southeastern United States, distribution grid feeder clustering
and characterization, and models for solar generation hosting capacity and power
production simulation. EPRI has also created “IntelliGrid” architecture which provides
methodology, tools, and recommendations for standards and technologies for utility use
in planning, specifying, and procuring IT-based systems, such as advanced metering,
distribution automation, and demand response. The architecture also provides a living
laboratory for assessing devices, systems, and technology. Several utilities have applied
IntelliGrid architecture including Southern California Edison, Long Island Power
Authority, Salt River Project, and TXU Electric Delivery.
• Oak Ridge National Laboratory (ORNL) – ORNL is owned by the U. S. Department of
Energy, Office of Science and is managed by the University of Tennessee and Battelle
Memorial Institute. The Lab conducts basic and applied research and development to
provide solutions that strengthen the nation's leadership in key areas of science, increase
the availability of clean, abundant energy, restore and protect the environment and
contribute to national security.
• Battelle Memorial Institute (BMI) – In addition to managing the Oak Ridge National
Laboratory, Battle also manages other laboratories which contribute to infrastructure
research including the Pacific Northwest National Lab, the Brookhaven National Lab, the
Idaho National Lab, and the Lawrence Livermore National Lab.
• Carnegie Mellon University’s Software Engineering Institute (SEI) became the steward
of The Smart Grid Maturity Model (SGMM) in 2009. The SGMM is a management tool
that utilities can leverage to plan their smart grid journeys, prioritize their options, and
measure their progress as they move toward the realization of a smart grid. Utilities use
the SGMM to assess their current state of smart grid implementation, define their goals
for a future state, and generate inputs into their road mapping, planning, and
implementation processes. In September 2010, the SEI published V1.1 of the SGMM
after pilot-testing it with more than 30 utilities, including American Municipal Power

6. (AMP), in Columbus, Ohio, and 22 of its member utilities to ensure the quality and
usability of the update.
THE CHALLENGES:
The smart grid simulation environment is designed as a three-layered architecture consisting of
the application, middleware and support layers. The application layer consists of high-level
applications or services such as advanced meter reading services, demand side management
services, and billing services. The services in the application layer make use of the middleware
layer. That middleware layer includes a communication interface which can be used to send
messages between components independent of the underlying networking technology (such as
ZigBee or PLC, TCP, or UDP) that is being simulated. The support layer consists of network and
electrical components and provides support functions for the layers above.
Power systems are fundamentally reliant on control, communications, and computation for
ensuring stable, reliable and efficient operation, and central to the smart grid concept is the
convergence of information and communication technology with power system engineering.
Communication network simulation environments are used to develop and evaluate new ICT
architectures and network protocols while power system engineers use simulation environments
for power system planning and operations. In a smart grid context, simulators allow for studying
complex interactions between these interconnected systems and the monitoring and control
elements on top of them.
Power system or communication network simulators are being used extensively in both domains.
However, one of the greatest challenges associated with smart grid simulation is that it requires a
combined simulation of both the power system and the ICT infrastructure. An additional
challenge stems from different models of time utilized by various simulators, i.e. continuous
simulation is common in power systems while communication network simulators typically are
discrete-event simulators. Another challenge, one which is easier said than done, is designing
models and simulators that are user friendly. At the very least, they should allow for the
integration of different programming languages. And last, but not least, modeling is further
complicated by the quality and availability of data, intricacy of the systems, and the complexity
of interactions between infrastructure sectors.
THE SOLUTIONS:
Modelers have developed a slew of innovative modeling approaches including agent-based
modeling, effects-based operations models, input-output models, building information models,
mathematical models, models based on risk, 3-D visualization models, and models based on
game theory. Development of M&S technology has also moved toward utilizing a combination
of software models running in synchrony to study the infrastructure as a whole. These
approaches are known as co-simulation and integrated simulation.
Co-Simulation:

7. In the context of a smart grid, a co-simulator consists of a specialized communication network
simulator and a specialized power system simulator. Each of them has their own distinct
simulation interface for things like data input, configuration, result output and control. The main
challenge is to connect, handle and synchronize data and interactions between both simulators
using their respective simulator interfaces, especially because each manages their simulation
time individually. The main advantage of this approach is that existing simulation models and
algorithms that have already been implemented and validated can be reused, and the majority of
development efforts can be devoted to the modeling of additional smart grid components and
systems such as PVs, wind turbines, and sub-systems such as low or medium voltage power
grids.
Integrated Simulation:
The integrated simulation approach is one in which the power system and communication
network are simulated in one environment with a single interface so that the management of
time, data, and power/communication system interactions can be shared among the simulator
constituents. The main challenge is to provide a simulation interface that provides sufficient
levels of detail. It has been suggested that a communication network, power system or other
platform can be selected as the basis for the smart grid simulator, and other components can be
implemented from scratch or by link to existing libraries or tools.
Co-simulation and integrated simulation are purely software-based approaches in which the
power grid and ICT infrastructure are simulated, and the physical world components are
abstracted. There are other approaches which provide support for emulation: real-time
simulation, and/or hardware-in-the loop simulations.
Real-time Simulation:
A real-time simulator must accurately produce the internal variables and outputs of the
simulation model within the same length of time as its real-world counterpart would. The
correctness of a real-time model not only depends upon a numerical computation but also on the
timeliness with which the simulation model interacts with external hardware and/or software
components. Applications of real-time simulation include testing of physical control and
protection equipment.
Hardware-in-the Loop (HIL) Simulation:
HIL simulation is a technique used to develop complex real-time embedded systems in which
some components are real hardware and others are simulated. Components may be simulated
because they are unavailable or because experiments with the real components are too costly,
time consuming, or too hazardous. Typically, a mathematical model of the simulated system is
used to provide electrical emulation of sensors and actuators that are connected to real hardware.
THE OFFERINGS:

8. This section might be too technical for some readers. It is not salacious reading, for sure.
Nonetheless, we are providing these tool and interface resources for our techie colleagues
because it is very difficult and time-consuming to find them through the best of research.
POWER SYSTEM SIMULATION TOOLS
Simulators for power system analysis have been extensively used by professionals for network
planning, operations and price forecasting. Over-voltages, harmonics, short circuits, transient
stability, power flow, and optimal dispatch of generating units are examples of variables that
need to be captured and parameterized in the simulations. The following resources are available:
• Matlab/Simulink environments are widely used as power system simulators based on
MATLAB. Tools include Power System Analysis Toolbox (PSAT), Power System
Toolbox (PST), Educational Simulation Tool (EST), SimPowerSystem, Power
Analysis Toolbox (PAT), Voltage Stability Toolbox (VST), and MATPOWER.
Several of these tools are open source but MATLAB is a commercial and closed
product. However, PSAT can run on GNU/Octave, which is a free Matlab clone.
• PSCAD/EMTDC is a commercial simulation tool for the power system CAD and
electromagnetic transients for DC. PSCAD/EMTDC can be coupled with external
tools like Matlab.
• DigSilent Power Factory allows the modeling of generation, transmission,
distribution and industrial grids, and the analysis of their interactions. Load flow,
electromechanical fluctuations and transient events can be simulated. Models of
voltage controllers, generators, motors, dynamic and passive loads and transformers
are part of DigSilent’s built-in electrical components library but the interior
algorithms are not accessible. However, users can create models using the DigSilent
Simulation Language (DSL), and DigSilent supports the exchange of power data with
external tools such as using an Open Process Control (OPC) interface for exchanging
data between simulators.
• Siemens Power Systems Simulator (PSS) product suite includes several software
solutions targeting different domains and time scales. Among others, PSS includes
PSS SINCAL and PSS E. PSS SINCAL is a commercial planning and analysis tool
(with special licenses for research and education) which targets utility distribution
system analysis with the capability to perform, among others things, power flow, load
balancing, load flow optimization and optimal branching simulations. Its COM-server
interface facilitates integration into existing IT architectures for analysis of distributed
generation and smart meter data. PSS SINCAL allows users to link each smart grid
equipment model, such as EV, micro-turbines, and smart meter, with their
corresponding generation and load profiles. For transmission system planning, the
PSS E tool allows users to perform load flow and transient analyses. PSS E can
interact with user scripts using the Python scripting language.

9. • EMTP-RV is a commercial software for simulations of electromagnetic,
electromechanical and control system transients in multiphase electric power systems.
Other potential uses of EMTP-RV include studies in insulation coordination,
switching surges, capacitor bank switching, and motor starting. Users can develop
customized modules and interface them to EMTP-RV via dynamic-link library (DLL)
functionality.
• PowerWorld Simulator is an interactive, visual-approach, power system package
designed to simulate high voltage power system operation on a time frame ranging
from several minutes to several days. PowerWorld’s add-on SimAuto allows the
control of the simulator from external applications, and SimAuto acts as a Component
Object Model (COM) object for interfacing with external tools such as Matlab or
Visual Basic.
• ETAP PSMS is a real-time power management system with more than 40 software
modules for load flow analysis, short-circuit analysis, device coordination analysis,
motor starting analysis, transient stability analysis, and harmonic analysis.
• Cymdist is designed for planning studies and simulating the behavior of electrical
distribution networks under different operating conditions and scenarios. It offers a
full network editor and is suitable for unbalanced load flow and load balancing
studies, and the software is fully customizable.
• EuroStag is a power systems dynamics simulator which allows a range of transient
and stability studies, and supplementary tools such as Smart FLow enable load flow
calculations.
• HOMER is a power generation simulator which can be used for designing hybrid
power systems containing a mix of energy sources such as generators, combined heat
and power, wind turbines, PV, batteries, and the like. It can simulate both grid-tied or
stand-alone systems.
• OpenDSS is an open-source distribution system simulator developed and maintained
by EPRI. It is designed to support power distribution planning analysis associated
with the interconnection of distributed generation to the utility system. Other targeted
applications include harmonic studies, neutral-earth voltage studies, and Volt/Var
control studies. Co-simulation interfaces such as COM and scripting interfaces are
provided, and users can define their own models.
• Opal-RT develops real-time digital simulators and hardware-in-the-loop testing
equipment. One of them, eMEGAsim, is a real-time hardware-based simulator which
studies, tests, and simulates large power grids and industrial power systems. It can
also be used for simulation of power electronics found in distributed generation such
as wind farms, PVs, and Plug-in Hybrid EVs. RT-LAB is the core technology behind
eMEGAsim and enables distributed real-time simulation and hardware-in-the-loop

10. testing of electrical, mechanical, and power electronic systems, and related
controllers. ARTEMIS is a suite of fixed-step solvers and algorithms that optimize
real-time simulation of SimPowerSystems models of electrical, power electronic, and
electromechanical systems. Opal-RT products are fully integrated with
MATLAB/SimuLink.
• The Real-Time Digital Simulator (RTDS) is a power system simulator that solves
electromagnetic transient simulations in real-time. It supports high-speed simulations,
closed-loop testing of protection and control equipment, and hardware-in-the-loop
applications. Power system equations are solved fast enough to continuously produce
output conditions that realistically represent conditions in the real network. RTDS
supports IEC 61850 device testing, and the simulator can be connected directly to
power system control and protective relay equipment.
COMMUNICATION NETWORK SIMULATION TOOLS
• OMNeT++ is an open-source discrete-event simulation environment which has been
designed for the simulation of communication networks (wired and wireless) and
distributed systems in general. The simulation environment is not limited to
simulating communication networks and has been used in various domains such as
wireless network simulations, business process simulation and peer-to-peer
networking. A comprehensive set of internet based protocols is provided by means of
the INET framework extension which includes support for IPv4, IPv6, TCP, UDP,
Ethernet, and many other protocols. Other extensions provide simulation support for
mobility scenarios (VNS), ad-hoc wireless networks (INET-MANET), and wireless
sensor networks (MiXiM, Castalia). Distributed parallel simulation is supported to
enable simulation of large scale networks. Additionally, federation support based on
the High-Level Architecture (HLA) standard is provided in OMNEST, the
commercial version of OMNeT++. OMNeT++ has received substantial attention from
the smart grid community for developing smart grid simulators.
• Network Simulator (ns-2 and ns-3) - The Network Simulator version 2 (ns-2) is a
widely used open source discrete-event network simulator and is targeted at
networking research with a strong focus on internet systems. Development of ns-3,
the successor to ns-2, is ongoing and includes new features such as support for the
Python programming language as a scripting interface, improved scalability, more
attention to realism, and better software integration. It is important note that ns-3 is
not backwards compatible with ns-2. In a smart grid context both ns-2 and ns- 3 are
adopted with a co-simulation approach for simulation of PLC networks. The
simulation model is based on transmission line theory (TLT) which relies on the
knowledge of the topology, wires, and the load characteristics of the power grid
underlying the PLC system. It supports networks with multiple node-to-node links,
and an interface to the ns-3 framework allows the integration of higher level protocols
such as TCP/IP.

11. • NeSSi (Network Security Simulator) is an open source discret-event network
simulator whose primary use involves network security related scenarios in IP
networks. However, distribution is supported to enable simulation of large scale
networks, and in the smart grid domain, provides a security analysis of a smart
measuring scenario through federated simulation and an integrated approach for
evaluating and optimizing an agent-based smart grid management system.
• OPNET ModelerR is a commercial discrete-event network simulator with built-in
validated models including LTE, WIMAX, UMTS, ZigBee, and Wi-Fi. It enables
modeling of various kinds of communication networks, incorporating terrain,
mobility, and path-loss characteristics in the simulation models and comes with an
open interface for integrating co-simulation and even hardware-in-the-loop
experiments. The Smart Grid Communications Assessment Tool (SGCAT) is a
simulation, modeling and analysis platform built on top of the OPNET Modeler and is
designed for utilities that want to develop a holistic smart grid communications
strategy. It has been developed to assess the performance of different smart grid
applications under various terrains, asset topologies, technologies and application
configurations.
SMART GRID SIMULATION TOOLS
• GridLAB-D is a smart grid analysis tool developed by Battelle at the Pacific Northwest
National Lab. It allows for the simultaneous simulation of power flow, end use loads, and
market functions and interactions. The software core can determine the simultaneous
state of millions of independent devices resulting in a detailed and accurate system
model. GridLAB-D is designed as a modular system, and the system can load additional
modules, such as power flow calculations and device control, end use loads and controls,
and data collection, which add specific functions and models to the simulation
environment. More advanced features such as consumer behavior models (like those
related to different types of demand profiles, price response and contract choice), energy
operations (such as distribution automation, load-shedding programs, emergency
operations), and business operations (retail rate, billing, and market-based incentive
programs) are also provided or under development. The original focus of GridLAB-D
was on the distribution system but research into the transmission system is also
supported. Although the current version of GridLAB-D does not support explicit
modeling of the communication network, a communication network module and a co-
simulation approach are underway for the next version. The addition of such a module
will enable users to determine the impact communications systems have on the operations
of smart grid technologies.
• GridSim simulates the power grid, the ICT infrastructure that overlays the grid, and the
control systems running on top of it in real-time. It focuses on the design and testing of
wide area control and protection applications using PMU and other high-rate time
stamped data to simulate components of the power system, substation, communication
and data delivery, and control center applications. GridSim uses different tools for their

12. simulations: TSTAT is a transient stability simulator used for power system simulation,
and GridStat is used to deliver data between the different components in GridSim.
DEMAND RESPONSE/DEMAND SIDE MANAGEMENT SIMULATION TOOLS
• IBCN is an integrated smart grid simulator that considers the combined simulation of the
power system and ICT infrastructure. It investigates the impact of voltage and load
profiles from distributed household generators, such as PV panels, on a distribution grid.
Another area for which the simulator has been used extensively is demand-side
management of electric vehicles.
• Smart Grids Information & Communication (SGiC) is a web-based software for
distributed decision support and performance analysis, and use cases for the framework
include power routing, power balancing, virtual power plants, and price based control.
The software enables the participation of residential and/or commercial customers by
providing an end-user interface which supports social network interactions and
appropriate incentives for consumers participating in DR, DSM, and virtual power plant
programs.
• GridSpice is a cloud-based simulation package developed to provide a framework to
model all interactions of a smart grid in the distribution and transmission networks. Built
on top of GridLab-D and MATPOWER, it targets renewable energy integration, home
area control, electric vehicle infrastructure, distributed energy resources, micro grids,
demand response and distribution operation, and utility scale storage.
CASE STUDIES:
NREL Utility Partnership Studies
NREL is collaborating with Duke Energy and Alstom Grid to implement a comprehensive
modeling, analysis, visualization, and hardware study using a representation of Duke Energy’s
utility feeder. This testing will make it easier for utilities to adopt smart inverters by addressing
the challenges of modeling them in GIS, DMS, OMS, and SCADA. Shrinking the current
inverter and making it cheaper to produce and install would enable more solar-powered homes
and more efficient distribution grids and help bring electricity to remote areas.
NREL is also working with San Diego Gas & Electric (SDG&E) to develop a real micro grid
scenario with high penetrations of PV that exist in SDG&E’s territory. The scenario will be
tested in the ESIF, and NREL scientists will investigate control cases for firming PV using
energy storage in the micro grid. The results of this project will give SDG&E insight on how to
effectively use high penetration PV in islanded micro grids through proper energy storage sizing
and placement.

13. In addition, NREL is working SolarCity and the Hawaiian Electric Companies to analyze high-
penetration solar scenarios using advanced modeling which will include load rejection
overvoltage and ground fault overvoltage testing. With the results of these tests, the Hawaiian
Electric Companies will be able to approve PV deployments for customers who have been
waiting to connect to high-penetration solar circuits.
EPRI and Tennessee Valley Authority (TVA)
For many years, EPRI has used M&S to analyze the impacts of distributed resources on
distribution systems. They worked with 14 utilities to analyze a wide range of PV deployment
scenarios across three dozen distribution systems and derived a streamlined method to capture
key impacts in an efficient manner reducing the time to analyze a feeder from weeks down to
less than 10 minutes.
EPRI first began applying the method earlier this year when working with the (TVA) who
launched a project in 2014 to determine the costs and benefits of integrating solar across its
service territory. The TVA and the 155 local power company customers that it serves felt it was
important to understand how higher penetrations of distributed generation would impact their
distribution systems and planning efforts. They engaged EPRI to perform the distribution
analysis portion of the project. EPRI applied their streamlined methods to further the learning by
enabling the efficient analysis of hundreds and thousands of distribution feeders. By working
with the local distribution companies, distribution planners will have the ability to quickly and
accurately assess their own unique systems as distributed generation becomes more impactful at
their locations.
American Electric Power and Battelle
As part of its Department of Energy-funded gridSMART program, AEP Ohio, a unit of
American Electric Power (AEP), investigated the impacts of distribution technologies which
included Volt/VAR optimization, energy storage, demand response (DR), electric vehicles (EV),
and distributed solar PV. They teamed-up with Battelle to accurately model the interactions of
these technologies on AEP’s diverse set of feeders, and to enhance the modeling of dynamic
resources such as solar and closed-loop conservation voltage reduction on distribution feeders
using GridLAB-D as the foundational software. The eventual result was a new offering from
Battelle called Grid Command Distribution (GCD) services and software for utilities. It was built
as a front-end addition for GridLAB-D, a distribution system simulation and analysis tool
developed at Pacific Northwest National Laboratory, a DOE lab that Battelle manages.
Duke and Alstom
Duke and Alstom have collaborated on a project to leverage the intelligence of, and information
provided by, sensors, energy boxes and smart meters to integrate DER for developing next
generation DMS to enhance the optimal performance of the emerging distribution system. The
project has six prioritized areas of scope: Management and forecasting of DER (DG, storage,
DR); Integration of network, market, and renewable resource models for next generation DMS;
Advanced distribution modeling capability to accurately simulate/model smart grid operations;

14. Accurate representation of the distribution system in real- or near real-time; Interoperability with
and seamless communication between other management systems and data bases used by the
utility; and the Simulation of distribution systems based on real-time operational planning to
analyze the benefits of smart grid assets.
Sacramento Municipal Utility District (SMUD)
In 2013, New Power Technologies, Inc. provided SMUD with an integrated transmission and
distribution modeling tool powered by their Energynet platform, as well as GRIDiant’s
Advanced Grid Management technologies. The combined technologies represent a new class of
software tools for modeling networks and producing actionable intelligence from strategic data
resources contained in legacy utility systems and smart grid investments. The goal is to allow
SMUD to more accurately assess, visualize, and manage current and future system impacts from
electric vehicle deployments, energy storage systems, demand response programs, distributed
generation, and solar PV generation within SMUD's service territory.
THE FUTURE:
Cybersecurity
In addition to the cyber attacks that we have seen as being recently successful on our nation’s
computer networks, there are efforts and planning underway with those that have malicious
intent to disrupt important infrastructure systems such as utilities and power grids.
In January of 2014, The DOE awarded $1.7 million to the Georgia Tech Research
Institute (GTRI) to help detect cyber attacks on our nation’s utility companies. By
partnering with the Georgia Tech School of Electrical and Computer
Engineering’s National Electric Energy Testing, Research and Applications
Center (NEETRAC) and the Strategic Energy Institute (SEI), the GTRI will work together
with experts in smart grid technology to develop protocols and tools to detect such attacks.
To detect adversarial manipulation of the power grid, the cyber security tool suite will
consist of advanced modeling and simulation technologies and a network of advanced
security sensors capable of acting to protect the power system in real-time on the basis of
this modeling and simulation. The system will build on past Georgia Tech research into the
monitoring, protection, control and operation of electric power utilities and their
automation infrastructure, as well as work on information security. Georgia Tech’s power
system control and automation laboratory will be used to develop methods to detect
intrusion and malicious commands before the system is field demonstrated in an actual
utility environment.
The project will consist of three phases, which include research and development, test and
validation at Georgia Tech, and technology demonstration at operational utility sites with
the assistance of multiple utility company partners. The Communications
Assurance and Performance [CAP] Group will work with GTRI researchers to develop, test

15. and deploy a context-aware network-based intrusion detection system [NIDS]. A cyber-
power co-simulator will be integrated where numerous cyber-attack mechanisms can be
simulated, including their effects in the physical power infrastructure. Real-time decision-
making algorithms will be developed that evaluate the impact of potential cyber-induced
power infrastructure malfunctions.
Battery Technology
The Joint Center for Energy Storage Research (JCESR) is looking to move beyond the lithium-
ion battery technology that has been around for more than 25 years and is working to develop a
transformational change in the technology. JCESR established an innovation hub at the Argonne
National Laboratory in 2012, and their goal is to develop two prototypes of batteries by 2016 -
one for electric vehicles and one for the electricity grid - which can be scaled to manufacturing
and can provide five times the energy and one-fifth the cost of commercial batteries. JCESR is
working with companies to get battery prototypes on the market, and is particularly working with
Johnson Controls Inc.
Wireless Charging of Electric Vehicles
A number of companies have generated plans for public-access inductive charging stations to be
situated on a garage floor or embedded underground in a paved parking spot that will allow
electric vehicle drivers to charge their vehicle when away from their wired charging source. In
this application, an EV equipped with an induction receiving pad would simply park over a
primary transmitting induction pad that is tied to the power grid and charging would commence.
These systems will use high resonance magnetic coupling technology to transfer power over a
large gap and, according to its proponents, will work over a wide range of adverse environments
including extremes of temperature, while submerged in water or covered in ice and snow.
Everyone seems to be jumping on the band wagon with this one, and the M&S of prototypes is
going on all over the place. Engineers at Delphi's Customer Technical Center in Champion,
Ohio, have installed a wireless energy transfer system featuring technology developed by
WiTricity Corp. (Watertown, MA.) on an all-electric THINK City test vehicle. Siemens, in
cooperation with carmaker BMW, has developed a 3.6kW induction charger, and a prototype is
to be tested in an EV with consumer trials following afterwards in Berlin to determine which
improvements are needed to integrate the system into series produced vehicles under real-life
conditions and to obtain customer feedback for future customer-oriented charging solutions.
Google has become the first trial customer for a new wireless EV charging station developed by
Wytheville VA-based Evatran called Plugless Power. The charging station, installed at Google’s
Mountain View CA. headquarters, replaces an electrical outlet with a charging pad so that a
specially equipped demonstrator vehicle can simply park over it to charge up. Even Rolls-Royce
Motor Cars wants in on the induction action and, in conjunction with the induction charging
technology from a New Zealand start-up company HaloIPT, has developed the model 102EX
Phantom Experimental Electric vehicle, a full electric concept car/technology demonstrator that
debuted at the Geneva Motor Show.

16. RECOMMENDATIONS/OPPORTUNITIES:
• The ever-increasing integration and deployment of wind, PV and EV resources into
the grid will force drastic changes in rate structures, government and utility incentives,
customer loyalty, the sale of excess consumer electricity to a utility, the ownership of
micro-grids, and the consumption of power. States would be well advised to begin
discussion and planning to address these variables before they are plopped in their laps as
realities. States such as Arizona and Hawaii are ahead of the ball in this area.
• Utilities may wish to consider hiring specialized utility engineers or outside consultants
to assist them with their M&S efforts or engage specialty vendors to assist them.
• Notable efforts are underway with simulation and power flow modeling vendors to
develop new and more effective simulation packages for designers and distribution
engineers.
• The basic models for various dynamic equipment such as smart inverters, battery storage
and grid power electronics must continue to evolve. Today, most of these models lack the
degree of accuracy necessary to simulate the effects of placing multiple devices on a
single feeder. Efforts to improve these models need to continue in the realm of open-
source analysis and simulation solutions such as OpenDSS and GridLAB-D.