The document discusses the integration of Distributed Energy Resources (DERs) into electric utility distribution networks, emphasizing the need for advanced software systems, such as Advanced Distribution Management Systems (ADMS), to optimize grid operations, manage load, and ensure reliability. It outlines how utilities can leverage real-time data and analytics to effectively forecast and manage energy distribution, while also addressing regulatory drivers and evolving customer expectations. The conclusion highlights the importance of cybersecurity and the need for a comprehensive approach to managing energy resources as utilities adapt to new challenges and opportunities in a changing power landscape.

1. Preparing for Distributed Energy
Resources
At the core: real-time network modeling with an Advanced Distribution
Management System (ADMS)
Make the most of your energy SM

2. Summary
Executive Summary . ................................................................................... p 1
Introduction ................................................................................................. p 2
The state of the Smart Grid ......................................................................... p 4
What’s needed to move Smart Grid implementations ahead?....................... p 5
Distributed energy resources are becoming a ‘new normal’.......................... p 6
Load transfer with distributed generation ..................................................... p 7
Getting ready with software.......................................................................... p 8
ADMS optimizes DER management; grid operations and planning . ............. p 9
Conclusion................................................................................................... p 11

3. Preparing for Distributed Energy Resources
Executive summary
More and more, as utilities face decreasing margin between system load and
system capacity, they are in need of innovative smart grid solutions that can help
them effectively disperse and store energy and manage load to meet resource
requirements. Many are incorporating Distributed Energy Resources (DERs) to
help fill the gap while, at the same time, meet requirements for reduced emissions
and energy independence; these utilities will require the capability to accurately
forecast and control DER contribution to the network, to assure security and grid
reliability.
Advanced smart grid software designed to support DER management and
optimize grid operations and planning works with a real-time network model,
based on an accurate geodatabase and incorporating data from operational
systems such as a supervisory control and data acquisition (SCADA) system
and outage management system (OMS). Along with real-time visualization and
monitoring of network status, this Advanced Distribution Management System
– ADMS – provides a host of analytical tools that recommend the most optimal
device operations, or optionally automate device operations, to maximize network
efficiency and reliability. For example, the utility can apply Volt/VAR control to
reduce feeder voltage automatically with no effect on the consumer. Detailed load
profiling and load forecasting based on integrated weather feeds yield network
load forecasting for effective renewables integration. Network simulation helps
forecast medium-term and long-term load and supports effective development
and planning.
ADMS functionality and tools are demonstrating that utilities can effectively
manage demand without building large-scale generation.
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4. Preparing for Distributed Energy Resources
Introduction
The deployment of Distributed Energy Resources (DERs) is growing as is
the impact on electric utility distribution networks. While DERs are increasing
renewable energy with their multitude of benefits, there are many concerns
utilities must tackle to assure successful management of a diverse and
distributed energy mix.
Here, we discuss how DERs will contribute to achieving a smart electric grid and
how proper network planning, monitoring, analysis, and control, through ADMS,
can transform distributed generation into an efficient asset.
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5. Preparing for Distributed
Energy Resources

6. Preparing for Distributed Energy Resources
The state of the Smart Grid
Up to now Terminology
Energy policies are evolving worldwide, with differing
regulations from country to country and within
Here’s how we are using some common
countries, helping drive Smart Grid investment industry terms –
priorities. While the impetus to embark on advanced
Smart Grid initiatives varies, and no two projects are Distributed Energy Resources (DERs) –
the same, there are some common drivers: small-scale power generation technology
that supplies less than 10 MW and is
• Regulations promoting reduced carbon emissions, located throughout the distribution network.
Increasingly, DERs consist of renewable energy
renewable technology and energy independence
and energy storage – making DERs a popular
component of Smart Grid implementations.
- ifferent locations have more advanced
D
regulations than others Distributed Generation (DG) – referring to
any dispersed generation less than 100 MW. In
- ind and solar have become viable energy
W this paper we are considering DG as a smaller-
sources scale, subset of DER
Electric Vehicles (EV) – serve as a source of
- torage is enabling permanent and on-demand
S
significant load but can also serve as a form of
load shifting
virtual generation (storage)
• timulus funding helping advance deployment of
S Demand Response (DR) – management of
advanced technology consumption, anywhere along a feeder, in
response to supply conditions
• onvergence of traditional generation capacity and
C
increasing system load Microgrid – a local network of DERs that
is a subset of the distribution network. It
can operate in an isolated manner or be
Near term connected. Microgrid management targets
local energy supply and demand.
Regional factors are likely to predominate:
• lectric vehicles (EVs) will begin to make an impact
E • s volatile renewables – those such as solar and
A
on the distribution system. Their initial effect is wind that are intermittent sources – see increased
not expected to be system-wide; instead, early deployment, Information Technology (IT) solutions
adopters are likely to be localized within specific will be integral to their success and storage
areas of a utility’s service territory, impacting the technology will have to advance.
network at the distribution transformer level. See
sidebar discussion for more about planning for the • olicy concerns – Customer privacy and cyber
P
deployment of EVs. security will continue to provide some challenge to
Smart Grid implementation.
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7. Preparing for Distributed Energy Resources
What’s needed to move Smart Grid
implementations ahead?
Many utilities are experiencing a common trend:
the margin between system load and system
Electric vehicles
capacity is decreasing and is expected to continue
to decrease. The utility can incorporate new power Utility thought leaders concur that penetration
of EVs will initially be concentrated in
‘sources’, purchased or generated; improve demand
localized areas (early adopter neighborhoods)
management; and add storage capability in order to
– impacting secondary networks of the
maintain a healthy margin between load and capacity. distribution system.
Management of demand is the option least utilized,
yet it poses significant potential because of the Nevertheless, penetration of EVs will require
several innovative ways it can be implemented. planning:
• attery-charging scenarios vary: the higher
B
Need to fill the gap the charging level, the faster the charge and
The Ontario Power Authority of Canada collected the greater the energy demand.
data identifying the existing power sources that have
• ermitting processes should be defined to
P
been meeting its resource requirements over the past
identify where EVs will reside.
few years and its forecast of available generation in
the coming years; see Figure 1. This report forecasts • ate structures are needed to help control
R
retirement of most existing nuclear facilities, a charging.
decrease in reliance on existing oil and gas and coal
sources and continuation of existing renewables. • eal-time monitoring can help model
R
demand accurately.
What power sources, including ‘virtual sources’, are
• etwork planning can preemptively address
N
to be added to meet the increasing annual peak
potential issues.
forecasts? What is going to fill the gap? Many utilities
will be required to disperse and store energy and • romotion by the utility can help encourage
P
manage load to meet resource requirements. desired charging habits.
The utility armed with mitigation strategies will
be best prepared to meet the demand and
supply challenges, and the environmental and
commercial benefits, of EVs.
Figure 1. Expected change in how existing power sources contribute toward resource
requirements (effective MW). Source: Ontario Power Authority
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8. Preparing for Distributed Energy Resources
Distributed energy resources are
becoming a ‘new normal’
Regulatory driver
While some utility customers are installing renewable
generation on their own initiative, the primary drive
is coming from regulations that push for reduced
emissions and energy independence. For example –
The Canadian province of Ontario has implemented
an aggressive feed-in tariff (FIT) that supports
penetration of DER (http://fit.powerauthority.on.ca/).
California’s general strategy of cutting GHG
emissions and creating green jobs includes these
2020 targets: 33 percent of energy sourced from
renewables; installation of one million solar rooftops;
and stimulation of EV deployment and battery storage
implementation
(http://www.energy.ca.gov/energypolicy/index.html)
New business model
Utilities incorporating DER will have to plan for
new connections and ways to achieve accurate
forecasting and the control needed for grid reliability
and security.
For these utilities, distributed energy resources will
become a major factor in the new utility business
model; see Figure 2. At the heart of the new model
is the centralized intelligence system that integrates
and manages devices, with intelligence moving out
to provide more comprehensive management and
collect more data.
Figure 2. Distributing energy resources is expected to be
the new paradigm in utility management. Source: Progress
Energy.
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9. Preparing for Distributed Energy Resources
Load transfer with distributed
generation
Figure 3 illustrates, very simply, the existence of DG
in the event of a feeder trip. The DG, along with the
neighboring feeder, might help back-feed the feeder
in question. Real-time data and accurate network
representation are needed to facilitate the response
decisions required for safe and reliable transfer of
load. The presence of DG will benefit from adaptive
relay protection to properly deal with the initial fault
and manage increasing load following restoration.
Figure 3. Managing load with distributed generation.
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10. Preparing for Distributed Energy Resources
Getting ready with software
The next logical question: how do utilities manage
load – to maintain the margin between load and
system capacity – and plan for and leverage DER,
to meet increasing demand?
Operating the electric distribution network
with a growing number of distributed energy
devices (DERs) is simply not feasible without
the deployment of advanced software analytics
– specifically, a real-time network model that
will support operations management, network
optimization and comprehensive planning. This
model resides at the centralized control center
illustrated in Figure 2 and is created and maintained
Figure 4. Distribution network load is expected to continue to increase, in large part due
by advanced Smart Grid software. With this to population growth and the proliferation of consumer technology. A smart IT control
software, utilities can integrate DER to defer capital system enables network management that will, in effect, increase system capacity and
expenditures for new generation sources; see maintain the margin between load and capacity without investment in new and costly
traditional generation facilities.
Figure 4.
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11. Preparing for Distributed Energy Resources
ADMS optimizes DER management;
grid operations and planning
ADMS is the large-scale IT control system that can
serve as the brain of the distribution network and
support network operating decisions. It leverages the
GIS as-built network model and integrates with many
operational systems such as supervisory control
and data acquisition (SCADA) systems and outage
management systems (OMS), to create a real-time
network model; see Figure 5.
Utilities minimize losses and maximize reliability and
safety by applying ADMS functionality to manage the
distribution network throughout the service territory in
a real-time manner. The ideal ADMS approach offers
three operating approaches to best meet reliability
and efficiency goals: Figure 5. ADMS model provides network visualization via geographic, schematic and
station one-line views.
• rovide users with the solution’s advanced tools
P
and visual context • lanning analysis: online to evaluate ‘what if’
P
scenarios and offline to assess historical activity and
• rompt users with recommended switching
P plan for future network enhancements
operations
• reparing for effective and secure deployment of
P
• ully automate network management with closed
F DER, including storage and microgrids
loop control functionality
An ADMS solution can deliver a host of analytical
The ADMS model delivers the information needed functions – some of which are identified below – that
across the utility enterprise for: will optimize grid efficiency and enable effective and
efficient integration of DERs.
• onitoring, analysis and control of network
M
operations Network operation control – including Fault
Location, Isolation and Service Restoration (FLISR)
• anaging load and adjusting the shape of the
M with optional closed loop control (automated)
demand curve switching, as well as large area restoration and load
shedding to help sustain system stability during
extreme peak periods.
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12. Preparing for Distributed Energy Resources
Network operation optimization – including
Volt/VAR Control to manage load tap changers,
Cutting-edge projects are putting
capacitors, and voltage regulators with optional demand management to work
closed loop control in a self-healing manner. An
ADMS also enables monitoring of renewable energy Automate peak load shaving. Using ADMS
through detailed load profiling and, with integrated, Volt/VAR Control functionality, one utility is
reducing feeder voltage automatically, with
real-time weather data, supports improved near-term
no effect on the consumer, and deferring,
and short-term load forecasting. It also supports or eliminating, the need to build large-scale
thermal energy storage and evolving battery generation. The ADMS model is helping the
technology. utility plan for ‘green’ MWs. According to a
utility spokesperson, “We see this project
Network operation analysis – including energy as something that could change the power
industry.”
losses, both technical and commercial; relay
protection through settings and device coordination;
Maximize Distributed Generation. This utility
reporting of harmonic distortion; and contingency serves a large, primarily rural territory and
and security assessment to identify re-supply options looks to support feed-in tariff regulations and
following faults. distributed renewable energy. It is deploying
ADMS modeling functionality to monitor the
Network planning – including simulation that high growth of DG and proactively plan for
effective dispatch and control of DGs. The
supports development; minimizing loss and detecting
utility is doing this in a way that also provides
overload for network reinforcement; medium-term economic benefits, by leveraging network load
and long-term load forecasting; and load growth forecasting based on meter load profiles and
analysis. integrated weather data.
Optimize network efficiency and reliability.
The most common benefit utilities realize
with ADMS deployment is enabling efficient
and reliable network operations in the face
of ever-growing constraints. A utility is
deploying ADMS to manage its distribution
network in a real-time manner to minimize
losses and maximize reliability and safety.
ADMS provides three operational approaches
this utility can use for device management:
availability of advanced tools and visual
context; recommendation of the most optimal
device operations; and automation of device
operations.
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13. Preparing for Distributed Energy Resources
Conclusion
Security is of utmost importance when deploying ADMS in a mission-critical
environment. From a standards perspective, much work remains to be done to OASyS DNA security
address Smart Grid cyber security. There is significant benefit in developing ADMS
technology that addresses the evolving NERC CIP and NISTIR requirements. Telvent collaborates with Idaho National
Laboratories (INL), the host of the United
States Department of Energy’s National
One of the best ways to address security concerns is deploying a single solution
SCADA test bed, in joint cyber security
that integrates ADMS with SCADA technology with a proven, high-level of security,
testing of Telvent Energy’s OASyS DNA
reliability and performance. Of course, the SCADA incorporated in this solution SCADA infrastructure and in developing and
must: documenting best practices.
• e able to support tens of thousands of intelligent field devices
B
• ave a robust reporting engine to deliver real-time data for critical business and
H
operational analysis and decisions
• upport a ‘self-healing’ network architecture
S
• erform system-wide health monitoring
P
• e designed for standards compliance that will support long-term deployment
B
A comprehensive ADMS solution applies this combined-technology approach. It
creates a single infrastructure and user interface for enterprise consistency and
efficiency. With its comprehensive set of tools, utilities can perform monitoring,
analysis, control, dispatch, planning and training for their distribution networks,
using real-time, planning, or study modes.
The most-advanced technology supports both three-phase balanced and
unbalanced state estimation. With it, the utility can take advantage of advanced
load management (DSDR), closed-loop control for self-healing automation, and
distributed energy resource modeling that supports economic decisions and
reliability management.
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14. ©2012 Schneider Electric. All rights reserved.
Schneider Electric USA, Inc.
4701 Royal Vista Circle
Fort Collins, CO 80528
Phone: -866-537-1091
1
+ (34) 9-17-14-70-02
Fax: 1-970-223-5577
www.schneider-electric.com/us
June 2012