Preparing for Distributed Energy
Resources
Executive summary
Many utilities are turning to Smart Grid solutions such as
distributed energy resources (DERs)—small-scale
renewable energy sources and energy storage—to
balance load and capacity without building large-scale
generation. This paper explains how successful DER
implementation depends upon an Advanced Distribution
Management System (ADMS) that enables real-time,
software-based network modeling to accurately forecast,
monitor, control, and analyze the contribution of
distributed energy to the grid.
998-2095-05-29-12AR0
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
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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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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Preparing for Distributed
Energy Resources
Preparing for Distributed Energy Resources
The state of the Smart Grid
Up to now
Energy policies are evolving worldwide, with differing
regulations from country to country and within
countries, helping drive Smart Grid investment
priorities. While the impetus to embark on advanced
Smart Grid initiatives varies, and no two projects are
the same, there are some common drivers:
• Regulations promoting reduced carbon emissions,
renewable technology and energy independence
- Different locations have more advanced
regulations than others
- Wind and solar have become viable energy
sources
- Storage is enabling permanent and on-demand
load shifting
• Stimulus funding helping advance deployment of
advanced technology
Terminology
Here’s how we are using some common
industry terms –
Distributed Energy Resources (DERs) –
small-scale power generation technology
that supplies less than 10 MW and is
located throughout the distribution network.
Increasingly, DERs consist of renewable energy
and energy storage – making DERs a popular
component of Smart Grid implementations.
Distributed Generation (DG) – referring to
any dispersed generation less than 100 MW. In
this paper we are considering DG as a smallerscale, subset of DER
Electric Vehicles (EV) – serve as a source of
significant load but can also serve as a form of
virtual generation (storage)
Demand Response (DR) – management of
consumption, anywhere along a feeder, in
response to supply conditions
• Convergence of traditional generation capacity and
increasing system load
Near term
Regional factors are likely to predominate:
• Electric vehicles (EVs) will begin to make an impact
Microgrid – a local network of DERs that
is a subset of the distribution network. It
can operate in an isolated manner or be
connected. Microgrid management targets
local energy supply and demand.
• As volatile renewables – those such as solar and
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
deployment of EVs.
• Policy concerns – Customer privacy and cyber
security will continue to provide some challenge to
Smart Grid implementation.
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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
‘sources’, purchased or generated; improve demand
management; and add storage capability in order to
maintain a healthy margin between load and capacity.
Utility thought leaders concur that penetration
of EVs will initially be concentrated in
localized areas (early adopter neighborhoods)
– impacting secondary networks of the
distribution system.
Management of demand is the option least utilized,
yet it poses significant potential because of the
several innovative ways it can be implemented.
Need to fill the gap
The Ontario Power Authority of Canada collected
data identifying the existing power sources that have
been meeting its resource requirements over the past
few years and its forecast of available generation in
the coming years; see Figure 1. This report forecasts
retirement of most existing nuclear facilities, a
Nevertheless, penetration of EVs will require
planning:
• Battery-charging scenarios vary: the higher
the charging level, the faster the charge and
the greater the energy demand.
• Permitting processes should be defined to
identify where EVs will reside.
• Rate structures are needed to help control
charging.
decrease in reliance on existing oil and gas and coal
sources and continuation of existing renewables.
What power sources, including ‘virtual sources’, are
to be added to meet the increasing annual peak
forecasts? What is going to fill the gap? Many utilities
will be required to disperse and store energy and
manage load to meet resource requirements.
• Real-time monitoring can help model
demand accurately.
• Network planning can preemptively address
potential issues.
• Promotion by the utility can help encourage
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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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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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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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
by advanced Smart Grid software. With this
software, utilities can integrate DER to defer capital
expenditures for new generation sources; see
Figure 4.
Figure 4. Distribution network load is expected to continue to increase, in large part due
to population growth and the proliferation of consumer technology. A smart IT control
system enables network management that will, in effect, increase system capacity and
maintain the margin between load and capacity without investment in new and costly
traditional generation facilities.
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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.
• Provide users with the solution’s advanced tools
and visual context
• Planning analysis: online to evaluate ‘what if’
scenarios and offline to assess historical activity and
• Prompt users with recommended switching
plan for future network enhancements
operations
• Preparing for effective and secure deployment of
• Fully automate network management with closed
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.
• Monitoring, analysis and control of network
operations
Network operation control – including Fault
Location, Isolation and Service Restoration (FLISR)
• Managing load and adjusting the shape of the
demand curve
with optional closed loop control (automated)
switching, as well as large area restoration and load
shedding to help sustain system stability during
extreme peak periods.
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Preparing for Distributed Energy Resources
Network operation optimization – including
Volt/VAR Control to manage load tap changers,
capacitors, and voltage regulators with optional
Cutting-edge projects are putting
demand management to work
closed loop control in a self-healing manner. An
ADMS also enables monitoring of renewable energy
through detailed load profiling and, with integrated,
real-time weather data, supports improved near-term
and short-term load forecasting. It also supports
thermal energy storage and evolving battery
technology.
Network operation analysis – including energy
losses, both technical and commercial; relay
protection through settings and device coordination;
reporting of harmonic distortion; and contingency
and security assessment to identify re-supply options
following faults.
Network planning – including simulation that
supports development; minimizing loss and detecting
overload for network reinforcement; medium-term
and long-term load forecasting; and load growth
analysis.
Automate peak load shaving. Using ADMS
Volt/VAR Control functionality, one utility is
reducing feeder voltage automatically, with
no effect on the consumer, and deferring,
or eliminating, the need to build large-scale
generation. The ADMS model is helping the
utility plan for ‘green’ MWs. According to a
utility spokesperson, “We see this project
as something that could change the power
industry.”
Maximize Distributed Generation. This utility
serves a large, primarily rural territory and
looks to support feed-in tariff regulations and
distributed renewable energy. It is deploying
ADMS modeling functionality to monitor the
high growth of DG and proactively plan for
effective dispatch and control of DGs. The
utility is doing this in a way that also provides
economic benefits, by leveraging network load
forecasting based on meter load profiles and
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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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.
One of the best ways to address security concerns is deploying a single solution
that integrates ADMS with SCADA technology with a proven, high-level of security,
reliability and performance. Of course, the SCADA incorporated in this solution
must:
Telvent collaborates with Idaho National
Laboratories (INL), the host of the United
States Department of Energy’s National
SCADA test bed, in joint cyber security
testing of Telvent Energy’s OASyS DNA
SCADA infrastructure and in developing and
documenting best practices.
• Be able to support tens of thousands of intelligent field devices
• Have a robust reporting engine to deliver real-time data for critical business and
operational analysis and decisions
• Support a ‘self-healing’ network architecture
• Perform system-wide health monitoring
• Be designed for standards compliance that will support long-term deployment
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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©2012 Schneider Electric. All rights reserved.
Schneider Electric USA, Inc.
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June 2012