Optimal Technologies (US), Inc.
RSI
How Resource Sensitivity Index (RSI) Technology
Benefits Electric Power Systems
1
Optimal Technologies (US), Inc.
Table of Contents
How Resource Sensitivity Index (RSI) Technology Benefits Electric Power Systems
4
What is the Problem?
4
Current Approaches to Power System Resource Ranking
5
The Need for a New Approach
6
Smart Grid Objectives
7
Smart Grid Tools: Resource Sensitivity Indexes
8
How Would Resource Sensitivity Indexes Work?
8
How can Resource Ranking Optimize System Performance?
10
Resource Sensitivity Indexes and System Stress
10
Summary
11
2
Optimal Technologies (US), Inc.
Preface
This white paper is intended for electric utility engineers and operators interested in
understanding Optimal’s view on how Resource Sensitivity Index (RSI) technology can play an
important role in the evolution toward an optimized smart grid.
The purpose of this white paper is to describe RSI technology and how it provides for a new level
of asset optimization, where the grid itself is the primary asset, and all component pieces (the
secondary assets) are tuned to maximize the objectives of the grid.
3
Optimal Technologies (US), Inc.
How Resource Sensitivity Index (RSI) Technology Benefits Electric Power
Systems
What Is The Problem?
When planning a road trip to a popular vacation attraction, a common strategy is to adjust the
start and arrival times to avoid potential delays from local rush hour traffic, especially in urban
areas. We have all encountered, much to our dismay, congestion resulting from traffic bottlenecks
and accidents on city streets. The proliferation of live-feed traffic Web casts, radio alerts, and
global positioning satellite (GPS) devices in our phones and vehicles attest to our paradoxical
fascination and frustration with getting from point A to point B quickly and safely, especially over
short distances. Yet, for the longer distances between local destinations, we take for granted the
speed and efficiency of interstate travel. Baring unpredictable weather events that could slow us
down, we worry more about driving boredom than driving gymnastics.
In many ways, our experience with and assumptions about energy resources are not unlike those
as drivers and passengers. Like a patchwork of routes linking travel destinations—from dirt roads
to modern interstates, the national power grid comprises a networked system—from low voltage
low capacity rural distribution line to high voltage high capacity transmission across a tri-state
grid. An overload or mechanical failure at just one site along the way, if timed during peak usage,
can result in major outages that cascade across the network like a multi-vehicle pileup. The wellknown Northeastern Blackout of 2003 that disrupted electrical power generation across the
northeastern US and parts of Canada resulted from a serendipitous coincidence of causes:
untrimmed trees and sagging power lines in Ohio, an energy-management software bug, and
peak power fluctuations in New York. In a sense, it was an accident waiting to happen.
With the push for a smart grid, the issues become even more complex. The smart grid will
introduce renewable generation and demand response at the distribution systems which are
generally not designed to handle any type of generation. In addition renewable generation based
on solar and wind resources are very unpredictable and often chaotic. Similarly, with the
introduction of energy efficiency programs and automated load management, the load patterns
are set to change over time.
4
Optimal Technologies (US), Inc.
In this new smart grid, power system tools must be able to determine power flow and optimize
systems in real-time, taking advantage of new technologies, in order to avert large-scale
blackouts and rolling brownouts. A tool that could sense and rank resource vulnerabilities both
locally and globally (in the context of a national power grid), would give power system operators
and engineers the ability to determine optimal resource control and allocation. A tool that could
also rank system resources by specific and multiple operational objectives including reliability and
costs, would give both utilities and consumers more efficient, cost-effective, and ultimately secure
electric power.
Current Approaches to Power System Resource Ranking
Historically, responses to power system dysfunction adhere to the prevailing conception that
virtually any upgrade or addition to the supply-side grid, including new generators, will improve
the overall power supply, power delivery capability, and reliability of the grid. However, the grid is
not transparent and free-flowing, but rather a complex network subject to a variety of design,
resource, and operational constraints. At present, it is difficult to predict the actual effect of any
specific increment or upgrade to the overall grid. In fact, any one change in generation or capacity
could product a negative ripple effect throughout the grid. Clearly, the power system and related
industries require improved analysis and optimization technologies to manage complex,
interconnected power grids.
System planners working on large, interconnected grids typically segment, analyze, and optimize
small pieces of the larger interconnected grid, then reassemble these disparate results. However,
this approach precludes definitive understanding of the effects experienced throughout
neighboring parts of the grid, even those that lie outside the parameters of the analysis. In many
cases, the negative nonlocal or system-wide effects far outweigh local benefits, though current
tools can only observe and measure the local benefits. Unfortunately, this piecemeal, locality-bylocality approach, though impossible to avoid, has led to development of a grid system that is far
from optimal in configuration and design. While current tools make it easy to focus on local power
system impacts, they cannot comprehend overall system impacts or properly rank the available
options.
5
Optimal Technologies (US), Inc.
The Need for a New Approach
In response to problems of grid congestion, and concomitantly, sharp fluctuations in energy
prices for producers as well as consumers, much of the power industry relies on location marginal
pricing (LMP) and bid-pricing strategies that estimate how much energy we will consume and at
what price. Whether they make these determinations using manual analysis or sophisticated
software algorithms, electric utilities need to know to allocate their resources most efficiently and
effectively. In other words, their chief concern is how much to spend, when to spend it, and
where.
Making these decisions can be extremely complex and tedious because of the interdependent
and sometimes chaotic nature of power systems. Upgrading a resource (a generator,
transmission line, or power plant, for example) at one location most certainly will increase local
costs. But the utility must also consider the effect on neighboring resources, which may need to
adjust their energy use, as well as costs, in order to maintain operating efficiencies and
competitive pricing.
If an electric utility decides that a new resource, such as a coal-firing or nuclear power plant will
best address spiraling energy demands and costs in the long-term, reality has shown that political
and environmental hurdles can prove more problematic than no action at all. For example,
consider legislative initiatives to provide industry and consumer incentives that would stimulate
the production and purchase of environmentally-friendly electric-powered vehicles. Realistically,
CO2 emissions from increased production at coal-fired plants would rise during the night as
vehicles in these areas recharge their batteries.
Additionally, congestions in the power flow, whether caused by mechanical or demand stresses,
increases the cost of electricity to some parts of the system. Accordingly, some customers may
actually pay more for electricity regardless of proximity to the generator if congestion reroutes the
transmission from a cheaper to a more expensive generator.
6
Optimal Technologies (US), Inc.
Clearly, there is a need for new optimization tools that can deal with and provide solutions for a
matrix of energy concerns. Analogous to the highway system, the point of greatest congestion
along any point in the grid may result from the most poorly maintained, out-of-date, or (ironically)
shortest route along the transmission path. Thus, balancing total system demand means that
electric utilities must be able not only to identify which resources are capable of performing
optimally (both locally and globally), but which resources are the most vulnerable and thus require
the most immediate attention
Smart Grid Objectives
Like any responsible business, electric utilities make asset allocation choices based on financial
as well as energy objectives. Sometimes, areas where generation is cheapest may not coincide
with areas that carry the biggest loads. Contrarily, resources that cost the least to run may
achieve only 60 percent of optimal production capacity if handicapped by high reactive power
losses along transmission lines in outlying areas.
A better analytical approach should be able to achieve the following objectives:
• Rank power system resources (e.g., generators, spot loads, capacitors, lines, transformers,
nodes) according to one or more objective functions as set by the utility
• Rank power system resources under varying constraints such as congestion, reliability, price
points and voltage limits
• Determine active and reactive power gains and losses to pinpoint most and least vulnerable
resources
• Determine sensitivities and optimization capacity of various grid resources towards
maintaining optimum reliability
• Determine the sensitivity of price boundaries with respect to power demands
• Assess the cost differences among various generation production and transmission schemes
per node
• Enable electric utilities to predict where best to locate, add, and maintain transmission and
generation facilities and meet demand response (DR)
7
Optimal Technologies (US), Inc.
Smart Grid Tools: Resource Sensitivity Indexes
A power system tool that incorporates resource sensitivity ranking capabilities could address
most of the limitations of current approaches to grid management. The power system engineer or
operator must be able to determine not only where so-called sensitive areas are located on the
grid, but more importantly which resources are more and less vulnerable. Using this tool, the
engineer could accomplish several essential tasks simultaneously: identify grid bottlenecks and
target specific measures for resolving them quickly and efficiently while optimizing system
performance. This may include allocating new resources as necessary as justified by many
different objectives and constraints.
Ranking available resources or components must be a key ingredient of a power system
optimization. The complex algorithms that determine optimal operational models should also be
capable of ranking (i.e., indexing) resources by how close they approach—or how far they stray
from—the optimization model. An electric utility should be able to determine where and how much
distributed generation (DG), demand response and other resources to locate at specific locations
in order to optimize system performance. Optimized performance may comprise multiple
objectives, such as increasing system load serving capability while reducing system congestion
and lowering energy price points.
Taken a step further, optimization algorithms should also be able to rank active and reactive
power resource sensitivity at every location in the system under a given set of system resources,
operating rules, and constraints. An underpowered generator in an outlying area may need to
quickly dissipate a voltage overload that is caused by excessive flow from other generation sites.
The operator must be able to quickly rank all of the variables that could potentially cause voltage
collapse from among an array of possibilities that might include switching on capacitors, shedding
load, redirecting load to other nodes, or increasing generation (if allowable).
How would resource sensitivity indexes work?
Resource sensitivity indexes must be able to analyze changes in objective function values that
result from changes in system resources, such as generation or capacitance, at each location.
They also must be able to indicate the corresponding resource stress condition at every location:
the higher the sensitivity, the greater the stress as indicated by a numerical index value at each
location.
8
Optimal Technologies (US), Inc.
As new resources are added at or removed from a location, its sensitivity to related resources
also changes. Whether a change in the stress condition at the location is reduced or increased
depends on the type of resource added or removed.
Figure 1 shows how a resource sensitivity index could rank the system stresses for each load in a
network at a specific point in time. The engineer's goal is to compute an electrical network steady
state so that physical and operating constraints are satisfied and an objective is optimized.
Figure 1. Resource sensitivity of spot loads in a model system
The plot renders index values visually so that the power system engineer can easily determine
the system-wide benefit of its loads, here represented as points along the plot. (The plot could
likewise display values for other system resources.) Generally speaking, values that fall above
the red line (steady-state operation) have a positive effect on the power system, while values that
fall below the red line have a negative effect. Individual points below the red line rank loads by
how well (or poorly) they achieve the objective functions established by the utility. Similarly, a
sensitivity index could rank resources by active power (generators), reactive power, and other
variables.
9
Optimal Technologies (US), Inc.
Although a location may meet its power system objectives with 50 MW of added generation, it
may show system decline with 100MW of added generation. This is in sharp contrast to the
smaller, segmented regional approach that is traditionally used in transmission and distribution
system analysis. Traditional analysis cannot optimize and rank both local and interregional effects
since these effects are rarely contained or caused only within the smaller region.
For power systems with existing devices that perform poorly, RSIs are instrumental in showing
precisely what changes are needed to provide and maximize a positive benefit.
How can resource ranking optimize system performance?
RSIs assure that all possible optimization options are included and understood, including those
that are not obvious and unavailable in competing mathematical methodologies. For example,
RSIs can be assigned various mathematical and economic prices. RSIs are produced within the
same near real-time run as the optimization itself. Additional steps are not required, which makes
RSIs measurable and defendable.
A system operator can use a resource sensitivity operational tool to decide which controls to
operate to relieve congestion or a constraint and yet maintain system optimal operation.
Optimally, a system engineer can optimize system performance for selected objectives, then
evaluate each newly-added resource and each rescheduled or relocated resource according to
how well it achieves each objective. The operator can decide where to place the next increment
of a new resource in the system in order to alleviate system stresses.
In a real-world system, resource sensitivity indexes reveal both negative and positive values.
Those positively- and negatively-ranked values that are furthest from optimum operation are
interpreted as either resource deficiencies or surpluses. In other words, resource sensitivity
indexes pinpoint the precise location of stress conditions at each location relative to system
optimization objectives.
Resource sensitivity indexes and system stress
In general, the larger the variations of the resource sensitivity index values, the worse the
condition of the system relative to its optimization objectives. That is, the system is less stable
and less predictable. For example, if ranking locations by reactive power in/outflow, those
10
Optimal Technologies (US), Inc.
locations with higher reactive power index values are prime candidate locations at which to add
new reactive power resources, such as capacitors and reactors. The location with the highest
index value, which has the highest stress, is also the most sensitive. If the engineer makes a
small resource change at this location, this one change will have a larger impact on the
optimization objective value than a resource change at any other location.
Summary
Resource Sensitivity Index (RSI) technology has the unique capability to rank node-specific
resources. RSIs can indicate quickly and precisely where in the system and to what amount both
supply-side and demand-side resources should be optimally aggregated, added, or reduced.
RSIs provide the electric utility engineer with a powerful predictive understanding of the entire
system or any subset of it. RSIs provide multi-dimensional, ranked indicators of magnitude,
direction, and location for local and system-wide improvement. This technology is not possible
using current sensitivity and ranking tools.
RSIs directly reflect the risk and benefit capability of the real system. RSIs provide both direction
and magnitude for each local component of each objective that is designated as critical for an
optimum system solution. They provide a unique understanding of the specific system-wide or
global impacts of individual components, as well as changes to individual resources and loads.
Thus, they can be used to measure the effectiveness of resources at every point in the system.
RSIs are maintained for all system resources and objectives, including feasible and infeasible
locations. Taking voltage profile objectives as an example, RSIs can measure the effort that is
required to bring each location voltage within the desired voltage profile. In other words, RSI’s
reflect the security of each location.
RSIs can be used to show precisely the sensitivities of resources at specific, individual locations
(nodes) for the entire system. With RSI technology, one can indicate the lowest-price approach
(magnitude and direction) for meeting planning and operational objectives, even when the system
is operating in an infeasible area. This gives users the ability to understand and rank the list of
available contingency actions and their related deployment costs, even under operating
conditions that are impossible to model (solve) with traditional tools.
11
Optimal Technologies (US), Inc.
Essentially, RSIs provide a new level of asset optimization, where the grid itself is the primary
asset, and all component pieces (the secondary assets) are tuned to maximize the objectives of
the grid. For example, using a single plot, an engineer can determine globally which specific
devices, loads, interchanges, and generators contribute positively or negatively – and to what
precise degree – to the current optimization objectives. RSIs, therefore, enable dramatically
superior analysis, optimization, and management of individual components and each separate or
linked distribution and transmission system.
About Optimal
Optimal’s AEMPFAST optimization and analysis software and services platform gives utilities the
right technology to plug into the Smart Grid. Using AEMPFAST, utilities can achieve greater
operational awareness, business agility, and automation – benefits that translate into more
reliable, efficient, and green electrical power. AEMPFAST is a bus compatible and open platform.
Its core functionalities support and accelerate achievement of a smarter grid by integrating with
varied “smart” distribution network systems (DMS, OMS, GIS, SCADA, MDMS). AEMPFAST
applies fast, non-linear analysis and optimization that runs very large (100,000+ buses), highly
detailed system models in near real time. AEMPFAST delivers ranked, bus-specific outputs of
critical value to distribution system operators.
AEMPFAST's competitive advantages among analysis and optimization technologies include:
Core engine: AEMPFAST offers a fast, repeatable, and accurate non-linear optimization engine
that analyzes and optimizes very large and detailed distribution and transmission networks in
near-real time.
Resource Sensitivity Indexes: AEMPFAST optimization and analysis outputs provide the
operational awareness network engineers and operators need in order to meet their business
objectives, e.g., loss minimization, congestion and load management, DG/DR/storage dispatch.
RSIs determine the impact of network assets and load toward prescribed objectives.
Real and Reactive Power: AEMPFAST provides simultaneous optimization and analysis of
system real and reactive power assets, helping to improve system voltage profiles, energy
efficiency, and reliability.
12
Optimal Technologies (US), Inc.
Fine Granularity: AEMPFAST analyzes the effects of home level loads and small amounts of DG
towards optimization objectives. AEMPFAST enables analysis and optimization of lateral
distribution feeders.
Additionally, AEMPFAST can: manage multiple optimization objectives, “plug and play” with third
party systems using standard communication protocols, perform 3-phase unbalanced and 1phase balanced network analysis, conduct optimized meter location analysis, and establish
distributed generation and load profiles.
Optimal Technologies (US), Inc.
801 Jones Franklin Road, Suite 210
Raleigh, North Carolina 27606
1.919.674.0883
www.otii.com
otii@otii.com
13