While reliability is perhaps one of the most watched performance indicators
in the utilities industry today, improvement efforts are often based on best
practices or trial and error. As a result, mountains of data are available to
monitor utility network reliability. Now analytical tools are needed to refine
this data into information that can be used to optimize restoration efforts
and simulate asset management strategies designed to ultimately improve service
reliability. These new tools will be used to analyze and assess trade-offs between
cost and reliability, providing utility network operators or owners with the
means to simulate restoration and asset management strategies and continuously
optimize tactics. First lets review how the various approaches to managing
reliability of utility networks have evolved.
Evolution of Network Operations
Over time, best practices in the industry have evolved, taking advantage
of new technologies and knowledge to deliver targeted service reliability. This
evolution will continue, and progressive utilities will lead the industry in
pioneering new tools and techniques to continually enhance reliability.
In the past, service reliability was supported by silo applications, such as
work and asset management systems (see Figure 1). These systems optimized tasks/activities
based on the data within their systems with manual interfaces to coordinate
activities outside of the specific area. For example, work management systems
would produce work schedules for each field service crew with some automated
interface from the customer information system. However, these schedules would
be manually faxed or otherwise distributed to the crews. The results of the
work would be subsequently recorded on multipart work orders by the crews and
manually entered back into the work management system. This disjointed manual
process prevented the ability to see across all activities to understand how
work could be optimized on a given day across the entire field service organization.
Recently, tools to support utility network reliability focused
on mobile workforce tools to improve the field-force work effectiveness by eliminating
some of the manual activities. Risks were assessed primarily based on known
asset attributes (e.g., age, cost). Sophistication grew in regard to maintenance
planning, establishing reliability and condition-based maintenance and replacement.
However, the basis for decision making related to sustaining or improving
reliability was still intuitive and based on limited scenarios. The relation
between action and result was determined through subjective processes.
The industry is now leveraging more knowledge and sophisticated
technologies to bring about more seamless and integrated decisions and actions
to support reliability. These integrated systems can improve work delivery,
but, interestingly, some of these systems are disabled during unplanned outages
in lieu of storm teams. The reason: They are not responsive enough, so utilities
resort to manual decision making to speed service restoration. In addition to
the integrated work management processes, asset management processes have also
evolved to optimize return on assets. These asset management processes and tools
are driven by increasingly sophisticated risk- and condition-based scenario
analysis. A key tenet of these analyses is that optimized return on assets will
also yield acceptable overall network reliability performance. These asset management
processes can be conducted more frequently than in the past due to improved
technology processing speeds, which produces large volumes of new data.
As the mountains of data grow, so do the possibilities. Therefore,
complex analytical capabilities are needed to refine the available data into
information that can be used to optimize restoration efforts in real time
and continuously. This same data can be used to simulate reliabilitybased asset
management strategies on a continual basis that balance reliability service
levels with return on assets, taking more of the guesswork out of decision making.
With the brain in place to conduct complex analyses continuously
using more current data, additional technologies can be leveraged to automate
network operations to an even greater degree. The possibilities include:
- Remote asset monitoring and control sensor technology;
- Large volumes of asset operational information;
- Consistent use of asset information for risk management;
- Pre-emptive action in advance of faults; and
- Dynamic asset reconfiguration.
Two Pillars of Optimization
are two major aspects to consider in optimizing reliability: restoration and
asset management. Both are key and often are considered independently.
Strategies and tools are needed to restore service as quickly as possible. Outage
management begins well before the outage (see Figure 2). Outage planning and
staging are needed to help ensure rapid deployment.
Once service is lost, early, accurate and continuous assessment is key. Many
utilities have assigned a statistically representative portion of assets within
each region to allow for a quick assessment and interpolation of damage. This
initiates predefined plans for deployment of resources and equipment. Mobile
dispatch systems notify crews and truck rolls. Advanced systems do not stop
there; as new information emerges and restoration tasks are completed, crews
are re-dispatched and resource deployment is optimized. Consideration of number
of customers versus duration of outage is traded off. Advanced utilities are
considering tools to support continuous assessment during an outage to balance
costs with restoration speed. As work is completed, the work orders are closed.
Once restoration is complete, the process starts again. Through post-mortem
analysis, outage performance is critiqued and outage plans are refined as needed.
Forecast and assessment tools are also revisited and adjusted.
There are two primary strategies for managing the avoidance
- The first is a design strategy. By sectionalizing the network (e.g., using
more fuses, reclosers and sectionalizers), the number of customers affected
by a given asset can be contained. This often entails expensive modifications
that must be spread over an extensive period (e.g., three to five years).
To optimize the return on these investments, operators must have a clear understanding
of their impact (the specifics, not just generalities) and prioritize the
investments according to reliability return.
- The second strategy is maintenance-related. Sophisticated operators employ
risk assessment mechanisms to identify and target maintenance programs on
high-risk assets. This strategy does not require large investments but, instead,
necessitates the collection and analysis of significant amounts of data related
to asset condition and reliability. Again, a long lead time is required.
strategies are often applied in a suboptimal manner; that is, the operator does
not have the necessary information to prioritize maintenance and design strategies.
Often priorities are based on industry best practices. Although a valid source
of ideas, specifics associated with individual network configuration, condition
and demands prevent equal transfer of concepts.
Optimizing Reliability and Investments
Analytical models can be developed to simulate and test strategies (see Figure
3). By establishing the mathematical relationship between individual reliability
and restoration strategies and their potential impact on specific networks,
operators can better understand and prioritize investments. To take it a step
further, these algorithms can be related to assess trade-offs between restoration
and reliability investments.
The Approach to Optimization
System analysis and development of tools to optimize reliability is a significant
undertaking. First of all, there are many aspects of each network, which make
it unique and limit the transferability of analytical models. The configuration
of the network, the condition of the assets, the structure of the organization
and the availability of data all factor into program development. A logical
set of comprehensive steps is required.
The starting place is where you are. What reliability
improvement initiatives are currently under way? How can they be enhanced? The
objective of this activity is to understand the current improvement initiatives,
planned and under way, and to identify possible enhancements by leveraging supporting
tools (e.g., weather forecasting, work management, dispatch and communications
technologies), analytical models, industry best practices, business case analyses,
and expertise internal and external to the organization. A root cause analysis
of recent interruptions will provide insights regarding cause and effect. Best
practices studies will provide ideas as to how others have addressed these causes.
Contrasting this insight with improvement initiatives under way will help identify
potential enhancements to the initiative. The goal is to improve the impact
of ongoing initiatives and develop more granular knowledge regarding the relationship
between causes and improvement initiatives.
Developing the Reliability Analysis System
There are five elements in
the development of analytical tools to optimize asset management and restoration
- Develop and optimize requirements Begin by interviewing key operations
staff and other experts to gather information relevant to the specific areas
of optimized scheduling and dispatch and asset management. The tasks will
be: (1) gather business knowledge, (2) determine data availability and data
format, (3) establish an appropriate linear objective function to be used
in the optimization, and (4) define hard and soft constraints that need to
be enforced on each aspect. There may be other issues that need to be discussed
as a result of interviews and meetings with the experts. These issues will
be addressed in the design detailing the high-level requirements for the overall
route optimization solution.
- Analyze data related to improved reliability Review data sources
that may contain data current and historical that could be used to build
analytical models. Utilize expertise in: data mining and modeling; an understanding
of the dynamics of risk, reliability and restoration; and the availability
of data to develop some potential applications of analytics to improve the
utilitys ability to predict reliability.
- Develop a restoration model Define optimized scheduling and dispatch
problem in mathematical terms. It is assumed that the objective function and
the constraints are linear. Model the problem as a mixed-integer program or
as a linear program. The decision will be made after conducting preliminary
analysis on available data. Conduct a similar analysis for probability of
failure and impact of failure. Develop draft mathematical model formulation
for optimized scheduling and dispatch. This mathematical model will have the
capability to serve as a basis for a prototype.
- Develop an asset management model Apply asset management concepts
(e.g., reliability-centered maintenance and commercial-based maintenance,
risk/reward capital allocation, etc.) in developing optimized investment and
maintenance in mathematical terms. The approach is similar to the restoration
model described above. The new dimension will be identifying interrelationships
between the asset management model and the restoration model.
- Analyze the potential for integration of other tools into the Reliability
Analysis System The objective of this activity is to analyze the environment
to determine the best way to customize the products and usage; to evaluate
dependency and integration issues for mathematical optimization component(s)
for the decision support system; and to fine-tune modeling.
and Prioritize Innovation Solutions
With the insights gained from the optimization tool, the next step is to identify
and prioritize potential solutions to enable the significant reduction of outages
and outage durations (See Figure 4).
- Apply insights from reliability analysis system development to enhanced
- Rank targeted solution areas to address reliability performance according
to expected reliability return on investment;
- Determine which combination of initiatives will potentially achieve reliability
- Conduct risk-assessment working session to determine the financial, operational
and technical risks of each key solution area;
- Prioritize and outline solution areas and options; and
- Develop an initial draft road map.
This cycle will repeat continuously. As the model is used, knowledge will expand.
Real-time analysis capabilities will provide a new paradigm for network operations.
New tools based on mathematical models will focus decisions on restoration and
reliability instead of on organizational silos and budgets. Coupled with new
business processes, these new tools will allow utilities to simulate investments
to optimize the balance between reliability and return on investment. Operators
will be able to fly by wire in emergency and restoration activities. And finally,
the mountains of data will begin to move.