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| Standing at the Intersection of Automated Fault Detection & Diagnostics and Demand Management: Savings Ahead!
Dynamic demand management strategies that consider energy pricing and the actual operations of a facility through the integration of Automated Fault Detection and Diagnostics enable large automated energy savings within defined comfort thresholds.
Energy Management Coordinator,
Why Should YOU Care About Managing Demand?
Electric demand is becoming increasingly imperative to manage for commercial energy consumers. In the demand response sector, utilities incentivize or provide rebates to those curtailing load during peak times on the grid. The lesser known consequence of the increasing importance of demand is that in the last 5 years, electricity consumption charges have decreased, but demand charges have significantly risen in both cost and percentage of the monthly utility bill. The increase in demand charges on the monthly utility bill is not always obvious to consumers as taxes and other line item charges that were once based on kWh are now based on the monthly peak kW instead.
Unaware of the potential impact demand can have on the monthly utility
bill, energy and facility managers often put emphasis instead on
managing consumption. However, due to the dual
importance of reducing demand on the grid during critical times and
reducing peak demand to lessen the monthly utility bill, managing
demand in a facility or portfolio can represent a significant
opportunity to save energy costs!
Obstacles to Demand Management Adoption
Demand management strategies are often fixed, formulated based on
operational assumptions and not on the current actual operations of the
facility. The actual operational conditions of a facility can
make static demand management strategies ineffective, leading to
undesirable comfort issues and minimized energy reduction (sometimes
even an increase in demand!). This fact has led to conservative
demand management deployments or none at all, leaving a significant
opportunity to earn savings or incentives.
There are a myriad of issues that would contribute to the failure of
the deployment of a static demand management strategy, including: if
the standard cooling setpoint or schedule has been changed, the
controller is offline, HOA lighting switches are set to manual, there
are already comfort issues in a space, equipment has failed potentially
causing increased zone temperature in the space and an adjacent zone
(this would cause the unit in an adjacent zone to “work harder” than it
normally would). These situational conditions provide only a few
of the issues that can potentially plague a static demand management
event, preventing optimized performance. With the numerous
variables that must be accounted for in order to achieve building
energy efficiency, how do we account for these and still provide an
AFDDI™ Enables Demand Management
Utilizing AFDDI™ provides significant insight into building operations
and greatly enhances the ability to quickly and reliably find problems
that can be prioritized across a portfolio for issues that contribute
to the electric demand. Often, identified faults do not have to
be eliminated in order to achieve Demand Management results; instead,
the dynamic demand management strategies take into account the analytic
outputs to adjust strategies accordingly and optimize load shed and
therefore monetary results.
Optimized Demand Response Deployment
The opportunity to get involved in Demand Response programming is well known in the industry with the allure of monetary incentives from utilities and the ease of solutions provided by vendors. Even though DR programs are being adopted all over the US and internationally, there are still many commercial buildings that are not participating or are not maximizing the opportunities associated with DR programming. In the Demand Response setting, Fault detection and diagnostics in an enterprise platform provides the ability to manage many events, vendors, and locations concurrently. The current state of equipment and the environment within a building is known, so load shed strategies can be adjusted accordingly providing dynamic load curtailment strategies that maximize event performance while ensuring that there are no adverse comfort issues. Thus, confidence is enabled so that a more aggressive solution that maximizes results can be achieved.
The Opportunity in Limiting Peak Demand
The increase in charges based on the peak kW during the month, combined
with the ability to deploy a dynamic strategy driven by fault detection
and diagnostics analytics has created a significant opportunity for
savings on a commercial building’s monthly electric utility bill.
If You Can Do DR, DL Can Work for You Too!
Often commercial buildings that are successful in demand response
programming do not implement any demand limiting strategies due to the
varying conditions in enrivonment, potential adverse implecations on
comfort in the space, and difficulties predicting times of peak
demand. Successful demand response deployment provides proof that
the strategies utilized are acceptable by sheding a determined amount
of load while not affecting comfort in the facility. These
existing strategies can also provide the same benefits in limiting
demand. The challenge with doing this is to know when to deploy
the strategy and for how long. These are variables that are
already determined with demand response, but are less pronounced in
demand limiting activities.
Determining the Opportunity for Savings
Demand Response often occurs during the hottest days of the year where
demand on the grid is the highest, while many times the greatest
opportunities for limiting demand are during months where the
environmental conditions create a few sharp peaks. Let’s explore
this concept further…
Many facilities have energy signatures that would require running a Demand Limiting curtailment strategy only a small percentage of the time throughout the month in order to achieve significant savings through the reduction of the peak demand. There are months where the opportunity for limiting demand is low because the energy signature displays a smoothed curve. The smoothed load necessitates elongated demand limiting curtailment events in order to shed the amount of load necessary to affect the monthly peak. Elongated event periods where HVAC and Refrigeration loads are used to curtail can also cause significant comfort issues, nullifying monetary gain. Therefore, during months where peak curves are smooth, demand limiting is not opportunistic.
Conversely, there are often more months throughout the year where the
energy signature provides sharp peaks for only a few days throughout
the monthly billing cycle. These sharp peaks provide the highest
opportunity to curtail load because they require short periods of load
shed that will not adversely affect comfort.
Why Don’t Traditional Demand Limiting Strategies Work?
Traditional demand limiting can entail setting a kW threshold in a
facility’s control programming that will enact a scheduled set of
actions to curtail load on specific pieces of equipment once the
facility reaches the pre-determined threshold. It is commonplace
to find facility managers not utilizing this feature because the
control strategy often does not take into account comfort thresholds
and the actual operational conditions of the facility. Another
limitation of such an approach is that in months where the peak does
not exceed the set limit, no demand reduction initiatives are initiated
and potential savings are unexploited. How do we adjust
traditional demand limiting control strategies to make them more
dynamic and adjust based on operational conditions?
AFDDI™ Driven Demand Limiting
Equipment and systems data that is collected every minute from the
building drive real time analytics that are utilized to provide
equipment and operational conditions to prediction models that
determine when to run a demand limiting strategy. If the strategy
is enabled prematurely then comfort thresholds may be breached before
the peak occurs; thereby causing the peak to not be avoided and
monetary gain nullified. However, if the peak is avoided, the
space may be uncomfortable and potential de-merchandizing can
occur. Conversely, if the strategy is enabled belatedly, the peak
for the month may be missed. In order to avoid the
potential implications of poorly timed strategy deployment, prediction
models may be employed that take AFDDI™ results as inputs so that the
demand reduction strategy can be dynamic and perform according to the
real-time operations of the facility. Thus, the strategy is
constantly tuned according to the operational conditions so that peaks
are avoided without having to fix identified faults.
A significant benefit to applying AFDDI™ analytics to equipment and controls data is that they can bring to light issues that are contributing to the demand peak for the month. Solving these issues can often be one time fixes or can be done through automated supervisory control. Poor equipment staging is a common issue that can be avoided through alterations to a control strategy that will not cause any adverse implications on comfort. This is very common as equipment is often controlled with separate thermostats or even control systems without efficient logic in place to avoid equipment running at once. A common example of such an occurrence is non-optimal start sequences. Staggering run times on units will often result in one unit being able to meet the demand before others even need to start. Strategies can additionally be put in place for refrigeration by optimizing defrosts cycles with no effect on meeting the needed setpoints.
The knowledge of the actual operational conditions of a facility
combined with energy data can additionally enable time of use and
real-time energy pricing optimization strategies to further maximize
the savings opportunities which dynamic strategies can provide.
As the importance of demand increases and the costs associated with it
continue to rise, managing demand will become more and more financially
advantageous to facility owners. However, the current static
demand management model needs to change, adapting to constantly
changing operational conditions that can potentially derail a
strategies. The solution…AFDDI™ drives demand management
solutions which provide insight into the real-time operational
conditions of a building, allowing for dynamic curtailment strategies
that do not adversely impact comfort. These dynamic AFDDI™
driven strategies can be easily scaled across a potfolio and managed
centrally to provide significant monetary benefit to the customer.
Contact Ezenics, Inc. to learn more about how AFDDI™ is enbling demand
management tools and startegies that achieve enterprise wide energy
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