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Abstract

Throughout the U.S. and in many parts of the world, there is a significant delta between energy consumed during on peak times and off peak times.  Because there has traditionally been no effective way to store electricity, it must be generated, distributed and used in sync in real time.  Upgrades to the generation and distribution infrastructure are costly and difficult to justify when additional capacity may only be needed to support peak loads.  As a result, the task of satisfying peak demand presents a challenge for everyone along the electric supply chain.

Grid constraints during peak times impact our economy in many ways; from higher energy costs for customers to increased CO2 emissions for generators, reliability concerns for utilities and excessive risk for competitive electricity suppliers.  Each participant in the electric supply chain is vulnerable to peak demand problems.  The result: billions of dollars of cost to our economy annually.

Utilities and grid operators have attempted to address the issue with incentive programs, rate structures, and awareness programs.  In California, utilities provide what is called Technical Assistance/Technology Incentives programs (TA/TI) to provide customers funding to analyze and implement demand reduction strategies. For example, Southern California Edison (SCE) encourages commercial customers to participate in demand response programs by offering free technical audits and demand response equipment.  Many utilities also have Permanent Load Shifting (PLS) programs, incentivizing building owners to implement expensive Thermal Energy Storage measures like ice plants, fuel cells, and lithium ion batteries.

Government organizations have also recognized the severity of our peak demand problem and attempted to address with energy policy and grants.  In 2009, the US government awarded $3.4 billion dollars towards smart grid projects though the American Recovery and Reinvestment Act (ARRA) and the US Department of Energy (DOE) announced and additional $620 million to fund new technologies such as grid-scale energy storage.   

Energy policy has also been used to address the issue.  In 2011, the Federal Energy Regulatory Commission announced Order 745, which will make load reduction equivalent to incremental supply on wholesale markets.  Once implemented, this will provide a powerful incentive for electricity consumers to reduce consumption at peak times.

Unfortunately, there are limitations with each of these solutions.  This paper will discuss those shortcomings and introduce a new solution to relieve grid constraints.
 
Current Situation

Peak electric demand is a pressing issue for consumers, suppliers, and generators.  Generators and energy suppliers are required to have enough electric capacity to satisfy demand at all times.  However, consumers are rarely operating at peak levels.  Figure 1 shows how the aggregate load in California peaks for a very short period of time in the afternoon, leaving significant electricity infrastructure idle for much of the day. 

The enormous disparity between on-peak and off-peak electric consumption can be explained using load factor.  Load Factor is a measure of how flat or consistent a load profile is.  It is a percentage of time that a building is near its peak.  A typical commercial or institutional building has a load factor of .55, meaning its peak electric consumption occurs for a very short period of time.  When consumers are not operating at peak, the grid has excess capacity and unfortunately, there is little use for electricity during pre-dawn hours.  If electricity could be efficiently stored like natural gas, electricity use could be shifted to off-peak hours and a building’s load factor would be closer to 1.0.  This would significantly reduce grid congestion and provide tremendous societal benefit in the form of reduced emissions and lower costs. 

As utilities have built new capacity over the years, they have retired older, less efficient generating plants.  These plants are mainly used to serve the peak load requirements. They are old, inefficient, and environmentally problematic.  As these “peaker” plants are being torn down, utilities need to build new generating capacity to meet demand.  It takes several years to plan and build power plants and transmissions and distribution systems and several millions of dollars. 

There is often public opposition to new infrastructure due to the NIMBY (Not in My Back Yard) philosophy.  We, as consumers, want reliable and inexpensive power, but don’t want the transmission lines running through our back yards.  This was especially evident in the years leading up to the California Energy Crisis. Metcalf Energy, for example, waited for approval to build a 600-MW combustion-turbine generating plant near Morgan Hill, California.  Though the Sierra Club and the American Lung Association endorsed the construction, CISCO Systems was a major opponent.  Though CISCO was a part of an industry that relied heavily on electricity for communications and manufacturing, they did not want a power plant next to their office park.   It is a catch 22 situation.

Suppliers and utilities face a huge risk when peak soars because they either need to buy on the spot market in real time when prices are high, or spend significant funds on hedging to insulate themselves from the risk.  Consumers want cost predictability almost as much as low prices, often feeling they can absorb increases in costs so long as they are aware of the increase beforehand.  When suppliers have to provide flat energy prices to their customers yet buy on wholesale markets that are not flat, they are exposed to risk.  They have obligations to provide power and positions in the market to meet those obligations, but are forced to either buy excess power or derivatives to cover their risk.  These costs are included in the flat rate they provide consumers.

Many times, the power plants that are being used to deliver peak load are inefficient, outdated plants.  They have less efficient transmission paths, and emissions from those plants are higher than that of the base load plants.  Utilities and generators are penalized for emissions and the higher the emissions, the higher their penalties.  Many times these penalties are passed through to consumers in rates, costing consumers millions of dollars annually.

The result is that electric customers get high electric bills, either on the supply portion of their bill or on the delivery of the power.  These charges can be seen in peak demand and peak consumption fees.

Problems/Shortcomings with Current

There are several solutions that have been introduced to address the peak load congestion.  In some cases, these solutions have provided incremental improvements but unfortunately have failed to sufficiently address the whole problem.  The following describes these approaches and associated deficiencies.

Solution – Buildings as Batteries

The traditional solutions pose fundamental flaws.  Thermal energy storage is expensive, demand response is not sophisticated, and utility rates are static.  Each was intended to address an underlying problem: lack of efficient method to store electricity. To truly solve the peak load problem, building operations must be integrated to grid operations with complete transparency between the systems.  There are three basic components that will enable this integration: 1) the building must have an effective method to reduce load without expensive capital investments;  2) the grid and building must communicate and have intelligence; and 3) the building needs the ability to predict load requirements to implement changes in advance of congestion events. 

1. Consumers need an inexpensive method to reduce load.  If only a select few buildings can afford to implement the solution, the aggregate benefit will not solve grid constraints.  Fuel cells, ice plants, and onsite generation are expensive and require significant capital.  Utilizing existing systems-- building automation, metering, building structure-- would provide a cost effective solution for all buildings and budgets.  
   
2. The electric grid and commercial buildings must communicate seamlessly and have intelligence so buildings can dynamically respond to, or work within, the operation of the grid.  The building needs to know when the grid is constrained and needs help, and the grid needs to know what it can count on from a building in the way of load reductions.  The grid and the building need the real time ability to dynamically adjust to what the real time requirements are.  
   
3. The grid and building need to know in advance what the conditions will be and have ability to identity and collaboratively implement a solution to avoid a condition versus respond to the condition.  This requires a predictive tool that can see future energy prices, weather patterns, and building impact.  
   

With these three areas addressed, grid constraints can be drastically reduced, which will save consumers money on demand charges and TOU rates, while increasing DR revenue.

How it Works

All buildings have an inherent capacity to store energy in their thermal mass.  Some buildings have more efficient thermal mass than others, with components such as building materials, insulation, window specification, tenant use, and solar position impacting the thermal mass efficiency.  The trick is to determine the efficiency of the thermal mass—to measure its ability to store energy-- which in turn impacts heat transfer between surfaces and air throughout the day.  For example, if the space temperature of a room is 73 degrees and the mass (concrete, drywall, books, desks, etc.) is 69 degrees due to cool thermal energy being stored there earlier in the day, then there is a positive heat transfer of 4 units between the thermal mass and the air.  This heat transfer takes heat out of the air and allows the space to remain comfortable long after the mechanical cooling has been reduced.

The process starts by capturing data about the building such as floor area, construction materials, window specifications, mechanical systems, ceiling materials and construction, and interior furnishings.  Also important is the position of building to the sun. The model includes hours of operation, number of tenants, percentage of conditioned space, and more.  Finally, coupling of thermal mass to space air and decoupling of outside air to thermal mass is a critical component to modeling buildings and the Energy Plus model includes such analysis.  Data is input into an online Building Survey and integrated into EnergyPlus, a Department of Energy whole building modeling tool that engineers use to design buildings and optimize energy and water use.

Once the building has been modeled to determine the thermal capacity of the mass, the building can integrate ongoing operational data from the building automation system, hourly weather forecasts, and energy market information such as day ahead or real time pricing, into the platform.  In markets that do not have dynamic pricing, the published rate would be used.  The platform algorithms simulate different load profiles to determine optimal times to cool the building.  For example, if energy prices are low between 4:00 am and 6:00 am and the outside temperature is 60 degrees with low humidity, then the system will precool the building using free outside air versus mechanical cooling.  By cooling during unoccupied hours, the building mass is sufficiently cooled and will provide favorable heat transfer in the building that will last most of the day.

Thermal Comfort

Maintaining a comfortable workplace is a critical component in facility management.  Thermal comfort will generally not be compromised for energy savings, as the adverse impact on productivity typically far outweighs the gains from energy savings.  Performing load shaping has traditionally impacted comfort as precooling delivers uncomfortably cool temperatures in the morning hours and peak load shedding often entails letting temperatures float to uncomfortably warm levels later in the day.  Dynamically shaping load throughout the day without impacting tenant comfort is accomplished by leveraging the thermal mass.    

ASHRAE 55 is a building standard that specifies the combinations of indoor thermal environmental factors and personal factors that will produce thermal environmental conditions acceptable to a majority of the occupants within a space.  The environmental factors addressed in the standard are temperature, thermal radiation, humidity, and air speed; the personal factors are those of activity and clothing.  In the standard, ASHRAE introduces Predictive Mean Vote (PMV), which is an index that predicts the mean value of votes of a large group of people.  PMV is essentially a prediction of how many occupants in a space will be comfortable.  A PMV of 0 is a thermally neutral sensation, which is the lowest portion on the curve in Figure 3, while positive and negative scores are too warm and too cold respectively.  In the region between plus and minus .5, thermal comfort is not compromised.  In a test by Morris et al, precooling and changing PMV by -.5 reduced peak demand by 40%, which shows that minor temperature adjustments can have a big impact on peak reductions without sacrificing comfort.  By leveraging thermal mass, CUE can be more aggressive in load shaping by storing and releasing energy throughout the day, delivering even greater savings.    

Maintaining control of a building is very important to building owners and facility managers.  Although CUE is a SaaS platform that sends commands to the buildings automation system, the commands are limited to space temperature setpoints that are within predefined thresholds set by the building operator.  As the automation system receives the setpoints throughout the day, it initiates control sequences to deliver the hourly temperature requirements.  The CUE algorithms work within the temperature bands given by the building operator.  There is no manipulation of control sequences or PID loops.      

CUE provides a dynamic load shaping solution that takes inputs from three sources: electric markets, weather, and buildings.  Algorithms run in the NOC throughout the day, as shown in Figure 4, and deliver optimal space temperatures back to the building.  In doing so, it becomes an operational tool that initiates energy savings every day, every hour.  No manual intervention is required and because the platform is a cloud application, there is no capital investment for building owners.

Case Study

CUE implemented its SaaS platform in a 976,000 ft² office building in downtown Chicago.  The building’s management team hoped that CUE technology would achieve energy expense savings, promote energy efficiency, and maintain the comfort of their tenants.  In the first month of operation, the CUE technology achieved all of these goals.

Using CUE technology, the building reduced peak demand costs by 30% and daily on-peak energy consumption by up to 30%, increasing demand response revenue, and making the HVAC system more efficient with its energy use.  As Figure 5 shows, the “pre-CUE” HVAC demand profile on two days, represented by the green and blue lines, had a high peak load.  After implementing the CUE Thermal Energy Storage solution, the peak demand and on-peak consumption was dramatically reduced, as is indicated with the black trend line.  The building manager maintained control of zone temperature setpoints and was able to achieve these reductions without impacting tenant comfort.  Additionally, CUE technology found untapped efficiencies in the building’s chiller operations.  While this building typically operated four chillers every day, they now only needed three. 

Summary

Constraints on the electric grid are a growing problem globally.  Demand is increasing, plants are being retired, and transmission infrastructure is dated.  The imbalance between supply and demand during peak times causes problems for all entities throughout the supply chain.  For generators, peak plants are typically less efficient and costly to operate and may incur emission penalties.  Competitive electric suppliers are exposed to significant risk associated with peak loads and spend millions annually to manage that risk.  Finally, consumers are subjected to high prices and potential brownouts. There have been numerous technological and legislative solutions introduced, rates altered, and utility programs implemented.  Each has shortcomings.  Clean Urban Energy provides a solution to grid constraints by utilizing a building’s inherent thermal mass as an energy storage medium to allow electricity to be used when prices are low, maintaining tenant comfort by releasing energy from mass throughout the day.   Because algorithms do the optimization with inputs from energy markets, weather forecasts, and building systems, no manual intervention is required by building operators.  It requires no capital investment, no expertise or unique intellectual aptitude, and no sacrifice to tenant comfort. 

Buildings as Batteries, managed by a sophisticated SaaS platform, solve the peak load problem and overcome the challenges other intended solutions encounter.  The result, significant savings and community benefit.

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