Demand response (DR) exists to provide value to electric utilities and their customers. In any of its manifestations, the effect of DR is to temporarily reduce end-user demand on utility supply resources.
Demand response (DR) currently exists for these purposes:
- to limit peak demand during temporary periods of peak heating or cooling
- to offset supply disruptions, transmission outages, or other capacity constraints
- to adapt energy systems to the intermittency of supply from solar and wind energy
DR experienced strong growth over the past 10 years. DR will grow strongly in this new decade as well, although the factors leading to growth must change. The obstacles that stand in the way of continuing with the established growth phase relate to ability to scale, regulations, and cost efficiency.
DR in the 2010s
Demand response in the 2010s was almost exclusively managed from the supply side. Strong growth in DR was facilitated mainly by Curtailment Service Providers (CSPs) that serve the power capacity markets. Growth was also made possible by advancements in controls technology, energy storage such as customer-sited batteries, and various government and utility incentives. The CSPs serve the capacity markets. Capacity markets ensure that load serving entities (LSEs, e.g. electricity transmission and distribution) commit to providing adequate supply, particularly peak supply, to balance demand throughout the year. As part of the capacity market, DR functions as if it were an LSE, although technically it is not.
On the demand side, the most typical DR placement was, and still is with industrial/manufacturing consumers of electrical power, which represent 50% of the market or more. By its very nature, industry is well suited for DR. Industry uses electricity in its processes, and when those processes aren’t needed 100% of the time, it’s often possible to shift power consuming processes to different times of day, or even different days altogether. Large batteries or other forms of back-up power can also be used if the savings from DR are high enough to cover the costs.
Limitations of the Status Quo
As noted previously, DR capability must be available to curtail load year round in order to be accepted in the capacity market. This is a regulatory requirement. This requirement for year round curtailment capability is only possible when the DR capability is a subset of Capacity Performance (CP). Today, 95% of DR revenue comes from CP. However, being reliant on capacity markets is problematic.
Scale Issues
According to PJM, power capacity accounts for only about 15% of the overall spend for power. If the power market (generation, transmission, and distribution) is a whole pie, the power capacity market is one slice of that pie, and DR is about 5% of that slice of pie, or just one bite of the whole pie. In other words, despite its growth, DR is still a relatively small part of the power market. If it is going to continue to scale, it will need to move beyond the capacity market.
Economic Efficiency
Another issue with DR being relegated to the capacity market is that the capacity market is not a market optimized for economic efficiency. The main objective of CP is to ensure sufficient capacity is available to meet demand at all times. For DR, this leads to a side effect that’s known as the double payment issue. Currently, when owners of the DR asset reduce demand, they not only reduce energy costs, but they also get paid to do so. In other words, the DR asset owner gets rewarded twice for the same action. This double payment effect can lead to gaming of the system for profit. Alternatively, if sought changes in demand were resulting from demand pricing alone, then the double payment issue would be avoided. As a result, economic efficiency would rise.
Regulatory Considerations
The FERC has regulatory oversight over the RTOs. The regional transmission organizations (e.g. PJM, ISO-NE) administer forward capacity markets. In Q4 of 2019, FERC weighed in on PJM and the capacity markets serving it. FERC placed burdens on new DR installations which effectively render them uneconomic (see 169 FERC ¶ 61,239).
Whether the new FERC regulations adhere long term remains an open question. Either way, these regulations demonstrate the uncertainty that is inherent in government regulatory authority. The best way to avoid regulatory involvement is to focus on markets that run themselves without the need for government involvement between parties.
DR in the 2020s and beyond
The
value of DR will continue to grow. The electricity market is a relatively small part of the overall energy mix, but it’s growing strongly. In Massachusetts for example, electricity makes up 17% of consumed energy with the other 83% coming from transport 44%, and thermal 39%. The overall trend is toward electrification of both transportation and space heating, so that the present 17% share of energy is going to move up substantially and relatively quickly. Demand Response has the capability to help facilitate this transition.
New Opportunities for Growth
Because of these strong trends, opportunities for growth in DR are best in new market segments, using different strategies and technologies. Take for example the fast growing trend in transportation powered by electricity. Demand for electrically-powered transportation, particularly cars and trucks, is expected to grow strongly for decades to come. The growing demand for electrical power in transportation will place transportation alongside the other large user segments of electrical power: industrial, commercial and housing.
The housing sector, specifically in heating, cooling and domestic hot water (DHW), is another growing area for electricity consumption. The growth is coming from more heat pumps being installed. Technologically speaking, heat pumps have progressed significantly over the past decade. Because they perform very efficiently, and they have the ability to be used for both heating and cooling, they are already growing strongly in supplemental retrofit situations. They are also being used more and more in new construction as well, and may well completely displace oil and gas within a couple of decades.
The market for electricity is dependent on its infrastructure. As the trend toward electrification gains momentum, there will be more demands made on the grid. It’s hard and expensive to build new transmission assets in built-up areas. Both efficiency and DR will make important contributions to meeting these new demands.
Moving Decision-making from Supply Side to Demand Side
Because demands on the electrical grid are becoming greater, especially during peak events, growth in DR is desperately needed. Some of the growth can be supported by the capacity markets. But as capacity markets are small relative to the expected increase in demand, there is a much larger opportunity for growth outside of the capacity markets.
Growth can come from any other part of the supply chain, such as generation, and LSEs (transmission and distribution), wherever there is some value either in lowering peak demand or in shifting demand away from peak hours. Value can be perceived by one or any combination of participants in the supply chain. The value from reducing peak demand can be temporary, such as just a few hours. And it can be either one-off or recurring, such as, for example any day where temperatures exceed 85°F, or fall below 20°F.
Achieving Economic Efficiency with DR
In the coming decade, the financial catalysts for growth need to change. The opportunity for growth will shift from the capacity market to the energy market; the energy market share of the pie is much bigger, and there’s more latent value to be realized. Suppliers can affect demand by varying the price. There’s an opportunity to improve economic efficiency by letting suppliers use market forces to determine peak demand pricing on a day ahead basis.
DR and Retail Energy Markets
Demand Side Management (DSM) offers potential for shifting demand away from peaks. With pricing signals, demand can be proactively reduced or shifted, depending on the goals of the entity making the price.
Keys to successful variable demand pricing includes interval metering, day ahead pricing, and technology that uses price signals to modulate demand. Also important is finding how elastic the demand is for classes of energy consumers. As day ahead demand pricing will be an important component of growth in DR in this decade, and because there is a learning curve to using it, it’s important to start planning and using it as soon as possible.
Be sure to follow if you’re interested in this subject as I will be following up with more on the future of DR growth for utilities in the weeks to come.
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Using Demand Flattening to Alleviate Capacity Constraints
Shifting natural gas demand away from demand peaks has several benefits for utilities. The most obvious benefit is that it helps alleviate natural gas constraints that can arise on very cold and very hot days.
Capacity constraints occur when demand is highest. They also can occur in local areas where older gas lines require a lower limit for safe pressure levels. (Higher pressure in older distribution lines may also contribute to higher rates of methane leaks.)
Flattening demand also can help reduce or eliminate low pressure at distal points in the distribution network.
Visualizing Peak Demand and Demand Flattening
The following chart shows an example of what demand looks like over the course of one winter day for a large multifamily property. Over the course of this 24 hours, the building consumes 1020 therms of gas. In this example, the peak consumption is in the morning, from about 6:00am to 10:00am. A second, smaller peak occurs in the late afternoon and early evening.
The example shows the exact same consumption over the 24 hour period. The only difference between the two is that the red line is flatter, spreading out the demand, and maxing out at 50 therms/hour, a 17% reduction from the 60 therm/hour peak.
Incentivizing Demand Flattening
Ideally, the best and most cost effective way to manage capacity constraints is to incentivize natural gas consumers to willingly shift their demand away from peak hours. This can be true during all times of the year.
This is already a common practice with power utilities, and to some extent with district energy utilities. In both cases, the utility is charging more for consumption during peak periods, in the form of a demand rate. What consumers of electrical power do to reduce their demand costs is either lower demand during peak summertime hours by raising thermostat settings, or they shift consumption to off-peak times of the day (e.g. making ice at night for cooling during the day).
As noted, power and district energy utilities use demand charges. This is the “stick” approach. Utilities could also offer a “carrot” in the form of either a monetary benefit, or some other kind of reward. There are many rewards, both tangible and intangible, that can be considered.
Using Leanheat AI Technology for Peak Demand Reduction
Leanheat technology success to date has been reducing peak demand in district heating networks. The technology applies equally well for natural gas distribution networks. The only difference is the source of energy: hot water, steam, or natural gas.
The technology works best is dense buildings with a constant, but not necessarily consistent demand for thermal energy like apartment buildings, nursing homes, and retirement communities. The consumer (or tenant) receives more consistent and comfortable indoor temperatures.
Leanheat uses artificial intelligence (AI), combined with lots of data; each building’s “energy fingerprint”, and near-term weather forecast data. Virtually any kind of incentive can be embedded in the software as long as the incentive can be digitized for the underlying algorithms.
Alternative Methods of Peak Demand Reduction
Lowering peak demand can be accomplished in several ways:
- In some cases it’s possible for a customer to shift to an alternative (supplemental) form of energy. This isn’t common.
- It can be possible to lower peak demand through conservation during peak periods.
An example of raising or lowering the thermostat settings by several degrees for a limited period of time has been field tested in Southern California. One downside of this approach is that it requires thousands of typically single family homes to sign on in order to make an impact. Another downside is that it only reduces peak demand for a few select days, as opposed to every single day with the Leanheat approach.
Conclusion
It makes sense for natural gas utilities to reduce the intra-day volatility of natural gas delivery. It reduces peak upstream gas pressure requirements on a daily basis, and provides more consistent pressures at the ends.
Leanheat is the logical technology choice for achieving meaningful reductions peak natural gas demand.
Making energy systems proactive is a logical step forward in improving building energy efficiency
Proactive building energy systems can increase energy efficiency, improve comfort, lower energy costs, reduce peak energy loads, and increase property values.
What is a proactive building energy system?
A proactive building energy system uses forward-looking input factors such as a building’s thermal momentum, near-term weather forecasts*, and demand costs to help determine system outputs. Artificial intelligence (AI) software uses strings of data inputs and self-learning to calculate outputs and optimize system performance.
A proactive energy system can be an add-on part of a very basic system, or it can can be integrated with a complex building management system (BMS) using open protocols or something else.
Contrast with today’s reactive energy systems
Nearly all building energy systems in North America today are reactive. A typical example would be a temperature set-point being reached which triggers a relay that starts or stops one or more devices (e.g. pump, boiler, chiller). There may be a variable component to the output, such as a distribution temperature selected from a heating curve. Commonly, a PID control determines the targeted output . The production and distribution technologies themselves (e.g. condensing boilers, variable speed heat pumps, low-temp hydronic distribution, etc.) may be high efficiency in a stand-alone sense. Nevertheless, they are limited by the narrow range of input data that are available to them.
Key differences between reactive and proactive system control
When external factors are changing quickly as is typical with nearly constant changes in weather factors (such as temperature), or when demand rates kick in, the reactive system loses its ability to optimize energy inputs.
Proactive energy systems on the other hand, use a range of data that includes forward-looking data that feed dynamic algorithms. With proactive systems, there can also be some automated learning involved as improvements build on past improvements. This is where AI can play an important role. The AI is used to determine thermal inertia and thermal momentum, both of which are important for maintaining targets without over or under performance. Reactive controls don’t have this ability.
Fortunately, there are more similarities than differences. Proactive systems and reactive systems use the same building assets. So from an upgrade perspective, it can be very easy to make the change.
Examples of proactivity in our lives
Although you may not have thought about it in this way, being proactive is a natural part of our everyday lives. There are countless examples of being proactive that illustrate this point.
Here are a just a few examples from one thing many of us do every day: drive a car. Imagine you’re behind the wheel of your car and you’re driving down the 
road. You see a sign that says “Stop Ahead”. This is an opportunity to be proactive. You can take your foot off the accelerator and let the momentum of the car carry you forward to the stop line. That’s proactive and saves a little bit of energy. Now imagine you’re on a highway and you see a sign that says “Exit Right 1 Mile”. If that’s your exit, you can look for a good opportunity to move over to the right lane. That’s also being proactive. A third example is when you drive with your high beams on at night, you’re gaining more “visual data”; you can be more proactive in dealing with upcoming road hazards.
Of course, being proactive doesn’t necessarily have to lead to greater efficiency. Proactive measures in energy are worth doing if they help achieve an objective like lower energy use, lower demand charges, or higher comfort.
Thermal momentum quantified and used proactively
Being proactive helps increase efficiency in cases where momentum (including thermal momentum) and inertia are significant factors. Thermal momentum is identified and used by AI for proactive control.
As momentum is the product of mass and velocity, higher mass leads to higher momentum. As an example of this concept, heavy trucks benefit more from that “Stop Ahead” sign than does a car, and the car benefits more than a bicycle. A pedestrian may not benefit at all. So the greater the mass, the greater the momentum, and this is true in buildings as well. Proactive AI control can account for momentum and inertia, and use it to improve overall efficiency.
A multifamily building is full of walls, floors, ceilings, carpeting, furniture, and plumbing. Therefore it is relatively dense. An empty airplane hangar, a big space filled with nothing but air, is not dense. As a result, changes in thermal energy can occur much more quickly in the hangar (assuming the building size, energy systems, and building envelopes are equivalent). Because the multifamily building is denser, it takes longer to heat up, and is slower to cool down. All that mass inside the building is a heat sink. That mass is absorbing and radiating back out thermal energy constantly. If a heating system reacts to a set point being reached, and stops pumping heat into the building, that building mass will continue to radiate heat back into the living space. If the outdoor temperature rises or sunlight streams in, the interior of the building can become overheated.
Consistent building temperatures with proactive control
Another thing we can say about the multifamily building is that steady temperature is important to the people that live there. Zeroing in on a set point temperature is a challenge that is easier with AI and proactive control. Multifamily building owners know that the alternative to consistent indoor temperatures is either complaints, open windows in winter, or both. It also can lead to higher energy costs, increased tenant turnover, and lower rents.
Lowering demand peaks in district energy with proactive control
In cases where there is an energy demand rate**, it’s possible for a proactive energy system to optimize operation to pre-load heating or cooling, and to hold back from the high demand peaks that drive demand charges. As noted earlier, this is a valuable tool where building mass can be used to advantage. This is most common for buildings connected to district energy networks, but is evident in other energy scenarios as well.
Other applications for proactive energy control
Besides thermal energy, as described above, there are other forms of energy consumption that can, and sometimes do benefit from proactive control.
- Natural gas consumption
- Electrical power (demand response)
- District energy (heating or cooling)
- Chilled water /sensible cooling
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*useful weather data may include the timing of upcoming temperature, sunlight, wind, and precipitation.
**energy demand rate – charged by some district energy utilities and electric utilities. Demand charges may be tiered, and can vary by time of day and time of year. In some cases demand rates change daily, with a day or less of advance notice. Demand charges help utilities pay for the higher marginal cost of supplemental energy sources, or the cost of infrastructure needed to meet peak demand.
© 2019
Optimizing district energy use from the building-side (i.e. point-of-use) has many benefits, including lower costs and higher building value.
District energy is the go-to energy source for many buildings in urban areas. For a new building, connecting with a district energy network can mean lower up front costs, as investing in boilers or furnaces, air conditioning units, chimneys, and large boiler rooms are rendered unnecessary.
District energy-connected buildings benefit from no energy equipment maintenance. The district energy provider of course maintains a large plant, and those maintenance costs that it incurs benefit from it’s economies of scale. Those costs are passed on to the customer in the price of energy.
District Energy Rate Structures
District energy providers can charge for the energy they provide in a variety of ways. First, they can charge differently depending on the type of customer, such as multifamily, industrial, or office building.
Second, there may be different rate structures depending on past energy use. Users of energy can be shifted by the provider from one rate group or another based on past consumption levels. Third, and adding some complexity, each one of those rate groups can consist of multiple tiers of energy use, where the first “x” amount of energy use is charged at one level, the next batch of energy used above that would have another rate, the third quantity of energy above that another rate, and so on.
A fourth component of district energy costs is time-of-use rate. Some district energy providers charge higher rates, for example, between 8am and noon on weekdays, at certain times of year. These rates are usually posted and published well in advance, and they usually don’t change except for seasonally.
A fifth component is the demand rate. District heating companies can charge for each unit of energy, such as the peak amount of steam, or BTUs passing through the meter in a short period of time, like 15 minutes, over the course of a billing period. Providers can define their demand rates differently, so the details matter.
Day-Ahead Rates
Another rate type that is not so common in North America at this time is the day-ahead rate. It’s possible for district energy providers to anticipate demand ahead of time. With this knowledge, they can post a rate or rates for the following day. For example, if the energy provider expects heavy demand from 8am to 10am the following day, they can notify customers of a higher rate during that time slot. Customers with appropriate technology can then respond by shifting more of their energy consumption to before and after that time slot if the difference in energy costs is significant.
Technology Solutions
Technology that uses artificial intelligence (AI) has been developed that makes it possible to reduce all of these charges, by using the district energy more smartly. A successful example of this is our Finnish technology partner Leanheat.
Leanheat’s technology has been successfully deployed in district heating-connected buildings that collectively house tens of thousands of apartment units. Those apartment units have their heating and cooling costs bundled into the rent by the building owners. The Leanheat technology saves the building owners from 10-20% over previous energy consumption, without any sacrifice in comfort.
Contact CIMI Energy for information about the Leanheat district energy optimizing solution.
Peak power loads are high points, sometimes spikes in demand for power. Peak loads are a problem for utilities and their customers alike. For utilities, peak loads must be balanced with supply to avoid power shortages. When the utilities can’t respond quickly enough with their own power, they need to source power on the open market, where price spikes of 1000% or more are possible. Utilities will often attempt to reduce the occurrence of demand spikes by charging power users higher rates for peak power. It’s then incumbent on the customer to reduce or eliminate spikes in power consumption to avoid those charges.
Peak Power Background
In buildings, peaks in power usage are often, but not always associated with a high demand for cooling on summer afternoons. Aggregations of power users all using their air conditioning at the same time result in demand peaks. It shouldn’t be surprising that these aggregations of power consumption are hardest to control because they are all independent of each other and are all responding to the weather, which is out of their control. On the other hand, larger consumers of power may be better able to reduce their peak power consumption by taking steps to reduce power purchases during these peak times. This is possible either by anticipating power peaks and taking proactive steps to reduce demand from the utility during those times, or by supplementing with other power sources.
Utility Rate Structures for Power
Energy Rate
Utilities charge for the power they produce by charging for delivered energy. Energy in the form of electricity is sold in units of kilowatt hours (kWh). Along with a charge for distribution, the energy charge covers most of the costs incurred by the utility for the power it produces and delivers. What these charges do not cover are the added fixed and variable costs associated with peak power production and delivery.
“Demand Rate” for Peak Power Consumption
Where the utility has reliable base load but is challenged in meeting peak loads, they may institute a demand rate billing structure. The demand rate structure imposes a higher charge for power consumption at peak times. The demand rate structure is common in industrial and large commercial application, and has been seen in some residential applications as well.
Responding to Demand Rates
As utilities impose demand rates as a response to their challenges is meeting peaks in demand, a logical response by the customer is to lower demand at those times. If a utility charges different rates depending on time of day, the natural response is to buy energy at the less expensive time of day, and use it when rates are higher. An example of this is when a power buyer makes ice at lower rates, and then melts the ice for cooling purposes during peak rate hours. This is great for cooling, but if electrical energy is what’s needed at peak hours, then other solutions are needed.
Combined Heat & Power (CHP)
CHP is a technology that large energy users can turn to for producing their own economical baseload power, while also reducing peak levels of purchased power. As described on the CHP post, the technology can be cost-effective on its own, in economically producing heat and power, and by increasing the reliability of power. The cost savings CHP can offer by avoiding costly demand rates can also be compelling.
SCADA
Demand control for large energy users can also be accomplished with SCADA applications. From a simple metering device with peak demand warning to a full monitoring network, SCADA can be used to reduce power usage automatically or manually, as needed. This type of demand response is appropriate in situations where reducing power consumption in an ad hoc manner is realistic. Therefore, this may not be a good option for hospitals, hotels, and commercial buildings.
Throttling down, or completely shutting off power to one or several powered items is called load shedding. It makes sense to shed load from one application in order to temporarily provide load somewhere else, and when the power is constrained in some way. The constraint may be peak power pricing, or it may be a limitation in the power infrastructure. In any case, load shed is most often a temporary measure, and SCADA can be used in that way.
Conclusion
Building owners and operators have multiple avenues available for reducing energy costs through peak shaving. Having a working knowledge of these options is a step in the right direction. Contact CIMI Energy to find ways to reduce your energy costs.
Additional Resources
The Peak Load Management Alliance exists, with more detailed information about peak loads and demand response.
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