Once you have distributed energy resources, optimal integration requires distributed intelligence and control, as well as always-on, always-operating grid optimization software.

You don’t need to spend thousands of dollars on analyst reports to know that deployment of distributed energy resources (DER) is on the rise and growing fast. All you need to do is to read the daily news.

In the two weeks before this paper was written, Hawaii passed lofty goals of having 100 percent renewable energy by 2045, New Jersey legislators were deliberating a goal of 80 percent renewable generation by 2050, and Sen-ator Angus King of Maine introduced legislation in Washington calling for all states to ensure the right of DER to connect to the grid “in a reasonable timeframe and only be charged just and reasonable fees, accounting for the benefit of DER to the grid and the grid to DER,” according to the bill’s summary on Senator King’s website.

“Whether it’s solar panels on the roof or battery storage in the basement, advanced technologies are expanding America’s energy future by literally bringing power generation to the people. But policies governing how these technologies connect to and interact with our nation’s electricity grid are stuck in the past and, as a result, are holding back the enormous potential for these technologies to flourish,” Senator King said. “My legislation would create a set of guidelines with deference to the states to protect the right of people to connect their technology to the grid, ensure that grid owners and operators receive their due compensation, and support the continued development of energy resources that will define our future.”

While rates and money issues catch much of the attention in popular press, utilities and grid operators know that there is an equally pressing issue to be addressed before DER adoption can significantly and safely progress much farther. How will we maintain the reliability and power quality of our electric

system with the introduction of often intermittent distributed generation?

Once you have distributed energy resources, optimal integration requires distributed intelligence and control, as well as always-on, always-operating grid optimization software that enables multiple applications to harness the true value of installed distributed resources. Those applications include re-newable firming, voltage management and frequency regulation.

What’s more, deploying such applications needn’t require large investments in new technology or storage. Such applications are possible by leveraging an integral and established part of the existing electric system: the customer loads that are served by it.

Demand response as a DER

Most definitions of distributed energy resources now include demand re-sponse among other DER technologies such as solar power or microgrids. The authors of New York’s ground-breaking initiative called “Reforming the Energy Vision” certainly use this definition. So do members of the North American Electric Standards Board, the Gridwise Alliance and other leading industry organizations.

More to the point, several key industry sources, including the American Public Power Association and the North American Electric Reliability Cor-poration have sided with the Federal Energy Regulatory Commission, which has gone on record to proclaim that “… demand-side options, like energy efficiency and particularly demand response, have a critical role to play in managing overall energy consumption and acting as a dancing partner for variable generation.”

Extracting Grid Value from Distributed Energy Resources

Already, it’s commonplace for utilities to see demand response as non-spin-ning reserves that displace generation deployed as operating reserves or reg-ulation service. Many utilities and system operators even see potential to use demand response as spinning reserves, reflecting time-tested reliability that has encouraged electricity suppliers to count on DER resources with certainty.

But, the trust in demand response doesn’t go far enough, and partly that’s because the use of demand response doesn’t go far enough. Too often, DR is seen as a program that’s available to operators only within certain time windows. Or it’s a program subject to rates, smart thermostats and, ultimately, customer override.

To truly leverage demand-side DER for support of other DER – including in-termittent renewables – you need to control loads in a number of ways that go beyond cycling off a household air conditioner or a building’s HVAC sys-tem every now and then. You need to use loads as process storage, which delivers a solution much more efficient than typical storage technologies.

Process storage circumvents the efficiency-draining AC/DC conversions that must take place when you use chemical or mechanical storage approaches, which average merely 70 percent efficiency. If you’re using a load such as a water pump as storage, you’re simply changing the timeframe in which the pump is used. That gives you storage with nearly 100 percent efficiency.

What’s more, you increase overall system efficiency, particularly when using process storage for integration of renewables. That’s because you can con-trol loads close to the source of a problem, such as a solar panel disabled by cloud cover. Instead of having to shore up the lost solar power with a gen-erator 500 miles away – and chalk up the line losses associated with that call for more power – you can manipulate load nearby, making more power available to the loads normally served by the solar installation. Again, you’re gaining a solution with close to 100 percent efficiency.

To gain this efficiency, a number of operational factors are necessary that make loads, especially commercial and industrial (C&I) loads, deliver their full DER potential. These include:

Seeing the bigger picture to enable multiple applications – As noted earlier, often demand response is an on-or-off matter designed to shed load and mitigate peaks. Yes, the electric system benefits. Generally, so do the customers who participate in the program, let their loads be in-terrupted and, in the case of C&I customers, may thereby avoid ratcheting up demand charges. Still, this on-off approach is limited in its applications.

That’s because participants may only be obligated to participate in a few hours of DR annually, or they may have equipment controlled by a smart thermostat, not a grid operator. Or they have override capabilities. These approaches lack precision, which means they’re not necessarily useful in support of a full suite of load shedding-related applications, such as the 15-minute signal-to-response requirements of a renewable firming pro-gram or the two-second signal-to-response control necessary to make regu-lation service work for a large independent system operator.

Gaining precise control – If load control is going to work for rapid-fire and finely tuned applications like regulation service or renewable firming, the demand response aggregator needs very precise control of loads partic-ipating in the DR program.

This requires translating what distributed assets look like into the power space so they can be controlled as power-based assets that work like dis-patchable generation. Think about a large HVAC system. What the equip-ment is controlling is the temperature of a building. That’s the control variable and, within that HVAC system, there is also a control system that takes temperature and converts it into equipment action. For highly precise control, it is the DR aggregator’s role to collect data related to the behavior of the equipment, correlate the operating equipment’s behavior with its

corresponding electrical load, and reverse engineer all the processes con-trolled by the equipment to generate a desired electrical response.

Given this HVAC example, maybe the system has 30 vents, each pushing out heated or cooled air as needed. Technically, you could turn those 30 differ-ent vents on and off to attain very precise power control. If you’re looking at the process anyway, it’s natural to use the process to drive the desired behavior of the equipment.

That doesn’t mean it’s automatically done, however. As noted, many DR companies circumvent load processes all together and en masse turn equip-ment on or off. That’s fine when you’re doing traditional demand response — i.e., load control — when your goal is to turn off as much equipment as possible. However, when you’re trying to provide applications that support the overall stability of the grid, this doesn’t work. For optimal leveraging of DER capabilities, you can’t merely control bulk energy behavior. You need to control precise power behavior.

This requires intimate knowledge of the process you’re controlling, which comes from a combination of engineering skill related to industry process control, big data analytics, as well as algorithmic machine learning so that the control system can become increasingly accurate with its forecasts. A top-tier program can do this without adding significant cost.

Looking ahead – Speaking of forecasts, these can and should be consid-ered an integral part of a grid-optimizing demand response technology de-ployment. When using demand response as a DER, it’s crucial to know how much energy storage and power capability there is in a portfolio of loads and processes. Fortunately, it’s also easy to collect data now, and analytics have become a powerful tool for statistical scrutiny of loads and processes.

In fact, by aggregating loads, there’s an averaging effect that operators can take advantage of, adding an even a higher degree of statistical confidence to the forecasts that support DR programs.

This is important because for DER integration and grid optimization ap-plications, demand response aggregators and utilities need to be able to fine tune the forecast confidence interval appropriately. With something like regulation service, you may want a 99.9 percent confidence level in the forecast so that you can say very precisely how much capacity there is to bid into the market. If the DR program is merely trying to avoid a peak demand day, any curtailment helps the grid, which means that from a stability per-spective, it may not be necessary to be so precise.

Running 24 x 7 x 365 operations

The grid can have as much need for support in the middle of the night as it does during the day, and that’s why continuous operations are crucial to DER integration applications that use demand response as a grid op-timizing DER.

Yes, energy consumption typically goes down in the middle of the night, but variability often goes up. If, in the middle of the day, a utility is

delivering 25,000 megawatts at noon and the variability is 250 mega-watts, that’s a 1 percent variability rate. Suppose in the middle of the night the utility is delivering 5,000 megawatts and the variability is still 250 megawatts. Suddenly the variability is now 5 percent, which means as a percentage of total generation, the variability can be higher at night without changes in the size of the variability itself, potentially impacting grid stability.

An even more dramatic – and increasingly common – development with potential to disrupt grid stability is oversupply. Germany, with an overabun-dance of wind and solar power, has been facing negative power rates as it sells its excess supply on the European market. Ontario has grappled with a similar problem. A May 2015 article on feed-in rates in The Globe and Mail, a nationally distributed newspaper in Canada, noted that, “much of the green power (in Ontario) comes on stream when it isn’t needed. This unneeded electricity is dumped into the United States at bargain-basement prices that Ontario’s Auditor-General found has already cost Ontario power consumers billions of dollars.”

Selling watts at a loss isn’t the only expense associated with oversupply in Ontario and other regions. There’s also an environmental cost. At times, Ontario electricity generators wind up discharging some 600 megawatts of steam into Lake Huron to keep the grid in balance without shutting down nuclear plants that have long ramp-up times.

Using distributed control of distributed resources

Once you have distributed energy resources, you also need distributed con-trol. You can’t get grid-optimization benefits if you are reliant on devices that are sending signals to a central computer system for analysis and waiting for

a reply. Computing power must be at the grid edge, where the DERs them-selves are located. It is vital to control devices throughout the power grid and take corrective action as close to the source of a problem as possible.

In the Enbala system, distributed computing nodes interface with DERs to execute local optimization routines and relay state information to optimiza-tion nodes located at a utility substation or other centralized location in the network. Optimization nodes host the software components for forecasting and optimization, resource control and dispatch, as well as energy mar-ket interfacing. These optimization nodes use real-time information from each DER to calculate optimal real and reactive power setpoints for each resource within the network based on real-time capabilities. All network communication takes place through a secure, low-latency, proprietary mar-shalling mechanism.

Once established, the network acts as a single, dispatchable resource for utilities and grid operators, which enhances system reliability and efficiency. The platform receives a control signal from the grid operator and continu-ously optimizes the dispatch of the DER portfolio. The optimization engine creates the opportunity for mutual benefits for utilities, grid operators, energy service providers and electricity consumers.

“It’s hard to use an intermittent energy resource to replace a firm resource. You need to back the intermittent resources with some form of storage, whether it’s batteries or controlled load.” – Malcolm Metcalfe, Enbala founder

Partnering with program participants for constraint-based control

No demand response program exists without cooperation of the electricity customers who participate in it. This is all the more true for DER integration and grid optimization programs that leverage process storage, or the energy that is already inherent in a customer’s load.

Why? Because such programs go beyond event-based load shedding where customers allow a utility or grid operator to shut down equipment for a few hours per year. In grid-balancing operations, DER must be available and controllable at all times, 24 hours per day, 365 days per year. For that rea-son it’s crucial that the control executed does not in any way impact custom-er operations or building comfort. Operating parameters must be tailored to each customer and to each controlled load. Done right, the system can optimize behavior of assets for the customer’s benefit.

This allows the customer to determine setpoints, plus it allows for precise forecasting algorithms that inform how to optimally dispatch the resource over time. A successful load-based DER program mandates that the pro-gram operator finds the flexibilities within the customer’s processes, har-nesses them and links them to market opportunities.

Since there can be multiple applications supported with customer premises devices, there should rightly be multiple ways for C&I customers to benefit, as well. Of course, the natural benefit comes from energy management that offers the customer demand-charge mitigation. At the same time, a pro-gram should strive to optimize the customer’s participation in ancillary grid services, such as operational reserves, contingency reserves and regulation service. Such an approach links load-based DER to markets, and the cus-tomer sees additional revenue streams, ensuring the program adds value for the customer and the grid alike. This is one more way load-based DER can benefit all participants in the electric delivery system.

And, let us not forget the environmental value of DER, which can displace fossil-fuel-based generation. Today, coal used for electricity generation is responsible for some 40 percent of total U.S. greenhouse-gas emissions. While it’s not easy to displace a firm, dispatchable generating source such as coal with one that is intermittent and not easily dispatched, new technol-ogy that helps us leverage all distributed energy resources will play a big role in this transformation.

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DER assets: What are they?

• Behind-the-meter power generation located at customer premises, such as roof-top solar power or site-based generators

• Microgrids serving as co-generation for specific sites -- such as a university, community, military installation or hospital – which have one or more generation resources, a single point of interconnection with the macrogrid and the ability to disconnect from the macrogrid in “island” mode

• Wind turbines

• Combined heat and power (CHP) or cogeneration systems

• Site-based battery storage

• Demand-side or process storage, which is energy that is stored in the form in which it is to be used, such as the process of heating and cooling a building or pumping water

Ancillary services from load-based DER

• 10-minute operating reserves

• Replacement reserves

• Regulation and frequency-response service

• Peak-demand mitigation