The opportunities and drawbacks of demand management using battery-solar solutions
The rebound effect has negative implications on demand efficiency — but a smarter battery charging system (SCS) can be the solution.
When a school district is located on mountainous terrain, extreme winds can create situations where blackout events happen. This was a glaring issue for a rural school that faced multiple power outages resulting in a complete evacuation of the students. In conjunction with the local utility, a plan was established to install a renewable energy system with solar and batteries to manage such unforeseen events as a battery charging solution. This included a collaboratively developed demand response program with the utility, peak shaving, and a back-up power solution.
The fast payback was attractive to the school while having the opportunity to improve its power quality. Although, the project was not as straightforward as originally proposed.
Demand response and peak shaving
Demand response is a trend that utilities use to curtail demand growth and thus delay capital-intensive upgrades. A benefit of demand response is how it lowers the total load factor of individual providers and the substation.
The load factor (not to be confused with power factor) is calculated by dividing monthly energy consumption by peak demand and 24 hours and the number of days in that month [Figure 1]. A load factor above 80 percent is considered excellent whereas a demand factor below 50 percent is not desirable.
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Figure 1: Load factor equation. Courtesy: Hedgehog Technologies
Utilities have created markets for demand response through price incentives. These include planned reduction events and offering price signals for peak demand with time-of-use (TOU) billing. These peak demand charges range from $9 per kW to $15 per kW on average.
A major concern associated with demand response is the bandwidth for businesses to modify their energy demand due to complex processes, 24-hour operations, or the capacity to develop new curtailments. While this is not always the case, demand response remains a challenge to integrate and yield a significant impact.
Alternatives to traditional demand management
With the lowering cost of photovoltaic (PV) solar and lithium batteries, it is possible to implement peak shaving and demand response while reducing the impact to a facility.
An example is installing solar panels on the rooftop of a healthcare facility or school building. A large rooftop can manage up to 0.5 MW of solar panels. Similarly, a battery system covering an area of 30 meters2 (325 sq ft) can support up to 4.0 MWh of storage.
Demand response typically occurs between the peak hours of 9:00 AM to 6:00 PM where the battery can be triggered on demand to produce power. The battery storage system has several added functionalities including peak shaving, backup power, and demand response that serves multiple markets. In some jurisdictions, carbon trading can also be applied to rooftop solar which allows the organization to claim carbon credits.
Rebound effect
An underlying issue found in demand response is called the rebound effect. Simply put, when demand response begins, the power consumption witnessed by the utility is reduced by triggering an off-switch for HVAC, heaters, and other building appliances. Once the demand curtailment concludes, these sources of energy reactivate and cause a major spike in power that can dramatically exceed the normal power.
For example, an HVAC is turned on at its highest setting to cool an overheated building after a demand response event ends. This can lead to demand peaks surpassing the previous benchmark creating an issue for both the customer that pays for the 15-minute average power each month and for the utility that created an unanticipated new peak event.
When the first few demand events happened at a school district, the battery system was set to recharge once the event concluded. The result was a dramatic spike in peak demand that almost doubled the previous peak. The following case study revealed that smarter charging is just as important as demand dispatching.
Case study: Demand response using batteries
A school district installed a solar battery system with the objective of improving demand response for the utility while creating backup power for the building that commonly faced blackouts. The system used a peak average demand of around 200kW. The system utilized a peak average demand of around 200kW. The demand would reach a peak around 10:30 AM and hit a minimum of 100kW around 8:00 PM [Figure 2].
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Figure 2: Demand Profile for a single day. Courtesy of: Hedgehog Technologies
After the battery was installed, it provided a demand response event from 9:00 AM to 1:00 PM before proceeding to charge the battery system as quickly as possible [Figure 3]. The solid line in Figure 3 shows how significant the charging peak can become. This peak, lasting four hours, will result in an increase in the school facility’s bill as their billing structure is based on monthly 15-minute average peak demand and accumulated energy.
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Figure 3: Uncontrolled charging event (1 pm – 4 pm) after demand response. Courtesy of: Hedgehog Technologies
An uncontrolled charging event as seen in Figure 3 created a financial burden to the school facility. It pushed the local feeder closer to its maximum capacity resulting in other downstream effects. The correct method of charging a battery after usage is to gauge the past monthly peak and charge the system based on that maximum.
Figure 4 seen below demonstrates how this can be completed by using the past day’s peak as the charging setpoint [Figure 4]. When there is a controlled charge, as seen on the black line of Figure 4, the charge time is extended but it does not overcharge or cause other issues. Demand response using batteries requires smart charging intelligence or it may not attain the benefits that both parties are seeking.
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Figure 4: Demand response showing controlled charge. Courtesy of: Hedgehog Technologies
Smarter dispatching solutions
Recharging a battery requires a thoughtful approach and an algorithm to anticipate other factors such as expected minimum charge time, the available demand response time, and calculating the charge signature. These are in place to ensure three objectives are met:
- Back-up power by morning.
- Capacity for peak demand reduction.
- Demand response capability.
We found there are limits to the number of daily demand response events and suggested to the utility that a battery system requires a limitation on demand response or else the customer will end up paying a premium for accelerated recharge times if it happens during peak production.
The solution is to install a smart battery charging system (SCS) as a charge and discharge controller. To achieve the benefits of using solar battery systems as back-up energy, the intelligence required for charging is just as important as the signals for demand support.
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