How commercial and industrial customers can reduce utility bills through Demand Charge Reduction from BTM storage?
Maximum Demand Charge is a fee proportional to the Peak Load that a facility uses from the utility. It may contribute more than 30% in the monthly electricity bill. Depending upon the utility, the peak demand is recorded in 30-min or 15-min intervals and it uses a monthly ratchet i.e. the record is reset at the end of each month.
Installation of PV power reduces energy costs. It may reduce the Demand Charges if peak of the solar power coincides with peak load of the facility. However, on some days due to cloud cover, the peak load and hence the demand charges cannot be reduced. Adding a properly controlled behind-the-meter energy storage can manage the facility peak demand and in turn reducing demand charges.
Solar-plus-storage provides saving opportunities where there are demand charges and TOU pricing in the tariff structure. Peak Shaving is one of the main revenue streams for PV-coupled BESS installations where the battery storage reduces Peak Demand and hence Demand Charges. In cases where the peak load coincides with electricity price peaks, peak shaving provides additional reduction in energy costs. The peak load can be reduced by discharging BESS during peak hours which in turn decreases the Max Demand Charge.
Now in order to devise a peak shaving scheme, a PV-BESS system needs to be dimensioned for the facility based on historical load profile i.e. hourly energy consumption pattern, the amount of peak power to be reduced, and the kW rating of PCS for charging and discharging. Software tools like NREL’s System Advisor Model (SAM), REOpt or some optimization method can be used to size the PV-BESS system.
Secondly, a controller is programmed with an intelligent Control Algorithm for Charging and Dispatch Strategy of the battery to reduce the peak demand of the customers. An adaptive control is implemented to optimize the peak shaving process. The controller determines the target power needed to discharge and at the same time looking at the battery state-of-charge to fully exploit the capacity of the ESS. Optimum shave levels are determined in real-time according to the stored energy in the battery for effectively shaving the target peaks.
From load forecasting based on historical operational log and weather data, the controller is not supposed to discharge the battery in smaller peaks and leave the bigger peaks intact. This situation can lead to increasing the peak loads instead of shaving them. Similarly, the controller tracks the current monthly recorded peak demand and ensures the battery does not discharge aggressively to shave a daily peak that is below the current monthly peak.
The controller can be programmed for various tariff structures. The controller will charge the battery from excess PV power. The battery charges from the grid only if sufficient PV energy is not available due to clouds during the sunshine hours of the day.
The programmed Dispatch Strategy makes efficient use of the battery by managing the interplay between available PV power and the electrical load, minimizing electricity usage from the grid. The PV power is normally scaled to some reasonable vale e.g. 50% of the facility peak load. However, for optimal sizing of battery storage, the software tools perform optimization over the whole year considering power demands for weekdays and weekends of different months. For financial viability and valuation of energy storage, the energy simulation software takes into a number of parameters including CAPEX, OPEX, federal and state incentives, discount rate, utility rate structure, depreciation, debt size and interest rate. Through optimization tools like REOpt or through iterative parametric simulation tool like SAM, an energy storage configuration is achieved that results in electricity bill savings, maximize return on investment, minimizes the payback period, and achieves a positive NPV with a reasonable value of IRR.
