4.4.1. Dynamic Prices

This test, conducted on 17 May 2020 evaluated the behavior of the SPO in response to dynamic prices. A grid signal containing a 24 hour price forecast for the event day was generated and published on WAVEMQ. The prices were based on the wholesale market prices obtained from the California Independent System Operator (CAISO), the entity responsible for the California energy markets. These prices reflect the duck-curve dynamics in California, which occur when the net electricity demand drops during mid-day due to large amounts of solar generation during that time. The demand ramps up rapidly in evening hours as the sun sets, but air conditioning loads continue to be present due to the thermal lags in buildings, and many people return home and begin evening activities.

Figure 8 shows the results of the dynamic price test. Section (a) of the figure shows the driving variables: dynamic prices and solar irradiance. Section (b) shows the battery state of charge, and the resulting net load controlled by the SPO, compared to the baseline power profile. Section (c) shows the SPO generated setpoints for the HVAC zones and their corresponding temperature profiles.

The expected behavior of the system is that the battery, the HVAC and the refrigeration loads will be controlled to minimize consumption during high price times and shift consumption to low price times, subject to the constraints on comfort and other factors. In the operational test, this behavior was observed in broad terms. Figure 8b shows how the net building load was lower at times of high prices and higher during the beginning and the middle parts of the day when prices were low. This was achieved through a combination of battery dispatch and modification of thermal setpoints. Deviations from this baseline are attributed to the behavior of the control system.

The baseline load was much lower the first 6 h of the day as the battery was charged to full capacity during these hours. This was in preparation of the increasing prices, starting from 06:00, when the battery discharges completely to support the building load (Figure 8b). The battery was controlled similarly during the high price periods that occurred later in the day as well. From the slope of the battery state of charge, it is evident that the rate of charging and discharging change also vary according to the the price fluctuations. In Figure 8c, it can be seen that the responses of HVAC systems were mainly for the second peak in the afternoon. From around 12:00 to 17:00, the battery was in charging mode and the cooling power usage was kept minimum as the indoor temperatures were increasing. Once the battery began to discharge, the HVAC systems began to bring the indoor temperatures back to the preferred temperatures slowly. Even though the HVAC systems used more power during the high price duration, the net energy use decreased as battery was discharging. Due to very strict constraints on the temperatures and also owing to undersized equipment, there were hardly any changes in the operation of refrigeration systems. However, the temperatures of both the freezer and refrigerator were maintained within the bound set by the convenience store operators.

**Figure 8.** (**a**) SPO's response to hourly dynamic prices and varying solar irradiance. (**b**) Through battery discharge and reduction in building load, where the net load is minimum during high price times and the battery charges during the low price times anticipating the high price period. (**c**) SPO changes the thermostat setpoints to vary the zone temperature. Preferred temperatures: 20.56 ◦C (69 ◦F) for the West Zone and 21.67 ◦C (71 ◦F) for the East Zone.

As there were no demand charges in this signal, the total cost of electricity constitutes only the energy cost, which is calculated by multiplying the hourly energy charge with the hourly energy consumption. The total cost for this day was \$59.14 for the SPO optimized actions as compared to the \$68.72 for the baseline load, generating a savings of 13.94%.
