*4.4. Experiments*

To evaluate the performance of the SPO, the system was subjected to different grid signals (as shown in Table 1) and the optimal setpoints that were generated were pushed to the equipment in the building. This paper presents the results of three tests: (1) a dynamic pricing signal; (2) a Time-Of-Use (TOU) tariff; and (3) a demand limiting signal. While most commercial buildings in the US are enrolled in a TOU tariff rate, some utilities are moving towards use of dynamic pricing for grid integration [80,81]. These prices are also considered as the replacement for event-based demand response programs. These use cases are a representative collection of legacy (i.e., TOU tariffs and event-based demand response programs) and emerging (i.e., dynamic pricing) interaction mechanisms between grid and building-level microgrids.

By specifying a linear regression model based on the weather, solar production and building load data from ten days prior to each experiment (excluding days when SPO was being run), a weather-normalized baseline has been calculated for each of the following experiments. The battery was excluded from the baseline formulation because it was not operated during the period of baseline data collection. For all the experiments, the preferred temperatures for the HVAC zones are 20.56 ◦C (69 ◦F) and 21.67 ◦C (71 ◦F) for the west and east zones, respectively. The key metric used to evaluate the performance of the SPO system is the total cost of electricity (including both energy and demand charges) since the primary objective of SPO's optimization engine is to minimize this cost. This is the cost incurred due to the net load consumption supplied by the grid. All variables in the following equation are assumed to be non-negative.

*netload* = *totalbuildingload* − *powerproducedbyPV* + *batterychargingrate* − *batterydischargingrate*.
