*4.1. Energy Performance*

The optimal control performance is evaluated by the energy saving percentage. The energy saving percentage is computed based on the base case (i.e., constant control settings without optimal control). The energy saving percentages of EDOC range from 5.83% to 12.06% for ten different load clusters. In general, as can be seen from Figure 11, SCOP-based EDOC outperforms PLR-based EDOC, and EDOC outperforms TDOC (1 h per optimal control).

The biggest energy performance difference can be 3%, as shown in load cluster no. 3. The possible reason of such superior performance is that the SCOP-based events can directly represent the system operation efficiency without mismatch compared with the PLR-based events. By continuous monitoring and tracking of the transient and cumulative SCOP deviations, a better timing of optimal control can be achieved. As a result, the system operation efficiency is always maintained at or near the ideal level. The next section will discuss the triggering time of optimal control in details.

**Figure 11.** Energy saving percentages.

#### *4.2. Triggering Time of Optimal Control*

It was observed that (see Table 4), in load cluster 1, both TDOC and EDOC approaches triggered seven times control actions. Thus, load cluster 1 is the best for analyzing the e ffect of di fferent optimal control triggering times.

In Figure 12, the optimal control time of EDOC is plotted. The Y axis refers to the time of the optimal control action, and the Y axis is updated each time when this optimal control is triggered. The X axis is the simulation time step in minutes. From Figure 12 (see dashed circles), SCOP-based EDOC tends to react in advance compared to the PLR-based EDOC. The possible reason is because SCOP is a more comprehensive energy e fficiency index than the PLR index. SCOP-based EDOC can capture the operation variation earlier than the PLR-based EDOC, leading to a better energy performance. Moreover, inside the solid circles of Figure 12, the SCOP-based EDOC did not take response, while this PLR-based EDOC triggered optimal control. In such circumstances, the PLR varies, but the SCOP still remains at the ideal level, which is unnecessary to trigger optimal control. The SCOP-based EDOC could avoid such unnecessary actions, while the PLR-based EDOC cannot (which is a demerit).

**Figure 12.** Optimal control time of EDOC (load cluster 1).

The total optimal control times of EDOC are shown in Table 4, which reflects the frequency of optimal control. Based on literatures [15,36,37], it can be found that 1 h per optimal control is the most common optimal control frequency used for TDOC. Thus, this study uses 1 h as the benchmark. Then, less than 24 times can be regarded as an acceptable triggering frequency for one day. On average, PLR-based EDOC (22.7 times per day) satisfies the condition, while SCOP-based EDOC (26.4 times per day) triggers more actions than the benchmark. It should be noted that, in load cluster no. 8, the SCOP-based EDOC performed 61 times of optimal control. The reason for such frequent triggering is possibly due to the uncertainty in "SCOP". As the parameter, SCOP is calculated based on the power meter and the load measurement, the combined uncertainty of power and load could be significant. Besides, the model of ideal SCOP also has model uncertainty. When the SCOP value fluctuated at the pre-defined threshold value, the optimal control action can be triggered frequently. From this, a demerit of SCOP-based EDOC is that frequent optimal control may be triggered compared with PLR-based EDOC. This problem could be mitigated by adding an additional time interval constraint between two adjacent optimal control actions (e.g., at least 10 mins between two control actions). Although SCOP-based EDOC obtains the highest energy saving, in terms of the energy saving performance, triggering frequency and robustness of optimal control, PLR-based EDOC can still be recommended to replace traditional TDOC for practical applications.


**Table 4.** Optimal control triggering frequency of EDOC.

## *4.3. Analyses of SCOP-based Events*

As both transient and cumulative SCOP deviations are used (Section 2.2.2), the event triggering times of the two events are analyzed in this section. From Figure 13, *e*1 and *e*2 were triggered differently under different load clusters, *e*1 sometimes dominates, and *e*2 sometimes reacts more. For example, in load cluster C1 and C2, only *e*2 was triggered. In load cluster C3–C10, *e*1 and *e*2 both appeared. If only using *e*1 in load cluster C1 and C2, the SCOP-based EDOC may not work. This validates the idea of combing *e*1 and *e*2 to accommodate broader operation conditions, since one event may fail sometimes.

Regarding the individual energy performance of the single event (*<sup>e</sup>*1 and *e*2), load cluster C7 was used as an illustration, where two events are triggered almost equally. Simulation results show: Using only *e*1 gives 9.84% of energy saving, and using only *e*2 gives 9.97% of energy saving. In Figure 11, combining *e*1 and *e*2 outputs 10.38% of energy saving. It can be seen that combining *e*1 and *e*2 will be beneficial for improving the energy performance of EDOC. The reason is because the single event may not capture the critical operation variations in a comprehensive manner.

(݁ଵ: transient SCOP deviation, ݁ଶ: cumulative SCOP deviation, C1: load cluster 1.)

**Figure 13.** Triggering times of SCOP-based events.
