# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 4 Discussion  
The PLC module used in this proposed system can be purchased for under USD 25 from the market; the current/voltage sensors and MCU (Arduino) cost roughly USD 18, while the Raspberry Pi 3B+ costs USD 40, making the proposed system cost-effective.  
The energy consumption and reduction (%) for all cases are summarized and plotted, respectively, in Table 6 and Figure 19. The energy reduction (%) is defined as per Equation (5). When no ADR program is involved, Case 2 consumes less energy than Case 1 by 8.8% (from 7.04 kWh to 6.41 kWh). Several factors contribute to this reduction. Increasing the AC temperature setting by 1 \({}^{\circ}\)C (from 25 \({}^{\circ}\)C to 26 \({}^{\circ}\)C) could be one major factor and cause significant energy reduction. The other factors are the outdoor and indoor temperatures. The outdoor temperature of Case 1 is higher than that of Case 2 by 1 \({}^{\circ}\)C, and the indoor temperature of Case 1 is also higher than that of Case 2. All these factors make the energy consumption of Case 2 lower than that of Case 1 by 8.8%. This result conforms with the statement that increasing the temperature setting of air conditioners by 1 \({}^{\circ}\)C would save 6% of power consumption [19].  
\[Energy\ Reduction\ (\%)=\frac{(energy\ of\ case\ 1)-(energy\ of\ case\ i)}{energy\ of\ case\ 1}\times 100\% \tag{5}\]  
Comparing Cases 1 and A under the same AC temperature setting at 25 \({}^{\circ}\)C, the same outdoor temperature, very similar indoor temperature, and the same operating time, the energy reduction in Case A is 45.94% (Table 6). Regardless of the type of AC, the duty cycle control strategy for demand control is effective. By observing Figures 14 and 15 (Cases 1 and 2), the current fluctuates from below 1 A (fanning mode) to 11 A (cooling mode) when a duty cycle demand control strategy is not applied. The current fluctuation, of course, has a certain impact on the power grid. When demand control is used in Cases A, B, and C, the current also fluctuates from below 1 A to 11 A; however, the duration of the current being below 1 A is shorter than that of Cases 1 and 2, resulting in less impact to the power grid.  
When participating in the OpenADR program, Cases A-C tested different cooling/fanning duty cycles from noon for 3 h (11 a.m. to 2 p.m.) on three different days. The energy reductions in Cases A-C compared with Case 1 (temperature setting at 25 \({}^{\circ}\)C) are in the range of 21-46%. If we look at the conditions of Case A carefully, its outdoor temperature is lower than those of Cases B and C by 1 \({}^{\circ}\)C. In addition, its duty cycle is the lowest among them. Its energy reduction, therefore, is the best, at 46%. However, the lowest indoor temperature of Case A is around 26.1 \({}^{\circ}\)C due to the short period of cooling mode. On the other hand, Case C has the least energy reduction due to its long period of cooling mode and its higher initial indoor temperature (higher than Cases A and B by almost 2 \({}^{\circ}\)C). Still, its lowest indoor temperature is 25.3, which is close to the AC temperature setting.  
Therefore, based on these results, the old-installed air conditioner tested in Cases A-C can participate in ADR programs to contribute an energy reduction of 21-46%, with the sacrifice of little comfort in room temperature, by adopting different cooling/fanning duty cycles through the developed automated DRC via OpenADR 2.0a. Cost-effective Raspberry Pi, Arduino, current/voltage sensors, and PLC successfully perform all proposed system functions. The proposed system can certainly be incorporated with any demand response algorithm to fulfill the requirement of demand management under the OpenADR program. For example, the proposed system can be utilized in a simple demand control strategy proposed in [20] for university classrooms with aggregated air conditioners. In addition to benefiting from the ADR program, the proposed system can also decrease or even avoid the over-demand penalty by shedding load during peak load periods.