**6. Simulation Results and Discussion**

The simulation results are presented for three cases based on optimal capacity planning and flexible operation feasibility using BESS and PHES, with and without DR. The optimal size of system components is determined under each case at minimum investment costs and maximum supply reliability (minimum LPSP) while satisfying the system operational and flexibility requirements. The results of the considered three case simulation scenarios are outlined and discussed below:


### *6.1. Case 1: BESS versus PHES without DRP*

Figure 4a,b shows the trade-off Pareto front plots for economic and reliability criteria with BESS and PHES, respectively, under case 1. From Figure 4, as expected, the system reliability condition improves (LPSP value decreases) as the total cost increases and vice visa. Hence, the cost-benefit relationship at different LPSP values is analyzed and discussed using the investment cost-savings approach. Table 3 summarizes the details of the cost-benefit analysis for case 1. The optimal selected points are derived after multiples execution of the optimization program for LPSP values in the range of 0% to 15%.

**Figure 4.** Pareto front plots for case 1.



A comparison of the two systems based on the ESS technology at maximum reliability condition i.e., LPSP = 0%; it can be seen that the choice of PHES instead of BESS results in a total investment cost reduction of about 34.28% from US \$ 1.38 × 10<sup>7</sup> to the US \$ 9.06 × 106. This a significant cost saving in the microgrid planning. Hence, PHES has been shown more economical compared to BESS.

### *6.2. Case 2: BESS versus PHES with CPP DRP*

In this case, the benefit of CPP DRP on capacity sizing optimization problem for both BESS and PHES-based microgrid is investigated, and pareto fronts plotted. Figure 5a,b shows the Pareto front plots with CPP DRP considering BESS and PHES, respectively. For both cases, it can be observed from the pareto plots that an increase in the LPSP value, the TPC decreases, this is due to the fact that the reliability index (LPSP) and planning cost (TPC) are conflicting objective. Table 4 summarizes the cost–benefits analysis for case 2 which involves the economic effects of critical peak pricing (CPP) DRP for the BESS and PHES-based microgrid configuration.

**Figure 5.** Pareto front for case 2.

**Table 4.** Techno-economic analysis for case 2.


For the comparative analysis of the two systems configurations at LPSP = 0% (maximum reliability) with the consideration CPP DRP; the selection of PHES as an ESS alternative to BESS in optimum capacity resulted in 34.22% reduction in the total investment costs. This significant cost saving signifies that PHES-based configuration is more economical and preferred investment option compared to BESS-based microgrid.

### *6.3. Case 3: BESS versus PHES with TADP DRP*

In this case, the prospects of TADP DRP in optimum component-sizing problem has been investigated. The renewable energy generation forecasting is a subset feature of the TADP DRP implementation. Hence, the GBRT prediction results for wind speed, solar irradiance and the consequent WT and PV powers are validated using error metrics (MAE, RSME and *r*2) in order to determine the suitable forecasting condition based on the learning rates *<sup>α</sup>gbr*. The total data set contained 17,520 data points with an hourly resolution; from which 75% of the data are adopted for training, and 25% are adopted for testing. Table 5 summarizes the forecasting results based on MAE, RSME and *r*2 for the GBRT forecasting model under three *<sup>α</sup>gbr* values i.e., *<sup>α</sup>gbr* = 0.1, 0.3, 0.5. As it can be noticed, the chosen value of *<sup>α</sup>gbr* significantly affects the precision of the GBRT forecasting model.


**Table 5.** Forecasting results of GBRT model based on MAE, RSME and *r*2 considering three *<sup>α</sup>gbr* values: *<sup>α</sup>gbr* = { 0.1, 0.3, 0.5 }.

The best wind speed and wind power forecast results are realized when the *<sup>α</sup>gbr* value chosen equals 0.1. The least error values indicated by MAE and RMSE of 0.22 (m/s) and 0.27 (m/s) for wind speed prediction and 35.03 kW and 47.98 kW for wind power forecast respectively confirms the consequences of the *<sup>α</sup>gbr* value chosen. The results accuracy are further validated using the *r*2 metric; the highest value of *r*2 = 0.96 further establishes that the GBRT at *<sup>α</sup>gbr* = 0.1 is an appropriate model for wind speed and wind power forecasting. Figure 6. shows a comparison of the actual wind speed versus the predicted wind speed with one-hour-ahead rolling forecasting horizon using the GBRT model when *<sup>α</sup>gbr* is set to 0.1 (for the best *<sup>α</sup>gbr* value).

**Figure 6.** Comparison between the actual versus the predicted wind speed with one-hour-ahead rolling forecasting horizon using the GBRT model at *<sup>α</sup>gbr* = 0.1 (from 1/12/2018 to 5/12/2018).

Also, for solar irradiance and PV power prediction, the best results are obtained when the *<sup>α</sup>gbr* parameter is set to 0.1. The minimum error values indicated by MAE and RMSE of 15.36 (W/m2) and 29.62 (W/m<sup>2</sup>) for solar irradiance prediction and 17.43 kW and 33.60 kW for PV power forecast, respectively, validate the parameter selection. Also, the highest *r*2 metric of 0.99 shows the goodness of fit and suitability of the model selection as being appropriate. Figure 7 shows a comparison of the actual versus the predicted solar irradiances with one-hour-ahead rolling forecasting horizon using the GRBT model when *<sup>α</sup>gbr* is set to 0.1 (for the best *<sup>α</sup>gbr* value).

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**Figure 7.** Comparison of the actual versus the predicted solar irradiances with one-hour-ahead rolling forecasting horizon using the GRBT forecasting model at *<sup>α</sup>gbr* = 0.1 (from 1/12/2018 to 5/12/2018).

Figure 8a,b shows the Pareto front plots with TADP DRP considering BESS and PHES, respectively; and Table 6 summarizes the cost–benefits analysis for the BESS and PHES-based microgrid configuration.

**Figure 8.** Pareto front plots for case 3.

**Table 6.** Techno-economic analysis for case 3.


According to the results of optimal capacity sizing considering TADP DRP at LPSP=0%, it can be noted that adoption PHES-based configuration will results in about 30.23% investment costs reduction compared to the BESS-based. Hence, PHES-based microgrid is the most cost-effective microgrid configuration compared to the BESS-based microgrid design. For all the scenarios (case 1–3) investigated, it is seen that PHES gives the lowest investment cost on ESS compared to BESS. Thus, for a cost-effective long-term investment, it can be deduced that the selection of the PHES-based microgrid has a better economic prospect compared to BESS-based configuration.

### *6.4. Techno-Economic Comparison for Each ESS Type Based on DRP Options at Maximum System Reliability (LPSP = 0%)*

In this section, different microgrid configurations based on the DRP options are evaluated based on the net investment cost for different ESS types. The prospect of each configuration in the long-term microgrid planning with the possibility of high renewable energy fraction is reflected in the net investment cost under each system configuration. Figure 9a,b shows the pareto front plots comparison, without and with DRPs, for BESS and PHES-based microgrid design, respectively.

**Figure 9.** (**<sup>a</sup>**,**b**) shows the Pareto front plots comparison for BESS and PHES-based microgrid design respectively, based on the DRP flexibility options.

Table 7 summarizes the investment cost under each configuration and flexibility options. The reference cases are the ones without DRP consideration (case 1) and the cost implication of introducing different types of DRP for each ESS-type microgrid are duly analyzed in terms of percentage cost reduction.


**Table 7.** Techno-economic analysis for each ESS type based on the DRP flexibility options.

For the BESS-based microgrid, introducing CPP DRP results in a cost saving of 0.53% of the investment from US \$ 1.38 × 10<sup>7</sup> (without DRP) to 1.37 × 107; this cost saving is as a result of 21.59% and 5.33% reduction in the PV and BESS component sizes, respectively. This is because CPP DRP decreased the load demand and consequentially, the BESS dependency during the peak demand periods. For the PHES-based microgrid, the introduction of CPP DRP results in 0.44% cost reduction from US \$ 9.06 × 10<sup>6</sup> to US \$ 9.02 × 106. The cost-benefit is because of the 5.8% and 0.14% capacity size reduction of WT and PHES, respectively, and an increase of 7.4% PV capacity. For the two cases, It should be noted that there is a decrease in the investment costs as the CPP DRP shifts the FDR to off-peak from the peak period of the system and ensure a more flattened load profile and prevent sub-optimal capacity sizing.

The potential superiority of TADP DRP over CPP DRP for microgrid design for high renewable energy penetration can be seen in the cost–benefits illustrated in Table 7. The inclusion of the TADP DRP in the BESS-based system resulted in 7.2% cost saving in the total planning costs. The planning cost reduction is due to a decrease of 17.58% and 2.5% for BESS and WT respectively with a slight increase of 0.11% in PV component size. Similarly, this trend is noted for PHES-based system with a total cost reduction of 1.48% resulting from 3.98% and 8.69% decrease in PHES and WT capacities, respectively. However, this results in an increase PV capacity of about 9.36%. Figure 10 illustrates the role and impact of DRPs in minimizing the gap between total generated RES power and load demand profiles. The prospects of TADP DRP over CPP DRP to reduce the mismatch between the load and the RES generated power profiles has been vividly portrayed by significant optimum component size reduction and hence the TPC minimization to realize the techno-economic benefit of a microgrid.

**Figure 10.** Role and impact of DRPs in minimizing the gap between total generated VRES power and load demand profiles.

Therefore, for the two system and types of DRP investigated, it can be inferred that the application of TADP DRP is more investment-worthy compared to the CPP DRP. TADP DRP short-term flexibility option takes into account the varying generation profile of WT and PV from the forecasting results; thus, the reason for its robustness.
