**6. Optimization Results**

Based on Figure 8, on average 25% of total energy savings can be achieved on a monthly basis which can be used to calculate the Return on Investment (ROI). GA optimization results on MDRed modelling revealed that optimal sizing of solar PV-battery system contributed to energy bill savings up to 20% of net consumption via solar PV self-consumption, 3% of maximum demand (MD) via MD shaving and 2% of surplus power supplied to grid via net energy metering (NEM) in regards to Malaysian electricity tariff scheme and cost of overall system.

**Figure 8.** Percentage of energy savings based on load profile.

GA optimization results from 10 population sizes and via 300 iterations are shown in Figure 9. In this paper, GA optimization studies is based on 21-years of load pattern assumption to cater for optimal sizing of solar PV-battery system. Since all actual MDs are captured during peak hours between 8.30 a.m. and 10.00 a.m., it does not show huge differences in solar PV-battery optimal sizing. Therefore, the total energy savings achieved in the range of 23% to 26% with the use of solar PV-battery system. However, the differences in optimal sizing are influenced by the load margin in between maximum demand and average load consumed during peak hours throughout the month.

**Figure 9.** GA optimization results of difference load profiles: (**a**) Jan-17 [Actual MD: 1260 kW], (**b**) Feb-17 [Actual MD: 1300 kW], (**c**) Mar-17 [Actual MD: 1220 kW] and (**d**) Apr-17 [Actual MD: 1290 kW].

The surplus energy savings under Net Energy Metering (NEM) achieved at minimal level due to low NEM billing rate compared to net consumption rate. The commercial sector may experience fluctuating load pattern with di fferent ranges of maximum demand. Therefore, the optimal solar PV-battery sizing at various load pattern falls in between 870 kWp and 970 kWp of solar PV and 230 kWh up to 330 kWh of total battery capacity. In addition, the new MD limit has been achieved in the range 1025 kW and 1098 kW of MD reduction using solar PV-battery system.

Load profile of the system influence the MDRed modelling optimal sizing on the solar PV-battery system using a GA algorithm. The load profile of the system influences the MDRed modelling optimal sizing on the solar PV-battery system using the GA algorithm. Based on Table 12, the month of March 2017 is proven to give the best optimal sizing for solar PV-battery system with highest percentage of total energy savings. This is due to the load profile changes especially the load margin in between maximum and average loading for every month. Based on Figure 10, load margin and maximum demand of March 2017 is lowest compared to other months which gives the significant impact on MD shaving compared to net consumption savings. Therefore, the acceptable sizing of solar PV and battery capacity will be 986 kWp and 330 kWh respectively. This will be the best choice to allow the MDRed model to work e fficiently at any load pattern to achieve high MD load reduction with yearly energy savings up to MYR 600,000.


 results for di

fferent load profiles.

optimization

**Table 12.** GA

**Figure 10.** Comparison data analysis of load profile.
