*3.1. Financial Model*

### 3.1.1. Electricity Tari ff Model

Most electricity tari ff categories for commercial and industrial sectors are designed to encourage customers to control their electricity demand at daytime peaks since MD charges are very high. Referring to Table 1, the electricity tari ff model is specified in Table 3 for net consumption and maximum demand calculation according to electricity tari ff categories on commercial and industrial sectors.


The total electricity bill (Cbill) for commercial and industrial customers as calculated as follows [6]:

$$\mathbf{C\_{MD\\_kW}} = \mathbf{E\_{MD\\_kW}} \ast \mathbf{P\_{MD}} \tag{1}$$

$$\mathbf{C\_{net\\_kWh}} = \mathbf{E\_{load\\_net}} \mathbf{\*} \mathbf{P\_{load\\_net}} \tag{2}$$

$$\text{C}\_{\text{bill}} = \int \text{P}\_{\text{load\\_net}} \, ^\ast \text{E}\_{\text{load\\_net}} + \int \text{P}\_{\text{MD}} \, ^\ast \text{E}\_{\text{MD}} \tag{3}$$

where the combination of maximum demand bill (CMD\_kW) in Equation (1) and net consumption bill (Cnet\_kWh) in Equation (2) will be added on a monthly basis. Equations (1)–(3) are formulated based on the Malaysian electricity tari ff category for the commercial and industrial sector. Based on Table 4 and Figure 3, a Malaysian higher learning institution's load profile under C1 electricity tari ff is chosen for the case study. Since the case studies is focusing on solar PV-battery sizing based on maximum demand (MD) reduction approach, the concept can be explained using one (1) type of load profile from any commercial or industrial sector. In this paper, C1 category load profile for four (4) consecutive months from January 2017 until April 2017 is used for validation studies. Based on energy data evaluation, highest load or maximum demand is recorded at 1300 kW during beginning of academic calendar for the higher learning institution. Therefore, four (4) months of load profile data is su fficient to cater for optimal sizing of solar PV and battery system based on the Malaysian electricity tari ff.

**Figure 3.** Energy consumption for C1 category commercial sector.

The MD is recorded between 1200 kW and 1300 kW during peak hours in between 8.30 a.m. and 10.30 a.m. Figure 3 shows the monthly net consumption and maximum demand recorded in electricity billing of the commercial building. Table 4 shows the maximum demand recorded throughout the months during peak hours only. It can be observed that the highest MD occurs in the month of February 2017 at 1300 kW. Besides that, the highest load has been consumed in between 8.30 a.m. and 10.00 a.m. for first four months in the year 2017. These data will be used in a MATLAB (2016a, The Mathworks, Natick, MA, USA) GA optimization algorithm for solar PV and battery sizing. The weather condition in Malaysia is suitable for solar PV generation. Based on Figure 4, the weather condition is almost predictable with intermittent sunlight from 8.30 a.m. until 5.30 p.m. The peak PV generation is normally achieved at 1000 <sup>W</sup>/m<sup>2</sup> and with solar PV module temperature and ambient temperature reaching 50 ◦C and 33 ◦C, respectively.

**Figure 4.** Actual solar irradiance measurement data at Nilai, Negeri Sembilan, Malaysia.


**Table 4.** Maximum demand captured during peak hours.
