**2. Building energy demand model**

The residential load profile used for this work is generated from measured aggregate hourly consumption data for 12 apartments in a residential building in Columbus, Ohio [8]. The apartments are on the third floor of a three-story building, which means that they will have higher heating loads in the winter and cooling loads in the summer. This choice represents a worst-case scenario in terms of the peaks in the residential load. One year of hourly metered power use for these apartments is available, starting at 12 am on Sunday, June 9, 2013. These apartments use electricity for hot water, heating, and cooling. The hourly commercial load profile is synthesized from typical load profiles for commercial kitchens [9]. The average demand from the commercial load is selected to be nearly the same as the average demand from the 12 residential apartments.

#### **2.1. Historical residential demand**

**Figure 1** illustrates the weekly average for the aggregate residential load data. Each day of the week, there is a peak in the morning at about 8 am, representing the electricity consumption as residents prepare for the workday. At the end of the day, at about 8 pm, there is a larger peak as residents return home for dinner and other electricity-consuming activities.

To determine the temperature-dependent component of the residential data, a piecewise linear regression is used. Each week, aggregate residential consumption data are averaged to create 52 single values. The same is done for the temperature. **Figure 2** shows a plot of the average weekly power versus average weekly outdoor temperature. The fit shown in **Figure 2** has five parameters as follows: a heating slope (HS), cooling slope (CS), heating temperature (HT), cooling temperature (CT), and baseline (B) [8]. The baseline component defines the hourly expected weather independent demand. The heating and cooling slopes KW per Fahrenheit degree (kW/°F) enable respective prediction of the hourly heating and cooling demand for a typical weather year.

**Figure 1.** Weekly average aggregate residential load.

The threshold temperatures and heating/cooling slopes depend on many factors, such as building construction and size. Values for the temperature-dependent five-parameter model were calculated to be: *HS* = 0.16 kW/°F., *CS* = 0.12kW/F<sup>∘</sup> , *HT* = 57 F<sup>∘</sup> , and *CT* = 48 F<sup>∘</sup> .

For the data used in this study, an average yearly LF is found by averaging the 12 monthly LF values. **Figures 4** and **5** show the comparison of the monthly LF values for the three types of loads considered: residential, commercial, and combined. **Figure 4** shows the effect of combining the residential load with only the baseline commercial load. In this case, the load factor of the combined is between the residential and commercial load factors each month. **Figure 5**

Renewable Energy Microgrid Design for Shared Loads http://dx.doi.org/10.5772/intechopen.75980 5

**Figure 4.** Comparison of monthly load factors for the three loads with no weather-dependent component.

**Figure 5.** Comparison of monthly load factors for the three loads with weather-dependent component.

**Figure 3.** Weekly average commercial load (baseline).

#### **2.2. Commercial kitchen demand model**

**Figure 3** illustrates the weekly average baseline consumption for the commercial kitchen. The same basic profile shape is used for each day, scaled to represent the different amounts of customer traffic for each day of the week. The peak consumption occurs at 8 pm as the kitchen serves dinner, and there is also a peak at 1 pm for lunch. The kitchen uses power more consistently than the residential load through the afternoon hours.

#### **2.3. Controlling load profile characteristics**

In order to compare the three individual loads, the load factor (LF) is used. LF is defined as the ratio of the average per-month consumption to the peak hourly consumption for that month.

**Figure 2.** Temperature dependence for residential data, along with a five-parameter fit.

**Figure 3.** Weekly average commercial load (baseline).

The threshold temperatures and heating/cooling slopes depend on many factors, such as building construction and size. Values for the temperature-dependent five-parameter model

**Figure 3** illustrates the weekly average baseline consumption for the commercial kitchen. The same basic profile shape is used for each day, scaled to represent the different amounts of customer traffic for each day of the week. The peak consumption occurs at 8 pm as the kitchen serves dinner, and there is also a peak at 1 pm for lunch. The kitchen uses power more consis-

In order to compare the three individual loads, the load factor (LF) is used. LF is defined as the ratio of the average per-month consumption to the peak hourly consumption for that month.

, *HT* = 57 F<sup>∘</sup>

, and *CT* = 48 F<sup>∘</sup>

.

were calculated to be: *HS* = 0.16 kW/°F., *CS* = 0.12kW/F<sup>∘</sup>

tently than the residential load through the afternoon hours.

**Figure 2.** Temperature dependence for residential data, along with a five-parameter fit.

**2.2. Commercial kitchen demand model**

**Figure 1.** Weekly average aggregate residential load.

4 Smart Microgrids

**2.3. Controlling load profile characteristics**

For the data used in this study, an average yearly LF is found by averaging the 12 monthly LF values. **Figures 4** and **5** show the comparison of the monthly LF values for the three types of loads considered: residential, commercial, and combined. **Figure 4** shows the effect of combining the residential load with only the baseline commercial load. In this case, the load factor of the combined is between the residential and commercial load factors each month. **Figure 5**

**Figure 4.** Comparison of monthly load factors for the three loads with no weather-dependent component.

**Figure 5.** Comparison of monthly load factors for the three loads with weather-dependent component.

shows the effect of combining the residential load with the commercial load, where the weatherdependent load is included with the commercial load. The load factor behavior varies more in this case. The LF for the combined load is still between the LF for the residential and commercial loads, except for January, March, and April.
