According to given data, the ON or OFF state for each device d∈D with respect to time slots and days is given in Figure 1. From Figure 1, we analyze that device 1 is ON in each time slot in 10 days. For device 2 it can be seen that it is ON for all days in time slot 18 and for 7 days in time slots 12, 15 and 21. Device 2 is also ON for 4 days in time slots 6 and 9 and for 3 days in time slots 5, 7, 8, 10, 12, 13, 19 and 22, while at time slots 1, 2, 3, 4, 11 it remain OFF. In this way we check the device ON and OFF status in each time slot. Similarly, the ON and OFF status of all other devices can also be known easily by analyzing the Figure 1.

Figure 1.

ON/OFF status of household load with respect to time slots and days

In device 3, it is seen that it remain ON for all 10 days in time slots 7, 8, 9, 15, 19, 20, 21 and 22 and 7 days in time slots 6, 16, 18, 23 and 24. Device 3 is also ON for 4 days in time slots 10 and 17 and for 3 days in time slots 1, and 14, while at time slots 2, 3, 4, 11, 12 and 13 it remain OFF. In device 4, it is seen that it remain ON for all 10 days in time slot 18 and for 4 days in time slot 6. It is seen that it remain ON for 3 days in time slots 7, 19 and 23 while except these time slots it remain OFF. Similarly, in case of device 5, it is seen that it remain ON for 7 days in time slot 18 and for 4 days in time slot 6. It is seen that it remain ON for 3 days in time slots 7, 19 and 23, while in remaining time slots it remain OFF.

According to predefined variable $Y_{t_s}^d=\{0,\text{1}\}$, the state of ON or OFF and operation of overall load in given 240 time slots for given real time data 10 days is defined. Figure 2 tells us the overall frequency profile (on status) of each appliance for our monitoring system for all 240 time slots. We have 240 time slots in 10 days. From Figure 2, we can easily analyze that the device 1 is ON in all time slots, device 3 is ON in 130 time slots in 10 days, and device 5 is ON in 120 time slots.

Figure 2.

Devices frequency

Device 4 is ON for minimum time slots, i.e. 20 time slots for 10 days. In this way we check each device frequency for specific period of time in given days. And then estimate the overall household consumption for real time data and vice versa.

Let us denote that the whole intended operation period for all devices is T. We are going to calculate the power consumption of each device in their intended operation time slot cycle, i.e. t_s∈T. For that purpose we categorize the given household residential load into several classes that are given below and have been discussed in previous section as well:

• Must run load;

• Interruptible load;

• Non-interruptible load.

After categorizing the overall household load, we consider a system without CCU deployment, where each device d is assumed to start operation right after it becomes awake at its nominal power (α_d).

Figure 3 shows the variation in electrical demand over particular period of time. In Figure 3 we can see that wattage consumed curve of device 1 is a straight line because it remains on for all 240 time slots and does not show the variation in demand for a specific time due to must run property.

Figure 3.

Power consumption profile of household load

Similarly, we can see in second interruptible device that it consumes maximum power of 7,500 W in time slot 18 for whole period of 10 days and consume 5,500 W for time slots 12, 15 and 21. Device 2 consumes 3,000 W in time slots.

We note that the operation of different devices is influences by the preference of user. Different parameters of our model may be considered to capture different types of preference. In time differentiating pricing tariffs, the intended operation period is divided into several time slots, and the price of electricity varies across different time slots. For example, the prices may correspond to off peak, mid peak and on peak hours. We have different rates for energy consumption in RTP. In RTP, rates are defined at different levels of time slots, i.e. off peak hours where the energy rates are kept low because consumption is low and on peak hours where the rates are kept high because consumption increased during that time slots.

For above profiles the intended period of operation is divided into peak time and off peak time. Peak hours from 5 PM to 10 PM and the off peak or rest hours for the day are off peak hours (12 AM to 4 PM and 11 PM). According to RTP, the prices varies with time. We defined the prices, i.e. 13 PKR for peak time and 7 PKR for off peak time. According to that data we calculate the overall cost of all devices. From Figure 4, we can see that each device is consuming different power so the energy cost for each device is different. The device which has greater energy consumption bill in PKR represents that it starts operation many times during peak time slots because during peak time slots energy rates are high, i.e. 13 PKR per kWh.

Figure 4.

Bill calculation when customer is informed by utility with respect to RTP

So the total energy cost after summing energy cost of all devices is 3,269.65 PKR that is great amount by the way due to lack of energy CCU with our system. After calculating overall cost of consumption of all the devices in given 240 time slots (off peak and peak time), we move forward on the load estimation of our system. In our load estimation section we define how the load damages the overall system performance as well as in economical point of view of consumer. We discuss how we shift the load from peak time slots to off peak time to give incentive in economical point of view to the consumer and best for the community growth and infrastructure.

Figure 5 explains the load curve for all devices collectively. In this way, we easily analyze the electrical demand in each time slot. This figure also shows the maximum and minimum electrical demand over a specific time for all the devices collectively. In figure we can see that the load is maximum at peak time slots and minimum at off peak time slots of total slots. As the consumption increases the calculated load increases, as a result PAR of a system increases and damage the system’s performance.

Figure 5.

Histogram of PAR when only RTP is applied without CCU

Recall from eq. (3), PAR is the ratio of the maximum aggregated load consumed in a certain time frame and the average of the aggregated load. PAR informs about the energy consumption behaviour of the consumers and the operation of the power grid. In Figure 5 we can see that the load is consuming more power at time 18, 19, 20 and 21 during peak time slots (5 PM to 10 PM), where energy tariffs rates are high, which is not better for economical point of view for consumer. During these time slots the PAR is high which overload the electrical grid system and its performance is slowed down. Our aim is to minimize PAR to improve system performance to run smoothly. We also aim to shift the peak load to off peak time slots because the rates during off peak time slots are very low as compared to peak time slots to make economical use of energy in smart grid system.

When CCU is deployed with respect to RTP at demand side, the result in the form of DSM minimize the energy cost for residential user.

When DSM is done through RTP, results in reduction in cost, but increases PAR, which damages the grid stability. For this purpose a combine model of RTP and IBR is implemented.

Figure 6 is a block diagram of basic DSM scheme. From Figure 6 it is clear that CCU does not have any type of physical connection with must run load. It only schedules the controllable (interruptible and non-interruptible) household load according to the circumstances.

Figure 6.

DSM through CCU

By deploying CCU, the load at the peak time slots, mainly 5 PM to 10 PM are shifted to off peak time slots to reduce the energy cost for residential users. Thus, we formulate a scheduling problem that minimizes the expected energy payment of the user with respect to demand uncertainties. In each time slot t_s as the demand information of the device is updated, the operation schedule of each controllable device can be rescheduled and the optimum power scheduling can be identified in real time as the solution of the following optimization problem for the minimization of expected cost from the current time slot t_s. In Figure 8 we can see that by deploying CCU energy, the energy is minimized, i.e. 2,771 PKR.

Figure 8.

Cost minimization by deployment of CCU

When RTP is charged during peak time when the demands of energy are usually high, PAR of a system has been disturbed which obviously disturb the overall system’s performance. To overcome this drawback and increase PAR, a combined model of RTP and IBR is implemented. According to Islamabad Electric Supply Company (IESCO), Pakistan, IBR are according to [33] as in Table 3.

Let the demand information of the device is known ahead of time and awakes devices in the upcoming time slots k>t_s that are currently sleeping, only the probability with each device becomes awake at each time slot before the operation cycle begins:

${\displaystyle \sum _{t_s=1}^T{p}_{t_s}^d+q_d}$ (5)

where q_d denotes the probability that device d does not become ON at any time within the DSM operation period.

In Figure 9, we can see that after deployment of CCU energy, the bar line at peak time slots is shifted to off peak time slots. When the load from more energy consumption slots is shifted to less and reliable slots, then we have reduction in PAR of overall aggregated load which improves the overall system’s performance.

Figure 9.

Reduction of PAR and shifting of pea values by using RTP + IBR