In this section, we present some simulation results to evaluate the system performance. We consider the power consumption of a house with a set of eleven electric appliances, where one is UC type, three are UM type, six are MN type, and the other is MD type. One hundred electric appliances are generated randomly from the set and inserted into different time slots during the simulation time. Table 5.1 lists the electric appliances set of our simulator. We set the instantaneous power threshold to 500 watt-second, and the simulation time is set to 168 hours (a week). In order to observe different scheduling results, we set the electric price is fluctuating every three hours as shown in Fig. 5.1.
We compare our iPM system with the non-schedule system in our iPM simulator to observe the power consumption and monetary cost. We define the power consumption as the accu-mulative power of the simulation time, and the monetary cost as the total cost of the power consumption.
The power consumption of our iPM system is similar to that of the non-schedule system as shown in Fig. 5.2. It is because all electric appliances are working well after scheduling.
Fig. 5.3 shows the instantaneous power during the simulation time. However, we find that the instantaneous power of our iPM system, with the three heuristic methods, is more evenly stable than that of the non-schedule system. Even though some power loads can not be shifted from the peak time, the power load would never exceed the power threshold after scheduling.
With regard to monetary cost, iPM can save more. Customers can decide which heuristic method they want, or they may use the default setting, choosing the lowest cost from these three heuristic methods. From Fig. 5.4, we can find that iPM can save 24.5% cost less than the non-schedule system.
Table 5.1: The electric appliance set
Type Execution time Deadline after start Power consumption
(minute) (minute) per watt-second
UC Uncontrollable Uncontrollable 30
UM 30 Uncontrollable 80
UM 40 Uncontrollable 60
UM 50 Uncontrollable 70
MN 50 360 70
MN 60 360 90
MN 70 360 45
MN 35 720 120
MN 20 720 120
MN 120 720 50
MD 110 720 50
45 720 35
0 0.5 1 1.5 2 2.5
0 20 40 60 80 100 120 140 160
Cost
Simulation time
Figure 5.1: The fluactuating prices during the simulation time.
0
Figure 5.2: The accumulative power of (a) Deadline heuristic, (b) Area heuristic, (c) Weight heuristic, and (d) incorporation.
0 20 40 60 80 100 120 140 160 180 200
Time (hour) Non-scheduleIncorpartion (a)(b)(c)
(d)
Figure5.3:Thepowerof(a)Deadlineheuristic,(b)Areaheuristic,(c)Weightheuristic,and(d)incorporationduringthesimulationtime.
21
0 100000 200000 300000 400000 500000 600000 700000
0 4 9 13 18 22 27 31 35 40 44 49 53 58 62 67 71 75 80 84 89 93 98 102 106 111 115 120 124 129 133 137 142 146 151 155 160 164
Cost
Time (hour) Non-schedule
Deadline Area Weight Incorpartion
Figure 5.4: The cost of power.
Chapter 6 Conclusions
In this paper, we propose an intelligent power scheduling system (iPM) based on pervasive meters. Each electric appliance connects to a wireless power meter. Through these wireless meters, the current power consumption of electric appliances can be transmitted to the control server. Our goal is to dynamically schedule the execution time of each electric appliance in a home to minimize the total monetary cost. Still, it poses several challenges: 1) user demand response, 2) load management, and 3) minimizing the monetary cost of electricity consumption.
Hence, we propose a power scheduling algorithm for smart usage of electric appliances in a home dynamically. We verify our results through simulations as well as a real prototype.
Specifically, we develop a power scheduling system based on the ZigBee Smart Energy Profile to monitor and schedule the usage of electric appliances.
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