Facilitación del comercio

The proposed design architecture for automated domestic EMS consists of some set of input data usually generated from the home area as well as data from energy retailers which are thereafter processed to produce some desired output. The goal is for an enhanced interaction among the key players in assisting consumers to participate more effectively in DR programs whereby the key outcomes that are effectively managed include the discomfort associated with participation in DR programs. Other output data generated includes forecast financial savings available as well as the generated load profile based on the load scheduler results in order to achieve maximum benefits.

Figure 3.2 is the proposed design architecture of a smart home whereby the key components are clearly identified as: Input Data, Output Data, Peripherals, Central Controller, Retailers, Smart Meter and Grid Supply. The Input data set is made up of demand load profile which is user-behaviour based; pricing information which is generated by the retailer as well as customer’s interaction which is conveyed via a keyboard system. The customer’s interaction contains a user-override capabilities as well as the household occupancy profile which

must be keyed in manually over all intervals required during the day. Alternatively data such as the occupancy profile can be derived by the use of sensors within the house especially at the entrance or exit point, which are capable of counting the number of people within the house at regular intervals.

Figure 3.2: Design Architecture

The energy demand load profile information is obtained using data extraction devices such as smart plugs or appliances and connected to the smart meter, all connected as IOT. The meter measures the energy consumed at regular intervals thereby generating the user’s energy demand load profile which is passed on as one of the inputs to the central controller. User interaction data can be supplied using a localized keypad or maybe from a mobile device while the pricing information can be acquired from the retailer’s database.

Central Controller: This is the heart of the system design that runs the algorithm which performs the required load scheduling. Microcontrollers are the ideal possible device to be used to achieve this control although the actual application was not implemented. Other popular controllers used in the industry includes Proportional Integral (PI) and Proportional Integral Derivative (PID), but they are more suited in process-type applications hence not used here.

The central controller block also contains storage and forecasting capabilities necessary for assimilation of input data, decision making and task execution via necessary communication protocol. Several independent algorithms can be designed and installed on this unit, and they should be able to coordinate the events occurring within the smart home by synchronising their individual activities with one another to achieve a particular aim. The highlight of this design is based on developing the functional algorithm for the controller. Stored data consists of recent pricing details as well as load profile data and the primary essence for this storage is to enhance load profile and price forecasting capabilities. Although there are various forecasting techniques available, in this work moving average forecasting technique was exclusively used whereby adequate priority is given to the most recent data available for the respective quantities being forecasted. During the load scheduling process, the algorithm ensures that the user’s specific characteristics and requests are met especially with regards to their accepted discomfort levels. In theory, consumers who accept higher discomfort levels tends to save more money than those that accept minimal discomfort levels. A no-discomfort means that the consumers are not participants in any form of DR activities hence, no financial savings

available to them. Finally, the central controller also computes the user participation levels which can be forwarded via the bi-directional communication link to the smart meter and then, to the retailers.

Controller Output: This consists of the operating times for the scheduled load which is then fed to the smart plug as a programmed time-of-day energy consumption. The schedule is also visible to the user who may decide to override schedules that they are not comfortable with. The accepted schedules then controls the loads as connected to the smart plugs while the actual real- time energy consumption details can be made available to the central controller for comparison with the forecasted load profile which may be applied to update the original schedule as a real-time scheduling algorithm. Although real-time load scheduling, is not covered here, but can be an interesting future work. Other output data includes forecast financial savings available, the discomfort level accepted per schedule as well as user participation or engagement levels and these are made accessible to the user via visual displays. The other key structures of the design and their respective functions are summarized further.

Smart Meter: This is the window to an enhanced interaction between the utility, the retailers as well as the smart home. Details of the daily load profile data measured by the smart meter is passed down to the central controller as well as to the retailer for billing purposes, while also allowing actual power supply to be made available to the rest of the domestic appliances via the distribution board and smart plugs.

Smart Plugs: This is the interface between the energy supplied and the respective loads. These plugs determine the switching patterns of respective loads in response to any scheduling command sent from the central controller to effect the load usage. It is essentially an effector whose basic functions include switching appliances ON and OFF while at the same time, being used to send vital load information based on consumption behaviour to the controller.

Communication Network: This is Important because the availability of adequate information is required in order to be able to co-ordinate the events happening both at the appliance side and the utility side. The network topology implemented could be star or mesh network and the devices that engage in these communications oftentimes talk to each other as much as they talk to the device directly above them in the communications hierarchy.

Figure 3.3 is basically a communication structure within the design architecture of Figure 3.2 and has been elaborated to show a hierarchical structure of the relationship between power supply routes and data transfer routes, in a vertical top-down format. In this design, the use of smart plug as data extraction devices was adopted per appliance because, apart from acting as a switching device to the appliances, it also offers a communication link between the appliances and the smart meter via Wi-Fi connection.