Group-based scheduling is a method applied when schedulable loads in several households within a community are controlled simultaneously based on prevailing energy demand and the requirements to shed load usually during peak demand. The household would usually receive a request or command to suspend the use of such appliances if the grid is under pressure and all householders who signed up to such schedule would have any identified load for such scheme temporarily disconnected from use. There are incentives to encourage users to participate and this section will discuss various applications of such methods in load scheduling.
Instances where group-based scheduling was implemented are presented in [67-72]. In [67] large groups of electrical loads with similar control characteristics can be controlled as a single entity. Thermostatically-controlled loads such as air conditioners, fridges and heaters are very good candidates for this exercise. According to the paper, the aim was to develop a mathematical model based on feedback control strategy whereby the aggregate power response of a population of a particular set of loads such as air conditioners, were characterised by a simultaneous step change in temperature set points and the off-set changes were then broadcast. These approximations are
thereafter used to simulate the dynamics of 10,000 air conditioners over a range of parameter values and then analysed. Result showed that aggregate power output of the loads can be controlled to reduce demand over a period of time.
Group-based load scheduling can also be of practical application within a community whose load network operates as a Micro Grid. In [68] the authors developed an online adaptive electricity scheduling algorithm for a community using Lyapunov optimisation method where residents were able to classify electricity demand into basic usage and quality usage, for load scheduling purposes. Customers were also allowed to set their load priority and preference according to their choices and while basic usage supply is uninterrupted, there will be permitted outages of loads classified as quality usage loads [69]. The micro grid control centre aimed to minimise operation cost by upholding outage- probability-of-quality-usage in order to reduce peak demand and evaluate savings available. This technique is described as quality-of-service in electricity [68]. With localised cluster of distributed renewable energy sources (DRERs) provided by the micro grid, the aim is to obtain a balance of electricity demand and supply, which is essential in micro grid management [70, 71].
The authors in [46] applied appliance scheduling for home EMS using distributed algorithm, whereby each user requires only the knowledge of the price of electricity to participate. This price depends on the aggregated loads of other users and not the load profiles of individual users. With the knowledge of the price of electricity in advance (day-ahead pricing strategy), consumers can adjust their load schedule according to the prices with the help of Energy
Management Controller (EMC) and Programmable Logic Controller (PLC) [47, 48, 54-56]. In this scenario, the EMC and PLC are able to identify the times when energy costs are lower based on the price forecast, and if the energy demand within the community is higher than a chosen threshold, dedicated loads as identified by the consumers cannot be turned ON is they are scheduled to be in use. Result showed a convergence with the help of a penalty term that penalised large changes in the user schedule between iterations.
Finally, the authors in [72] described the possibility of achieving a joint scheduling for home appliances, EVs as well as DERs such as wind turbines and photovoltaic cells within in a smart micro grid. They proposed a centralised scheduling method to control electricity consumption of all the EVs within the community and other household appliances depending on the amount of energy available for supply at various times in the day. Addition of storage facilities usually helps to sustain the grid especially at night times when the EVs are not able to supply energy thereby supporting energy supply from wind turbine. The problem was formulated as an MILP problem and the result showed a better management of electricity consumption by shifting loads from high demand periods to low demand periods thereby maintaining both load consumption and energy supply regulation.
In conclusion, several methods of load scheduling have been discussed in this section. In some instances, a third party was responsible for initiating a group- based control signal dissemination which thereafter, affects every appliance connected under the scheme designed for scheduling purposes. One obvious
disadvantage of group-based scheduling is an increased discomfort to consumers because the primary decision making about appliance use and scheduling is made by a third party and the timing of the scheduling events may not be favourable to all the customers who signed up for this events, at all times. This therefore casts a shadow over the long-time application of group- based scheduling while proposing that the future of scheduling of domestic appliances in smart grid is expected to be developed around implementation of scheduling programs using localized appliance control per household.
On the other hand, the major advantage of group-based scheduling over individually-based control is the relative ease of implementation since the burden of decision making lies with an Independent System Operator (ISO) who acts as a third party, and can be more easily deployed rather than trusting the participation and responses from individual householders. The next section is a review of various authors’ contributions on the various algorithms used in performing load scheduling for more active participation in DR programs.