## 6 Proposed Schemes
### Multi-Verge Optimizer (MVO)
### The Total Electricity Cost

Figure 8 shows the electricity cost for the unscheduled and scheduled (with MVO, SCA, GA, and ACO) loads. The results show that the electricity cost of the meta-heuristic algorithm-based scheduled load is very low compared to the unscheduled load cost. In Figure 8a, the operation of morning session of the campus for a single day is considered. In this case, MVO-based scheduling shows better results compared to all scheduled and unscheduled costs. Similarly, in Figure 8b the operation of evening session of the campus for a single day is considered. In this case, MVO has reduced it compared to all scheduling techniques, except GA.

Figure 6: Hourly load. Figure 7: Hourly consumed energy price. ### Par

Figure 9 shows PAR results, which show the stability of a grid. When the PAR value increases or decreases, it affects the stability of a grid. Due to the reduction in cost, PAR is not reduced. MVO-scheduled load reduces the PAR, as for an unscheduled load, all scheduling techniques considered in this work do. The figure also depicts a comparison of the proposed algorithm's PAR with the most modernistic optimization algorithms, such as GA and ACO. However, using the same scenario, the MVO-scheduled load PAR is the least out of all the algorithms. ### Waiting Time

Figure 10 shows the average waiting times of machines for each technique. It is that time interval wherein a consumer switches on an appliance, but due to scheduling for the reduction of cost, the appliance does not start. Thus, the consumer has to wait for a specific amount of time \(T_{w}\). The figure shows that the waiting time of MVO is very much less compared to SCA. It shows that MVO has not reduced the cost too much; however, it has far less waiting time in the morning session of the campus load. In case of evening session, MVO is not too good. In this case, SCA showed better results compared to all techniques. Thus, for those consumers who want to reduce their waiting time, SCA-scheduling is good. Figure 10 also depicts a comparison of the proposed algorithms with GA and ACO algorithms. SCA gives better results in the case of waiting time. \[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{22}\]0 Total cost for un-scheduled load and scheduled with MVO, SCA, GA and ACO algorithms. (**a**) Total cost for morning session of the campus for 1 day. (**b**) Total cost for evening session of the campus for 1 day. \[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{22}\]1 Peak to average ratio (PAR). ### The Total Daily Average Load

Figure 11 shows that the total daily average load is the same in the case of unscheduled and scheduled with different algorithms. It is clear from the figure that, irrespective of the scheduling algorithms, the total daily load remains the same. Scheduling algorithms only shift the load to low cost or low demand hours; however, they do not reduce the total daily load of the campus. ### Hourly Power Demand with and without RES

Figure 12 depicts the unscheduled load curve along with the generated RES and battery bank charging and discharging. It is clear from the figure that the extra stored energy is used during night time to further reduce the electricity cost. Table 2 depicts a comparison of the proposed algorithms with the unscheduled load in terms of minimization of consumed energy price, PAR and average user wait time. A comparison of the proposed algorithms with recently applied algorithms like GA and ACO algorithms is also shown in the table. \[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{22}\]2 The total daily average load. \[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{22}\]3 Unscheduled load with renewable energy sources and the battery bank. \[TDR=1-\frac{l^{1/p}}{L^{1/p}} \tag{22}\]4 Waiting time. ### Feasible Regions

A feasible region is a set of all possible points. Due to the scheduling of the load, cost minimization is decided on the bases of the pricing signal issued by the utility, using different algorithms. We have considered the DAP signal for our calculations. Figure 12(a) shows the feasible region for the MVO algorithm.