## 6 Proposed Schemes
### Multi-Verge Optimizer (MVO)
Point \(P_{1}\)(20.53, 387.2) gives the minimum load with minimum cost, and point \(P_{2}\)(20.53, 598.0) gives the minimum load with maximum cost in any interval of time. It usually happens when the minimum load is running in peak hours with high energy cost. Similarly, point \(P_{3}\)(71.96, 3297.0) gives the maximum load with maximum cost in the case of the unscheduled load. Point \(P_{4}\)(71.96, 1341.3) gives the maximum load during off peak hours with minimum cost. \(P_{5}\)(53.6, 2297.0) puts a threshold on the maximum cost after scheduling with MVO. It continues until point \(P_{6}\) (71.96, 2297.0) is reached, which gives a point of the maximum load with reduced cost. Figure 12(b) shows all these points for SCA scheduling. It is clear from these figures that SCA performs better compared to MVO in the case of cost minimization. Table 3 depicts the computational times of the proposed algorithms, MVO and SCA, using an Intel (R) Core (TM) i5 processor, with 4.00 GB of installed memory (RAM) and the 32-bit Windows 7 Operating system. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time** & **Techniques** & **Cost** & **Cost** & **PAR** & **PAR** & **Waiting Time** \\
**Sessions** & **Techniques** & **(S)** & **Reduction** & **PAR** & **Reduction** & **(h)** \\ \hline \multirow{4}{*}{Morning} & Un-scheduled & 101.32 & – & 6.49 & – & – \\  & MVO-scheduled & 88.47 & 12.68\% & 4.73 & 27.11\% & 1.01 \\ \cline{1-1}  & SCA-scheduled & 90.03 & 11.14\% & 4.73 & 27.11\% & 1.37 \\ \cline{1-1}  & GA-scheduled & 88.71 & 12.44\% & 5.52 & 14.94\% & 1.69 \\ \cline{1-1}  & ACO-scheduled & 93.24 & 07.97\% & 6.49 & 0.0\% & 1.22 \\ \hline \multirow{4}{*}{Evening} & Un-scheduled & 131.41 & – & 6.49 & – & – \\  & MVO-scheduled & 89.39 & 31.98\% & 4.73 & 27.11\% & 1.52 \\ \cline{1-1}  & SCA-scheduled & 102.16 & 22.26\% & 4.73 & 27.11\% & 1.18 \\ \cline{1-1}  & GA-scheduled & 77.53 & 41.00\% & 5.52 & 14.94\% & 1.42 \\ \cline{1-1}  & ACO-scheduled & 121.49 & 07.55\% & 6.49 & 0.0\% & 1.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An evaluation of the anticipated algorithm for appliance scheduling in terms of unscheduled load and scheduled load with MVO, SCA, GA and ACO algorithms. Figure 13: Feasible regions. (**a**) Feasible region for the MVO-scheduled load; (**b**) feasible region for the SCA-scheduled load. ## 8 Conclusions, Challenges and Future Work

Demand side management strategy for the energy optimization problem has been addressed in this work. The electrical load of the university is divided into various units of (electrical) load for which two bio-inspired algorithms, i.e., MVO and SCA, have been proposed and applied (practically). The function of these algorithms was to schedule the shiftable appliances as per the pricing (i.e., day ahead pricing) signal provided by the electricity suppliers. The performance of the proposed schemes was compared to the unscheduled load, which is judged on the basis of PAR, total energy consumption, cost and users' comfort (in terms of time to wait). Moreover the performances of well-known algorithms, i.e., GA and ACO, have been compared with our proposed solution and it could be seen from the results that our proposed algorithms efficiently minimized the price of energy consumption without disturbing their operation, via shifting of some load to certain low-demand hours. This in-turn reduces the PAR and maximizes user comfort, which causes in reduction of burden over the utilities. Due to the random number generation at the beginning, and the stochastic nature of the bio-inspired algorithms, every time the algorithm runs, it will give a new solution. Therefore, an infinite number of solutions is possible, as in each iteration, the algorithms look for local best, and then assign that best solution to the previous one achieved and store the global best solution if applied. Therefore, the performance of an algorithm is measured upon taking an average of more than ten (10) repetitions in different consumer scenarios.