## 2 Related Works
### Multi-User Load Management Systems  
Other contributions have been made regarding the multi-user load management context for only two objectives. In [12], the authors presented a multi-objective problem, which is determined as a mixed integer linear programming (MILP). The objective of this work is to provide an integration between an aggregator and demand response program to minimize the overall electricity cost. This led to enhancing the efficiency and supports an economic structure regarding the smart residential distribution context. The simulation outcomes showed that the peak-to-average ratio (PAR) is minimized from 2.6 to 2.22. The authors of [13] suggested an implicit demand flexibility paradigm regarding residential clients within practically high electricity prices during winter in Norway. The paper developed an econometric technique, which compares pre-crisis levels with demand. This has been achieved through managing some factors that impact electricity consumption. These factors are like outdoor temperature besides using an extensive data set, which maintains an hourly-based electricity demand statistic. The analyses indicate energy saving that reaches 11.4% while the peak hour minimization approaches 10.4%. In [17], the authors proposed a centralized DR scheduling model that utilizes fuzzy clustering in order to determine the heterogeneity of residents' energy consumption. The proposed demand response scheduling model managed to minimize the energy cost by 10%. This helps to realize a low peaking-to-average ratio and manages to provide a satisfaction level for numerous residents' power demands. Other contributions have considered the multi-user concept for more than two objectives. In [14], a load scheduling model regarding a domestic area is presented, which addresses end-users' preferences. The target of the proposed model is to minimize three objectives: peak load demand, energy cost, and end-user inconvenience. According to those three objectives, both end-users and utility companies will be able to change their reduction priority for one over another. Three multi-objective optimization models in this work have been applied, which are the normalized weighted-sum approach, compromise optimization, and preemptive optimization. The simulation analyses showed an enhanced performance when compared to three related approaches in the literature. A combination between the honey badger algorithm (HBA) and Mexican axololotl optimization (MAO) has been realized in [18]. The proposed hybrid approach is an energy management model that optimizes user energy consumption behavior to reduce each of the following: electricity costs, peak power consumption, and carbon emissions. The results indicate a carbon emissions minimization to 1.8 pounds and realize a minimum cost at 310 cents. In addition, it managed to gain a high load classification accuracy of 98.2%. The authors of [15] developed a triple-objective load scheduling optimization problem (LSOP) that supports customer inconvenience, peak demand management, and optimal cost. According to the multi-objective context, a Henry gas solubility optimization (HGSO) model is utilized to solve LSOP. Multi-criteria decision making (MCDM) is used to sort the sets of optimal solutions. To compute the weights, an entropy weighting method (EWM) has been adapted and is based on multiple criteria that dominate LSOP. The sorted solutions are ranked depending on the technique for order of preference by similarity to ideal solution (TOPSIS). The provided model acquires an energy cost saving of 5.5%. Additionally, a 9.7% reduction to the peak demand is realized while non-cooperative users have turned into cooperatives. A combined demand-side operational model has been studied in [16], which addresses five residential houses in South Africa. The study is based on two parts; the first one presents a power management scheme that composes a demand-side management scheme. The second part aims to reduce the client's cost and minimizes the energy consumption regarding the grid side.