## 2 Literature Review
[29] applied a GA, BPSO, and a Cuckoo search optimization algorithm (CSOA) to develop an optimal scheduling strategy for residential appliances in the presence of an ESS and RERs. The objective of the implemented idea was to reduce the consumers' electricity cost under a ToU tariff scheme and minimize the PAR. [PERSON] et al. [30] presented a model to solve an appliance scheduling problem by using the Dijkstra algorithm and reducing the system complexity. Integration of the ESS and RERs were not considered in their proposed model. The minimization of the consumption cost of electricity in a smart grid environment using an evolutionary technique was discussed in [PERSON] and [PERSON] [31] through the integration of an ESS. During a low electricity tariff time, energy was stored in the ESS and it was utilized during the peak-tariff time. Charging and discharging limits of the battery per time slot were also defined by the authors to avoid battery damage. However, the authors did not consider the installation cost of the storage system. An innovative technique was proposed in [PERSON] and [PERSON] [32] to keep the supply demand balance in residential, commercial, and industrial areas. The authors compared the electrical consumption of different residential consumer's datasets via a GA-assisted DSM and a simple DSM. Simulation results demonstrated that a DSM with a GA (DSM-GA) gave better results than a DSM without GA. The PAR and consumer comfort were not considered by the authors in their proposed model. [PERSON] et al. presented a strategy for residential load management with the integration of RERs [33]. Their proposed scheme used a GA to optimally schedule the load demand using a day-ahead pricing (DAP) tariff to meet the cost-minimization and PAR-reduction objectives. [PERSON] and [PERSON] [34] proposed a novel scheduling mechanism for home appliances to reduce the consumption costs and the PAR. The scheduling model is implemented in inclined block rate (IBR) and real-time pricing schemes to avoid a peak demand during low-tariff times, which would improve the PAR index as well. A non-dominated sorting genetic algorithm (NSGA) was adopted to solve the optimization problem in a MATLAB simulation environment. [PERSON] et al. [35] proposed a scheduling technique for minimizing the residential consumer electrical energy consumption and reducing the PAR with the help of the heuristic techniques of a GA, CSOA, and a Crow search algorithm (CSA) under RTP and CPP tariff schemes. However, the integration of RERs was not considered in their proposed model. [PERSON] et al. [36] formulated a scheduling problem for residential consumer home appliances using a GA, BPSO, wind-driven optimization (WDO), and hybrid framework of a GA and WDO. The minimization of the electricity consumption cost and the decrease in the PAR were the main objectives in the RTP and inclined block rate (IBR) tariff schemes. The simulation results demonstrated that the above-mentioned methodology achieved the defined objectives in an effective way. However, in the proposed scheme, the authors did not consider the H2G energy exchange environment. [PERSON] and [PERSON] [37] developed a scheduling scheme for residential consumer home appliances using convex programming (CP) to minimize the electricity consumption cost and decrease in the PAR. In this model, the RTP tariff scheme was used for the calculation of the electricity consumption cost. It can be concluded from the results that the proposed model is able to effectively achieve the objectives. To maintain the balance between the residential electrical energy demand and supply, a fuzzy logic (FL)-based model is presented in [PERSON] et al. [38]. An ESS and PV system are also integrated in their scheduling scheme. The proposed scheme has the ability to schedule the home appliances, ESS, and PV in an optimal way. [PERSON] et al.