## 2 Related Work  
In the literature, the demand response (DR) and incline block tariff (IBT) concepts [24; 25] considering DGs are introduced for energy management (EM) via shifting and curtailing loads during peak hours to avoid extra economic costs [26; 27; 28]. However, before EM load forecasting, human-factors identification in driving, and transmission resilience enhancement are indispensable [29; 30; 31]. Similarly, optimization algorithms are presented for EM problems in [32; 33; 34; 35]. The authors used a heuristic forward-backward algorithm for optimizing thermal appliances to curtail peak energy consumption [36; 37; 38; 39]. However, measurement applications and transmission system faults [40; 41] are ignored. The genetic algorithm (GA) is used to solve EM problems for cost and discomfort minimization [42; 43]. An ESS is integrated with photovoltaic (PV) to solve the EM problem in [44; 45]. Likewise, an optimization strategy is introduced for scheduling appliances to optimize the operational cost and peak-to-average demand ratio (PADR) [46]. Moreover, the power usage scheduling problem is solved by engaging consumers from commercial and residential areas [47]. The EM considering DGs is performed via a particle swarm optimization technique to reduce the PADR and cost [48; 49; 50]. However, vehicle driving modes and charging/discharging scheduling are not addressed [51; 52; 53; 54]. In [55], the power usage problem is solved using real-time pricing and IBT for peak energy consumption and user discomfort minimization. An automation system is introduced to improve buildings' efficiency, allowing consumers to control and monitor consumption in [56; 57]. A literature review pertinent to the topic is conducted, and some existing literature studies are discussed in detail. For example, in research studies [58], authors proposed a multi-integer linear programming technique for PADR reduction. The results illustrate that the PADR is reduced by 49%, and the net energy cost is reduced by 45% compared to the base case. A demand-side management (DSM) strategy based on GA, binary particle swarm optimization (BPSO), WDO, and GPSO is developed in [59]. The results illustrate that the electricity bills and PADR are reduced by 21.55% and 19.94%, respectively. However, the carbon emission reduction is ignored [60]. The authors developed a model to solve an ongoing pollution routing problem and environment polarization characteristics in [61; 62]. The electricity cost and PADR with the user's discomfort minimization are solved using the Nash game theory-based optimization model in [63; 64; 65]. The results illustrate that the electricity cost is reduced by 9.1% in the summer and 9.68% in the winter, respectively. Moreover, the PADR is reduced by 1.76% and 1.81% in the summer and winter seasons, respectively. However, an essential parameter, carbon emission, is ignored in this study. The ECS based on various optimization techniques such as GA, BPSO, WDO, etc., is presented in [66; 67], which shows better results in terms of PADR and electricity cost reduction. The findings reveal that the electricity cost is reduced by 36% and PADR is reduced by 34%, respectively. However, the authors ignored user comfort and carbon emission, which are interdependent objectives [68; 69; 70; 71; 72]. In our earlier works, enhanced differential evolution (EDE), and a hybrid algorithm of EDE and GA, namely EDGE, are developed in [73; 74], respectively, to solve load scheduling problems in RE-integrated SPG. The developed model's performance is evaluated by comparing with BPSO, GA, WDO, and DE in perspectives of the utility bill, PADR, carbon emission, and user discomfort minimization. Likewise, in [75], the authors developed ACO to solve the DSM problem optimally using a flat pricing scheme (FPS) in SPG. The developed model outperforms MILP and W/O scheduling cases in the utility bill, carbon emission, and PADR alleviation perspectives.