## 1 Introduction  
Due to technology and population growth, the energy demand is growing rapidly worldwide [1]. Mostly, energy generation is fossil fuel-based, which is costly and produces almost 27% carbon emissions after transportation [2]. Therefore, distributed generators (DGs) are introduced in the power system for power generation to avoid such problems, which are dangerous for the environment and can lead to global warming [3; 4; 5; 6]. However, DGs, especially renewable DGs, are intermittent, and their generation is unstable [7; 8; 9]. To overcome the fluctuations and smooth out generation from DGs, the integration of energy storage systems (ESSs) and electric vehicle driving modes are needed [10; 11; 12; 13; 14]. This becomes possible with emanation of smart power grid (SPG), which combines the traditional grid and ICT [15]. The SPG accommodates renewable, distributed, and hybrid generations and enables a two-way power and communication flow [16].  
The SPG intelligently engages end-users and electric utility companies (EUCs) in electricity markets to exchange information and electricity actively in real-time [17]. The intelligent engagement of consumers and EUCs in the electricity market is beneficial for both consumers and EUCs because it provides cheaper, green, and sustainable energy [18; 19]. Recently, different optimization techniques have been introduced to ensure consumers' active participation in the electricity market for various objectives such as operational cost, pollution emission, availability of DGs, user comfort, etc. [20; 21; 22; 23].  
The frameworks mentioned above are vital for solving EM via optimal scheduling in SPG. However, each framework has its own characteristics and constraints, and is appropriate for specific problems, objectives, and assumptions. Additionally, no framework exists that is perfect in all aspects, and can simultaneously cater conflicting objectives. Thus, a model is needed that solves the EM problem and caters simultaneously conflicting objectives. In this regard, a system model is proposed based on a novel algorithm, a genetic wind-driven optimization (GWDO) algorithm, for EM problems. The developed model simultaneously caters to energy cost, PAR, carbon emissions, and discomfort minimization. Additionally, the generation of DGs is intermittent, making it more challenging to handle. Thus, ESS is integrated with DGs to overcome the problem of energy generation fluctuations and meet the need of the load smoothly. The consumer's load is classified as interruptible portable, uninterruptible portable, and has consistent applications for participating in the DR to solve the EM problem. For validation and applicability, the proposed GWDO-based ECS is compared with the existing heuristic algorithms such as GA, particle swarm optimization (PSO), bacterial foraging algorithm (BFA), ant colony optimization (ACO), and wind driven optimization (WDO) in aspects of solving energy problems. Due to architectural similarities, newly designed hybrid algorithms such as genetic particle swarm optimization (GPSO), genetic bacterial foraging algorithm (GBFA), and genetic ant colony optimization (GACO) are also used as benchmarks. The proposed system model simulations are carried out in two scenarios: in the first scenario, the home is not equipped with DGs, and in the second scenario, the home is equipped with DGs. The findings reveal that the proposed GWDO-based ECS outperforms the existing algorithms in solving EM problems.  
The remaining paper is arranged as: Related work is discussed in Section 2. In Section 3, the developed framework for electricity market EM is presented. The overall problem formulation and mathematical modeling are briefly discussed in Section 4. The simulation results for two scenarios are presented in Section 5. Finally, the paper is concluded in Section 6.