Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid. However, system inputs like renewable energy generation process, conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort. Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics. The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs. The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS). Extensive simulations are conducted to validate the employed algorithm. Simulation results illustrate that the proposed algorithm performs real-time energy optimization and reduces the time average energy cost of 20.15% while meeting the user’s energy and comfort requirement.

A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO ₂ in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user-comfort (UC) improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in aspects of indoor air quality, average delay, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy cost, reduce carbon emission, mitigate peak loads, and improve UC.

With the smart grid development, the modern electricity market is reformatted, where residential consumers can actively participate in the demand response (DR) program to balance demand with generation. However, lack of user knowledge is a challenging issue in responding to DR incentive signals. Thus, an Energy Management Controller (EMC) emerged that automatically respond to DR signal and solve energy management problem. On this note, in this work, a hybrid algorithm of Enhanced Differential Evolution (EDE) and Genetic Algorithm (GA) is developed, namely EDGE. The EMC is programmed based with EDGE algorithm to automatically respond to DR signals to solve energy management problems via scheduling three types of household load: interruptible, non-interruptible, and hybrid. The EDGE algorithm has critical features of both algorithms (GA and EDE), enabling the EMC to generate an optimal schedule of household load to reduce energy expense, carbon emission, Peak to Average Ratio (PAR), and user discomfort. To validate the proposed EDGE algorithm, simulations are conducted compared to the existing algorithms like Binary Particle Swarm Optimization (BPSO), GA, Wind Driven Optimization (WDO), and EDE. Results illustrate that the proposed EDGE algorithm outperforms benchmark algorithms in energy expense minimization, carbon emission minimization, PAR alleviation, and user discomfort maximization.

The advancement of the smart grids (SGs) is enabling consumers to schedule home appliances to respond to demand response programs (DRs) offered by distribution system operators (DSOs). This way, not only will customers save money on their energy bills and be more comfortable, but the utility company will also be able to regulate peak-hour demand and reduce carbon emissions (CE). Designing an optimization scheme to reduce the electricity bill cost, peak-to-average ratio (PAR), CO2 emission, wait time, and enhance the user comfort in terms of delay, luminance, and thermal comfort is not only the aim of this work but also the need of demand-side management. This research focuses on energy usage, scheduling, and management under the DR program of an electric utility, as well as renewable energy sources integration, i.e., solar energy (SE), thermal, controllable heat and power (CHP), and wind energy (WE). Moreover, the integration of renewable energy sources will reduce electricity bills and also lower the environmental impact of CE. In this context, a smart appliances scheduler and energy management controller (ASEMC) is proposed which is based on heuristic algorithms, i.e., genetic algorithm (GA), wind-driven optimization (WDO), particle swarm optimization (PSO), bacterial foraging optimization (BFO) and our proposed hybrid of GA, PSO, and WDO (HGPDO) algorithm. The performance of the proposed scheme and heuristic algorithms is evaluated via simulations. Results show that in Scenario 1, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 25.7%, 36.39%, and 20.74%, respectively, while in Scenario 2, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 35.25%, 31.72%, and 36.30%, respectively. Furthermore, in Scenario 1, user comfort in terms of cumulative delay, indoor air freshness quality, thermal, and visual comfort improves by 26.77%, 3.28%, 13.33%, and 31.66%, whereas in Scenario 2, user comfort improves by 23.33%, 3.30%, 10%, and 45%, respectively.

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission.