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Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort. [ABSTRACT FROM AUTHOR]

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions. • Presenting a multi-objective model for energy management via day-ahead scheduling in smart grids. • Enhancing day-ahead scheduling with renewables and demand response strategies. • Introducing a probabilistic model for solar and wind energy uncertainty prediction. • Proposing a hybrid demand response to lower peak energy demand and prevent rebound peaks. • Utilizing MOWDO algorithm for optimal Pareto fronts exploration to achieve tri-objective optimization: cost, emissions, and user comfort. [ABSTRACT FROM AUTHOR]

Demand response (DR) is currently gaining widespread attention because it persuades consumers to participate actively in the electricity market, intending to balance power demand with available generation. The DR shaves peak energy use by shifting or curtailing demand or driving energy conservation concerning price variation or incentive payments to meet growing energy demand with the available generation, and provides new opportunities for power usage scheduling. To avail this opportunity, this work proposes DR energy management scheme (DREMS) based on multi-objective wind-driven optimization (MOWDO) algorithm for the power scheduling problem of the smart home, which has smart appliances: controllable (power and time) and critical appliances. The first class of appliances are controllable with adjustable power and time. On the other hand, critical appliances do not tolerate adjustment in time or power. The first class contributes to utility bill payment and peak-to-average demand ratio (PADR) minimization, and the second class contributes to comfort enhancement. The aim is to reduce utility bills, PADR, and discomfort, and achieve the desired trade-off between payment and PADR, and payment and discomfort. The power usage scheduling problem is formulated as an optimization problem with integer programming for four modes of operation. The DREMS based on the MOWDO algorithm resolves the optimization problem, producing an optimal operation schedule for four modes of operation. To assess its effectiveness, the MOWDO algorithm is compared with the existing MOPSO algorithm using various performance metrics. Simulation results demonstrate that the MOWDO returned an optimized operation schedule that successfully achieves the desired trade-off between payment and PADR, as well as payment and discomfort. • Proposes closed-loop DREMS using MOWDO for scheduling flexible and critical appliances. • Formulates objective functions and constraints for residential load scheduling. • MOWDO explores optimal trade-offs between payment, PADR, and discomfort. • Validates DREMS by comparing it with other scheduling strategies in various modes. [ABSTRACT FROM AUTHOR]