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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]

In this study, we investigate the phenomenon of mixed convection in viscous fluid flow by a vertical channel, considering the presence of magneto‐hydro‐dynamics and porosity. Mixed convection occurs when both buoyancy forces and external forces, such as pumps or fans, influence the flow behavior. Understanding and accurately predicting mixed convection is crucial for optimizing heat exchanger design and performance. To model the temperature equation, we utilize the concept of the first law of thermodynamics. Additionally, we incorporate effects such as Joule heating, heat generation, and radiative heat flux in the energy equation modeling. The resulting physical liquid equations are solved using the shooting technique with the RKF (Runge–Kutta–Fehlberg) scheme. We present graphical representations of the flow variables, discussing their behavior in detail. The introduction provides an overview of the paper's roadmap, while the conclusion highlights the main and significant results obtained from our study. We found that, the temperature is more when liquid motion in between channel for large numerical values of thermal radiation parameter and Reynolds number. [ABSTRACT FROM AUTHOR]