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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]

Accurate load forecasting plays a crucial role in the effective energy management of smart cities. However, the smart cities' residents' load profile is nonlinear, having high volatility, uncertainty, and randomness. Forecasting such nonlinear profiles requires accurate and stable prediction models. On this note, a prediction model has been developed by combining feature preprocessing, a multilayer perceptron, and a genetic wind-driven optimization algorithm, namely FPP-MLP-GWDO. The developed hybrid model has three parts: (i) feature preprocessing (FPP), (ii) a multilayer perceptron (MLP), and (iii) a genetic wind-driven optimization (GWDO) algorithm. The MLP is the key part of the developed model, which uses a multivariate autoregressive algorithm and rectified linear unit (ReLU) for network training. The developed hybrid model known as FPP-MLP-GWDO is evaluated using Dayton Ohio grid load data regarding aspects of accuracy (the mean absolute percentage error (MAPE), Theil's inequality coefficient (TIC), and the correlation coefficient (CC)) and convergence speed (computational time (CT) and convergence rate (CR)). The findings endorsed the validity and applicability of the developed model compared to other literature models such as the feature selection–support vector machine–modified enhanced differential evolution (FS-SVM-mEDE) model, the feature selection–artificial neural network (FS-ANN) model, the support vector machine–differential evolution algorithm (SVM-DEA) model, and the autoregressive (AR) model regarding aspects of accuracy and convergence speed. The findings confirm that the developed FPP-MLP-GWDO model achieved an accuracy of 98.9%, thus surpassing benchmark models such as the FS-ANN (96.5%), FS-SVM-mEDE (97.9%), SVM-DEA (97.5%), and AR (95.7%). Furthermore, the FPP-MLP-GWDO significantly reduced the CT (299s) compared to the FS-SVM-mEDE (350s), SVM-DEA (240s), FS-ANN (159s), and AR (132s) models. [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]