demand side management, demand response, load scheduling, real time pricing, genetic algorithm, dynamic incentives, artificial fish swarm algorithm, economic load dispatch, hybrid genetic–artificial fish swarm algorithm, multi-objective optimization, sustainable power generating system, photovoltaic (PV), partial shading, maximum power point tracking (MPPT), dragonfly optimization algorithm (DOA), adaptive cuckoo search optimization (ACSO), fruit fly optimization algorithm combined with general regression neural network (FFO-GRNN), improved particle swarm optimization (IPSO), voltage source inverter (VSI), total harmonic distortion (THD), energy optimization, day ahead energy prediction, artificial neural network, renewable energy sources, microgrid, smart grid, electricity price forecasting, energy management, electricity load forecasting, convolutional neural network, corona virus herd immunity optimization, sustainable cities, requirement-gathering tool, qualitative comparison, requirement engineering, software engineering, requirement-management tools, automatic generation control, wind energy, transmission line security, dispatch strategies, Pakistan power system, PI-PD controller, load frequency control, automatic voltage regulator, nature-inspired optimization, multi-area interconnected power system, class imbalance, gated recurrent units, electricity theft detection, non-technical losses, smart grids, deep learning, GRU, healthcare, MLP, PRECON, smart cities, smart meters, sustainable society, electric vehicles, flexible load, optimization, renewable energy, forecasting, machine learning, energy efficiency, sustainability, low carbon emission, distribution generation, Garra Rufa ptimization, PSO, GA, power system, urban sustainability, local growth, regional cities, cultural heritage, real estate, intelligent regions, innovation systems, circular economy, regional policies, Research and information: general, Physics

This reprint covers the following topics in the field of smart grids: 1. Optimal dg location and sizing to minimize losses and improve the voltage profile using garra rufa optimization. 2. Solar and wind energy forecasting for the green and intelligent migration of traditional energy sources. 3. Optimized micro-grid’s operation with electrical-vehicle-based hybridized sustainable algorithm. 4. The detection of nontechnical losses in smart meters using a MLP-GRU deep model and augmenting data via theft attacks. 5. A hybrid deep-learning-based model for the detection of electricity losses using big data in power systems. 6. Load frequency control and automatic voltage regulation in a multi-area interconnected power system using nature-inspired computation-based control methodology. 7. Line overload alleviations in wind energy integrated power systems using automatic generation control. 8. Electric price and load forecasting using a CNN-based ensembler in a smart grid. 9. Day-ahead energy forecasting in a smart grid considering the demand response and microgrids. 10. A dragonfly optimization algorithm for extracting the maximum power of grid-interfaced pv systems. 11. An economic load dispatch problem with multiple fuels and valve point effects using a hybrid genetic–artificial fish swarm algorithm. 12. Incentive-based dynamic pricing in a smart grid.