A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 54, P. 101461 - 101461

Published: July 1, 2024

Increasing electrical energy consumption during peak hours leads to increased losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced reduce hours. This study proposes an efficient optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses load-shifting technique for as a way improve patterns SUBs. The proposed system's goal is optimize SUBs appliances order effectively regulate load demand, with end result being reduction average ratio (PAR) consequent minimization electricity costs. accomplished while also keeping user comfort priority. system evaluated by comparing it Grasshopper Optimization (GOA) unscheduled cases. Without applying algorithm, total cost, carbon emission, PAR waiting time are equal 1703.576 ID, 34.16664 (kW), 413.5864s respectively RTP. While, after GOA, improved 1469.72 21.17 355.772s ISCA Improves PAR, 1206.748 16.5648 268.525384s respectively. Where 13.72 %, 38.00 13.97 % And method, 29.16 51.51 35.07 According results, created algorithm performed better than case GOA scheduling situations terms stated objectives was advantageous both utilities consumers. Furthermore, has presented novel two-stage stochastic model Moth-Flame (MFOA) co-optimization capacity planning systems storage would be incorporated grid connected smart urban buildings.

Language: Английский

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Reinforcement learning for battery energy management: A new balancing approach for Li-ion battery packs

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102532 - 102532

Published: July 11, 2024

This study investigates the challenge of cell balancing in battery management systems (BMS) for lithium-ion batteries. Effective is crucial maximizing usable capacity and lifespan packs, which essential widespread adoption electric vehicles reduction greenhouse gas emissions. A novel deep reinforcement learning (deep RL) approach proposed passive with switched shunt resistors. Notable RL algorithms capable handling discrete actions, such as Trust Region Policy Optimization (TRPO), Proximal (PPO), Deep Q-Network (DQN), Augmented Random Search (ARS), Asynchronous Advantage Actor Critic (A3C), are investigated. TRPO demonstrates superior performance compared to other rule-based methods both charging discharging scenarios without requiring fine-tuning, optimizing balance between switch changes. It achieves up 16.8% improvement pack capacity, 69.4% state-of-charge variance among cells, 40.4% decrease number switching operations simulation results five li-ion cells connected series. The introduces an innovative application balancing, a comprehensive modeling technique, tailored multi-objective reward function that balances costs. work represents significant advancement applying systems, providing framework further research practical implementation energy storage systems.

Language: Английский

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A review of IoT-enabled smart energy hub systems: Rising, applications, challenges, and future prospects

Renewable energy focus, Journal Year: 2024, Volume and Issue: 51, P. 100634 - 100634

Published: Sept. 12, 2024

Language: Английский

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ANFIS-Optimized Control for Resilient and Efficient Supply Chain Performance in Smart Manufacturing

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104262 - 104262

Published: Feb. 1, 2025

Language: Английский

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Dual-layered deep learning and optimization algorithm for electric vehicles charging infrastructure planning

International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 166, P. 110545 - 110545

Published: Feb. 20, 2025

Language: Английский

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An Integrated Price- and Incentive-Based Demand Response Program for Smart Residential Buildings: A Robust Multi-Objective Model

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105664 - 105664

Published: July 14, 2024

Language: Английский

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Transforming Smart Homes Via P2P Energy Trading Using Robust Forecasting and Scheduling Framework

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102766 - 102766

Published: Aug. 24, 2024

With the advent of smart grids, advanced information infrastructures, metering facilities, bidirectional exchange information, and battery storage home area networks have all transformed electricity consumption energy efficiency. There is a significant shift in management structure from traditional centralized infrastructure to flexible demand side driven cyber-physical power systems with clean system. These changes significantly evolved (HEM) space. Consequently, stakeholders must define their responsibilities, create efficient regulatory frameworks, test out novel commercial strategies. P2P trading appears be feasible solution these circumstances, allowing users trade one another before becoming completely reliant on utility. offers more stable platform for by facilitating between prosumers consumers. This research proposes generation prediction techniques HEMS optimal using Multi-Objective Optimization model. An enhanced Wild Horse technique was first used summarize historical records' qualities. Then, Bi-LSTM predict values. Furthermore, Grasshopper optimization (GHO) approach employed fine-tune model's hyperparameters. The framework offered probabilistic fault evaluation that upholds load flow balance need supply continuous operations. It results an intelligent community transforming cities into ones, opening new avenues scientific terms technological developments.

Language: Английский

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Smart Grid Stability Prediction Using Adaptive Aquila Optimizer and Ensemble Stacked BiLSTM

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103261 - 103261

Published: Oct. 1, 2024

Language: Английский

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Energy optimization using hybrid demand response, renewable energy, and storage battery: A tri-objective optimization approach

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106145 - 106145

Published: Jan. 1, 2025

Language: Английский

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Enhanced Energy Management System in Smart Homes Considering Economic, Technical, and Environmental Aspects: A Novel Modification-Based Grey Wolf Optimizer

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1071 - 1071

Published: Feb. 22, 2025

Increasingly, renewable energy resources, storage systems (ESSs), and demand response programs (DRPs) are being discussed due to environmental concerns smart grid developments. An innovative home appliance scheduling scheme is presented in this paper, which incorporates a local with wind turbines (WTs), photovoltaic (PV), ESS, connected an upstream grid, schedule household appliances while considering various constraints DRP. Firstly, the specified as non-shiftable shiftable (interruptible, uninterruptible) loads, respectively. Secondly, enhanced mathematical formulation for management considers real-time price of grids, WT, PV, also sold from microgrid. Three objective functions considered proposed management: electricity bill, peak-to-average ratio (PAR), pollution emissions. To solve optimization problem, novel modification-based grey wolf optimizer (GWO) proposed. When wolves hunt prey, other wild animals try steal prey or some part hence they should protect prey; therefore, modification mimics battle between hunted prey. This improves performance GWO finding best solution. Simulations examined compared under different conditions explore effectiveness efficiency suggested simultaneously optimizing all three functions. Also, both improved (IGWO) scenarios, shows that IGWO improvement has better more robust. It been seen results framework can significantly diminish costs, PAR, emissions simultaneously.

Language: Английский

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