ArticleScientific reports2025
Comprehensive framework for smart residential demand side management with electric vehicle integration and advanced optimization techniques.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ELLS-NSBA three-stage framework for optimal DG/SC placement and EV integration in distribution networks.Scientific reports · 2026Article
- Integrated energy scheduling for grid-connected microgrids using battery degradation-aware optimization and coordinated control strategies.Scientific reports · 2025Article
- Lightweight machine learning framework using temporal features for electric vehicle demand response forecasting on edge devices.Scientific reports · 2025Article
- Mitigating distributed denial of service attacks using attribute subset selection with temporal convolutional networks.Scientific reports · 2025Article
- A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management.Scientific reports · 2025Article
- Multi objective moth swarm algorithm for optimizing electric vehicle integration in distribution grids.Scientific reports · 2025Article
- Novel control strategies for electric vehicle charging stations using stochastic modeling and queueing analysis.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The exponential deployment of electric vehicles (EVs) in the residential sectors in recent years allows better energy utilization in the decentralized and centralized levels of distribution systems due to their bidirectional operation and energy storage capabilities. However, to execute these, it is necessary to adopt residential demand side management (RDSM) to schedule energy utilization effectively to fetch economical and efficient energy consumption and grid stability and reliability, particularly during peak load conditions. The paper aims to formulate a robust and efficient RDSM technique to provide an energy utilization scheduling considering various influential factors and critical roles of EVs in RDSM. A Binary Whale Optimization Algorithm (BWOA) approach is proposed as an efficient algorithm for EV's impact on the RDSM for better energy scheduling. A single-objective formulation is presented with detailed modelling considering economic energy utilization as the primary objective with all possible equality and inequality system operational constraints. Secondly, the impact of EVs on the RDSM is studied from various perspectives in result analysis, considering EVs as load, storage devices, and different bidirectional modes of operation with other vehicles, residential components, and grids. In addition, the EVs role and the mutual influence with the integration of renewable energy sources (RES) and energy storage devices (ESDs) are extensively analyzed to provide better residential energy management (REM) in terms of economic, environmental, robust, and reliable points of view. The load priority based on consumer choice is also incorporated in the formulation. Extensive simulation is done for the proposed approach to show the effect of EVs on REM, and the results are impressive to show the EV's role as a load, as a storage device, and as a mutually supportive device to RES, ESD, and grid.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.