Green transportation has become our top priority due to the depletion of the earth’s natural resources and rising pollutant emission levels. Plug-in electric vehicles (PEVs) are seen as a solution to the problem because they are more cost and environment friendly. Due to rapid industrialization and government incentives for zero-emission transportation, a significant challenge is also constituted in power grids by the self-interested nature of PEVs, with the asymmetry of information between the charging power demand and supply sides. In this paper, we propose an optimal strategy in industrial energy management system, based on evolutionary computing, to characterize different charging situations. The proposed approach considers stochastic, off-peak, peak, and electric power research institute charging scenarios for attaining the vehicle-to-grid capacity in terms of optimal cost and demand. An extensive scheduling of charging cases is studied in order to avoid power outages or scenarios in which there is a significant supply-demand mismatch. Furthermore, the proposed scheme model also reduces the greenhouse gases emission from generation side to build a sustainable generation infrastructure, which maximizes the utility of fuel-based energy production in the presence of certain nonlinear constraints. The simulation analysis demonstrates that PEVs can be charged and discharged in a systematic manner. The participation of transferable load through the proposed methodology can significantly reduce the economic costs, pollutant impacts, efficiency, and security of power grid operation.

Microgrids are power distribution systems that can operate either in a grid-connected configuration or in an islanded manner, depending on the availability of decentralized power resources, such as sustainable or non-sustainable power sources, battery backup systems, and power demands. The extensive adoption of inverter-based systems poses numerous technological challenges, necessitating a centralized management system to assure the system reliability and monitoring of the energy delivery networks. Thus, this research begins by highlighting these significant obstacles and then analyzes the present-day advances in multilevel control architecture for delivering on promised functionality. This article also discusses the development of innovative control technologies, such as introducing collaborative distributed approaches and reducing conventional three-stage patriarchal administration to fewer stages of system integration and functioning.

In recent times, significant progress has been achieved in the domain of intelligent and eco-friendly transportation. Electric mobility emerges as a viable and effective solution, offering cost-efficient means of transportation. However, the rise in fuel expenses, climate change, and unregulated charging of electric vehicles (EVs) have necessitated a transformative shift in the operation of smart grids. The exponential rise in EV charging requirements has the potential to adversely affect power grids, resulting in peak demand, grid overload, energy hub emissions, and possible infrastructure overburden. This study presents a predictive cost model that utilizes a hybrid search and rescue (SAR) and adaptive neuro-fuzzy interface system (ANFIS) denoted as SAR-ANFIS approach. The model is designed to effectively model complex dynamic energy emission dispatch scenarios that are integrated with transient charging loads. These scenarios include peak, off-peak, electric power research institute (EPRI), and stochastic scenarios. The proposed methodology aims to minimize the cost of charging scenarios while also providing policymakers with a tool to create financial budgets for forthcoming electric vehicle loads. This is achieved through the use of a fuzzy model that has the ability to predict costs. The ANFIS exhibits robust predictive capabilities owing to its aptitude for acquiring and representing intricate non-linear associations between input and output variables. The incorporation of ANFIS into a SAR algorithm results in improved predictive capacity through the optimization of the learning process, enhancement of model accuracy, and facilitation of efficient parameter tuning. The integration of ANFIS and SAR algorithms enhances predictive accuracy and robustness by leveraging the former’s adaptive and learning capabilities and the latter’s global search and optimization capabilities. The model takes into account various charging strategies and dispatch constraints within an energy hub. The attainment of the 24-hour pricing scheme is achieved by solving a minimum-cost optimization problem, which serves as the initial training data for the development of the proposed model using an adaptive neuro-fuzzy approach. The proposed approach effectively coordinates the various charging behaviours of electric vehicles, including those identified by the EPRI, as well as stochastic, peak, and off-peak charging, at the system level. The proposed methodology offers several advantages, including the facilitation of coordination among various charging scenarios for EVs and the creation of a cost prediction model that can assist policymakers in devising budgetary plans for future EV loads. One of the benefits of this technology is its capacity for autonomy, which enables vehicle owners to charge their electric vehicles in a cost-effective manner, regardless of the specific scenario they find themselves in. Furthermore, the suggested plan has the potential to mitigate the release of greenhouse gases from the power generation sector, thereby facilitating the establishment of a viable charging network that is environmentally sustainable. The present study examines the efficacy of the SAR-ANFIS approach and model on a standardized test system across a range of load profiles.

This paper deals with the event-triggered (ET) consensus of generic linear multi-agent systems (MASs) subject to heterogeneous sector-restricted input nonlinearities over directed graphs. A new multiplicative input uncertainty-based model is derived to contemplate each agents' sector-restricted input nonlinearity. The proposed approach has been considered for an ET consensus of MASs under input nonlinearities to achieve an efficient control bandwidth by decreasing the sampling frequency. A consensus controller design condition with non-identical control gains is developed to ensure exponential stability with the ET mechanism for generic linear MASs with heterogeneous actuators. To the best of our knowledge, this is the first investigation of the consensus of linear MASs with heterogeneous sector-restricted nonlinearties at the control input, representing any continuous-time function bounded between two linear functions. The addition of the ET mechanism has further strengthened our contribution. This study also demonstrates that the designed ET condition under heterogeneous nonlinear inputs for a strongly connected topology can effectively exclude the Zeno behavior. Finally, a simulation example employing six spacecrafts is provided to illustrate the efficacy of the suggested consensus controller.