Plug-in Hybrid Research Articles

Optimal allocation of PV units using metaheuristic optimization considering PEVs charging demand

The distribution system is seeing a dramatic shift as a result of the increasing use of distributed generators (DGs) and plug-in electric vehicles (PEVs), or plug-in hybrid electric cars. The research endeavors to optimize the allocation of photovoltaic (PV) based DGs within radial distribution systems (RDS) while accommodating the load demand stemming from PEVs. A weighted-sum based multiobjective (WMO) technique is employed in this study to optimize three fundamental technical metrics of the distribution network: achieving the best possible voltage stability index (VSI) while reducing real power loss and total voltage variation to a minimum. Initially, the study investigates the impact of both conventional and PEVs load demand, considering PEVs load demand on distribution system performance under three charging scenarios: a situation involving peak charging, scenario involving off-peak charging, and scene of random charging. Subsequently, PV units are strategically planned, taking into account the PEVs demand within the distribution system utilizing an innovative weighted multiobjective electric eel foraging optimization (WMOEEFO) algorithm, its effectuality is validated with weighted multiobjective differential evolutionary (WMODE) and weighted multiobjective grey wolf optimization (WMOGWO) algorithms on standard test system IEEE 33-bus.

Energy savings and emission reduction benefits of China's aluminum use paths in transport sector.

Energy savings and emission reduction benefits of China's aluminum use paths in transport sector.

Optimal planning and feasibility analysis of a grid-connected PV-biomass hybrid renewable energy system for rural electrification with electric vehicles: A case study of Hainan, China

Optimal planning and feasibility analysis of a grid-connected PV-biomass hybrid renewable energy system for rural electrification with electric vehicles: A case study of Hainan, China

Co-optimization of speed planning and energy management for connected plug-in hybrid electric vehicles on slippery roads

Co-optimization of speed planning and energy management for connected plug-in hybrid electric vehicles on slippery roads

Design and test of adaptive energy management strategy for plug-in hybrid electric vehicle considering traffic information

Design and test of adaptive energy management strategy for plug-in hybrid electric vehicle considering traffic information

Advanced scheduling of energy storage, renewable generation, and hydrogen management in microgrids with plug-in hybrid electric vehicle charging integration

Advanced scheduling of energy storage, renewable generation, and hydrogen management in microgrids with plug-in hybrid electric vehicle charging integration

Mitigating backfire occurrence in HHO-gasoline plug-in hybrid motorcycle engine

Mitigating backfire occurrence in HHO-gasoline plug-in hybrid motorcycle engine

A two-stage approach for the bike repositioning problem with plug-in hybrid electric trucks

A two-stage approach for the bike repositioning problem with plug-in hybrid electric trucks

Battery integration and grid-connected hybrid power plant with an optimal energy management system integrated into a multilevel configuration

Battery integration and grid-connected hybrid power plant with an optimal energy management system integrated into a multilevel configuration

Retraction notice to “Power generation cost minimization of the grid-connected hybrid renewable energy system through optimal sizing using the modified seagull optimization technique” [Energy Rep. 6 (2020) 3365–3376

Retraction notice to “Power generation cost minimization of the grid-connected hybrid renewable energy system through optimal sizing using the modified seagull optimization technique” [Energy Rep. 6 (2020) 3365–3376

Review of energy storage integration in off-grid and grid-connected hybrid renewable energy systems: Structures, optimizations, challenges and opportunities

Review of energy storage integration in off-grid and grid-connected hybrid renewable energy systems: Structures, optimizations, challenges and opportunities

Adaptive hybrid particle swarm optimization and fuzzy logic controller for a solar-wind hybrid power system

This paper presents the best modeling and control strategies for a grid-connected hybrid wind-solar power system to maximize energy production. For variable wind speeds, determine the optimal power point using fuzzy logic control, adopt an adaptive hill climb searching method, and compare it with an optimal torque control method for large inertia wind turbine (WT). The role of fuzzy logic controller (FLC) is to adjust the hill climbing search (HCS) technique's step-size according to the operating point. The doubly-fed induction generator (DFIG) control system has two subsystems: rotor-side and grid-side converters. The active and reactive power have been indirectly regulated by adjusting the current on the d-q axis. The rotor side converter (RSC) controllers are responsible for controlling the WTs rotational speed to achieve the maximum power output. The grid side converter (GSC) manages the voltage at the DC link and keeps a unity power factor between the grid and GSC. Optimal hybrid power point tracking technique for use with photovoltaic systems in both constant and variable shade circumstances, based on particle swarm optimization (PSO) and perturb and observe (P&O). The optimal power point tracking (OPPT) approach is compared to three other methods: PSO, P&O, and hybrid P&O-PSO. The model has a total capacity of 2.249 MW, with wind capacity of 2 MW and solar capacity of 0.249 MW, and its efficiency is analyzed.

Determination of Rational Operating Modes for Hybrid Electric Vehicles

Plug-in Hybrid Vehicles (PHEV) are the bridge between Internal Combustion Engine Vehicles (ICEVs), and EVs. This paper describes Energy management, and possible energy management strategies of these vehicles to achieve the desired emission goals.Environmental pollution is an increasingly significant issue in today's world. Failure to change daily routines- especially transportation habits-this could have severe consequences within a few decades. People are often posing the question about what the optimal balance between our fast-paced modern world could be and minimizing environmental impact, particularly in terms of vehicle selection. The solution may lie somewhere in the middle of the spectrum of regular cars with internal combustion engines (ICE), and pure electric vehicles (EVs). Due to the importance of the topic, this paper delves deeper into plug-in hybrid vehicles (PHEV).

Demand side management with electric vehicles and optimal renewable resources integration under system uncertainties

The rapid growth of integrating electrical vehicles (EVs) into the distribution network has introduced complexities and power flow inefficiencies. To address these challenges, optimal renewable energy resources (RERs) integration along with applied demand-side management (DSM) contribute to managing load profiles and generation thus reducing costs. This should be smartly attained through selecting efficient optimization techniques to improve power quality, voltage profile, and reliability. This paper aims to investigate the effect of integrating EVs and applying peak load shifting (PLS) as a DSM strategy with the optimal allocation of distributed energy resources, specifically wind and photovoltaic (PV) systems, as distributed generators (DGs) on distribution networks. Taking into consideration the stochastic behavior of RERs, EVs demand elasticity of charging and discharging scenarios and load variance. The main objective of this work focuses on power loss reduction and implementing PLS to flatten the load profile and form a new loadability to reduce costs. The study is demonstrated on a typical IEEE 69-bus system, considering the load, EVs, and RERs profiles during weekdays in winter and summer seasons. The study examines the optimal size and location of combining two DGs (wind and PV), in addition to incorporating bidirectional plug-in hybrid electric vehicles into the system. The study utilizes the Zebra optimization algorithm (ZOA), in comparison with the Whale optimization algorithm (WOA), Grey wolf optimization algorithm (GWO), and Genetic algorithm (GA). The latter is employed only as a reference for comparison. For each season, the simulation is divided into two parts, each part consists of four cases. Part (1) is simulated assuming constant power integration for the RERs while part (2) considers their stochastic behavior. Also, optimal charging strategies for EVs are examined for cost-effectiveness during high penetration levels for the IEEE 123-bus system. The results demonstrated the effectiveness of the proposed algorithm in reducing power loss. Moreover, shifting peak hours flattens the load profile, thereby reducing costs and power loss across the distribution network. Furthermore, the performance of the ZOA dominates the WOA, GWO, and GA.

Hybrid renewable energy systems for sustainable energy: a techno-economic analysis from western India

Abstract This paper presents a detailed techno-economic analysis of grid-connected hybrid microgrid systems in Western India, specifically targeting remote regions including Charanka, Samana, Navagam-Kevadia, Vighakot, Methan, and Panadhro. Leveraging the capabilities of HOMER Pro software, the study evaluates various configurations integrating solar photovoltaic systems, wind energy systems, and energy storage systems. The assessment focuses on optimizing energy integration to achieve cost-effective and environmentally sustainable outcomes. Key performance indicators such as net present cost, cost of energy, and grid reliability are analyzed. The findings reveal that hybrid configurations combining solar photovoltaic systems and wind energy systems, supported by energy storage systems, substantially reduce cost of energy and enhance system reliability. Cost of energy values across the studied sites range from INR 4.24 to INR 6.74, demonstrating a viable equilibrium between cost, efficiency, and environmental benefits. This study highlights the scalability of hybrid microgrid systems for renewable energy integration, offering valuable insights for sustainable energy planning and implementation in similar contexts globally.

Evaluating the Damage Cost of Vehicle to Grid Integration in Indonesia Power Grid for Sustainable Energy System

The cost incurred in the EV Charging is currently only from the energy consumed consideration. While there is extensive research on the impact of EV charging on grid infrastructure and operation, few studies quantify these effects in terms of explicit 'damage costs'. However, besides energy problems, the analysis of the environmental impact of EVs has not been quantified monetarily. This paper aimed to investigate the damage cost (DC) of EVs in comparison with Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) based on Indonesia Java Bali's grid conditions. Using the existing data in Indonesia, the characterisation parameter were determined using cradle-to-gate life cycle analysis (LCA) and ReCiPe 2016 then converting the endpoint indicators impacts to DC using a monetary weight factor (MWF) of those two EVs. Cradle to gate LCA is a method for evaluating the environmental impact of battery from the extraction of raw material to the factory gate and ReCiPe 2016 is a widely used of LCA method, which converts emission and resources extraction into certain indicators. Results show that DC for a 20 kWh BEV = 0.12 ± 0.01 USD/kWh compared to an 8 kWh PHEV = 0.08 ± 0.005 USD/kWh. Therefore, the DC of an extra 0.02 USD/kWh for BEVs and 0.015 USD/kWh for PHEVs should be considered in formulating the EV charging tariff.

Energy scheduling of renewable integrated system with hydrogen storage in distribution grid including charging and hydrogen stations of eclectic vehicles

In this article, the energy management of the intelligent distribution system with charging stations for battery-based electric vehicles (EVs) and plug-in hybrid EVs, hydrogen station for fuel cell-based EVs, and renewable integrated energy systems (IESs) with hydrogen storage devices in accordance with the estimation of economic, operational, security and environmental goals of distribution system operator is presented. Hydrogen storage is used to store electric energy and feed hydrogen consumers. The methodology adopted here is expressed as a multi-objective formulation to be solved. Objective functions are minimizing the cost of buying energy by distribution system from the upstream network, minimizing distribution system energy losses, minimizing environmental emissions, and maximizing voltage security in the distribution system. In this issue, AC power flow model, operation and voltage security boundaries in the network, performance model of charging station for EVs, hydrogen station model for fuel cell vehicles, and renewable IES operation model with hydrogen storage is the boundaries specific to the problem. The problem in the single-objective model uses the Pareto optimization that relies on the sum of weighted functions method. Next, the fuzzy decision-making technique extracts an optimal compromised solution between the operational, economic, security and environmental objectives of the network operator. In the present scheme, load, energy prices, renewable phenomena, electric vehicles have uncertainty. In this article, stochastic optimization based on Unscented Transform is incorporated to provide a suitable modeling of the uncertain parameters appearing in the problem. Modelling of the performance of EVs charging station and hydrogen fulling station, using hydrogen storage as electricity energy storage and feeding hydrogen loads, energy management of renewable bio-waste and tidal units in IES, considering the different objectives of network operator, and using Unscented Transform approach to model of uncertainty parameters are the innovations of this article. Findings show that the method improves the technical, environmental and economic conditions of the grid, and the integrated system with its optimal performance is able to enhance the economic, environmental, security and operation status of the distribution system up to roughly 45.8%, 38%, 32–45% and 10.6%, respectively.

Driving circular economy in electric vehicle batteries

Driving circular economy in electric vehicle batteries MARBEL project has designed, developed and demonstrated new modular, compact, lightweight and high-performance battery packs together with flexible and robust battery management systems for battery electric vehicles and plug-in hybrids, while maintaining safety levels, allowing fast, high quality and cost-effective large-scale production and following ecodesign principles.

Techno-economic Evaluation of Grid-connected Hybrid Energy System Based on Run-of-River and Solar Energy Plants for Sustainable Electrification of a Rural Community

The connection between energy access and greenhouse gas emissions is an issue that continues to garner attention. Presently, hundreds of millions of people globally do not have access to sufficient electricity, and those who do, rely on expensive fossil resources characterized by greenhouse gases. A viable solution is to explore renewable energy (RE) sources to satisfy the electricity demand and curtail the effect of greenhouse gases. This study performed a techno-economic analysis of a grid-connected hybrid RE system that included micro-hydro and solar photovoltaic power plants for a Nigerian rural community. The optimal system, according to the analysis done with HOMER software tool, has an overall NPC, operating cost, and LCOE of $3,202,139.00, $37,515.81, and $0.06053/kWh, respectively. A 98.1 kW micro-hydro turbine, a 150 kW converter, 100 kW solar panels, and 704 battery strings constitute the system components. An annual emission of 4,483 kg of CO2, 0.356 kg of CO, 22.5 kg of SO2, 4.86 kg of NO, and 1.66 kg of particulate matter will be released into the atmosphere. The implementation of this hybrid power system will not only increase access to energy but also help lessen greenhouse gas emissions.

Intelligent power control strategy based on self-tuning fuzzy MPPT for grid-connected hybrid system

Introduction. This paper investigates various methods for controlling the Maximum Power Point Tracking (MPPT) algorithm within the framework of intelligent energy control for grid-connected Hybrid Renewable Energy Systems (HRESs). The purpose of the study is to improve the efficiency and reliability of the power supply in the face of unpredictable weather conditions and diverse energy sources. Intelligent control techniques are used to optimize the extraction of energy from available sources and effectively regulate energy distribution throughout the system. Novelty study is employing intelligent control strategies for both energy optimization and control. This research distinguishes itself from conventional approaches, particularly through the application of Self-Tuning Fuzzy Logic Control (ST-FLC) and fuzzy tracking. Unlike conventional methods that rely on logical switches, this intelligent strategy utilizes fuzzy rules adapted to different operating modes for more sophisticated energy control. The proposed control strategy minimizes static errors and ripples in the direct current bus and challenges in meeting load demands. Methods of this research includes a comprehensive analysis of several optimization techniques under varying weather scenarios. The proposed strategy generates three control signals that correspond to selected energy sources based on solar irradiation, wind velocity and battery charging status. Practical value. ST-FLC technique outperforms both conventional methods and standard Fuzzy Logic Controllers (FLCs). It consistently delivers superior performance during set point and load disturbance phases. The simulation, conducted using MATLAB/Simulink. The results indicate that fuzzy proposed solution enables the system to adapt effectively to various operational scenarios, displaying the practical applicability of the proposed strategies. This study presents a thorough evaluation of intelligent control methods for MPPT in HRESs, emphasizing their potential to optimize energy supply under varying conditions. References 27, tables 6, figures 18.