A day-ahead economic scheduling of microgrids equipped with plug-in hybrid electric vehicles using modified shuffled frog leaping algorithm

Zeng, Xie; Nazir, Muhammad Shahzad; Khaksar, Mehrdad; Nishihara, Kentaro; Tao, Hai

doi:10.1016/j.est.2020.102021

Cited by 31 publications

(10 citation statements)

References 32 publications

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“…Based on the above microgrid topology, renewable generation, power consumption, and availability of GEVs, V2G scheduling is carried out to improve energy utilization efficiency. Performances of three different V2G schemes, including conventional rule-based method [4] (Case 1), optimization-based [17] method (Case 2), and the developed mixed learning method (Case 3), are compared in this section.…”

Section: B Power Balancing and Battery Anti-aging Performance Of V2g ...mentioning

confidence: 99%

“…With the development of communication and computation technologies in recent years, many efforts have been made to reduce battery aging by using optimizationbased scheduling methods [15,16]. A heuristic algorithm-based V2G scheduling method is developed in [17] to schedule the charging behavior of GEVs in the microgrid. The V2G scheduling is modeled as a multi-objective optimization problem under a 24-hour time scale, and the battery aging is mitigated by constraining the number of cycles (NOC) and depth of discharge (DOD).…”

Section: Introductionmentioning

confidence: 99%

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Battery Protective Electric Vehicle Charging Management in Renewable Energy System

Zhao

et al. 2023

IEEE Trans. Ind. Inf.

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The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This paper develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a UK microgrid with actual energy generation and consumption data. This work can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.

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Section: B Power Balancing and Battery Anti-aging Performance Of V2g ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

Zhao

et al. 2023

IEEE Trans. Ind. Inf.

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“…Extensive exploration of RER scheduling is well-documented within the existing literature [10][11][12][13][14][15][16][17][18][19]. Reference [10] delves into the realm of stochastic energy management and scheduling strategies for MGs, which addresses the on-grid scenarios.…”

Section: Introductionmentioning

confidence: 99%

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Jokar,

Niknam,

Dehghani

et al. 2023

Energies

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Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

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“…These literature studies aim at either minimising the total cost of the system or maximising the profit due to contributing to the electricity market. To this end, a problem formulation to reduce the total cost by reducing the reliability costs has been proposed [18]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Optimal day‐ahead scheduling in micro‐grid with renewable based DGs and smart charging station of EVs using an enhanced manta‐ray foraging optimisation

Bastawy

Ebeed

Ali

et al. 2022

IET Renewable Power Gen

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Micro-grids (MGs) are small parts of the electrical power system that work along with the electric system or autonomously based on environmental or economic conditions. The renewable-based distributed generators (RDGs) and electric vehicle charging stations (EVCSs) are wildly incorporated in MGs. Optimal day-ahead scheduling of the MG is ahead corner of energy management for cost reduction. In addition, solving the economic load dispatch and day-ahead scheduling of the MG is a complex optimisation problem, especially considering the RDGs, EVCS, and uncertainties in the electrical system. This paper aims to optimise the day-ahead scheduling of the MG with and without a smart charging strategy for electric vehicles. An enhanced manta-ray foraging optimisation (EMRFO) algorithm is proposed to solve this optimisation problem. EMRFO depends upon boosting population diversity and the searching ability of the standard MRFO using strategies. The proposed strategies are based on quasi-oppositional-based learning and local chaotic mutation. The studied MG consists of wind turbines, fuel cells, and diesel generators. The day-ahead scheduling of the MG is solved with and without considering the uncertainties of the load demand and the wind speed. The proposed algorithm for day-ahead scheduling of the MG is compared to well-known algorithms such as anti lion optimisation, particle swarm optimisation, whale optimisation algorithm, sine cosine algorithm, and harmony search algorithm. The simulation results demonstrate that the proposed algorithm is superior to these algorithms for solving the optimisation problem. The results show that the generation cost is reduced considerably from 77,745.

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A day-ahead economic scheduling of microgrids equipped with plug-in hybrid electric vehicles using modified shuffled frog leaping algorithm

Cited by 31 publications

References 32 publications

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Optimal day‐ahead scheduling in micro‐grid with renewable based DGs and smart charging station of EVs using an enhanced manta‐ray foraging optimisation

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