A multi-level multi-objective strategy for eco-environmental management of electricity market among micro-grids under high penetration of smart homes, plug-in electric vehicles and energy storage devices

Fatemi, S. M. J. Rastegar; Ketabi, Abbas; Mansouri, Seyed Amir

doi:10.1016/j.est.2023.107632

Cited by 37 publications

(2 citation statements)

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“…Furthermore, the ANRVs in all cases meet the desired values compared to those of model C. Consequently, incorporating the DRP into the GMS plan proves to be a superior choice. However, GenCos need to find efficient solutions for operating the DRP in real scenarios, considering recent works proposing DRP applications [41][42][43][44] that can be later adapted to the GMS field.…”

Section: The Results Of Models C and Ementioning

confidence: 99%

Optimizing Generation Maintenance Scheduling Considering Emission Factors

Prukpanit,

Kaewprapha,

Leeprechanon

2023

Energies

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Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos.

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Section: The Results Of Models C and Ementioning

confidence: 99%

Optimizing Generation Maintenance Scheduling Considering Emission Factors

Prukpanit,

Kaewprapha,

Leeprechanon

2023

Energies

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“…This integration holds immense potential to not only enhance the reliability and efficiency of electric vehicle charging but also to contribute to the overall resilience and sustainability of the broader energy ecosystem. This introductory exploration delves into the synergistic relationship between MGs and EV charging stations, shedding light on the transformative impact they can collectively exert on the landscape of modern transportation and energy infrastructure [3,4].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Microgrid Inverter-Integrated Charging Station Performance through Optimization of Fractional-Order PI Controller Using the One-to-One Sine Cosine Algorithm

Aldosary

2024

Fractal Fract

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This paper is dedicated to optimizing the functionality of Microgrid-Integrated Charging Stations (MICCS) through the implementation of a new control strategy, specifically the fractional-order proportional-integral (FPI) controller, aided by a hybrid optimization algorithm. The primary goal is to elevate the efficiency and stability of the MICCS-integrated inverter, ensuring its seamless integration into modern energy ecosystems. The MICCS system considered here comprises a PV array as the primary electrical power source, complemented by a proton exchange membrane fuel cell as a supporting power resource. Additionally, it includes a battery system and an electric vehicle charging station. The optimization model is formulated with the objective of minimizing the integral of square errors in both the DC-link voltage and grid current while also reducing total harmonic distortion. To enhance the precision of control parameter estimation, a hybrid of the one-to-one optimizer and sine cosine algorithm (HOOBSCA) is introduced. This hybrid approach improves the exploitation and exploration characteristics of individual algorithms. Different meta-heuristic algorithms are tested against HOOBSCA in different case studies to see how well it tunes FPI settings. Findings demonstrate that the suggested method improves the integrated inverters’ transient and steady-state performance, confirming its improved performance in generating high-quality solutions. The best fitness value achieved by the proposed optimizer was 3.9109, outperforming the other algorithms investigated in this paper. The HOOBSCA-based FPI successfully improved the response of the DC-link voltage, with a maximum overshooting not exceeding 8.5% compared to the other algorithms employed in this study.

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