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Optimal Low-Carbon Scheduling for Smart Microgrids With Dynamic Thermal Capacity Constraints

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Bibliographic Details
Title: Optimal Low-Carbon Scheduling for Smart Microgrids With Dynamic Thermal Capacity Constraints
Authors: Peng Xie, Hongwei Liu, Chun Chen, Mingjun Liu
Source: IEEE Access, Vol 13, Pp 94106-94117 (2025)
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year: 2025
Subject Terms: microgrid, dynamic thermal capacity, electric vehicle, carbon emission factor, Low-carbon, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Description: This study aims to integrate electric vehicles, photovoltaic and battery energy storage systems, and distribution network information in a microgrid to achieve decarbonized optimal operation. Under the different operating states of distribution networks, the paper proposes a decarbonized two-stage deeply integrated operational mode for a photovoltaic, battery energy storage system, and electric vehicles integrated microgrid, incorporating the electricity market to optimize overall revenue. In the optimization process, this paper introduces statistical node carbon emission factors and dynamic thermal capacity constraints to explore microgrids’ decarbonized potential and maximize system equipment’s utilization efficiency. This study proposes a two-stage day-ahead robust optimization framework for the microgrids, integrating tri-level min-max-min modeling to address multidimensional uncertainties through parameterized uncertainty sets to balance conservatism and optimize costs under worst-case scenarios. This work develops an adaptive threshold tightening mechanism for Column-and-Constraint Generation algorithms to balance computational efficiency and solution precision. Additionally, this paper proposes an adaptive dynamic pricing framework that integrates electric vehicle users’ cost-adaptive behavior with supply-demand fluctuations, using incentive-driven temporal optimization to maintain real-time grid equilibrium and enhance microgrid flexibility. Simulation results show that the proposed approach improves system profitability by up to 55.2% while enhancing transformer safety and operational flexibility.
Document Type: Article
ISSN: 2169-3536
DOI: 10.1109/access.2025.3572952
Access URL: https://doaj.org/article/4cc831457d524aaf96b8e59ae28f18df
Rights: CC BY
Accession Number: edsair.doi.dedup.....58864dc1e2cf4adbeb84f79289a7fd0e
Database: OpenAIRE
Description
Abstract:This study aims to integrate electric vehicles, photovoltaic and battery energy storage systems, and distribution network information in a microgrid to achieve decarbonized optimal operation. Under the different operating states of distribution networks, the paper proposes a decarbonized two-stage deeply integrated operational mode for a photovoltaic, battery energy storage system, and electric vehicles integrated microgrid, incorporating the electricity market to optimize overall revenue. In the optimization process, this paper introduces statistical node carbon emission factors and dynamic thermal capacity constraints to explore microgrids’ decarbonized potential and maximize system equipment’s utilization efficiency. This study proposes a two-stage day-ahead robust optimization framework for the microgrids, integrating tri-level min-max-min modeling to address multidimensional uncertainties through parameterized uncertainty sets to balance conservatism and optimize costs under worst-case scenarios. This work develops an adaptive threshold tightening mechanism for Column-and-Constraint Generation algorithms to balance computational efficiency and solution precision. Additionally, this paper proposes an adaptive dynamic pricing framework that integrates electric vehicle users’ cost-adaptive behavior with supply-demand fluctuations, using incentive-driven temporal optimization to maintain real-time grid equilibrium and enhance microgrid flexibility. Simulation results show that the proposed approach improves system profitability by up to 55.2% while enhancing transformer safety and operational flexibility.
ISSN:21693536
DOI:10.1109/access.2025.3572952