Optimal Power Flow Algorithm Based on Second‐Order Cone Relaxation Method for Electricity‐Gas Integrated Energy Microgrid

Zhang, Fan; Shen, Zhuzheng; Xu, Wen; Wang, Guofeng; Yi, Biyi

doi:10.1155/2021/2073332

Cited by 4 publications

(4 citation statements)

References 28 publications

(31 reference statements)

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“…Some cases of this optimization technique are cited below. In [198], an optimal programming strategy for an HMG is proposed using the second-order cone programming relaxation method to perform a linear transformation to minimize the total cost of the proposed HMG. In [199], the authors use the coupling relationship between a PV and a WT system where an integrated optimization model is established in HMGs.…”

Section: Optimal Power Flow (Opf) Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources

Hernández-Mayoral,

Madrigal-Martínez,

Mina-Antonio

et al. 2023

Sustainability

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Microgrids (MGs) are systems that cleanly, efficiently, and economically integrate Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) to the electrical grid. They are capable of reducing transmission losses and improving the use of electricity and heat. However, RESs presents intermittent behavior derived from the stochastic nature of the renewable resources available on site. This can cause power-quality issues throughout the electrical grid, which can be solved by different optimization techniques and/or control strategies applied to power converters. This paper offers a detailed review of the literature regarding three important aspects: (i) Power-quality issues generated in MGs both in islanded mode and grid-connected mode; (ii) Optimization techniques used in the MGs to achieve the optimal operating conditions of the Energy Management System (EMS); and (iii) Control strategies implemented in the MGs to guarantee stability, mitigation of power-quality issues, power balance, and synchronization with the grid. It is worth mentioning that in this paper, we emphasize hybrid MGs (HMGs) since they combine the benefits of AC–MGs and DC–MGs while increasing system reliability. As the utility grid moves toward an optimal design of MG structures, this paper will serve as a foundation for future research, comparative analysis, and further development of novel techniques regarding HMGs.

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Section: Optimal Power Flow (Opf) Optimizationmentioning

confidence: 99%

“…Minimizes energy cost by using renewable energy sources [168,[194][195][196][197][198][199][200][201]…”

Section: • Consider Non-linear Characteristics •mentioning

confidence: 99%

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A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources

Hernández-Mayoral,

Madrigal-Martínez,

Mina-Antonio

et al. 2023

Sustainability

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“…Common optimal scheduling models encompass MILP [5], dynamic programming [6], distributed optimization [7], etc. Likewise, common model-solving algorithms involve intelligent algorithms [8], second-order cone relaxation methods [9], Lagrange relaxation methods, etc. However, as the uncertainties of the source-load dual-side within MGs escalate, solving the optimal scheduling problem under such uncertainties becomes a more realistic and challenging research problem [10].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

Chen,

Wang,

2023

Energies

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With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.

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Optimal Scheduling of Gas and Electricity Distribution Networks in Microgrids: A Decomposition Approach

Shahbazbegian

Ameli

Shafie‐khah

et al. 2022

2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Syst

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Optimal Power Flow Algorithm Based on Second‐Order Cone Relaxation Method for Electricity‐Gas Integrated Energy Microgrid

Cited by 4 publications

References 28 publications

A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources

A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources

A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

Optimal Scheduling of Gas and Electricity Distribution Networks in Microgrids: A Decomposition Approach

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