Robustly Multi-Microgrid Scheduling: Stakeholder-Parallelizing Distributed Optimization

Qiu, Haifeng; Gu, Wei; Xu, Yinliang; Wu, Zhi; Zhou, Suyang; Pan, Guangsheng

doi:10.1109/tste.2019.2915585

Cited by 61 publications

(15 citation statements)

References 35 publications

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“…Additionally, for managing the optimization problem, the authors have presented a mixed-integer two-stage stochastic system. Qiu et al (2019) have demonstrated a stakeholder-parallelizing distributed adaptive robust optimization for the scheduling of hybrid AC/DC MG. The power issues of the lower-layer were tended to by a nested column-and-constraint generation algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

Kumari

Babu

2020

Transactions of the Institute of Measurement and Control

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This paper presented an effective technique for considering optimal scheduling of microgrid (MG) with islanding constraints. The proposed optimal scheduling approach is the combination of both the modified dragonfly algorithm (MDA) and whale optimization algorithm (WOA)-based approach named as (MDAWO). In this work, the searching conduct of the dragonflies is altered by the effective WOA approach. This WOA is used to the optimal scheduling of MG and also significantly reduces the computational burden. In this paper, the MDAWO is the appraisal technique used to set up the definite schedule of the MG coupling subject to the power assortment. The equality and inequality constraints are utilized to characterize the target capacity of a projected approach using the system information. Batteries act as an energy source to adjust the MG with continual running at an unfaltering and stable output control. Finally, the performance of the proposed technique is executed through the MATLAB/Simulink working platform with two different scenarios. With these two different scenarios, the proposed technique performance is compared with existing techniques such as dragonfly algorithm, firefly and gravitational search algorithm. Furthermore, the statistical analysis of the proposed technique based on cost and fitness are analyzed. Numerical simulations demonstrate the effectiveness of the proposed MG optimal scheduling model and explore its economic and reliability merits.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

Kumari

Babu

2020

Transactions of the Institute of Measurement and Control

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“…The authors in [30] propose nested column-constraint and generation (CC&G) method for distributive scheduling of MMG, which is based on stakeholder-parallelizing distribution optimization. This method uses an enhanced analytical cascading method to achieve energy optimization.…”

Section: Cga Approach For Multi-microgrid Systemmentioning

confidence: 99%

Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

et al. 2020

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Multi-microgrid (MMG) system is a new method that concurrently incorporates different types of distributed energy resources, energy storage systems and demand responses to provide reliable and independent electricity for the community. However, MMG system faces the problems of management, real-time economic operations and controls. Therefore, this study proposes an energy management system (EMS) that turns an infinite number of MMGs into a coherence and efficient system, where each MMG can achieve its goals and perspectives. The proposed EMS employs a cooperative game to achieve efficient coordination and operations of the MMG system and also ensures a fair energy cost allocation among members in the coalition. This study considers the energy cost allocation problem when the number of members in the coalition grows exponentially. The energy cost allocation problem is solved using a column generation algorithm. The proposed model includes energy storage systems, demand loads, realtime electricity prices and renewable energy. The estimate of the daily operating cost of the MMG using a proposed deep convolutional neural network (CNN) is analyzed in this study. An optimal scheduling policy to optimize the total daily operating cost of MMG is also proposed. Besides, other existing optimal scheduling policies, such as approximate dynamic programming (ADP), model prediction control (MPC), and greedy policy are considered for the comparison. To evaluate the effectiveness of the proposed model, the real-time electricity prices of the electric reliability council of Texas are used. Simulation results show that each MMG can achieve energy cost savings through a coalition of MMG. Moreover, the proposed optimal policy method achieves MG's daily operating cost reduction up to 87.86% as compared to 79.52% for the MPC method, 73.94% for the greedy policy method and 79.42% for ADP method.

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“…where constraints (36) and (37) specify the minimum and maximum thermal charging and discharging limits, P th c/d, min and…”

Section: Multi-energy Management System (Mems)mentioning

confidence: 99%

“…A distributionally robust optimisation-based algorithm is developed by Liu et al [36] to provide a robust solution to the detailed scheduling decisions in the proposed transactive energy framework of networked microgrids under uncertainty. The stakeholder-parallelising distributed adaptive robust optimisation model is proposed in [37] for multi-microgrids. The uncertainty associated with the selling/ purchasing price of power from the power market is handled by robust optimisation keeping the minimal operating cost of MG [47].…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing and multi‐energy management strategy for PV‐biofuel‐based off‐grid systems

2020

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This study proposes a comprehensive framework for developing a multi-energy off-grid microgrid with the decoupled flow of thermal and electrical energies in a rural setting. A carbon-neutral microgrid with a hybrid generation system constituting a photovoltaic unit and a biofuel generator is proposed. In order to enhance the fuel utilisation efficiency, the biofuel generator is operated in combined cooling, heating, and power mode, and the recovered thermal energy forms the heat distribution network in the microgrid. The flexibility of system operation is improved by suitable multi-energy (electrical and thermal) storage. Firstly, an optimal sizing framework has been developed for the system as a mixed integer linear programming model. Secondly, a coordinated multi-energy management system (MEMS) has been developed for combined optimal dispatch of multiple generation and storage resources. The MEMS has been developed as a mixed integer non-linear programming model, which minimises system operational cost while considering minimum battery degradation to prolong its lifetime. Finally, a detailed economic analysis of the proposed system has been presented, highlighting the levellised cost of energy and net present value. Extensive case studies and simulation results depict the effectiveness and suitability of the proposed MEMS for the rural off-grid microgrid.

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Robustly Multi-Microgrid Scheduling: Stakeholder-Parallelizing Distributed Optimization

Cited by 61 publications

References 35 publications

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

RETRACTED: An efficient modified dragonfly algorithm and whale optimization approach for optimal scheduling of microgrid with islanding constraints

Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

Optimal sizing and multi‐energy management strategy for PV‐biofuel‐based off‐grid systems

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