Fast learning optimiser for real‐time optimal energy management of a grid‐connected microgrid

Tan, Zhukui; Zhang, Xiaoshun; Xie, Baiming; Wang, Dezhi; Liu, Bin; Yu, Tao

doi:10.1049/iet-gtd.2017.1983

Cited by 22 publications

(5 citation statements)

References 35 publications

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“…Since EV in the Vehicle-To-Grid (V2G) system can consume power in charging and supply power in discharging, then it can represent an autonomous microgrid with a storage unit. Tan et al [110] used a module-free, e-greedy, reinforcement learning of Q-learning solution as a non-convex top layer of a fast-learning optimizer, in order to implement the real-time optimal energy management (OEM) of a connected microgrid. The proposed strategy was an intelligent contribution for a combined management method of classical control and an intelligent model-free reinforcement learning, which in turn, enhanced the speed and the value of the quality optimization.…”

Section: E-greedy Policymentioning

confidence: 99%

“…Q-learning has been involved in microgrid power management with the aim of achieving fast and high-quality optimization. The three proposed strategies [108][109][110], presented in the previous section all followed a Q-learning method of their module-free reinforcement learning involvement. The introduction of Q-learning RL of optimizing power flow for an EV charging station highlighted its development compared to classical, programmingbased optimization [111].…”

Section: Q-learningmentioning

confidence: 99%

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Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

2021

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Distributed Energy Storage Systems are considered key enablers in the transition from the traditional centralized power system to a smarter, autonomous, and decentralized system operating mostly on renewable energy. The control of distributed energy storage involves the coordinated management of many smaller energy storages, typically embedded within microgrids. As such, there has been much recent interest related to controlling aspects of supporting power-sharing balance and sustainability, increasing system resilience and reliability, and balancing distributed state of charge. This paper presents a comprehensive review of decentralized, centralized, multiagent, and intelligent control strategies that have been proposed to control and manage distributed energy storage. It also highlights the potential range of services that can be provided by these storages, their control complications, and proposed solutions. Specific focus on control strategies based upon multiagent communication and reinforcement learning is a main objective of this paper, reflecting recent advancements in digitalization and AI. The paper concludes with a summary of emerging areas and presents a summary of promising future directions.

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Section: E-greedy Policymentioning

confidence: 99%

Section: Q-learningmentioning

confidence: 99%

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

2021

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“…The study in Cucuzzella et al presents a sliding mode control approach for microgrids because of its robustness properties against a wide class of uncertainties, which is an appropriate solution to overcome uncertainties in generation forecast of RES. In Tan et al, a fast learning two‐layer optimizer is proposed to achieve high‐quality optimum and a short execution time optimization. In Elsied et al, a new strategy based on a genetic algorithm is proposed for an RT energy management system for microgrids to optimize the energy cost, emissions, and the integrated power of the available RES.…”

Section: Real‐time Control and Energy Managementmentioning

confidence: 99%

“…Microgrid energy management optimization with considering both renewable uncertainty and RT market price 71 Virtual queues from Lyapunov optimization A new auction model is presented for market RT operation 73 Fast learning optimiser (FlO) A two-layer optimization using model-free Q-learning is employed for knowledge learning and decision making is proposed for gridconnected microgrid, 57 Chance-constrained stochastic optimization New RT microgrid planning and control considering uncertainties of the PV power predictions 74 Fuzzy logic controller Decentralized reactive power control and stability improvement 47 Making a game with Nash equilibrium A new distributed RT electricity allocation (DRTA) scheme is presented 49 Heuristic optimization methods…”

Section: Robust Optimizationmentioning

confidence: 99%

A review on real‐time simulation and analysis methods of microgrids

Ahmadiahangar

Rosin

Niaki

et al. 2019

Int Trans Electr Energ Syst

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Summary Microgrid is a recently developed concept for future power systems. The main characteristics of the microgrid are the capability of integration of renewable energy sources and the ability to operate in two grid‐connected and islanded modes. A significant challenge of microgrid implementation is developing comprehensive control methods to ensure efficient, stable, and reliable operation. Real‐time studies are a promising approach in this case. In this paper, various real‐time energy management approaches have been thoroughly explained following a new categorization of them. A significant literature review of real‐time simulation and modeling methods has also been presented. A review of different applications of hardware‐in‐the‐loop testing of the microgrid is included in the present study. Finally, a discussion on demand for further research has been made. The presented work is organized to allow a reader to understand the importance of real‐time studies of microgrids and highlight trends in literary works without delving deeply into each one.

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“…e multi-energy microgrid can coordinate the allocation of various types of resources, such as electricity, gas, heat, and cold, and improve the exibility of system energy supply and ability to meet users' energy demand. It is an important method for the development on a large scale and application of distributed renewable energy in the future [6,7].…”

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation of Multi-Energy Microgrid under the Background of New Power System

2022

Mathematical Problems in Engineering

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As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited’s region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3.

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Fast learning optimiser for real‐time optimal energy management of a grid‐connected microgrid

Cited by 22 publications

References 35 publications

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

A review on real‐time simulation and analysis methods of microgrids

Research on the Evaluation of Multi-Energy Microgrid under the Background of New Power System

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