A Microgrid Energy Management System with Demand Response for Providing Grid Peak Shaving

Shen, Jingshuang; Jiang, Chuanwen; Liu, Yangyang; Qian, Jie

doi:10.1080/15325008.2016.1138344

Cited by 73 publications

(33 citation statements)

References 16 publications

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“…There is a wide range of studies covering the issue of energy management for DN with microgrids (MGs) [9][10][11][12][13][14][15][16][17]. Most of the work is focused on economic scheduling for different entities.The operating cost of microgrids in the DN is minimized through effective energy management considering the real-time electricity price in [11].…”

Section: Related Workmentioning

confidence: 99%

“…Most of the work is focused on economic scheduling for different entities.The operating cost of microgrids in the DN is minimized through effective energy management considering the real-time electricity price in [11]. In [14], energy management considering the demand response for a microgrid, is achieved for the economic operation of the microgrid, and also for peak shaving of the distribution grid. Multi-agent approaches and game theories have been widely applied to the decision-making and energy management of AC and DC MGs [15][16][17].…”

Section: Related Workmentioning

confidence: 99%

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Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

Yue

et al. 2019

Applied Sciences

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The increasing penetration of distributed energy resources in next-generation distribution networks has resulted in an explosion of the Internet of Things to upgrade their control and monitoring systems. This poses new challenges for the efficient energy management and reliable decision-making of these systems. This is due to the potentially large amount of data that cannot be handled by the traditional architecture of control and data acquisition systems, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. Neural networks were used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

Yue

et al. 2019

Applied Sciences

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“…A local controller at each generating unit acts as a lower level control, and a centralized controller as upper level control. In [38] an operational scheduling problem was formulated in accordance with various incentives like peak time rebate (PTR) and demand response (DR) programming for responsive loads and constraints like optimal scheduling of the batteries and diesel generators.…”

Section: Power Quality Controlmentioning

confidence: 99%

Optimization Techniques for Operationand Control of Microgrids � Review

Ramprabhakar

2018

JGE

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Microgrid systems show great promise in integrating large numbers of distributed generation systems, based on renewable energy sources into future power systems. The characteristics of power generated by renewable energy sources is random, unregulated and highly unreliable, vowing to the stochastic nature of the sources like wind, solar etc. While integrating the locally generated power into grid sounds very attractive, there are many operational and planning concerns. Also, a major chunk of power generated is weather dependent, this makes an energy storage system and/or backup power generation system an imperative part of microgrids. A proper planning and designing is the first step towards integrated power. Optimization techniques justify cost of investment of a Microgrid by enabling economic and reliable usage of resources. This paper summarizes various optimization methodologies and criterion for optimization of Microgrids. Extensive study of published literature show that computational alternatives like evolutionary, heuristic and non-classical algorithms show better results when compared to other conventional methods. The study of multi-objective optimization problems shows superior performance by combining intelligent optimization algorithms with adaptive techniques.

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“…Similarly, a hybrid electrothermal storage system that uses a battery and a hot water tank was developed in Reference 11 to optimize power exchange with the grid in an electrothermal residential MG. In References 8 and 12 a peak power pricing mechanism was used with the short‐term forecasting of renewable generation to smooth power exchange with the grid. In the aforementioned studies, a mixed‐integer linear programming problem was integrated into a model predictive control framework to reduce forecasting errors.…”

Section: Introductionmentioning

confidence: 99%

Dynamic control of grid‐connected microgrids for tie‐line smoothing

Islam

Yang

Ամին

2020

Int Trans Electr Energ Syst

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Summary This paper presents an interlink inverter control method for providing a constant tie‐line smoothing service in a grid‐connected residential microgrid (MG) to mitigate the fluctuating nature of renewable power generation and load demand. A grid power controller is designed for an MG to keep a constant grid power on typical days of the year by maintaining the charging/discharging of the battery. To achieve this objective, the MG controller sends the reference to the interlink inverter controller based on load dynamics, average PV generation and battery capacity. Moreover, a state‐space model is developed from the dynamic model of the MG system, and an eigenvalue‐based stability analysis is performed for different system parameters. The proposed control method is verified through extensive case studies by using real irradiance and typical residential customer load data in Queensland, Australia. In addition, a comparison with a dynamic energy management‐based method that aims to achieve the same objective is presented to demonstrate the performance of the proposed control method. Results show that the proposed method provides a constant tie‐line power for a grid‐connected residential MG for typical days of the year.

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A Microgrid Energy Management System with Demand Response for Providing Grid Peak Shaving

Cited by 73 publications

References 16 publications

Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

Optimization Techniques for Operationand Control of Microgrids � Review

Dynamic control of grid‐connected microgrids for tie‐line smoothing

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