Architecture of a microgrid energy management system

Jimeno, Joseba; Anduaga, Jon; Oyarzabal, José; Gil-de-Muro, Asier

doi:10.1002/etep.443

Cited by 97 publications

(70 citation statements)

References 16 publications

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“…To maintain the frequency between acceptable limits, the V/f inverter connected to storage device will adjust the active power in the network. It will inject active power when frequency falls from the nominal value and will absorb active power if the frequency rises above its nominal value [10]. …”

Section: F Frequency and Active Power Controlmentioning

confidence: 99%

Investigations on Issues in Microgrids

Gaur¹,

Singh²

2017

JOCET

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Abstract-This paper presents many technical issues and shortcomings confronted in installation of micro-grids particularly in rural areas. It discusses the technical challenges involved in grid integration of micro-grids and highlights some aspects of stability in micro-grids. There are different types of micro-grid applications. The system layout and the control design vary depending on the application and so does the facet of stability in a micro-grid. It briefly encompasses the stability aspects of remote, utility connected micro-grids depending on the modes of operation, control topology, types of renewables and the network parameters. In the end, a brief insight into IEC 62257 specifications has been given.Index Terms-Power quality, smart grid, solar power, stability, wind power.

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Section: F Frequency and Active Power Controlmentioning

confidence: 99%

Investigations on Issues in Microgrids

Gaur¹,

Singh²

2017

JOCET

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“…In order to provide high-quality and economical electricity, the microgrid operation needs to be scheduled reasonably [4]. As the brain of the microgrid, the energy management system (EMS) takes charge of coordinating the output power of the distributed resources and the exchanged power with the main grid to minimize the operational cost or maximize the total profits, according to the information from load prediction, power prediction of renewable energy and electricity price [5][6][7]. As far as the architecture of EMS is concerned, there exist three kinds of structures: the centralized structure [8], the hierarchical structure [7,9] and the distributed structure [5,10].…”

Section: Introductionmentioning

confidence: 99%

“…As the brain of the microgrid, the energy management system (EMS) takes charge of coordinating the output power of the distributed resources and the exchanged power with the main grid to minimize the operational cost or maximize the total profits, according to the information from load prediction, power prediction of renewable energy and electricity price [5][6][7]. As far as the architecture of EMS is concerned, there exist three kinds of structures: the centralized structure [8], the hierarchical structure [7,9] and the distributed structure [5,10]. The authors in [11] give a comprehensive review about the EMS of microgrids and summarize the recent state of the Power generation scheduling in the EMS plays an important role in the microgrid operation, which is used to dispatch various energy resources to meet the load demands at the minimum cost subject to the physical constraints of the microgrid [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

et al. 2017

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Abstract:In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based optimization model for maximum profits is developed. To reduce the computational complexity in solving the model, the possibility degree comparison between an interval and a real number is used to convert the interval constraints into the general ones; meanwhile, some slack variables and complementary conditions are introduced to eliminate the absolute-value operation. Unlike the stochastic optimization, the interval optimization only needs the upper-lower bounds of the uncertain variables instead of their probability distribution functions, which is beneficial to the practical application. Furthermore, the possible profit interval and the expected optimal profit can be determined by solving the optimization model. Numerical simulations are performed on a microgrid system modified from the benchmark low voltage network in the European Union project "Microgrid", and the results demonstrate the effectiveness of the proposed method.

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“…The advantages of Multi-Agent System (MAS) use in control applications described in [2] demonstrate the effectiveness of this approach in the organization and management of the intelligent distribution grid in large-scale applications.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous, Short-Term and Predictive Long-Term Power Balancing Techniques in Intelligent Distribution Grids

Suzdalenko

Galkin

2013

IFIP Advances in Information and Communication Technology

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Abstract. An increased number of distributed small generators connected to the power grid allows higher total efficiency and higher stability of electrical power supply by exporting energy to the grid to be achieved during peak demand hours. On the other hand, it poses new challenges in structuring and developing the control approaches for these distributed energy resources. This paper proposes an improved method of real-time power balancing targeted to reaching long-term energy management objectives. The novel long-term energy management technique is proposed, that is based on load categorization and regulation of energy consumption by regulating electricity price function estimated with the proposed mathematical model. The method was evaluated by a Lab-VIEW model by simulating various types of loads. The price function for the defined energy generation pattern from renewable energy sources was obtained.Keywords: Intelligent Distribution Grid, Nanogrid, Energy Management, Instantaneous Power Balancing, Short-Term Energy Management, Pricing. IntroductionThe dynamic of worldwide installed power utilizing renewable energy sources (RES) is increasing year by year [1], showing global awareness of climate change and the footprint of human behavior on the nature: like mining activities that change the landscape and damages caused by oil plants. Another problem is related to the total effectiveness of energy from the primary fuel. It relates to the total system efficiency of energy delivery to the end-user from a mining site, which includes energy losses at middle stages, like use of energy at the mining stage, energy conversion losses, transmission (mechanical and electrical) losses, as well as end-device efficiency. Consequently, locally generated energy (especially from renewable energy sources) is preferable because it excludes most of the mentioned losses. As more distributed small generators are connected to the power grid, higher total efficiency and higher stability of electrical power supply by exporting energy to the grid during peak demand hours. On the other hand, it poses new challenges in structuring and developing the control approaches for these distributed energy resources.

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Architecture of a microgrid energy management system

Cited by 97 publications

References 16 publications

Investigations on Issues in Microgrids

Investigations on Issues in Microgrids

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

Instantaneous, Short-Term and Predictive Long-Term Power Balancing Techniques in Intelligent Distribution Grids

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