Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting

Arcos-Avilés, Diego; Pascual, Juan I.; Guinjoan, F.; Marroyo, Luis; Sanchís, Pablo; Marietta, Martin P.

doi:10.1016/j.apenergy.2017.07.123

Cited by 127 publications

(98 citation statements)

References 65 publications

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“…Arcos-Avilés et al [111] presented an energy management algorithm based on low-complexity fuzzy logic control for a residential grid-connected MG, which includes renewable distributed generation and batteries.…”

Section: Energy Management Based On Artificial Intelligence Techniquesmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

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Section: Energy Management Based On Artificial Intelligence Techniquesmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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“…Very few solutions presented in literature focus on actually building physical microgrids to study the concepts used for management algorithms or the optimization techniques. In [22] a fuzzy logic-based energy management system used in a residential microgrid is proposed looking to improve the life time of the batteries by maintaining the SOC close to 75%. As observed in [23][24][25] the simulated operation of microgrids is well-reported in literature but data analysis gathered from physically installed sites is neglected.…”

Section: Fig 1 Islanded Renewable Energy Microgridmentioning

confidence: 99%

An islanded microgrid energy management controller validated by using hardware-in-the-loop emulators

Petreuş

Etz

Pătărău

et al. 2019

International Journal of Electrical Power & Energy Systems

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A novel microgrid emulator used to test multiple microgrid configurations and energy management control strategies is presented. The system includes Hardware-In-the-Loop (HIL) emulators for geothermal and biogas energy sources. It also includes actual photovoltaic energy together with a lead acid battery bank for storage and it is controlled by a two level control system. The control system consists of a primary level voltage-reactive power and frequency-active power and a secondary level energy management algorithm based on the balance between the power produced by the renewable energy generators, state of charge of the battery bank and the loads. The energy management control strategy is based on cycle-charging the batteries at a reference value in order to efficiently use the available resources. The primary energy source is considered the geothermal energy, while the most cost effective one is the photovoltaic energy. A buffer zone is kept in the battery bank in order to store as much energy produced by the photovoltaic system as possible. The presented microgrid, used for testing the energy management strategies, employs emulators for the geothermal and biogas generators; however, the results are relevant and can be scaled for a real-life microgrid.Keywords: microgrid, power management, renewable energies, two level control, cycle charge control. Auxiliary equipment and communicationA method to send the commands to the microgrid and to measure the process variables from the microgrid is needed for the control algorithm based on battery cycle charging implementation. A bidirectional three wire RS485 serial communication network is built for this purpose. The communication protocol used in the network uses Modbus compatible data frameworks but is also compatible with the data protocols used by the inverters. Beside the communication modules needed by the inverters, there is a need for other equipment in order to give the geothermal and biomass emulators communication capabilities. The command signals are fed to the emulators through Modbus to analog converters and Modbus output compatible power meters are used for the electrical measurement. The network communication master is the central computing unit. The program that implements the master's communication functions and the cycle charging battery management algorithm is written in ANSI C, thus being highly portable. The physical interface between the network and the master is made by an USB to RS-485 interface. The connections of the equipment are presented in Fig. 22. The set points for the SMA equipment are directly sent through the communication network, but the emulators need Modbus to analog transducers for their commands.

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“…Given the two major renewable energy sources are the sun and wind, the effective production of electricity from such sources is one of the main objectives of the energy industry [4]. Although these energies seem free, with less negative influence, it can only be determined by how these energies are collected and utilized whether or not the system is sustainable [5]. In this context, it can be stated that if the overall system's output is great and energy generated is handled in an approach that minimizes negative influences on the power grid, utilized whether or not the system is sustainable [5].…”

Section: Introduction and Importance Of The Smart Home Energy Managemmentioning

confidence: 99%

“…Although these energies seem free, with less negative influence, it can only be determined by how these energies are collected and utilized whether or not the system is sustainable [5]. In this context, it can be stated that if the overall system's output is great and energy generated is handled in an approach that minimizes negative influences on the power grid, utilized whether or not the system is sustainable [5]. In this context, it can be stated that if the overall system's output is great and energy generated is handled in an approach that minimizes negative influences on the power grid, the energy is effectively sustainably harvested from these resources [6].…”

Section: Introduction and Importance Of The Smart Home Energy Managemmentioning

confidence: 99%

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

2020

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This article provides a fuzzy expert system for efficient energy smart home management systems (FES-EESHM), demand management, renewable energy management, energy storage, and microgrids. The suggested fuzzy expert framework is utilized to simplify designing smart microgrids with storage systems, renewable sources, and controllable loads on resources. Further, the fuzzy expert framework enhances energy and storage to utilize renewable energy and maximize the microgrid’s financial gain. Moreover, the fuzzy expert system utilizes insolation, electricity price, wind speed, and load energy controllably and unregulated as input variables to enable energy management. It uses input variables including insolation, electrical quality, wind, and the power of uncontrollable and controllable loads to allow energy management. Furthermore, these input data can be calculated, imported, or predicted directly via grid measurement using any prediction process. In this paper, the input variables are fuzzified, a series of rules are specified by the expert system, and the output is de-fuzzified. The findings of the expert program are discussed to explain how to handle microgrid power consumption and production. However, the decisions on energy generated, controllable loads, and own consumption are based on three outputs. The first production is for processing, selling, or consuming the energy produced. The second output is used for controlling the load. The third result shows how to produce for prosumer’s use. The expert method can be checked via the hourly input of variable values. Finally, to confirm the findings, the method suggested is compared to other available approaches.

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Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting

Cited by 127 publications

References 65 publications

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

An islanded microgrid energy management controller validated by using hardware-in-the-loop emulators

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

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