A Study of Energy Management in Domestic Micro-grids Based on Model Predictive Control Strategies

Bruni, G.; Cordiner, Stefano; Mulone, Vincenzo; Rocco, Vittorio; Spagnolo, Fanny

doi:10.1016/j.egypro.2014.11.1013

Cited by 72 publications

(20 citation statements)

References 9 publications

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“…Bruni et al [13] developed a numerical model for power management in a domestic off-grid system with photovoltaic cell, fuel cell, and batteries, considering energy cost and in-house comfort as optimization targets. The modulation strategy to reduction of requested power and smoother battery pack charge and discharge cycles are obtained.…”

Section: Energy Conservation and Efficient Utilizationmentioning

confidence: 99%

Clean, efficient, affordable and reliable energy for a sustainable future

Lee

Yan

Chou

et al. 2015

Energy Conversion and Management

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Section: Energy Conservation and Efficient Utilizationmentioning

confidence: 99%

Clean, efficient, affordable and reliable energy for a sustainable future

Lee

Yan

Chou

et al. 2015

Energy Conversion and Management

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“…A cogeneration system is designed with the purpose of satisfying the predicted demands of electricity and sanitary hot water (SHW) of a given consumer [10]. This way, recently there have been reported several approaches for optimizing the use of electric [7,47,12,32,43] and thermal energies [22,9,34] as well as cogeneration systems in housing complexes [35,44,45,46,17,27].…”

Section: Introductionmentioning

confidence: 99%

Optimal design of integrated CHP systems for housing complexes

Fuentes-Cortés

Ponce-Ortega

Nápoles‐Rivera

et al. 2015

Energy Conversion and Management

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“…AVG MIN WT W P P T = − ⋅  (27) being T W the time window of one day. Furthermore, for the controller output, P FLC (n), nine triangular MFs shown in Fig.…”

Section: B Fuzzy Controller Designmentioning

confidence: 99%

“…Furthermore, other works consider scenarios with more degrees of freedom where the EMS drives different storage elements (batteries, fuel cells…), controllable loads (electrical load management, heat pumps…) or a combination of both as in [23]-[25], to carry out Demand Side Management (DSM) and Demand Response (DR) strategies. The control methods used in this case are usually sophisticated as Model Predictive Control (MPC) and, include both generation and demand forecasting as in [26], [27].In contrast, the power architecture addressed in this work focuses on a residential grid-connected MG with wind and solar generation as well as a residential load where only the battery can be controlled. Moreover, the data available are historical yearly records of RES power generation and load consumption (i.e.…”

mentioning

confidence: 99%

Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids

Arcos-Avilés

Pascual

Marroyo

et al. 2018

IEEE Trans. Smart Grid

276

167

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Abstract--This paper presents the design of a low complexity Fuzzy Logic Controller of only 25-rules to be embedded in an Energy Management System for a residential grid-connected microgrid including Renewable Energy Sources and storage capability. The system assumes that neither the renewable generation nor the load demand is controllable. The main goal of the design is to minimize the grid power profile fluctuations while keeping the Battery State of Charge within secure limits. Instead of using forecasting-based methods, the proposed approach uses both the microgrid energy rate-of-change and the battery SOC to increase, decrease or maintain the power delivered/absorbed by the mains. The controller design parameters (membership functions and rule-base) are adjusted to optimize a pre-defined set of quality criteria of the microgrid behavior. A comparison with other proposals seeking the same goal is presented at simulation level, whereas the features of the proposed design are experimentally tested on a real residential microgrid implemented at the Public University of Navarre.Index Terms--Distributed power generation, energy management, fuzzy control, microgrid, renewable energy sources, smart grid. I. INTRODUCTION HE ENVIRONMENTAL and economic benefits related to the reduction of both carbon dioxide emission andThis work was partially supported by the Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación SENESCYT and the Instituto de Fomento al Talento Humano del Ecuador under the grant No. 2013-AR2Q4081. Government of Navarra and the FEDER funds under project "Microgrids in Navarra: design, development and implementation". Spanish Ministry of Economy and Competitiveness under grant DPI2013-42853-R; European Union under the project FP7-308468, "PVCROPS-Photovoltaic Cost reduction, reliability, operational performance, prediction and simulation"; grant RUE CSD2009-00046, Consolider-Ingenio 2010 Programme, Spanish Ministry of Science and Innovation, and the grants DPI2013-41224-P, DPI2012-31580 from the Spanish Ministry of Economy and Knowledge.D. Arcos-Aviles is with the Departamento de Ingeniería Eléctrica y Electrónica, Propagation, Electronic Control and Networking (PROCONET) research group, Universidad de las Fuerzas Armadas ESPE, 171-5-231B Sangolquí, Ecuador (e-mail: dgarcos@espe.edu.ec).J. Pascual, L. Marroyo and P. Sanchis are with the Department of Electrical and Electronic Engineering, Public University of Navarre, Pamplona, Spain (e-mail: juliomaria.pascual@unavarra.es; luisma@unavarra.es; pablo.sanchis@unavarra.es.).F. Guinjoan is with the Department of Electronics Engineering, Escuela Técnica Superior de Ingenieros de Telecomunicación de Barcelona, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain (e-mail: francesc.guinjoan@upc.edu).transmission losses have made distributed renewable generation systems became a competitive solution for future smart grids [1]. In this context, microgrids are considered as the key building blocks of smart grids [2] and have aroused g...

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A Study of Energy Management in Domestic Micro-grids Based on Model Predictive Control Strategies

Cited by 72 publications

References 9 publications

Clean, efficient, affordable and reliable energy for a sustainable future

Clean, efficient, affordable and reliable energy for a sustainable future

Optimal design of integrated CHP systems for housing complexes

Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids

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