Fuzzy-based energy management of a residential electro-thermal microgrid based on power forecasting

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

doi:10.1109/iecon.2018.8591575

Cited by 10 publications

(7 citation statements)

References 17 publications

(51 reference statements)

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“…Thus, a persistence technique for both renewable energy and load forecasting is presented in [44] which is based on historical power data instead of weather data. Other techniques presented in the literature for load and generation forecasting include fuzzy logic [45], [46], statistic approach [47], [48], intelligent algorithm [49], adaptive neuro-fuzzy inference system (ANFIS) [50]. However, further accurate load and generation forecasting for DC microgrids can be the future research directions combining those methods or models as a hybrid one.…”

Section: A Generation and Load Forecastingmentioning

confidence: 99%

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Al–Ismail

2021

IEEE Access

338

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In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cuttingedge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic. INDEX TERMS DC microgrids, renewable energy sources, batteries, supercapacitors, DC bus voltage, power management, state of charge, microgrid operation, and planning.

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Section: A Generation and Load Forecastingmentioning

confidence: 99%

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Al–Ismail

2021

IEEE Access

338

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“…However, it can be noted that almost all possible energy scenarios will be covered along a year. Moreover, the use of the CS algorithm leads to a simulation time reduction of around 33% compared with the previous heuristic trial and error procedure presented in [20]. Furthermore, a weighting factor of w = 2 has been implemented in the cost function defined in (12) to prioritize the minimization of P G,MAX , P G,MIN , and MPD over APD, PPV, and PVR since are dependent of the first three ones and because the main objectives of the EMS is to minimize the power ramp-rates, power peaks, and fluctuations in the power exchanged with the grid.…”

Section: Simulation and Comparison Resultsmentioning

confidence: 98%

“…MGs are currently defined as a low-voltage distribution network consisting of loads, distributed generation elements, and energy storage systems (ESS) that are connected to the main supply network at a single point of common coupling (PCC), with an associated energy management system (EMS) that allows them to operate reliably, safely, and economically [10], [11]. ENT STRATEGY e microgrid ar in [20]. Howe ption of this ar en next.…”

Section: Introductionmentioning

confidence: 99%

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Adjustment of the Fuzzy Logic controller parameters of the energy management strategy of a grid-tied domestic electro-thermal microgrid using the Cuckoo search algorithm

Arcos-Avilés

García-Gutiérrez

Guinjoan

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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During the last century, population growth, together with economic development, has considerably increased the energy demand and, although renewable energies are becoming an alternative, still total energy supply is mainly non-renewable, causing well-known negative effects such as pollution and global warming. On the other hand, technological advances have allowed the development of increasingly efficient distributed generation systems and the emergence of microgrids, whose studies have been focused on architecture, elements, and objectives of the associated energy management strategies. In this regard, energy management strategies based on a Fuzzy Logic controller have been developed for electro-thermal microgrids where parameter optimization has been carried out through heuristic procedures of trial and error with acceptable results but involving a high computational cost. To solve the aforementioned drawbacks, in the present work the use of Cuckoo Search optimization nature-inspired algorithm that allows the adjustment of Fuzzy Logic controller parameters and ensures a higher quality of energy management is proposed. Obtained results show encouraging outcomes for the use of these meta-heuristic optimization algorithms.

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“…The thermal storage capacity associated with the water tank temperature, is responsible for improving the system performance by providing a thermal buffer to alleviate the solar availability or load mismatch [46], [49]. In this regard, the water tank and the controllable load (i.e., EWH) provide extra possibilities to control the grid power profile.…”

Section: Electro-thermal Microgrid Power Architecturementioning

confidence: 99%

An Energy Management System Design Using Fuzzy Logic Control: Smoothing the Grid Power Profile of a Residential Electro-Thermal Microgrid

et al. 2021

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This work deals with the design of a Fuzzy Logic Control (FLC) based Energy Management System (EMS) for smoothing the grid power profile of a grid-connected electro-thermal microgrid. The case study aims to design an Energy Management System (EMS) to reduce the impact on the grid power when renewable energy sources are incorporated to pre-existing grid-connected household appliances. The scenario considers a residential microgrid comprising photovoltaic and wind generators, flat-plate collectors, electric and thermal loads and electrical and thermal energy storage systems and assumes that neither renewable generation nor the electrical and thermal load demands are controllable. The EMS is built through two low-complexity FLC blocks of only 25 rules each. The first one is in charge of smoothing the power profile exchanged with the grid, whereas the second FLC block drives the power of the Electrical Water Heater (EWH). The EMS uses the forecast of the electrical and thermal power balance between generation and consumption to predict the microgrid behavior, for each 15-minute interval, over the next 12 hours. Simulations results, using real one-year measured data show that the proposed EMS design achieves 11.4% reduction of the maximum power absorbed from the grid and an outstanding reduction of the grid power profile ramp-rates when compared with other state-of-the-art studies.

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Fuzzy-based energy management of a residential electro-thermal microgrid based on power forecasting

Cited by 10 publications

References 17 publications

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

Adjustment of the Fuzzy Logic controller parameters of the energy management strategy of a grid-tied domestic electro-thermal microgrid using the Cuckoo search algorithm

An Energy Management System Design Using Fuzzy Logic Control: Smoothing the Grid Power Profile of a Residential Electro-Thermal Microgrid

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