A Review of Fuzzy-Based Residential Grid-Connected Microgrid Energy Management Strategies for Grid Power Profile Smoothing

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

doi:10.1007/978-981-13-3284-5_8

Cited by 26 publications

(24 citation statements)

References 45 publications

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“…Another parameters is the maximum power bypass (MPD) which is defined as the range of maximum change in the main grid profile. In other words, it is the maximum absolute value of the slope during the sampling time represented by 9 [49]- [51]:…”

Section: Discussionmentioning

confidence: 99%

“…This number (the truth value) applies to the consequence [51], [52]. The advantages of using this type of fuzzy system: a) it is very intuitive, b) it could be trained by an artificial neural network or by hand, and c) it is adapted to incorporate knowledge and experience.…”

Section: B Fl Epc Designmentioning

confidence: 99%

“…The first strategy is FLC NPT (Fuzzy logic controller based on Net Power Trend) [49], [51]. The diffuse controller of this strategy considers the tendency of the power flow in the MG to minimize the power exchange with the main grid; while extending the life of the BB against the charging and discharging cycles, Figure 11 (a).…”

Section: Hil Validationmentioning

confidence: 99%

“…The second strategy is FLC EROC (Fuzzy logic controller based on Energy Rate of Charge) [50], [51]. It aims at minimizing power spikes and fluctuations in the power grid while maintaining the SOC within safe limits, and reducing the complexity of the FLC, Figure 11 (b).…”

Section: Hil Validationmentioning

confidence: 99%

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Fuzzy Logic Power Management Strategy for a Residential DC-Microgrid

et al. 2020

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Power management strategies (PMS) are applied to keep a balance, between different energy sources (i.e. solar, wind, geothermal, hydro), storage units (i.e. fuel cell, batteries, fly wheel) and loads. Up to date, there has been reported several advance techniques to solve this task, for instance: Fuzzy Logic, Deep Learning, Droop Control, Bayesian Networks, among others. Nevertheless, some of those PMS are over simplified and others are too complex to be programmed in devices with limited resources. To solve these issues, this paper proposes a PMS based on Fuzzy Logic, which keeps a balance between those two goals. Characteristics of the proposed PMS are a small number of rules; fulfillment of the demanded power at every time; reducing use of the storage unit; and keeping a balance between the different sources, storage unit and loads. The proposed PMS is numerically evaluated by using SIMULINK-MATLAB®, in a 10kW residential DC Microgrid (MG), and validated by using a Hardware in the Loop platform (NI myRio-1900 and Typhoon HIL402). A comparison with three popular advance techniques demonstrates the feasibility of the proposed PMS.

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Section: Discussionmentioning

confidence: 99%

Section: B Fl Epc Designmentioning

confidence: 99%

Section: Hil Validationmentioning

confidence: 99%

Section: Hil Validationmentioning

confidence: 99%

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Fuzzy Logic Power Management Strategy for a Residential DC-Microgrid

et al. 2020

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“…In [14], a game theoretic non-cooperative distributed coordination control (NCDCC) scheme is suggested, trying to address multi-operator energy trading for microgrids. There is some MAS research utilizing fuzzy-logic controllers to mitigate the effects of unpredictability in microgrid energy management which can be found in comprehensive reviews [15,16]. Optimal operation of energy storage systems has also gained popularity in microgrid management [17].…”

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confidence: 99%

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

et al. 2019

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In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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