An adaptive fuzzy logic system for residential energy management in smart grid environments

Keshtkar, Azim; Arzanpour, Siamak

doi:10.1016/j.apenergy.2016.11.028

Cited by 103 publications

(40 citation statements)

References 23 publications

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“…The FLC is a computational intelligence techniques converting the human experience into an advanced control method. Unlike the binary system, the FLC is capable of addressing issues in uncertain and heuristic ways [38][39][40][41]. In the single household PV storage system, the battery power and grid power are regarded as controllable variables i.e., power source or power sink to satisfy power balance and the load demand as in Equation (3).…”

Section: Design Of the Fuzzy Controllermentioning

confidence: 99%

A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

et al. 2018

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Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC), battery state-of-charge (SOC) and the variation of SOC (∆SOC) are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO) to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year.

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Section: Design Of the Fuzzy Controllermentioning

confidence: 99%

A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

et al. 2018

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“…• dynamic pricing- 2,[7][8][9][10][11][12][13][14] • time of use- 10,[15][16][17] • maximum demand- 18 The time of use tariff sets the prices of energy for different times in a day well in advance. It does not represent the actual conditions in the energy market at a given time.…”

Section: Introductionmentioning

confidence: 99%

Economic analysis and control strategy of residential prosumer

Badar

Sanjari

2020

Int Trans Electr Energ Syst

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Summary A smart home requires energy management systems to operate optimally. It has an integrated renewable energy source along with a storage system. The output of the renewable energy source cannot be controlled. The ESS is optimally scheduled for charging and discharging in order to derive the maximum utilization of it. Optimal scheduling of battery integrated smart home is proposed in this paper to minimize the energy costs for a given set of daily electricity tariffs scheme. The sizing of the battery installed in a prosumer system is investigated. An SHEMS is modelled, and an appropriate objective function is defined to reflect the operational costs of a prosumer. The battery efficiency is related to the applied tariff for deciding the operational characteristics of the storage system. The optimal control of the battery operation is then proposed considering its constraints. The sensitivity analysis is performed on the battery to observe its performance in different conditions. The results show the effectiveness of the proposed algorithm to reduce the operational costs of the smart home.

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“…It is a partially infrastructure-based network where some nodes are static and other nodes are dynamic. 4 Random deployment of dynamic node is shown in Figure 2B, and fusion of both the scenarios is shown in Figure 2C where BS helps FIGURE 1 Peer-to-peer wireless network to enable static and dynamic nodes. Figure 2A shows arrangement of static nodes including BS in a 3×3 grid structure.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2A shows arrangement of static nodes including BS in a 3×3 grid structure. 4 Random deployment of dynamic node is shown in Figure 2B, and fusion of both the scenarios is shown in Figure 2C where BS helps FIGURE 1 Peer-to-peer wireless network to enable static and dynamic nodes. This concoction of architecture helps to overcome numerous issues in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques

Das

Tripathi

2017

Int J Communication

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Hybrid Ad-hoc NETwork (HANET) is a fusion of both the static and dynamic topologies. Each node of this network consists of low capacity battery. Because of heterogeneous characteristic of the topology, network parameters are imprecise in nature. This, in effect, the performance and lifetime of the network degrade. To overcome these issues, this paper proposes an optimized energy efficient routing (OE2R) method. This method is inspired by artificial intelligence techniques such as multiobjective optimization, geometric programming, aspiration level, and tolerance limit. The fusion of such stated artificial intelligence techniques provide an effective tool to optimize multiple conflict objectives and estimate imprecise parameters of the network, simultaneously. The proposed technique OE2R is simulated using LINGO optimization software. To justify the effectiveness of the proposed technique, its performance is compared with some existing methods such as approximate linear programming technique for average cost bounds and linear programming-based efficient message delivery approach in hybrid network in several passes of performance metrics. The simulation results show that the proposed OE2R method performs much better with the comparative methods and may be implemented as energy-efficient routing for HANET. KEYWORDSapproximation method, aspiration level, geometric programming, hybrid ad hoc network, multiobjective optimization, optimset Int J Commun Syst. 2017;30:e3340.wileyonlinelibrary.com/journal/dac

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An adaptive fuzzy logic system for residential energy management in smart grid environments

Cited by 103 publications

References 23 publications

A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

Economic analysis and control strategy of residential prosumer

Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques

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