Analysis of energy management in micro grid – A hybrid BFOA and ANN approach

Roy, Kallol; Mandal, Kamal Krishna; Mandal, Atis Chandra; Patra, Sankar Narayan

doi:10.1016/j.rser.2017.07.037

Cited by 47 publications

(14 citation statements)

References 31 publications

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“…The proposed technique is compared with existing strategies such as squirrel optimization with gravitational search–aided neural network (SOGSNN) and adaptive neuro‐fuzzy interference system and advanced salp swam optimization algorithm (ANFASO) . In this section, the energy resources of PV and MT are resolved from Roy et al…”

Section: Resultsmentioning

confidence: 99%

“…So the multiobjective function is required for optimizing the configuration of MGs with minimum fuel cost, ie, MT, FC, and DE fuel cost functions. PSO trains the network of ANN . Here, we are training the ANN using the target power demand with the corresponding input time intervals of a day, ie, daily demand dataset based on the available PV and WT energy.…”

Section: Mathematical Modelling Of Mg Connected System Architecture Wmentioning

confidence: 99%

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Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

Roy

2019

Int J Numerical Modelling

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This paper presents a hybrid technique for optimal power flow management and production cost minimization of microgrid (MG)-connected system with renewable energy sources (RES). The hybrid technique is the joint execution of both the particle swarm optimization (PSO)-aided artificial neural network (ANN) and grasshopper optimization algorithm (GOA) named as GOAPSNN technique. The proposed technique is concerned with the mathematical optimization problems that involve more than one objective function to be optimized. At first, the PSO-ANN technique predicts the load demand in the MG-connected system by using the inputs of MG-connected system like photovoltaic (PV) system, wind turbine (WT), microturbine (MT), diesel engine (DE), and battery. According to the load demand, GOA is utilized to choose an optimal configuration of MG, ie, fuel cost minimization, emission factors, operating, and maintenance cost. The proposed technique is executed in MATLAB/Simulink platform and compared with the existing strategies, such as squirrel optimization with gravitational search-aided neural network (SOGSNN), adaptive neuro-fuzzy interference system and advanced salp swam optimization algorithm (ANFASO). In this paper, the maximum generated power of PV, WT, MT, and battery is 7. 9, 9.8, 15.7, and 4.56 kW, respectively. The state of charge (SoC) of the proposed technique is investigated, which is around 83%. In light of the load demand, the proposed approach has less production cost.

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Section: Resultsmentioning

confidence: 99%

Section: Mathematical Modelling Of Mg Connected System Architecture Wmentioning

confidence: 99%

Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

Roy

2019

Int J Numerical Modelling

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“…For example, an artificial neural network provided encouraging results in [29] by significantly reducing the primary energy consumption, emissions and operating costs of a microgeneration system coupled with renewable energy. A similar energy system and objective function were optimized by Roy et al in [30]. Artificial neural network effectiveness was also shown by Seo et al [31].…”

Section: Literature Reviewmentioning

confidence: 86%

Optimal Management of the Energy Flows of Interconnected Residential Users

Manservigi¹,

Cattozzo²,

Spina³

et al. 2020

Energies

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In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and an auxiliary boiler and the national electrical grid are employed as supplementary systems. In this paper, a simulation model, which accounts for component efficiency and energy balance, was developed to replicate the interaction between the users and the energy systems in order to minimize primary energy consumption. The simulation model identified the optimal operation strategy of two residential users by investigating different energy system configurations by means of a dynamic programming algorithm. The reference scenario was compared to three different scenarios by considering independent energy systems, shared thermal and electrical energy storage and also the shared prime mover. Such a comparison allowed the identification of the most suitable energy system configuration and optimized operation strategy. The results demonstrate that the optimized operation strategy smoothes the influence of the size of thermal and electrical energy storage. Moreover, the saving of primary energy consumption can be as high as 5.1%. The analysis of the economic feasibility reveals that the investment cost of the prime mover can be as high as 4000 €/kW.

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“…The factor of the random and nondispatchable nature of the RES is the primary test in the MGEM. The economic dispatch (ED), unit commitment (UC), and demand‐side management (DSM) are the fundamental factors in the optimal energy management for MGs, yet without seeking after a hearty definition against RES vulnerability . Subject to the Weibull circulation of wind speed and wind speed‐to‐power yield mapping, an ED issue is intended to limit abundance control and lessen the accessible air control.…”

Section: Introductionmentioning

confidence: 99%

“…The economic dispatch (ED), unit commitment (UC), and demand-side management (DSM) are the fundamental factors in the optimal energy management for MGs, yet without seeking after a hearty definition against RES vulnerability. 10,11 Subject to the Weibull circulation of wind speed and wind speed-to-power yield mapping, an ED issue is intended to limit abundance control and lessen the accessible air control.. 12 So as to adapt to the changeability of RES, the stochastic programming is likewise utilized. Single-time-possibilityrestricted ED issues for RES are analyzed in Aktas et al 13 and offer likelihood ensures that the load will be paid.…”

Section: Introductionmentioning

confidence: 99%

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

et al. 2019

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This paper proposes an efficient hybrid approach-based energy management strategy (EMS) for grid-connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasioppositional-chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS-RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid-connected MG system is continuously tracked by the RF technique.The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two-strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques. KEYWORDS energy management strategy, grid-connected MG, power flow constraints, random forest, quasioppositional-chaotic symbiotic organisms search algorithm How to cite this article: Chen J, Zhou Z, Karunakaran V, Zhao S. An efficient technique-based distributed energy management for hybrid MG system: A hybrid QOCSOS-RF technique. Wind Energy. 2020;23:575-592. https://doi.

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Analysis of energy management in micro grid – A hybrid BFOA and ANN approach

Cited by 47 publications

References 31 publications

Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

Optimal Management of the Energy Flows of Interconnected Residential Users

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

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