Optimal Design of an Islanded Microgrid With Load Shifting Mechanism Between Electrical and Thermal Energy Storage Systems

Mohandes, Baraa; Acharya, Samrat; Moursi, Mohamed Shawky El; Al-Sumaiti, Ameena Saad; Doukas, Haris; Sgouridis, Sgouris

doi:10.1109/tpwrs.2020.2969575

Cited by 61 publications

(23 citation statements)

References 44 publications

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“…A recurrent neural network is trained in [51] to predict temperature and solar irradiance based only on the date of the year and the time of the day. After that, the trained network is used to generate random weather scenarios.…”

Section: B Scenario Generation From a Multinomial Distributionmentioning

confidence: 99%

Renewable Energy Management System: Optimum Design and Hourly Dispatch

Mohandes

Wahbah

Moursi

et al. 2021

IEEE Trans. Sustain. Energy

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Section: B Scenario Generation From a Multinomial Distributionmentioning

confidence: 99%

Renewable Energy Management System: Optimum Design and Hourly Dispatch

Mohandes

Wahbah

Moursi

et al. 2021

IEEE Trans. Sustain. Energy

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“…Despite the fact that the PV has brought great benefits to the grid/micro-grid [5][6][7] and the electricity markets, it suffered from a remarkable challenge related to the operating efficiency which varies according to the weather conditions under which the SC works [8,9]. This is in addition to its high maintenance cost as reported in Ref.…”

Section: Introductionmentioning

confidence: 99%

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

et al. 2020

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Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547x10-4 , 7.64801x10-4 , and 7.59756 x10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

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“…Operators of the microgrid make an operation schedule of the controllable components, relying on the assumed profiles of the uncontrollable components (or the assumed profile of the net load) in advance, and adjust the schedule by reflecting the actual behavior of the net load. In the process, the BESSs take on an extremely important role that compensates the power surplus or shortage in the microgrid by their charging or discharging function, in addition to contributions to the reduction of operational costs and peak shaving [8][9][10][11]. By contrast, the BESSs, as is well known, have issues in their investment costs and operating lifetime, and these create a bottleneck in design and management of the microgrid.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components

et al. 2021

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Battery energy storage systems (BESSs) are key components in efficiently managing the electric power supply and demand in microgrids. However, the BESSs have issues in their investment costs and operating lifetime, and thus, the optimal sizing of the BESSs is one of the crucial requirements in design and management of the microgrids. This paper presents a problem framework and its solution method that calculates the optimal size of the BESSs in a microgrid, considering their cooperative operations with the other components. The proposed framework is formulated as a bi-level optimization problem; however, based on the Karush–Kuhn–Tucker approach, it is regarded as a type of operation scheduling problem. As a result, the techniques developed for determining the operation schedule become applicable. In this paper, a combined algorithm of binary particle swarm optimization and quadratic programming is selected as the basis of the solution method. The validity of the authors’ proposal is verified through numerical simulations and discussion of their results.

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Optimal Design of an Islanded Microgrid With Load Shifting Mechanism Between Electrical and Thermal Energy Storage Systems

Cited by 61 publications

References 44 publications

Renewable Energy Management System: Optimum Design and Hourly Dispatch

Renewable Energy Management System: Optimum Design and Hourly Dispatch

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components

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