Optimization of capacity and operational scheduling for grid-tied microgrid using pumped-storage hydroelectricity and photovoltaic

Nugraha, Petrus Yuri; Hadisupadmo, Sutanto; Widyotriatmo, Augie; Kurniadi, Deddy

doi:10.1109/ascc.2015.7244857

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Also, the ESS is supposed to work preferably around a reference design energy. This is the case, for example, of power store systems or of energy stored by a flywheel preferably working close to an angular reference speed [31], and of the water level in an elevated tank storing potential energy [32].…”

Section: Mg Network Model Descriptionmentioning

confidence: 99%

Stochastic constrained linear quadratic control in a network of smart microgrids

Bersani

Dagdougui

Ouammi

et al. 2020

IET Renewable Power Generation

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Challenges of microgrids (MGs) energy management have gained more relevance with the presence of uncertainties in power generation and local loads. These problems significantly increase when related to network of smart MGs (NSMG). To address these challenges, this study presents a stochastic constrained control problem for the optimal management of a cooperative NSMG with interconnections allowing power exchanges. In this model, each MG can exchange power locally among each other as well as with the main electric grid. The proposed control approach is based on a linear-quadratic Gaussian problem definition for the optimal control of power flows under quadratic constraints limiting the variability of the power exchange as well as of the stored energy in each MG. The developed framework is applied to a cooperative network of four smart MGs to test and validate its effectiveness and performance. The network is connected to the main electric grid allowing power exchanges. The results demonstrate that the role of energy storage systems is undoubtedly becoming more and more relevant in the context of reacting to the stochastic behaviour of the balance between produced and consumed powers in MGs.

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Section: Mg Network Model Descriptionmentioning

confidence: 99%

Stochastic constrained linear quadratic control in a network of smart microgrids

Bersani

Dagdougui

Ouammi

et al. 2020

IET Renewable Power Generation

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“…Shams et al use a simple Gaussian randomization to make their demand data reflect uncertainty, as well as more specific distributions for irradiation and wind speeds [14]. A number of other methodologies appear in literature, such as employing a Monte Carlo simulation [15] or deriving multiple scenarios and corresponding realization probabilities from historical data [16]. The scope of the microgrid models found in literature varies greatly.…”

Section: Introductionmentioning

confidence: 99%

Micro-energy Systems (MESs); Modeling and Optimal Operating Based on NSGA-II

Al‐Gabalawy

Adly

Abdelaziz

et al. 2021

Digital Transformation Technology

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In this paper, an optimization tool for Micro-energy systems (MESs) is designed and implemented in a modular and scalable architecture. A flexible modeling tool suitable for optimizing a microgrid setup under diverse conditions is developed. In addition to modeling of the small-scale generation or storage unit, where the modeling tool reflects the trade between connected entities as well as behavior such as load shifting and curtailment. It is then applied to a case study set in a village in North Rhine-Westphalia which is due to be destroyed by lignite mining. The case study furthermore indicates the potential of a decentralized energy system, especially as the investment costs of the applied technologies further decrease, and 'energy quality' factors, such as CO 2 emissions are becoming increasingly more relevant. Finally, the objective of the model will be to meet the demand of all connected entities at a minimum cost. Moreover, with these functionalities at a small solving time, the modeling tool should meet the criteria of being scalable, flexible in regard to the form and quality of input data and as user-friendly as possible. At the last, a multi-objective optimization has been executed based on the non-dominated sorting genetic algorithm-II (NSGA-II).

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Optimization of capacity and operational scheduling for microgrid system using two-stage stochastic linear programming

Nugraha

Widyotriatmo

Samsi

2016

2016 International Conference on Instrumentation, Control and Automation (ICA)

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Optimization of capacity and operational scheduling for grid-tied microgrid using pumped-storage hydroelectricity and photovoltaic

Cited by 5 publications

References 18 publications

Stochastic constrained linear quadratic control in a network of smart microgrids

Stochastic constrained linear quadratic control in a network of smart microgrids

Micro-energy Systems (MESs); Modeling and Optimal Operating Based on NSGA-II

Optimization of capacity and operational scheduling for microgrid system using two-stage stochastic linear programming

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