A
            <scp>clustering‐based technoeconomic</scp>
            analysis for wind farm and shunt capacitor allocation in radial distribution systems

Sadeghian, Omid; Oshnoei, Arman; Tarafdarhagh, Mehrdad; Khezri, Rahmat

doi:10.1002/2050-7038.12708

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…By considering the reactive power of the capacitor banks (namely Q C (𝜆(n)), the active/reactive power injected into buses is as [39]…”

Section: Load Flow Calculationsmentioning

confidence: 99%

“…By considering the reactive power of the capacitor banks (namely

{{Q}_C}(\lambda ( n )

), the active/reactive power injected into buses is as [39]

\begin{equation}P(i) = P_L^i \forall i\end{equation}

\begin{equation}Q(i) = Q_L^i \forall i \notin {{B}_n}\end{equation}

\begin{equation}Q({{B}_n}) = Q_L^{{{B}_n}} - {{Q}_C}(n) \forall i \in {{B}_n}\end{equation}

in which

P( i )

and

Q( i )

are active/reactive power injected into buses, respectively. In addition,

P_L^i

and

Q_L^i

are the active/reactive loads of the system.…”

Section: Load Flow Calculationsmentioning

confidence: 99%

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Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Sadeghian,

Safari

2024

The Journal of Engineering

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This paper studies the effect of the number of switching (NOS) per day of capacitor banks on loss reduction in radial distribution systems. To this aim, the daytime (more precisely, 24 h) is divided into different numbers of time segments (equal to the same NOS) for capacitors’ size switching. The resulting non‐linear programming with discontinuous derivatives (called DNLP) model is solved subject to related constraints. The results reveal the impact of hourly switching of capacitor banks on further loss reduction (namely 118.4435, 83.7856, and 101.738 MWh for three IEEE systems) and higher net savings (i.e. k$5.6067, k$4.2772, and k$5.3542 for the same systems) of radial distribution systems compared to daily switching. Then, the hyper‐tuned Random Forest model is trained based on the IEEE 69‐bus network, fine‐tuned by the IEEE 10‐bus network, and fitted by the IEEE 33‐bus network to have an intelligent multi‐classification task with the highest accuracy. Numerical simulation, in both classic and intelligent parts, is presented to demonstrate the performance of DeepOptaCap. For the final step, DeepOptaCast is compared to other intelligent models of Light Gradient Boosting Method (LGBM), Decision Tree, and XGBoost, regarding KPIs of mean absolute percentage error, root mean squared percentage error, mean absolute error, root mean squared error, and coefficient of determination to demonstrate the model's superiority.

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“…By considering the reactive power of the capacitor banks (namely Q C (𝜆(n)), the active/reactive power injected into buses is as [39]…”

Section: Load Flow Calculationsmentioning

confidence: 99%

“…By considering the reactive power of the capacitor banks (namely

{{Q}_C}(\lambda ( n )

), the active/reactive power injected into buses is as [39]

\begin{equation}P(i) = P_L^i \forall i\end{equation}

\begin{equation}Q(i) = Q_L^i \forall i \notin {{B}_n}\end{equation}

\begin{equation}Q({{B}_n}) = Q_L^{{{B}_n}} - {{Q}_C}(n) \forall i \in {{B}_n}\end{equation}

in which

P( i )

and

Q( i )

are active/reactive power injected into buses, respectively. In addition,

P_L^i

and

Q_L^i

are the active/reactive loads of the system.…”

Section: Load Flow Calculationsmentioning

confidence: 99%

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Sadeghian,

Safari

2024

The Journal of Engineering

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“…The payback period denotes the time required for the return on an investment to repay the total cost of the BESS and is expressed by eqn. ( 22) [40][41].…”

Section: Iiiv Payback Period Calculation For the Bessmentioning

confidence: 99%

An Iterative Heuristic Optimization Method for the optimum Sizing of Battery Energy Storage System to augment the dispatchability of Wind Generators

Kashyap,

Ghose

2024

IEEE Access

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This research aims to devise a comprehensive methodology for optimizing the size of a Battery Energy Storage System (BESS) supporting Wind Energy Systems (WES) to enhance power commitment flexibility in the energy market. The methodology involves three essential steps: (i) estimating rated kW, (ii) initializing rated kWh of the BESSs, and (iii) iteratively adjusting the BESS size based on heuristic rules to prevent State of Charge (SoC) limit violations following the load cycle. Three realistic load cycles for the BESSs out of which one load cycle is generated based on maximum error values and the other load cycles are generated based on the mean and 1σ of the Normal Distribution Curve (NDC) of forecast errors of WES located at Agasthianpalli, Tamil Nadu, India. Two simple yet effective heuristic rules have been proposed to optimize the size of the BESS. The BESS size also ensures to start every day with maximum SoC and maintain SoC within the limits throughout the day. This leads to two scenarios considering a single set of the BESS to serve the load cycle and two sets of the BESS operating alternatively, reaching maximum SoC on the subsequent day by charging from the grid. Validation using Genetic Algorithm (GA) is done by comparing the costs of BESSs, emphasizing the suitability of the proposed technique. The cost analysis indicates that scenario 1 is optimal in terms of both cost and BESS size, surpassing scenario 2 by 8.89% and 9.95%, respectively.

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“…Additionally, lower energy losses of BDFRMs have been reported in the literature. However, the installing costs such as foundation cost and landing cost are required for all wind turbine types [141]. In addition to wind turbines, BDFRM is a potential solution for the development of Electric Vehicles (EVs), which are an alternative to conventional vehicles.…”

Section: Role Of Bdfrm In Decarbonizationmentioning

confidence: 99%

A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine

et al. 2021

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The Brushless Doubly-Fed Reluctance Machine (BDFRM) has been widely investigated in numerous research studies since it is brushless and cageless and there is no winding on the rotor of this emerging machine. This feature leads to several advantages for this machine in comparison with its induction counterpart, i.e., Brushless Doubly-Fed Induction Machine (BDFIM). Less maintenance, less power losses, and also more reliability are the major advantages of BDFRM compared to BDFIM. The design complexity of its reluctance rotor, as well as flux patterns for indirect connection between the two windings mounted on the stator including power winding and control winding, have restricted the development of this machine technology. In the literature, there is not a comprehensive review of the research studies related to BDFRM. In this paper, the previous research studies are reviewed from different points of view, such as operation, design, control, transient model, dynamic model, power factor, Maximum Power Point Tracking (MPPT), and losses. It is revealed that the BDFRM is still evolving since the theoretical results have shown that this machine operates efficiently if it is well-designed.

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A clustering‐based technoeconomic analysis for wind farm and shunt capacitor allocation in radial distribution systems

Cited by 11 publications

References 40 publications

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

An Iterative Heuristic Optimization Method for the optimum Sizing of Battery Energy Storage System to augment the dispatchability of Wind Generators

A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine

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