DG placement and size with continuation power flow method

Arief, Ardiaty; Nappu, Muhammad Bachtiar

doi:10.1109/iceei.2015.7352566

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…In addition, the proposed technique analyzes the impact of PV systems on the generation system reliability evaluation and an optimum capacity of the PV system for optimum reliability indices. In the first model, the optimum PV system capacity is 300 MW that is installed at bus 9, which results in an optimum voltage profile as proved in [45]. The max.…”

Section: A One Pv System Installation (Ieee_eps_24 Bus_1_pvmentioning

confidence: 99%

“…At the same time, the repairing and failure rates are recorded and tabulated by the maintenance team in the power station. These data are available for some typical systems like IEEE 14-bus and 24-bus systems [42][43][44][45][46]. The block diagram technique, Markov process, and MAACPSO algorithm in the optimal reliability study presented in this paper can be summarized through the block diagram shown in Fig.5.…”

Section: Maacpso Algorithmmentioning

confidence: 99%

“…The capacity of generation units for the IEEE_EPS_24_bus system is given in table (1) [42][43][44]. The failure and repair rates of generation units and PV plants are shown in table (2) [45][46]. This study focuses on assessing the reliability of a grid-tied PV system.…”

Section: Maacpso Algorithmmentioning

confidence: 99%

“…This part of the proposed study studies the reliability evaluation of the generation side for IEEE_EPS_24 bus_2_PV, which has two PV systems (second model) and analyzes the impact of PV systems on system reliability evaluation. The total optimum PV systems capacity is 300 MW [45]. This power capacity is divided into buses 4 and 9.…”

Section: B Two Pv System Installation (Ieee_eps_24 Bus_2_pv)mentioning

confidence: 99%

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Optimal Reliability Study of Grid-Connected PV Systems Using Evolutionary Computing Techniques

et al. 2021

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Integrating renewable energy sources (RESs) into electrical power systems has gotten highly noticeable among researchers and those interested in electrical energy production due to the increase in energy demands, fossil fuel exhaustion, and ecological effects. PV-based renewable energy generation is one of the essential RESs that has appeared and had played a vital role in electrical power systems recently due to their advantages. In this regard, this paper presents a multi-objective computation problem for optimal siting and the design of grid-tied PV systems to achieve optimum generating reliability, considering some states of different generation probabilities. The proposed paper studies the evaluation of the grid-tied PV systems reliability, the states of generation probabilities, the generation buses availabilities, the capacities of the generation's system in or out of service for each failure state, and the frequency and mean duration of generation failure states. The presented multi-objective computation problem is optimized using a modified adaptive accelerated particle swarm optimization (MAACPSO) algorithm. The effectiveness of the proposed method is demonstrated through IEEE_EPS_24_bus integrated with PV systems. Results revealed the ability of MAACPSO to solve the multi-objective optimization problem presented, consequently supporting the system reliability.

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Section: A One Pv System Installation (Ieee_eps_24 Bus_1_pvmentioning

confidence: 99%

Section: Maacpso Algorithmmentioning

confidence: 99%

Section: Maacpso Algorithmmentioning

confidence: 99%

Section: B Two Pv System Installation (Ieee_eps_24 Bus_2_pv)mentioning

confidence: 99%

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Optimal Reliability Study of Grid-Connected PV Systems Using Evolutionary Computing Techniques

et al. 2021

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“…Karena penurunan signifikan terhadap bahan bakar fosil dan kekwatiran terhadap dampak hijau, maka saat ini seluruh dunia beralih ke sumber energi terbarukan seperti energi matahari, angin, laut, hydro, dan panas bumi [10][11][12]. Diantara sumber energi terbarukan yang ada, sumber energi matahari banyak dilirik oleh para insyinyur karena kinerjanya tidak mencemari lingkungan dalam menghasilkan listrik dan mampu memanen energi dari matahari, yang merupakan sumber energi bebas setelah fasilitas tersebut terpasang [13,14].…”

Section: Pendahuluanunclassified

Penempatan Photovoltaic yang Optimal Menggunakan Metode Continuation Power Flow

Rachman

Nappu

Arief

2017

jpe

Self Cite

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Makalah ini menyajikan sebuah metode aliran daya untuk memperkirakan lokasi penempatan pembangkit Photovoltaic (PV) yang optimal pada sistem distribusi. Metode ini sebagai metode untuk mengindentifikasi bus sistem yang paling sensitif. Pada Analisis dan simulasi ini dilakukan pada 44 bus, 47 line, dan 7 generator. Adapun efek penempatan PV yang tepat dengan metode Continuation Power Flow (CPF) adalah dapat meningkatkan profil tegangan dan dapat mengatasi ketidakstabilan tegangan, sehingga analisis dan evaluasi kestabilan tegangan dapat terlihat pada kondisi sebelum dan sesudah penambahan PV. Penelitian ini mengidentifikasi daerah-daerah yang memiliki radiasi matahari yang baik dari SOLARGIS data. Daerah yang memiliki irradiance yang baik menjadi input PV. Dari hasil analisa power flow menemukan daerah yang memiliki irradiance yang baik dan sensitivitas yang baik, dan bisa direkomendasikan untuk penempatan PV untuk membantu pelayanan beban yang semakin meningkat tiap tahunnya.

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Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Yang

Chen

et al. 2020

Int J Energy Res

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Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.

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DG placement and size with continuation power flow method

Cited by 19 publications

References 26 publications

Optimal Reliability Study of Grid-Connected PV Systems Using Evolutionary Computing Techniques

Optimal Reliability Study of Grid-Connected PV Systems Using Evolutionary Computing Techniques

Penempatan Photovoltaic yang Optimal Menggunakan Metode Continuation Power Flow

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

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