Reliability assessment of load points including solar and wind DGs

Yahaya, Abass Afolabi; Al-Muhaini, Mohammad

doi:10.1109/sedst.2015.7315207

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, industrial and government loads are immune to seasonal effects. A per unit load demand of any consumer type at hour h , day d and week w is given as follows:

L ()(, h, d, w) = P_{w} P_{d} P_{h}, normal∀ h, d, w,

where

P_{h}

is the hourly load,

P_{d}

is the daily load and

P_{w}

is the weekly load, given as percentages of daily peaks, weekly peaks and annual peaks, respectively [19].…”

Section: Case Study: Results and Analysismentioning

confidence: 99%

“…Stochastic simulations via MCSs are employed to help capture the uncertainties in the reliability of network components. The required parameters such as time‐to‐failure and time‐to‐repair are usually treated as exponentially distributed indices, and they determine the up states and down states of each component [19]. This exponential distribution is described by (12), and the probability that the system uses time t in any state is given in (13)

f ()(, t) = λ e^{- λ t}

F ()(, t) = 1 - e^{- λ t},

where

F false(t false)

is the probability that the system will reside in a state for a period of time (≤ t ) before entering another state.…”

Section: Proposed Reliability Assessment Approachmentioning

confidence: 99%

See 1 more Smart Citation

Optimal design of hybrid DG systems for microgrid reliability enhancement

Yahaya

Al-Muhaini

Heydt

2020

IET Generation, Transmission & Distribution

Self Cite

View full text Add to dashboard Cite

“…In contrast, industrial and government loads are immune to seasonal effects. A per unit load demand of any consumer type at hour h , day d and week w is given as follows:

L ()(, h, d, w) = P_{w} P_{d} P_{h}, normal∀ h, d, w,

where

P_{h}

is the hourly load,

P_{d}

is the daily load and

P_{w}

is the weekly load, given as percentages of daily peaks, weekly peaks and annual peaks, respectively [19].…”

Section: Case Study: Results and Analysismentioning

confidence: 99%

f ()(, t) = λ e^{- λ t}

F ()(, t) = 1 - e^{- λ t},

where

F false(t false)

is the probability that the system will reside in a state for a period of time (≤ t ) before entering another state.…”

Section: Proposed Reliability Assessment Approachmentioning

confidence: 99%

Optimal design of hybrid DG systems for microgrid reliability enhancement

Yahaya

Al-Muhaini

Heydt

2020

IET Generation, Transmission & Distribution

Self Cite

View full text Add to dashboard Cite

Reliability Assessment in Rural Distribution Systems With Microgrids: A Computational- Based Approach

et al. 2022

View full text Add to dashboard Cite

Rural distribution systems, especially in developing countries, tend to be less reliable than urban distribution systems because customers are (1) located remotely and (2) connected to weak aerial networks with radial topologies without redundancy. To improve reliability in rural areas, microgrids (MGs) are being integrated into conventional power systems. This study evaluates the effect on the reliability of rural distribution systems when MGs are introduced considering different penetration levels for renewable and nonrenewable distributed generation, and under rated power of energy storage. Here, we first formulate a reliability model for a rural distribution system with MGs. Based on this model, an interactive method using a sequential Monte Carlo simulation method is proposed and applied to calculate different conventional reliability indices. We show that this approach facilitates the selection of the parameters of the different systems constituting the MGs in order to comply with a predefined reliability objective. For instance, by introducing only photovoltaic distributed generation systems to the rural distribution systems under study, achieving the reliability objective is next to impossible. However, when correctly dimensioned-hybrid MGs are introduced, such an objective is successfully achieved. In the future, our model and the results provided herein could be combined with technical and economic studies to obtain an optimal solution that meets a certain reliability objective.

show abstract

Reliability Evaluation in Distribution Networks with Microgrids: Review and Classification of the Literature

2020

View full text Add to dashboard Cite

Modern power systems must provide efficient, reliable, and environmentally responsible energy. Recently, the inclusion of Microgrids (MGs) has allowed us to overcome some difficulties and face important challenges in this direction, especially related to the use of alternative energy sources. Increased and probabilistic demand, as well as limited energy supply, pose the need to evaluate the reliability of any distribution system (DS) when MGs are introduced. Here we reviewed and classified the state-of-the-art of reliability assessment (RA) in MGs. Initially, we contextualize RA in distribution systems. Next, each of the MGs subsystems components are introduced and the questions (1) why is it important to evaluate the reliability in Microgrids? (2) how do Microgrids influence the reliability of distribution systems? and (3) how does each of its subsystems influence the reliability of the Microgrids? are addressed. A total of 1395 research studies were published between 2002 and 2020. Using the PRISMA model, 147 met the inclusion criteria (71% correspond to research papers and 29% to reviews; 62% were published in journals, 34% were conference papers and 4% were books). The first study dates from 1971. Despite immense advances in MGs, we identified that (1) real test systems constitute an emerging trend; (2) although new MG configurations sound promising, the development and application of new RA techniques are a necessary step towards the identification of potential pitfalls of such architectures; (3) new RA methods or variations of the existing ones, whether analytical or simulation-based, are constantly being proposed, but their comparison for a particular DS, including MGs, are yet to be performed; and (4) more research studies are needed to assess how new control strategies and information and communications technology impact MGs reliability. Future lines of research could build upon these gaps to enhance reliability, especially when alternative energy resources are available.

show abstract

Reliability assessment of load points including solar and wind DGs

Cited by 3 publications

References 12 publications

Optimal design of hybrid DG systems for microgrid reliability enhancement

Optimal design of hybrid DG systems for microgrid reliability enhancement

Reliability Assessment in Rural Distribution Systems With Microgrids: A Computational- Based Approach

Reliability Evaluation in Distribution Networks with Microgrids: Review and Classification of the Literature

Contact Info

Product

Resources

About