Probability Characterization of Wind Farm Power Output and Impact on System Reliability

Alamri, Abdullah; Alowaifeer, Maad; Meliopoulos, A. P. Sakis

doi:10.1109/pmaps.2018.8440311

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…The computational complexity resides in the probabilistic evaluation of each state at a stage. This evaluation uses a selective enumeration of contingencies algorithm [71][72] as shown in the upper right corner of the figure and probabilistic computational algorithms [73][74][75][76], which are integrated to provide a number of probabilistic performance measures (metrics) for the state at that stage. The metrics are numerous.…”

Section: B Selective Enumeration Methodsmentioning

confidence: 99%

Composite Power System Reliability with Renewables and Customer Flexibility

Meliopoulos

Papic²,

Tindemans

et al. 2022

2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Composite Power System Reliability is defined as the computational procedure that quantifies the probability that the power system will perform the function of delivering electric power to customers adequately, on a continuous basis and with an acceptable quality. This definition leaves many details undefined and exemplifies the ambiguity in reliability analysis. The increasing deployment of wind and PV creates additional uncertainties that make reliability analysis a rather complex issue. Because of increased uncertainty the need for composite reliability analysis and utilization of results in power system planning is critical. New approaches are emerging for dealing with these problems from the operational point of view, including demand response programs, tapping on customer and distributed resource flexibility and new control approaches. The key question to be addressed is: how the new operational paradigms affect composite power system reliability. This paper presents the ongoing work of the IEEE Composite System Reliability Task Force of the IEEE PES Reliability, Risk, Probability Application (RRPA) Subcommittee.

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Section: B Selective Enumeration Methodsmentioning

confidence: 99%

Composite Power System Reliability with Renewables and Customer Flexibility

Meliopoulos

Papic²,

Tindemans

et al. 2022

2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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“…The method for computing the PDF of each season wind speed (ρV(v)) is similar to the method used for computing ρG(g) [4]. Once ρV (v) is known, the WTS curve, conditional upon the availability of the WTS, can be utilized to compute the PDF of the WTS power (ρWG(pWTS)).…”

Section: Wf Probability Distribution Functionmentioning

confidence: 99%

“…Notably, it is assumed in this paper that the WTSs in the WF are arranged in such a way that makes the wake effect negligible. As explained in detail in our previous work [4], the WTS is modeled as a 2-state model: either the unit is available with probability equal to 1−qWTSi and capacity of pWTS.rated, or unavailable with capacity equal to zero and probability equal to qWTSi. The probability that the WTS is unavailable is computed as follows [18]:…”

Section: Wf Probability Distribution Functionmentioning

confidence: 99%

See 1 more Smart Citation

Analytical Method for Energy Storage Sizing and Reliability Assessment for Power Systems with Variable Generation

Alamri¹,

Alowaifeer²,

Meliopoulos³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper presents a mixed integer linear program (MILP) to optimally size power and energy of energy storage systems (ESSs). The sizing model takes into account conventional generation (CG) operation constraints in addition to seasonal and locational wind speed and solar radiation variations, and variable generation (wind turbine systems (WTSs) and solar cell generators (SCGs)) forced outages. Subsequently, the outcomes of the ESS sizing model are inputted to the probabilistic production method (PCC) to assess the reliability of the integrated system. All aforementioned analyses have been applied to a system with different penetration levels. The method is demonstrated with case studies on a system consisting of 10 CG units and VG penetration levels of 20% and 30%. For each penetration level, ESS sizing is computed and then reliability assessment is performed.

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