Probabilistic load flow in distribution systems containing dispersed wind power generation

Hatziargyriou, Nikos D.; Karakatsanis, T.S.; Papadopoulos, Michael

doi:10.1109/59.221262

Cited by 137 publications

(95 citation statements)

References 6 publications

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“…The goals of this study were to (i) use the LCF model to select three diverse types of biomass feedstocks available in a geographic region and (ii) build a robust predictive model to maximize sugar yields by identifying optimal deconstruction process conditions for a given biomass blend or vice versa. Other forms of renewable energy producing systems, including solar and wind, have long been developing and applying predictive models to accept varied levels of feed input (Al-Amoudi and Zhang, 2000;Chow and Leung, 1996;Hatziargyriou et al, 1993;Landberg, 1999). Developing a predictive model on biomass deconstruction and integrating it to the established LCF model allows for a robust and sustainable platform for commercial-scale bio-based manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Narani

Coffman

Gardner

et al. 2017

Bioresource Technology

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Feedstock blending can enable nationwide production of biofuels. Predictive model can identify ideal blend ratios to achieve high sugar yields. A low ratio of high-quality feedstock can substantially improve sugar yields. g r a p h i c a l a b s t r a c t a r t i c l e i n f o b s t r a c tCommercial-scale bio-refineries are designed to process 2000 tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. In this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120°C for 14.8 h. By using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160°C for 2.2 h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.

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Section: Introductionmentioning

confidence: 99%

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Narani

Coffman

Gardner

et al. 2017

Bioresource Technology

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“…The load is assumed to be a random variable (P L,i ) normally distributed within each hour for a given time period [17]. Then, the probability distribution function (PDF) of the power demand of the i-th load (P L,i ) is given by the following expression:…”

Section: Uncertainties Modellingmentioning

confidence: 99%

Inertial frequency response provided by battery energy storage systems: Probabilistic assessment

González-Longatt

Alhejaj

Bonfiglio

et al. 2017

2017 6th International Conference on Clean Electrical Power (ICCEP)

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“…In particular, the power supplied by wind is widely random following the random nature of weather conditions; therefore a probabilistic approach during planning seems much more appropriate [3].…”

Section: Introductionmentioning

confidence: 99%

Wind generation and power system interaction analysis using probabilistic techniques

Ortiz¹,

Ríos²,

Acosta³

2024

RE&PQJ

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Abstract. Energy power from renewable sources, especially wind turbine generators, are being considered as an important generation alternative in the electrical power systems around the world due to their non contaminant nature and low environmental effects. In particular, the power supplied by wind generators is widely random following the random nature of weather conditions; therefore a probabilistic approach during planning seems much more appropriate than the deterministic approach actually used in the electrical utilities. This paper presents a methodology that allows the application of probabilistic analysis, thru the use of programming tools, in the load flow planning studies. The methodology described provides the opportunity to obtain the steady state response in systems where wind power exists using a wind power generation model (WPGM) and an iterative routine to the load flow analysis considering each value of generation of the wind sources.A wind power generation model (WPGM) was developed using a random wind speed generator. The random wind speed generator describes the Weibull distribution of wind measurements, and then using Montecarlo simulation techniques, a program in Visual Basic executes the automatic evaluation of random samples of speed of wind considering the characteristic power curve of a wind turbine, obtaining a widely database of wind generation conditions. As a result of the investigation, a computational tool was developed in order to use it, in combination with the PSS/E load flow analysis software, for steady state power system analysis. The methodology was used as an assessment tool to identify the effects of wind turbines generation into the substation voltages and power flow profiles in the power network, for a wind farm project intended to be installed in Margarita Island. The analysis aimed to identify the transmission adjustments, reinforcements and expansions needed to guarantee the wind farm project integration successful into the power system.

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Probabilistic load flow in distribution systems containing dispersed wind power generation

Cited by 137 publications

References 6 publications

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Inertial frequency response provided by battery energy storage systems: Probabilistic assessment

Wind generation and power system interaction analysis using probabilistic techniques

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