Probabilistic OPF Incorporating Uncertainties in Wind Power Outputs and Line Thermal Ratings

Fang, Duo; Zou, M.; Djokić, Saša Ž.

doi:10.1109/pmaps.2018.8440232

Cited by 10 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [1,3,14,21] show that WDF can be calculated from CDF by derivative it as in (3). Equation (32) is used to apply a total derivation for bivariate WCDF, and obtain bivariate wind Weibull which is proposed in the form illustrated in (33)…”

Section: Bivariate Wind Wdfmentioning

confidence: 99%

“…Since the wind stream applied to the wind turbine have impacts on both node voltage and branch power of microgrid, so the prediction of microgrid power flow is much needed to provide information for the energy management system of microgrid [31]. Probabilistic power flow (PPF) can be used to obtain the probability distributions of microgrid variables, that are functions of load variation, and generation uncertainties (load forecast error, generator failure and stochastic of renewable energy sources) [31][32][33][34][35][36]. Probabilistic measurements based on PPF are more reliable than deterministic values [35], because of the instantaneous…”

Section: Bivariate Wdf Application To Power Flowmentioning

confidence: 99%

See 1 more Smart Citation

Wind speed estimation based on a novel multivariate Weibull distribution

Salim

Dorrah

Hassan

2019

IET Renewable Power Generation

View full text Add to dashboard Cite

Good practice in smart grids is networked sensors topologies which give the ability to fuse sensors to obtain a new feature based on sensors' complementarity. Moreover, to get a prediction based on other sensors, possibly in other areas, in case of sensor failure. This ability may be achieved by correlating sensor data based on their historical observations. This study addresses the problem of stochastic multivariate modelling within the framework of cyber-physical systems. Weibull distribution function (WDF) was found to be the best probability distribution function to fit wind variations. The main idea of this study is to propose a generalised multivariate stochastic model to describe the joint probability between multi-stochastic variables. A novel multivariate WDF for wind modelling is proposed to estimate wind speed in one location based on wind speed data on other location(s). The suggested approach is applied to three cases to demonstrate the efficacy of the developed technique. It is pointed out that this approach could be generalised for the multivariate WDF of wind modelling situations. Finally, it is shown that there are existing structures resemblances between the wind Weibull versus normal distribution models arising from the fact WDF reflects a natural phenomenon with some restrictions. Nomenclature A ω area which rotor blades cover (m 2) C p power coefficient C scale factor f v Weibull distribution function (WDF) F v cumulative distribution function f r j frequency of wind speed in a histogram K shape factor M number of locations that have wind data µ mean value N number of wind data n j number of wind points that fall inside the bin characterised by the wind speed ρ correlation factor ρ 1 correlation factor between variables 1 and 2 ρ 2 correlation factor between variables 1 and 3 ρ 3 correlation factor between variables 2 and 3 ρ ω air density σ standard deviation Σ covariance matrix v series wind speed v i wind speed X variable in Gaussian function IET Renew. Power Gener.

show abstract

Section: Bivariate Wind Wdfmentioning

confidence: 99%

Section: Bivariate Wdf Application To Power Flowmentioning

confidence: 99%

Wind speed estimation based on a novel multivariate Weibull distribution

Salim

Dorrah

Hassan

2019

IET Renewable Power Generation

View full text Add to dashboard Cite

show abstract

“…However, manufacturer power curves are obtained in controlled conditions (air-tunnels), where impact of variations in wind speeds and wind directions, WT dynamics and other site and application specific factors are not considered. In order to fully represent uncertainties in WF power outputs, probabilistic models developed in [22] and [37] are applied to estimate WF output generation profiles and their uncertainties, for generated input wind speed profiles, with Figure 8 giving an example for WF1.…”

Section: Wind Profiles Load Profiles and Their Uncertaintiesmentioning

confidence: 99%

“…A probabilistic MCS-based OPF is then implemented to identify PDFs required for optimal dispatch solutions. In this MCS-based P-OPF, generated power of each WF is sampled according to distribution functions developed in [22], while loads are sampled with normal distributions, where standard deviations are estimated according to 0.95 and 0.05 quantiles in Figure 5. For each 30-minute time interval, 5,000 MCS solutions for all uncertain variables (eight WF generations and two load demands, as well as wind speeds and wind directions at OHLs) are inputted into the OPF solver.…”

Section: Comparison Between Aa-based Opf and Mcs-based P-opfmentioning

confidence: 99%

“…As mentioned, these AA interval solutions are usually too conservative, as they include all possible values of uncertain variables, regardless of their actual probabilities. Therefore, this paper uses Monte Carlo Simulations (MCS') for evaluation of probabilities and uncertainties in input values (and risks in output values), based on the methods developed in [22]. This allows to specify related uncertainties and risks with suitable confidence levels by considering corresponding AA-intervals and P-OPF solutions, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Handling uncertainties with affine arithmetic and probabilistic OPF for increased utilisation of overhead transmission lines

Fang

Zou

Coletta

et al. 2019

Electric Power Systems Research

Self Cite

View full text Add to dashboard Cite

Large-scale integration of variable and unpredictable renewable-based generation systems poses significant challenges to the secure and reliable operation of transmission networks. Application of dynamic thermal rating (DTR) allows for a higher utilisation of transmission lines and effectively avoids high-cost upgrading and/or reinforcing of transmission system infrastructure. In order to efficiently handle ranges of uncertainties introduced by the variations of both wind energy sources and system loads, this paper introduces a novel optimization model, which combines affine arithmetic (AA) and probabilistic optimal power flow (P-OPF) for DTR-based analysis of transmission networks. The proposed method allows for the improved analysis of underlying uncertainties on the supply, transmission and demand sides, which are expressed in the form of probability distributions (e.g. for wind speeds, wind directions, wind power generation and demand variations) and related interval values. The paper presents a combined AA-P-OPF method, which can provide important information to transmission system operators for evaluating the trade-off between security and costs at a planning stage, as well as for selecting optimal controls at operational stage. The AA-P-OPF methodology is illustrated for a day-ahead planning, using a case study of a real transmission network and a medium size test distribution network.. .

show abstract