Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation

Abdullah, M. A.; Agalgaonkar, Ashish P.; Muttaqi, Kashem M.

doi:10.1016/j.renene.2013.01.010

Cited by 44 publications

(19 citation statements)

References 21 publications

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“…Due to the particularity of the wind power system, the characteristics of the wind turbines must be taken into account in the power flow calculation: (i) Induction generator does not have excitation device, and it relies on the power grid providing reactive power to create the magnetic field. In other words, SCIG does not have voltage regulation capability and cannot be regarded as a PV bus with constant voltage magnitude like a conventional synchronous generator; (ii) From Equations (23) to (26), it can be seen that P e > 0 and Q e < 0, i.e. induction generator needs to absorb a certain amount of reactive power during injecting active power to the power system, and the amount of the reactive power absorbed from power grid is closely related to the slip s and the bus voltage magnitude U s , which cannot be regarded as a PQ bus with constant reactive power.…”

Section: Deterministic Power Flow Solution With Wind Farmmentioning

confidence: 99%

“…A modified flat start way was proposed to improve the power flow convergence. M. A. Abdullah et al . proposed a PLF framework for distribution feeders incorporating renewable distributed generation.…”

Section: Introductionmentioning

confidence: 99%

“…A modified flat start way was proposed to improve the power flow convergence. M. A. Abdullah [26] et al proposed a PLF framework for distribution feeders incorporating renewable distributed generation. A transformation matrix based load flow was formulated and conducted to evaluate the cumulants of the branch flows.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy power flow solution considering wind power variability and uncertainty

Weng

Shi

Zheng

et al. 2014

Int. Trans. Electr. Energ. Syst.

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Summary With the rapid development of wind power, the variability and uncertainty of wind power have brought new problems and challenges to the secure and stable operation of the power systems. In this paper, based on the deterministic power flow solution, the probability theory and fuzzy sets theory are employed to implement systematic and thorough studies on power system fuzzy power flow solution incorporating uncertainties of wind power from different types of wind turbines squirrel cage induction generator, doubly fed induction generator and permanent magnet synchronous generator. The Weibull distribution with specific parameters is adopted as wind speed probability density model. With the possibility/probability consistency principle, the probability distribution of wind speed can be transformed into the possibility distribution of wind speed. In accordance with the simplified power vs wind speed curve of wind turbine, and with consideration of wake effect of wind farm, the fuzzy modeling for power output of wind farm is then presented. The proposed fuzzy power flow model incorporating wind power variability and uncertainty is linearized further for simplicity. A sensitivity analysis based method is employed to solve the fuzzy power flow involving different power characteristics of wind turbines. Finally, simulations are carried out on a real‐sized China's Jiangxi power grid, and some meaningful and important conclusions and comments are drawn based on the simulation results. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Deterministic Power Flow Solution With Wind Farmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy power flow solution considering wind power variability and uncertainty

Weng

Shi

Zheng

et al. 2014

Int. Trans. Electr. Energ. Syst.

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“…wind power, which affect power system can be predicted and modeled using efficient methods [3,4]. Furthermore, in systems with high penetration of wind power, spatial and temporal correlation of wind power should be considered for extra accurate and reliable results [5,6].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

et al. 2016

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a b s t r a c tIncreasing penetration of wind power in power systems causes difficulties in system planning due to the uncertainty and non dispatchability of the wind power. The important issue, in addition to uncertain nature of the wind speed, is that the wind speeds in neighbor locations are not independent and are in contrast, highly correlated. For accurate planning, it is necessary to consider this correlation in optimization planning of the power system. With respect to this point, this paper presents a probabilistic multiobjective optimal power flow (MO-OPF) considering the correlation in wind speed and the load. This paper utilizes a point estimate method (PEM) which uses Nataf transformation. In reality, the joint probability density function (PDF) of wind speed related to different places is not available but marginal PDF and the correlation matrix is available in most cases, which satisfy the service condition of Nataf transformation. In this paper biogeography based optimization (BBO) algorithm, which is a powerful optimization algorithm in solving problems including both continuous and discrete variables, is utilized in order to solve probabilistic MO-OPF problem. In order to demonstrate performance of the method, IEEE 30-bus standard test case with integration of two wind farms is examined. Then the obtained results are compared with the Monte Carlo simulation (MCS) results. The comparison indicates high accuracy of the proposed method.

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“…Quantified descriptions of the system performance, together with external relevant factors such as environmental impact, social and economic benefits etc., enter into the decision-making process and have an impact on operations, short-term planning and long-range planning [3]. These assessments are traditionally carried out by Monte Carlo simulation techniques [6,7,8], and by analytical methods [9].…”

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confidence: 99%