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

Increasing 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...

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Veröffentlicht in:Renewable energy 2016-08, Vol.94, p.10-21
Hauptverfasser: Shargh, S., Khorshid ghazani, B., Mohammadi-ivatloo, B., Seyedi, H., Abapour, M.
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container_issue
container_start_page 10
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creator Shargh, S.
Khorshid ghazani, B.
Mohammadi-ivatloo, B.
Seyedi, H.
Abapour, M.
description Increasing 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 multi-objective 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. •A new method is proposed to solve probabilistic multi objective optimal power flow.•Wind power and load uncertainty and their correlation are considered.•Emission and fuel cost are considered as objective functions.•Point estimate method and Nataf transform are used for uncertainty handling.
doi_str_mv 10.1016/j.renene.2016.02.064
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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. 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subjects Algorithms
Correlated wind power
Correlation
Nataf transformation
Optimal power flow
Optimization
Probabilistic methods
Probabilistic multi-objective optimization
Probability density functions
Probability theory
Wind power
Wind speed
title Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties
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