Evaluating the integration of wind power into distribution networks by using Monte Carlo simulation

Mokryani, Geev; Siano, Pierluigi

doi:10.1016/j.ijepes.2013.04.019

Cited by 44 publications

(22 citation statements)

References 53 publications

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“…The sensitivity analysis is performed to choose the proper combination of the weighting factors in (5 Table II that the method is not very sensitive to the weightings selection. The mean of energy generated by PVs over the planning horizon in different scenarios is shown in Fig.7.…”

Section: Case Study and Simulation Resultsmentioning

confidence: 99%

“…In [5], the authors proposed a combined MCS and optimal power flow (OPF) to maximize the social welfare by integrating DR scheme considering different combinations of wind generation and load demand over a year. A stochastic formulation of load margin taking into account the uncertainties related to RES integration into the network is proposed in [6].…”

Section: B Literature Reviewmentioning

confidence: 99%

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Active distribution networks planning with integration of demand response

Mokryani

2015

Solar Energy

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-This paper proposes a probabilistic method for active distribution networks planning with integration of demand response.Uncertainties related to solar irradiance, load demand and future load growth are modelled by probability density functions. The method simultaneously minimizes the total operational cost and total energy losses of the lines from the point of view of distribution network operators with integration of demand response over the planning horizon considering active management schemes including coordinated voltage control and adaptive power factor control. Monte Carlo simulation method is employed to use the generated probability density functions and the weighting factor method is used to solve the multi-objective optimization problem. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. Index Terms -

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Section: Case Study and Simulation Resultsmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

Active distribution networks planning with integration of demand response

Mokryani

2015

Solar Energy

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“…WTs' life time, installation cost, depreciation time, interest rate, are considered as summarized in Table VI. The annual cost for WTs is calculated as follows [33][34][35][36][37][38]:…”

Section: A Calculation Of the Wts Offer Price From The Point Of Viewmentioning

confidence: 99%

Active distribution networks planning with high penetration of wind power

Mokryani

Pillai

et al. 2017

Renewable Energy

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“…Wind speed as a time series shows chaotic characteristics and wind direction often change due to its stochastic nature [21,22]. Yaw control systems need to be employed to capture the maximum wind power and to improve the wind turbine efficiency [23].…”

Section: Yaw Position Forecastingmentioning

confidence: 99%