%0 Electronic Article
%A Baoxia Qi
%A Jiajia Chen
%A Yanlei Zhao
%A Pihua Jiao
%K stochastic distribution network planning
%K wind turbines
%K stochastic DNP
%K Latin hypercube sampling method
%K MVSbEM model
%K simulated annealing particle swarm optimisation algorithm
%K forecasting approach
%K voltage deviation
%K uncertain wind power
%K power factors
%K mean-variance-skewness based expectation maximisation model
%K substations upgrading
%K forecasting error
%K expectationâ€“maximisation model
%K continuously increasing demand
%K wind power penetration
%K network loss
%K different forecasting wind
%K sample uncertain wind speed
%X The penetration of wind power is increasing in distribution network for reducing reliance on fossil fuels and covering continuously increasing demand for energy. However, it is argued that the forecasting error of wind power cannot be avoided even using the best forecasting approach. Therefore, in this study, a mean-variance-skewness based expectation maximisation (MVSbEM) model has been proposed by maximisation of the mean and skewness while simultaneously minimisation of the variance to obtain the optimal trade-off relationship between the profit and risk of distribution network planning (DNP) considering uncertain wind power integrated. In the MVSbEM model, the indexes of network loss, voltage deviation, and investment cost are concurrently taken into account under several kinds of actual operation constraints. In addition, the authors have made a full investigation on the MVSbEM by considering different forecasting errors, power factors of wind power, the different forecasting wind speeds, the number of wind turbines as well as the lines and substations upgrading. Furthermore, in order to reduce the computational burden, the Latin hypercube sampling method is used to sample uncertain wind speed. The feasibility and effectiveness of the MVSbEM model have been comprehensively evaluated on a modified IEEE 33-bus system.
%@ 1751-8687
%T Expectationâ€“maximisation model for stochastic distribution network planning considering network loss and voltage deviation
%B IET Generation, Transmission & Distribution
%D January 2019
%V 13
%N 2
%P 248-257
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=27e1g85kurrak.x-iet-live-01content/journals/10.1049/iet-gtd.2018.5813
%G EN