Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties |
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| Affiliation: | 1. Huadian Electric Power Research Institute, Hangzhou, Zhejiang 310030, China;2. Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China;3. Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;1. Ocean Engineering and Technology Research Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran;2. Griffith School of Engineering, Gold Coast Campus, Griffith University, QLD, 4222, Australia;1. RWTH Aachen University, Institute of Semiconductor Electronics (IHT), Sommerfeldstr. 24, Aachen, Germany;2. IMEC, Kapeldreef 75, 3001, Leuven, Belgium;3. RWTH Aachen University, Institute of Materials in Electrical Engineering and Information Technology II (IWE II), Sommerfeldstr. 24, Aachen, Germany;1. Department of Thermo Fluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, UTM, 81310 Skudai, Johor D.T., Malaysia;2. Faculty of Engineering, Computing and Science, Swinburne University of Technology, 93350 Kuching, Sarawak, Malaysia;3. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia |
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| Abstract: | 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. |
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| Keywords: | Optimal power flow Probabilistic multi-objective optimization Correlated wind power Nataf transformation |
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