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Wanliang Fang
Researcher at Xi'an Jiaotong University
Publications - 38
Citations - 1014
Wanliang Fang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Electric power system & Probability distribution. The author has an hindex of 13, co-authored 35 publications receiving 713 citations.
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An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data
TL;DR: Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs, and the estimated results of the proposed PV power forecasting model coincide well with measurement data as discussed by the authors.
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Distributionally Robust Chance-Constrained Approximate AC-OPF With Wasserstein Metric
TL;DR: In this paper, a distributionally robust chance constrained approximate ac-OPF is proposed for variable renewable energy (VRE) uncertainties, where the ambiguity set is constructed from historical data without any presumption on the type of the probability distribution, and more data leads to smaller ambiguity set and less conservative strategy.
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Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment
TL;DR: In this paper, a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors is proposed to minimize expected total operation costs, including the costs of generation, reserve, wind curtailment, and load shedding, while guaranteeing the system security.
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Distributionally Robust Chance-Constrained Approximate AC-OPF with Wasserstein Metric
TL;DR: In this paper, a distributionally robust chance constrained approximate AC-OPF was proposed for variable renewable energy (VRE) uncertainties in the presence of VRE uncertainties, where the power flow model employed in the proposed OPF formulation combines an exact AC power flow at the nominal operation point and an approximate linear power flow to reflect the system response under uncertainties.
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FACTS Devices Allocation via Sparse Optimization
TL;DR: An algorithm based on alternating direction method of multipliers is proposed to solve the sparsity-constrained OPF problem and employs Lq(0 <; q ≤ 1) norms to enforce sparsity on FACTS devices setting values to achieve solutions with desirable device numbers and sites.