Wanliang Fang

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.

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.

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.

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.

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.