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This paper proposes an interval prediction technology of photovoltaic (PV) power based on parameter optimization of extreme learning machine (ELM) model. First, the weighted Euclidean distance is proposed as the evaluation index of PV power prediction interval. The historical sample units are screened and the ELM training set is optimized. Then, a hybrid optimization algorithm for ELM parameters is proposed. The hidden layer input and output weights and biases parameters of the ELM model are optimized by using the elitist strategy genetic algorithm and quantile regression, and the trained model is used to predict the PV power range. Finally, an actual calculation example is constructed based on the historical data of PV power plants and weather stations. The PV power interval is predicted, and the results are compared with those obtained by other methods. The results of the calculation example show that the method proposed can greatly improve the accuracy of interval prediction while increasing the reliability of interval prediction.

Aimed at the problem of uncertainties in the regional development stage and the difficulties in quantifying regional investment demand in different development stages, a robust optimization method for power grid investment decision-making considering regional development stage uncertainties is proposed to promise the matching degree between power grid investment decisions and development needs, and to improve the ability of decision-making results to deal with portfolio risks and uncertainties in regional development stage. First, investment risk constraints are constructed based on the modern portfolio theory. Then, a box uncertainty set is used to characterize uncertainties in regional development stage, and a robust optimization model for power grid investment decision-making considering uncertainties in development stage is established. In the optimization model, the outer minimization problem is used to solve the uncertain variables in regional development stage in the worst scenario, while inner maximization problem is used to obtain the decision-making plan that can maximize investment return in the worst scenario. Furthermore, according to the strong duality theory, the double-layer optimization model is transformed into a single-layer model that can be solved directly, and the big-M method is used to solve the model proposed. Finally, an actual example of 13 cities in an eastern coastal province verifies the applicability and effectiveness of the power grid investment decision-making model.

Line hardening and energy storage configuration are important parts of the pre-disaster planning defense strategy, which can effectively improve the disaster prevention and emergency response capabilities of the hybrid AC-DC distribution system (HDS). Under the background of frequent extreme events, a method to improve the resilience of hybrid AC-DC distribution system considering line hardening and energy storage resource allocation is proposed, and a two-stage robust optimization model is constructed. Essentially, the model is a tri-level mixed integer nonlinear programming problem. The outer level evaluates the active behavior of HDS to determine the line hardening and energy storage system configuration strategies, the middle level determines the worst line failure set after the extreme event occurs, which is the passive behavior of HDS, and the inner level evaluates the active behavior of HDS to determine the emergency response and the operation strategies. Based on the nested column and constraint generation algorithm (nested column and constraint generation, NC&CG), the 3-level mixed integer linear programming model is solved. Finally, a simulation analysis is conducted with a 9-node DC distribution network and an improved IEEE-33 node hybrid AC-DC distribution system coupled with a ring AC distribution network as an example. The results show that the proposed method can effectively improve the resilience of the distribution network and ensure its safe and reliable operation in extreme events.

This article, published in the British Journal of Dermatology, discusses the deceptive pigment changes observed in a patient undergoing neoadjuvant sonidegib therapy for basal cell carcinoma (BCC). The patient, a 71-year-old Asian male, experienced a significant decrease in tumor volume after three months of sonidegib treatment, resulting in a black plaque. However, complete surgical excision revealed complete tumor regression with abundant pigment granules and histocytes. The authors suggest that pigmented BCCs that rapidly regress after HHI therapy may retain pigmentation, potentially leading to misjudgment of incomplete tumor regression. The article does not disclose any conflicts of interest and the data underlying the study can be obtained upon request. [Extracted from the article]