Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction and data marking. The negative abnormal points, whose wind speed are greater than cut-in speed and power are below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k-means, local outlier factor (LOF), combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency and universality of the proposed method.

To accomplish more practical scheduling of microgrids under source-load uncertainties, this paper firstly proposes a novel recourse-cost constrained adaptive robust optimization (RC-ARO) model with binary recourse variables. The dispatch plan in the nominal scenario is optimized in the first-stage to get the minimal operation cost, then the adjustment plan in the worst scenario is determined in the second-stage that minimizes the recourse-cost. It has overcome the defect that conventional ARO can only get the scheduling plans in the worst scenario. Secondly, a spatiotemporal correlation model of wind power uncertainty is further developed based on the similarities of power time sequences, aimed at avoiding impossible scenarios in reality and reducing the conservativeness of independent uncertainty sets. Thirdly, a new column-and-constraint generation (C&CG) algorithm with alternating optimization procedure (AOP) is developed to directly obtain the binary solution, which helps accelerating the solution of RC-ARO model using traditional nested-C&CG. Finally, case studies demonstrate the effectiveness and superiority of the proposed RC-ARO model, the developed uncertainty sets and the novel solving algorithm. The solving time of C&CG-AOP reduces by half compared with nested-C&CG, and a larger scale of decision variables under uncertainties brings more significant speedup by the proposed algorithm.

With the rapid development of wind farm worldwide, monitoring the status of numerous wind turbines becomes the essential work. Abnormal data in wind power curve (WPC) are quite important for wind farm operations and maintenances because they usually reveal wind turbine failures or some extreme conditions. This paper proposes a new algorithm of WPC abnormal data detection and cleaning by image thresholding based on minimization of dissimilarity-and-uncertainty-based energy (MDUE). The basic idea is to transform the scatter data into a digital image and the problem of data cleaning is turned into an image segmentation problem. For all data pixels, the confidences of being classified as normal class are computed and make up a grey level feature image. Then the optimum threshold is determined by searching through the energy space based on intensity-based class uncertainty and shape dissimilarity. Finally, the normal and three types of abnormal data are marked after applying image thresholding to the feature image. The algorithm is compared with several data-based algorithms and a recently published image-based algorithm. A large number of experiments conducted on real-world WPC data collected from 37 wind turbines in two wind farms verified the superior performance of the proposed method.