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The increasing penetration of variable renewable energy sources (RES) such as wind power and photovoltaic cell in the microgrid poses a great threat to the power balance. The microgrid operator has to consider the system operational flexibility in the dispatch to overcome the uncertainties. For a multi-energy microgrid, the coupling relationship and complementarity of different energy provides potential capacity for operational flexibility. In this paper, we propose an operational flexibility metric to quantify the ability of the combined heat and power (CHP) microgrid to overcome RES uncertainties in operation. An intraday rolling dispatch strategy for the off-grid CHP microgrid is proposed, which consists of a dispatch decision (DD) stage and a real-time adjustment (RTA) stage. The operational flexibility constraints are incorporated in the DD model to ensure that the uncertainties in the real-time operation can be suppressed. The RTA model is based on a linear programming model. Case studies demonstrate that the proposed dispatch strategy can overcome the uncertainties efficiently and ensure the secure and economic operation of the system.

Multifocus image fusion utilizes the clear parts of multiple images from the same scene to generate a new image, which includes more information about the scene than any of the individual source images. In this paper, a multifocus image fusion method based on robust sparse representation (RSR) and an adaptive pulse-coupled neural network (PCNN) is presented. Each source image is first decomposed with RSR to obtain a sparse coefficients matrix and a residuals matrix. Second, the spatial frequency of the residuals matrix is calculated as the motivation for PCNN neurons, and a salience map of the source image is proposed as an adaptive linking strength for the PCNN. The initial decision map is acquired by comparing the ignition frequency maps of the source images obtained through PCNN. Then, the final decision map is achieved through morphological opening and closing operations. Finally, the fused image is obtained by employing a weighted fusion rule. Experimental results indicate that the proposed method is effective and better than other existing popular fusion methods regarding both objective and subjective evaluations.

Multifocus image fusion generates a single image by combining redundant and complementary information of multiple images coming from the same scene. The combination includes more information of the scene than any of the individual source images. In this paper, a novel multifocus image fusion method based on extreme learning machine (ELM) and human visual system is proposed. Three visual features that reflect the clarity of a pixel are first extracted and used to train the ELM to judge which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Second, we measure the similarity between the source image and the initial fused image and perform morphological opening and closing operations to obtain the focused regions. Lastly, the final fused image is achieved by employing a fusion rule in the focus regions and the initial fused image. Experimental results indicate that the proposed method is more effective and better than other series of existing popular fusion methods in terms of both subjective and objective evaluations.

This paper presents a novel framework based on the non-subsampled contourlet transform (NSCT) and sparse representation (SR) to fuse the multi-focus images. In the proposed fusion method, each source image is first decomposed with NSCT to obtain one low-pass sub-image and a number of high-pass sub-images. Second, an SR-based scheme is put forward to fuse the low-pass sub-images of multiple source images. In the SR-based scheme, a joint dictionary is constructed by integrating many informative and compact sub-dictionaries, in which each sub-dictionary is learned by extracting a few principal component analysis bases from the jointly clustered patches obtained from the low-pass subimages. Thirdly, we design a multi-scale morphology focus-measure (MSMF) to synthesize the high-pass sub-images. The MSMF is constructed based on the multi-scale morphology structuring elements and the morphology gradient operators, so that it can effectively extract the comprehensive gradient features from the sub-images. The “Max-MSMF” is then defined as the fusion rule to fuse the high-pass sub-images. Finally, the fused image is reconstructed by performing the inverse NSCT on the merged low-pass and high-pass subimages, respectively. The proposed method is tested on a series of multi-focus images and compared with several well-known fusion methods. Experimental results and analyses indicate that the proposed method is effective and outperforms some existing state-of-the-art methods.