Aiming at tapping the potential of energy complementary, maximizing renewable energy sources utilization and jointly minimizing the operating cost, a coordinated operation model of a multi-integrated energy system based on linearized coupling relationship is established in this paper. In order to establish an accurate model for interconnected energy hubs, an integrated strategy is proposed by the combination of the linear weighted sum and grasshopper optimization algorithm to solve the energy management problem, which improves comprehensive energy efficiency and realize regional coordination optimization. Eventually, the case study indicates that the operating cost of the multi-integrated energy system with coordination is reduced by nearly 3.2%, which can effectively validate the scalability, flexibility, and economic performance of the presented integrated strategy.

In this paper, the wireless power transfer system (WPT) system with dynamic loads such as batteries is studied comprehensively. An integrated control technology of load estimation and power tracking of LCC compensated is proposed, which realizes load estimation, mode judgment and charging control at the transmitter, and standard load setting and decoupling control at the receiver. Based on the influence of reflection impedance on the output current of the inverter, a method of identifying coupling coefficient and equivalent load is proposed and a mathematical model is established. Receiver controller provides standard reference load for load estimation. Transmitter controller judges battery status according to the estimation of equivalent load and adopts double closed-loop control to regulate power and current. Receiver decouples control when battery charging voltage reaches the threshold, and providing mode conversion sign for transmitter controller to realize constant current (CC) and constant voltage (CV) charging of battery. Dual-sides integrated control scheme has no data communication between transmitter and receiver, so it can control independently, which reduces the complexity of the system and is suitable for different charging modes. The proposed controller is more efficient as it maintains a track current, and dynamically alters the pick-up characteristics to suit the load demand. Finally, the simulation and experimental results validate the feasibility of proposed control method, which realizes the estimation of the load and CC/CV charging of the battery. The proposed WPT system achieved the efficiency at 91.16% while delivering 2kW to the load with a vertical air gap of 150mm.

Due to the atmospheric scattering and absorption, hazy weather often occurs in our everyday life, thus reducing the visibility of scenes. Single image dehazing is considered as an ill-posed and challenging problem in computer vision. To restore visibility in inclement weather, we propose an attention-to-attention generative adversarial network (AAGAN) whose motivation is the human visual perceptual mechanism. More specifically, a dense channel attention model is embedded into the encoder. Moreover, its output is projected forward to the corresponding multiscale spatial attention model in the decoder. Both attention models form an attention-to-attention mechanism to implement attention projection, thus capturing global feature dependencies of the whole network. Besides, we analyze the dehazing mechanism based on the atmospheric scattering model, and then utilize an improved RaLSGAN to recover more realistic texture information and enhance visual contrast for different hazy scenes. Finally, in order to improve the visual performance of image restoration, we remove all the instance normalization layers to avoid unnecessary artifacts, and then introduce spectral normalization for all the convolution layers to stabilize the entire training process. Qualitative assessments and analyses demonstrate that our proposed approach can achieve remarkable dehazing performance on both synthetic and real-world scenes against previous state-of-the-art methods.

The increasing complexity of energy internet integrated with multiple energy systems requires a more flexible energy management system. To exploit the potential of energy complementarity, maximize the consumption of renewable energy sources (RES) and minimize the operating costs, a cooperative operation model for integrated multi-energy systems (IMES) with transmission losses is proposed in this paper. On this basis, a novel multi-energy management strategy is developed to improve the energy utilization efficiency of the systems for regional operation by integrating the alternative decomposition-based decoupling method for multi-energy flow and the dynamic wolf pack algorithm (DWPA). Furthermore, the operating costs are formulated as a multi-objective optimization problem based on the Pareto efficiency to obtain more diverse solutions. Finally, case studies considered perform sensitivity analysis and demonstrate that the proposed cooperative operation model and strategy can effectively improve the economy and the flexibility of a regional energy system.

The formation and development of the energy internet through energy hubs enhance energy efficiency and improve system operating flexibility to accommodate more renewable energy sources (RESs) under the pressure of energy crisis. To further tap the potential of energy complementary and optimize the total operating cost in an operational model of multi-energy hubs, an improved energy management system model for the energy conversions of flexibility-based energy hubs using the linearized coupling relationship is proposed in this paper, which could realize regional coordination optimization and high proportion of RES consumption. Furthermore, an algorithm, which combines a Linear Weighted Sum algorithm and the Kuhn-Tucker condition, is presented to utilize surplus energy among the different energy hubs rationally and solve the optimal energy management problem of the constrained nonlinear multi-objective dispatch model. Finally, the results of the simulation indicate that the total operating cost of multi-energy hubs participating in energy complementary is reduced by nearly 15.23% in summer and approximately 8.56% in winter, and the proposed approach can successfully reduce the waste of RES.