Global Energy Interconnection最新文献

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer (PEMEL) under the fluctuating input of renewable energy, this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic (PV) electrolysis of water. First, voltage and performance attenuation models of the PEMEL are proposed, and the degradation cost of the electrolyzer under a fluctuating input is considered. Then, the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost. Finally, a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system. In the up-layer optimization, the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi. In low-layer optimization, the power allocation between the PEMEL and battery energy storage system (BESS) is optimized using a non-dominated sorting genetic algorithm (NSGA-II) combined with the firefly algorithm (FA). A better optimization result, characterized by lower degradation and total costs, was obtained using the method proposed in this study. The improved algorithm can search for a better population and obtain optimization results in fewer iterations. As a calculation example, data from a PV power station in northwest China were used for optimization, and the effectiveness and rationality of the proposed optimization method were verified.

In an integrated energy system, source-load multiple uncertainties and correlations lead to an over-limit risk in operating state, including voltage, temperature, and pressure over-limit. Therefore, efficient probabilistic energy flow calculation methods and risk assessment theories applicable to integrated energy systems are crucial. This study proposed a probabilistic energy flow calculation method based on polynomial chaos expansion for an electric-heat-gas integrated energy system. The method accurately and efficiently calculated the over-limit probability of the system state variables, considering the coupling conditions of electricity, heat, and gas, as well as uncertainties and correlations in renewable energy unit outputs and multiple types of loads. To further evaluate and quantify the impact of uncertainty factors on the over-limit risk, a global sensitivity analysis method for the integrated energy system based on the analysis of covariance theory is proposed. This method considered the source-load correlation and aimed to identify the key uncertainty factors that influence stable operation. Simulation results demonstrated that the proposed method achieved accuracy to that of the Monte Carlo method while significantly reducing calculation time. It effectively quantified the over-limit risk under the presence of multiple source-load uncertainties.

Multi-energy microgrids (MEMG) play an important role in promoting carbon neutrality and achieving sustainable development. This study investigates an effective energy management strategy (EMS) for MEMG. First, an energy management system model that allows for intra-microgrid energy conversion is developed, and the corresponding Markov decision process (MDP) problem is formulated. Subsequently, an improved double deep Q network (iDDQN) algorithm is proposed to enhance the exploration ability by modifying the calculation of the Q value, and a prioritized experience replay (PER) is introduced into the iDDQN to improve the training speed and effectiveness. Finally, taking advantage of the federated learning (FL) and iDDQN algorithms, a federated iDDQN is proposed to design an MEMG energy management strategy to enable each microgrid to share its experiences in the form of local neural network (NN) parameters with the federation layer, thus ensuring the privacy and security of data. The simulation results validate the superior performance of the proposed energy management strategy in minimizing the economic costs of the MEMG while reducing CO2 emissions and protecting data privacy.

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of wind- driven generators, a min-max-min two-stage robust optimization model is presented, considering the unit commitment, source-network load collaboration, and control of the load demand response. After the constraint functions are linearized, the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method. The minimum-maximum of the original problem was continuously maximized using the iterative method, and the optimal solution was finally obtained. The constraint conditions expressed by the matrix may reduce the calculation time, and the upper and lower boundaries of the original problem may rapidly converge. The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately; otherwise, it is easy to cause excessive accommodation of wind power at some nodes, leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power. Thus, the most economical optimization scheme for the worst scenario of the output power of the generators is obtained, which proves the economy and reliability of the two-stage robust optimization method.

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible

Smart cities depend highly on an intelligent electrical networks to provide a reliable, safe, and clean power supplies. A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery, which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts. A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future. However, most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid. To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids, this article proposes a model of smart city development needs from the perspective of residents’ needs based on Maslow’s Hierarchy of Needs theory, which serves the primary purpose of building a smart city. By classifying and reintegrating the needs, an evaluation index system of smart grids supporting smart cities was further constructed. A case analysis concluded that smart grids, as an essential foundation and objective requirement for smart cities, are important in promoting scientific urban management, intelligent infrastructure, refined public services, efficient energy utilization, and industrial development and modernization. Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions, such as electric vehicle charging facilities and wireless coverage.

The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation. Of the currently available technologies, solar energy has promising application prospects owing to its merits of being clean, safe, and sustainable. Solar energy is converted into electricity through photovoltaic (PV) cells; however, the overall conversion efficiency of PV modules is relatively low, and most of the captured solar energy is dissipated in the form of heat. This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells. To overcome the shortcomings, an efficient approach involves combining a PV cell with a thermoelectric generator (TEG) to form hybrid PV-TEG systems, which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs. Based on a thorough examination of the literature, this study comprehensively reviews 14 maximum power point tracking (MPPT) algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion, namely conventional, mathematics-based, metaheuristic, artificial intelligence, and other algorithms. This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

The reliability of geographic information system (GIS) partial discharge fault diagnosis is crucial for the safe and stable operation of power grids. This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects. First, the non-subsampled contourlet transform (NSCT) algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge (PRPD) spectrum with richer information. Subsequently, the VAE structure was introduced into the traditional GAN, and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN. Then, the self-attention mechanism was integrated into the VAE-GAN, and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state. Finally, the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples. The eigenvectors of the sets were substituted into the long short-term memory (LSTM) network for partial discharge fault diagnosis. The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method. The structure similarity index measure (SSIM) index is increased by 4.5% and 21.7%, respectively, and the average accuracy of fault diagnosis is increased by 22.9%, 9%, 5.7%, and 6.5%, respectively. The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy

To address the significant lifecycle degradation and inadequate state of charge (SOC) balance of electric vehicles (EVs) when mitigating wind power fluctuations, a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm. First, a swing door trending (SDT) algorithm based on compression result feedback was designed to extract the feature data points of wind power. The gating coefficient of the SDT was adjusted based on the compression ratio and deviation, enabling the acquisition of grid-connected wind power signals through linear interpolation. Second, a novel algorithm called IDOA-KM is proposed, which utilizes the Improved Dingo Optimization Algorithm (IDOA) to optimize the clustering centers of the k-means algorithm, aiming to address its dependence and sensitivity on the initial centers. The EVs were categorized into priority charging, standby, and priority discharging groups using the IDOA-KM. Finally, an two-layer power distribution scheme for EVs was devised. The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals. The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles. The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals, smoothing wind power fluctuations, mitigating EV degradation, and enhancing the SOC balance.