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In the context of achieving “carbon peaking and carbon neutrality”, the low-carbon transformation of the energy system is the development direction in the future. Hydrogen, known for its high calorific value and low pollution, has received extensive attention in recent years. Based on the carbon emission flow theory, a bi-level optimization operation model of multi-integrated energy system with hydrogen (H2-IES) is proposed considering dynamic carbon emission factors. At the upper level, an economic dispatch model is established by the main energy grid based on the principle of optimal benefit, and the energy prices and carbon emission factors of each park are determined and distributed to the lower level. At the lower level, a multi-park low-carbon cooperative operation model is established based on the Nash negotiation theory, and the adaptive alternating direction method of multipliers (A-ADMM) is used for distributed solution to determine the energy demand of each park and provide feedback to the upper level. The coordinated operation of both levels is realized in multiple iterative interactions. To equitably distribute the benefits of cooperation, a revenue distribution method based on comprehensive bargaining power is proposed. The analysis of a case study shows that the bi-level optimization method proposed in this paper can realize the coordinated operation between the upper and lower levels, and take into account the low-carbon and economical properties of multi-parks operation. Because the income is reasonably distributed, the enthusiasm of parks to participate in cooperation can be guaranteed.

Integrated energy system (IES) is an important means to achieve the goal of “carbon peaking and carbon neutrality”. However, different types of users in the system have different energy consumption behaviors, which makes the coordinated optimization and low-carbon operation of the integrated energy system more difficult. In order to give full play to the subjective initiative of users, the user behavior of the integrated energy system is modelled based on user behavior analysis, and users are classified into aggressive and conservative types by convolutional neural network (CNN). Then, the decision model of integrated energy system operator is constructed to determine the supply mode of electric heating energy, and the corresponding energy package is designed for different types of users. Finally, the effectiveness of the above models and methods is analyzed based on actual data, and the value of user classification in low-carbon operation of integrated energy systems is verified.

Hydrogen energy has broad development prospects as a clean, carbon-free, flexible and efficient secondary energy and industrial raw material. Although the technologies of hydrogen production by water electrolysis, hydrogen storage and hydrogen supply have been relatively mature, the technology chain of hydrogen production-storage-supply is still in its infancy. It is of great importance to explore the technology chain in power system for the cooperative utilization of hydrogen energy and traditional electricity. This paper firstly introduced the basic principles, classifications, advantages and disadvantages of the technologies including hydrogen production by water electrolysis, hydrogen storage and hydrogen supply, and summarized the development of hydrogen production by water electrolysis, hydrogen storage and hydrogen supply technologies in the United States, Japan and the European Union. Then, the current status of above technologies in China was analyzed, and three possible application modes of hydrogen production by water electrolysis, hydrogen storage and hydrogen supply in power system in China were discussed. Finally, based on the current situation, the suggestions for promoting the development of hydrogen production by water electrolysis, hydrogen storage and hydrogen supply in power system in China were put forward, which provide a reference for optimizing the development of the whole technology chain of hydrogen energy production-storage-supply-use.

In the wireless charging system, the power transmission follows the magnetic coupling relationship between the transmitting coil and receiving coil. When there are metal foreign objects between the transmitting coil and receiving coil, the eddy current effect or magnetic focusing effect of the foreign objects will affect the transmission performance and operational stability of the wireless charging system, and in severe cases, it can also endanger charging safety. In order to clarify the influence mechanism of metal foreign objects with different materials on the performance of wireless charging system and effectively analyze the influence law of foreign objects, an accurate modeling method of metal foreignobjects is urgently needed to improve the detection accuracy of metal foreign objects. Therefore, an equivalent modeling method of metal foreign objects field is proposed, taking into account the key factors of metal material, location, equivalent area and so on. By constructing the coupling equation of wireless charging system under the influence of metal foreign objects, the expression of phase difference between the transmitting coil current and receiving coil current before and after the introduction of foreign objects is obtained. On this basis, the equivalent coil model of ferromagnetic metal and non-ferromagnetic metal and its parameter configuration method are further proposed, which can accurately obtain the current phase difference under the condition of metal foreign objects intervention. Finally, the accuracy and effectiveness of the modeling method are verified by simulation and experiment. This method provides a new idea for the characteristic analysis and online detection of metal foreign objects introduced in wireless charging system.

When False Data Injection (FDI) attacks inject false data into the controllers of a microgrid, it can lead the controllers to make decisions based on inaccurate information, ultimately resulting in system collapse. This study presents a novel approach to dynamically adjust output voltage in Campus Microgrids (CMG). Initially, we use feedback linearization techniques to address the inherent nonlinear complexities in Distributed Generation (DG) dynamics. Moreover, during voltage regulation, controllers and sensors may be susceptible to FDI attacks. To address this, an elastic follower observer is introduced to mitigate the impact of false information injection attacks on voltage coordination, thus ensuring the robustness and stability of the system. Notably, our method reduces dependence on global information and fault boundaries within the communication network, thereby enhancing system elasticity and efficiency. In conclusion, through comprehensive simulation experiments, we evaluate the effectiveness and performance of our proposed strategy. The simulation results validate the success of our approach in voltage regulation and its efficiency in fault management. These findings bear significant implications for the advancement of voltage regulation strategies in distributed generation systems. [ABSTRACT FROM AUTHOR]

Flexible air conditioning energy use, leveraging building thermal inertia and thermal energy storage, can effectively reduce building carbon emissions. The carbon reduction potential of flexible energy use in air conditioning is influenced by uncertainties, such as dynamic electricity carbon emission factors. To accurately quantify this potential, a methodology for calculating the carbon reduction achievable through flexible energy use in air conditioning, considering these uncertainties, is proposed. First, the sources of uncertainty in air conditioning energy use are identified and mathematically described. Next, the relationship between uncertainty, load, and carbon emissions is analyzed. Subsequently, the carbon reduction mechanisms of various flexible air conditioning energy use methods, based on building thermal inertia and energy storage, are, respectively, explained, and corresponding mathematical models are established. Finally, a case study is conducted to verify the feasibility of the method and reveal the characteristics of the carbon reduction potential of various flexible energy use methods, considering uncertainty. [ABSTRACT FROM AUTHOR]

The authors have expressed the wish to retract this article on the ground that there are some technical issues that require to be corrected. 1. The indicator does not provide a specific quantification method. 2. The indicator weight is obtained through subjective evaluation where there is a significant deviation between the actual project verification and the current comprehensive energy system operation results. 3. The post evaluation index values in the article are only obtained based on the average value obtained from expert scoring, and the fitting results do not have reference significance. The Publisher and Editor(s) approved to the retraction.

Hydrogen, being the most abundant element in the universe, holds great promise as an energy carrier for decarbonizing various economic sectors. In particular, green hydrogen production through water electrolysis is essential for achieving this goal, with polymer electrolyte membrane (PEM) water electrolyzers playing a crucial role. PEM water electrolyzers are known for their rapid response, enabling them to effectively adapt to fluctuations in renewable energy sources. Nevertheless, rapid load changes can result in the rapid build-up of heat within the electrolytic cell, leading to a sharp increase in temperature and potentially harming the cell. To address this challenge, we developed an electrolysis water system model using MATLAB and validated its accuracy through experiments. This model allowed us to explore the factors influencing stack temperature and propose a fast and secure dynamic process control strategy. By laying the groundwork for subsequent control studies on PEMEC (Proton Exchange Membrane Electrolysis Cell) stacks and systems, this research facilitates further progress in their control and regulation.

The authors have expressed the wish to retract this article on the ground that there are some technical issues that require to be corrected. 1. The indicator does not provide a specific quantification method. 2. The indicator weight is obtained through subjective evaluation where there is a significant deviation between the actual project verification and the current comprehensive energy system operation results. 3. The post evaluation index values in the article are only obtained based on the average value obtained from expert scoring, and the fitting results do not have reference significance. The Publisher and Editor(s) approved to the retraction.

Environmental conditions can significantly affect the performance of photovoltaic (PV) hydrogen production systems, resulting in fluctuations in PV output and suboptimal hydrogen production. In order to solve these problems, a voltage stabilization control based approach has been implemented for a photovoltaic integrated hydrogen production system, which is based on an existing hydrogen production model coupled to PV storage. The aim of this approach is to enhance system stability, improve the quality of photovoltaic power generation, and optimize hydrogen production. The strategy includes maximum power point tracking (MPPT) control for the PV system, as well as coordinated control of the electrochemical energy storage system to ensure stable bus voltage and efficient use of solar resources. The effectiveness of this control strategy was demonstrated by simulation.

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems. [ABSTRACT FROM AUTHOR]