Complete analysis of the dynamic characteristics of the proton exchange membrane electrolytic cell (PEMEC) is significant for its efficient and flexible utilization. To fully reflect the dynamic process including thermo-electric interactions within PEMEC, this paper disassembles this process and simplifies it for representation through a clear diagram of dynamic power flow. On this basis, we proposed a novel combined qualitative and quantitative analytical method for the comprehensive response by defining the evaluating indexes for PEMEC's response performance. Meanwhile, we analyzed the change pattern of dynamic response behavior, response time and the influence of thermo-electric interaction under multi-scenarios, like different voltage abrupt change magnitudes, different cathode operating pressures, and different inlet water temperatures. The results show that the PEMEC has the biggest response behavior with the longest response time under the largest external voltage variation magnitude. Besides, there is the shortest response time and smallest parameters total changes after response when the cathode operating pressure is 15bar Moreover, when the inlet water temperature is 40 degrees C it has the characteristic of quick action time and small response magnitude. The model, analysis method, and findings in this paper provide an effective reference for the operational regulation of PEMEC's thermal and electrical parameters.

Conducting research on advanced cycle of distributed energy system and improving energy utilization efficiency is an important approach to promote the development of distributed energy systems. The paper proposed a novel solar-driven supercritical carbon dioxide-heat pump cogeneration system based on solar power tower system and performed the overall modeling of the system using the heat current method. The overall exergy loss calculation model of the system was established by applying the exergy analysis method incorporating the standard thermal resistance-based entropy generation rate expression. The calculation results present that under rated condition, irreversible loss mainly occurs in the solar energy conversion part, accounting for 73.83 %, and the system exergy efficiency is 34.69 %. Besides, the paper investigated the steady state performance of the system under different working conditions and the influence of the turbine inlet temperature and the intermediate pressure on the system performance. When the direct normal irradiance increases, the proportion of exergy loss in solar energy conversion part increases by 62.13 %, while the change of system exergy efficiency depends on the operating state of the system. The optimum intermediate pressure is 11.72 kPa, when the net power output and the exergy efficiency of the whole system reaches the maximum. Finally, the proposed system contributes to the improvement of cascade utilization of energy, and the modeling method is effective for solar-driven supercritical carbon dioxide cogeneration systems.

Analyzing the dynamic characteristics of pairs of polymer electrolyte membrane fuel cell (PEMFC) is crucial for enhancing the efficiency and flexibility of the system in which it is utilized. In order to accurately reflect its dynamic process and investigate the interaction between the heat and electricity, this paper adopted the standardized thermal resistance method to build a dynamic model of PEMFC. On this basis, a simulation comparison with the experimental data is performed to verify the accuracy of the proposed model. Moreover, we propsoed a two-stage comprehensive response analysis method of the PEMFC under thermo-electric coupling with corresponding key parameters, such as the fuel inlet temperature, gas excess coefficient and load variation amplitude in multiple scenarios. The results show that the comprehensive response characteristics are better when fuel gas inlet temperature is 40celcius or excess oxygen coefficient is 2.4 in the corresponding scenarios. The model, analysis method and findings in this paper provide an effective reference for the development of optimal control strategies for the PEMFC stack temperature as well as other parameters to improve its comprehensive dynamic response performance.

Improving the melting and solidification rate of phase change material is crucial for enhancing the charging or discharging power of a latent heat storage system. This research proposed a novel two-tube heat exchanger latent heat storage unit with longitudinal fins of horsetail stem based on the bionic structure of horsetail stem cross-section. Numerical modeling of transient melting of phase change material in horsetail stem fins by natural convection was established. The model’s accuracy was verified and compared with horsetail stem fins and conventional straight fins. Moreover, we optimized and designed the new fin structure based on the response surface method with minimum melting time as the objective function. The optimization results show that the optimized ponytail fins have a more uniform temperature distribution of the phase change material, significantly eliminating the influence of the heat transfer hysteresis zone, and reaching the steady state more quickly under the heat storage condition. Compared to straight fins, optimized horsetail stem fins reduced the heat storage area by only 3.80% and shortened the phase change material melting time by 44.30%. The solidification time was also reduced by 54.59%. This study provides a new idea for the structural design of phase change heat storage fins.

Efficient and reliable utilization of renewable energy at the user's end is the key to achieving a low-carbon life. This paper proposed a new distributed energy system around the comprehensive utilization of solar energy by integrating solid oxide fuel cell (SOFC), energy storage equipment, photovoltaic thermal (PVT) collector, and heat pump. By integrating the use of SOFC and PVT, we can further minimize reliance on fossil fuels, while employing the coupling of PVT and heat pump effectively mitigates the inherent challenges of solar energy's variability and intermittency, all while enhancing overall system efficiency. On this basis, we apply the heat current method to construct a cross-scale heat current model of the components and the system by considering the energy transfer, conversion, and storage characteristics of the system. By employing this model, we simulate the system's operation throughout an entire typical day, assess the COP enhancement of the PVT-coupled heat pump system, analyze the influence of diverse operating conditions on daily system performance, and evaluate the economy of the energy storage devices in the system.

Distributed power sources and diversified loads flexibly and controllably interact in the active distribution network with symmetrical characteristics, and their spatial-temporal characteristics are significant in optimizing the grid configuration and enhancing the active regulation capability of the distribution network. This contribution proposes an active distribution network architecture that considers symmetrical source and load access and constructs an active distribution network optimization scheduling model by considering the constraints of photovoltaic, energy storage, electric heat pumps, fuel cells, micro-gas turbines, and other equipment. On this basis, the overall energy balance and heterogeneous energy transfer constraints are considered with the goal of minimizing the operation cost of the system. By introducing the IEEE-33 node model, including influences distribution power source and energy storage location, dynamic characteristic of heat pump, operational performance of fuel cell, and heat load variation on the active power and reactive power of active distribution network are analyzed. When the COP increases by 0.4, the day operating cost of the system equipment decreases by 14.5%. The simulation results show that considering the temporal and spatial distribution characteristics of the equipment can improve the active distribution network operation and scheduling, promote renewable energy accommodation, and enhance the economy and flexibility of the system.

Deep exploration and effective coordination of customer-side electrical and thermal loads significantly promote renewable energy consumption. This paper proposes a distributed integrated energy system (DIES) by coupling a power distribution grid, heat supply network, wind power, photovoltaic, and combined heat and power generation. Based on the standardized thermal resistance method, the dynamic thermal power flow model of the heating network and the overall power flow model of the DIES are constructed. On this basis, the flexible heating load under the user-following resilient heating scheme is obtained by considering users' behavior and physical comfort. Based on the resilient heating scheme, a dual-layer dispatching model of the DIES is constructed, and a multi-scenario day-ahead dispatching is conducted to maximize renewable energy consumption. Compard with traditional heating schemes, the dispatch results show savings in heating demand of 7.8 % and 6.2 % with the basic resilient heating scheme and ultimate resilient heating scheme, respectively, and an increase in regional PV power consumption of 27.5 % and 35.8 %, respectively. Overall, improving heating schemes in DIES can bring comprehensive benefits to the energy saving and enhance the flexibility of system dispatching.

Deep exploration and effective coordination of customer-side electrical and thermal loads significantly promote renewable energy consumption on the customer side. This paper proposes a distributed integrated energy system (DIES) by coupling a power distribution grid, heat supply network, wind power, photovoltaic, and combined heat and power generation. Based on the standard thermal resistance method, the dynamic thermal power flow model of the heating network and the overall power flow model of the DIES are constructed. On this basis, the flexible heating load under the user-following intelligent heating scheme is obtained by considering users' behavior and physical comfort. Based on the elastic heating load, a dual-layer dispatching model of the DIES is constructed, and a multi-scenario day-ahead dispatching is conducted to maximize renewable energy consumption. Compard with traditional heating schemes, the dispatch results show savings in heating demand of 7.8% and 6.2% with the intelligent heating scheme and ultimate intelligent heating scheme, respectively, and an increase in regional PV power consumption of 27.5% and 35.8%, respectively. Overall, improving heating scenarios in DIES can bring comprehensive benefits to the system regarding energy saving and emission reduction and increase the flexibility of system dispatching.

This paper addresses a typical dyeing and printing industrial system and introduces a system efficiency and carbon efficiency calculation model based on the standard thermal resistance method. Initially, the standard thermal resistance method is employed to conduct an in-depth analysis of the heat exchange process for each device. The data is reduced in dimension through a reverse deduction approach, leading to the derivation of the equipment's thermal resistance, power consumption, and other unknown parameters. Utilizing these parameters, each device's standard thermal resistance model is constructed and subsequently integrated to form a comprehensive power flow model for the entire dyeing and printing plant. This model can evaluate the plant's energy consumption conditions, pinpointing segments and equipment with high energy consumption. Further derivations provide expressions for system efficiency and carbon efficiency characterization in conjunction with system structure and operational feature parameters, creating a standard thermal resistance model for the equipment and system based on external characteristic parameters. This paper presents a tri-fusion technique rooted in expert experience analysis, survey data identification, and the physical standard thermal resistance method. This technique can calculate energy efficiency and carbon efficiency indicators even in scenarios with incomplete system information.

Various heating disturbances and faults in the heating network are necessary to be controlled and handled by some intelligent heating strategies with the increasing complexity of the heating network. This paper constructed the dynamic modeling of the heating system using standard thermal resistance and obtained a dynamic heat current model of the heating system. On this basis, we analyzed the heat transfer performance of the heating system. Five disturbances are selected, including the behavior of users, indoor heat source, heat exchanger heat transfer performance deterioration, pipe blockage, and pipe leakage. The effects of different disturbances on the overall system and user side water supply temperature were obtained by establishing a dynamic model for segmented heating pipelines. Feasible control methods for the heating network and load side are sorted out, mainly changing the water supply's temperature and the water supply's flow rate to reduce the water supply's fluctuation. Five specific control strategies are proposed for five types of disturbances. Comparing the case without control and the case with control, the results show that the fluctuation of water supply temperature is significantly reduced, and the control strategies can reduce the impact of disturbances on the heat network system and customers and improve the comfort of customers in the presence of disturbances. under the disturbance of pipeline leakage, the method proposed in this article reduces the temperature fluctuation amplitude by 75% and the fluctuation duration by 60%.

This paper constructs the prediction models of three neural networks: full connected, Recurrent Neural Networks (RNN) and Long Short-Term Memory network (LSTM). The actual wind power output is taken as the experimental data for prediction and analysis. Through multi-angle quantitative comparison, it is found that the three methods can achieve an accuracy rate of more than 95% and a qualification rate (relative error less than 15%) of more than 98%. The training rounds of the fully connected neural network are much higher than those of the other two methods. The LSTM has a strong single training ability. The researchers can select the appropriate super parameters on a small number of data sets to obtain an ideal learning effect quickly.

As essential parts of the combined heat supply network with multiple heat sources, the electrical-heating and heat storage technology can simultaneously achieve the goal of clean heating and renewable energy accommodation. The paper introduces the integrated electrical-thermal system with multiple heat sources that consist of combined heat and power (CHP), coal-fired boiler (CB), and electrical boiler (EB) with thermal energy storage (TES). Considering the integration of electrical and thermal networks, we construct a comprehensive model that includes the electrical energy transmission and the overall heat transfer and storage processes based on the heat current method. Based on the comprehensive model, the optimal dispatch of the integrated system is discussed, and an iteration solving method is proposed, which can achieve acceptable convergence and solving efficiency. The simulation results show the combined operation of multiple heat sources has higher system operation efficiency but not necessarily a higher utilization rate of renewable energy. In the test system the combined operation of CHP and coal-fired boiler can reduce the coal consumption of the system by 2%. However, the wind utilization of the system decreases by 4.17%. Moreover, the introduction of an electrical boiler with a TES device can improve the system's flexibility and reduce wind curtailment, wherein the test system is 12.11% more wind consumption. Further, the heat load level can affect the operation efficiency of CHP, the combined operation of multiple heat sources can achieve the balance between reducing coal consumption and increasing renewable energy power generation.

More and more renewable energy is connected to the distribution network (DN), and some DNs in suburban or rural areas connect multiple electrothermal loads to balance renewable energy power. The electrothermal load includes heat storage, heat exchanger, and heating network. In this paper, the power flow method is used to analyze the heat transfer process of electrothermal load. Considering the minimum voltage deviation, the minimum active power loss of DN, and the minimum carbon emission cost, the overall electrothermal hybrid optimal power flow analysis is carried out to achieve multi-objective optimization. In this paper, the power flow method and carbon emission cost method are applied to the optimal power flow analysis of the electrothermal hybrid network. The effectiveness and correctness of the model are verified by an improved IEEE 33 buses case.

Heat transfer characteristics analysis of supercritical carbon dioxide (sCO2) in variable cross-section tubes is critical for its promotion and application. This paper proposed three horizontal heat exchange tubes, i.e., straight tube, converging tube, and diverging tube. On this basis, we used numerical simulation to analyze the convective heat transfer performance of sCO2 in the horizontal tube with variable cross-section under heating conditions. The diverging tube effectively enhances the overall heat transfer performance compared with the uniform cross-section tube, and the heat transfer rate is increased by 19.26% compared with that of the straight tube. In contrast, the converging tube weakens the heat transfer ability. Moreover, we proposed quasi-air film and eddy blockage concepts to reveal the fluid's heat transfer deterioration mechanism near the top bus. Wherein the quasi-air film is full of sCO2, which has a low thermal conductivity close to that of air at room temperature, and generates near the top bus. Besides, the criterion factor, But was proposed by double corrections of density and temperature for judging the occurrence of eddy blockage, and the Nu-based evaluation shows that the eddy blockage can deteriorate the heat transfer performance at the top of the horizontal tube by 17%. The physical mechanism of heat transfer deterioration under the heating condition is clarified by the dual-effect of quasi-air film and eddy blockage on this account.

This paper proposed a novel finite data mapping-based multi-objective optimization approach of flow channel structure to achieve accurate prediction and optimization of the PEMFC by numerical simulation and machine learning. A 3D-mathematical model of a single cell is built to model the fuel cell performance and provide some finite simulation data. On this basis, the artificial neural network method is introduced into the response surface analysis and combined with the central composite design to obtain the relationship between the flow channel geometry size and the PEMFC output performance. The optimal solution is obtained by the NSGA-II algorithm with a multi-objective functions. The multi-field synergy analysis results show that the optimized PEMFC improves the peak power by 8.5% and expands the current density operating range by 15.5%. Meanwhile, the overall heat transfer capacity increased by 5.95%, enhanced the mass transfer capacity at the cathode side, and the average mass fraction of oxygen at the intersection of the diffusion layer and flow channel increased by 28.57%. In addition, the total drainage increased by 5.35%. In all, the proposed finite data mapping optimization method and multi-field synergy theory analysis can effectively guide and evaluate the optimization of fuel cells.