The distributed energy supply system with efficient coupling of electric, hydrogen and thermal energy integrates the advantages of electricity, hydrogen, and thermal energy, which can significantly improve energy efficiency and reduce environmental pollution. In order to achieve the fine control of the model, a control model of the distributed energy supply system based on alternating direction multiplier method (ADMM) is proposed. According to the characteristics of multi-agent electric-hydrogen-thermal system, a double-layer optimal scheduling model is developed, and the adaptive alternating direction multiplier method is proposed to solve it. The experiment results show that the iteration times of the proposed algorithm fluctuate around 43. When the sample size was 40,000 times, the average iteration time of the model was 0.11 s, respectively, showing a relatively stable performance. The total carbon emission reduction of the energy system in the experimental park reached 4.53 tons within 24 hours, and the corresponding carbon trading income was 546.18 yuan. The results verify the applicability and potential of the model in both environmental and economic win-win aspects, and provide an important theoretical and practical reference for optimizing distributed energy management and promoting low-carbon transition.

As the industry with the highest share of carbon emissions in the industrial sector, the steel industry plays a critical role in achieving China's dual carbon goals. Accurate carbon emission forecasting for the steel sector is therefore of great importance. However, such forecasting faces dual challenges of incomplete and delayed user data. To address these issues, this paper proposes a dynamic carbon emission prediction method for steel enterprises based on electricity consumption data. First, typical process flows in the steel industry are analyzed, and an energy-carbon emission process knowledge graph is constructed to uncover the relationships between key equipment and carbon emissions. Then, an electricity-carbon coupling model is proposed, using energy as a bridge. Based on the process knowledge graph, major carbon sources and critical equipment are identified, and a coupling model is established using a Gated Recurrent Unit (GRU) and multivariate regression. The energy-carbon conversion coefficients of each process are predicted using a rolling Long Short-Term Memory (LSTM) network. Grey relational analysis is employed to achieve carbon emission forecasting for steel enterprises. Finally, a case study of a representative steel enterprise in southern China is conducted to validate the proposed method. The results show that the prediction accuracy exceeds 95%.

In response to the issues of voltage flicker and exceeding limits in distribution network nodes caused by the large-scale integration of distributed energy, this paper introduces virtual power plants as the key subject of regional voltage regulation to cope with the reactive power control challenges brought by fluctuations in new energy. This article evaluates the reactive power output potential of wind turbines, photovoltaic units, energy storage, and flexible loads in a virtual power plant, and establishes an optimized operation model with economic objectives based on constraints such as power balance and voltage threshold. Validate the effectiveness of the strategy in an IEEE 34 node distribution network through numerical analysis. The results show that this strategy can reduce the load peak valley difference by $11.2 \%$, significantly improve voltage deviation, and reduce the total operating cost of the virtual power plant by $6.2 \%$, improving the system’s operational economy and safety, providing a solution for voltage regulation under high proportion new energy integration.

ABSTRACTIntegrated modeling and operation optimization of building energy systems is significant for improving the energy utilization efficiency and reducing carbon emission. This paper introduces the standardized thermal resistance to construct an overall heat current model of the building cooling system with coupled heat transfer, mass transfer, and energy conversion processes. Based on the heat current model, we derive the holistic thermal energy transfer and conversion constraints on the system level and reduce the intermediate parameters of the system model. Moreover, the genetic algorithm is introduced to optimize the system operation conditions under the given system structure parameters. The optimization results provide the optimal mass flow distribution of cooling water, return water, and ambient air and meanwhile show that the compressor power consumption can reach 76.5% of the total system power consumption. The change of user behavior by raising the room temperature to 4°C can reduce the total system power consumption by 20%. The results are in line with the theoretical reality and prove the feasibility and effectiveness of the method proposed in this paper, which provides a practical reference for the energy‐saving operation of the building cooling system.

ABSTRACT Continuous improvement of transient supercooling effects in thermoelectric cooling is important for solving thermal management problems such as chip hot spots. In this paper, a new I‐type thermoelectric cooling structure is investigated, and its transient cooling performance is deeply investigated by a simulation method with the minimum cold end temperature as the index. We systematically analyze the cooling performance difference between the I‐type structure and the conventional π‐type structure under various pulse currents, and investigate the effects of structural parameters (such as the length of the thermoelectric legs and copper thickness) and current amplification on the minimum cold end temperature of the I‐type structure. The results show that, within a certain range, the decrease of copper thickness and the increase of the length of the thermoelectric legs are conducive to the reduction of the minimum cold end temperature, and the cooling performance of the I‐type structure is better than that of the π‐type structure under various pulse currents, especially when the current amplification factor is 20, the cold end temperature of the new structure is nearly 30 K lower than that of the conventional structure. The research demonstrates that the innovative design enhances the transient cooling efficiency, with the minimum cold end temperature serving as a definitive metric. This new structure not only exhibits a lower cold end temperature but also experiences a slower temperature increase as the pulse current diminishes. This study provides theoretical support for the application of thermoelectric cooling technology in the fields of high‐power cooling and high‐speed cooling.

Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of PV generation is conducted, and outlier data is handled using the 3σ rule. Subsequently, a novel approach that combines temporal convolution and LSTM networks is introduced, with TCN extracting spatial features and LSTM capturing temporal features. Finally, a real spatiotemporal dataset from Gansu, China, is established to compare the performance of the proposed network against other models. The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy, with a single-step Mean Absolute Error (MAE) of 1.782 and an average Root Mean Square Error (RMSE) of 3.72 for multi-step predictions.

Abstract The wind-solar-storage hybrid power generation system is mainly composed of wind turbines, solar cell arrays, electrolytic cells, fuel cells, battery packs, intelligent controllers, multi-functional inverters, cables, supports and auxiliary parts to supply power to the load. The system utilizes wind and solar energy for power generation without an external power supply, and an energy storage module composed of an electrolytic cell, fuel cell, and battery is used to assist the power supply. In this paper, a fuel cell model, a wind power generation model and a solar power generation model are respectively constructed, and a small experimental platform was built to validate the model. The pulse width modulation (PWM) control method, combined with the auxiliary regulation of the system by the battery, reduces the impact of the volatility of wind power generation on the electrolytic cell and fuel cell, and makes the load current more stable under different working conditions. The simulation results show that the system has good compression resistance, fast response and good stability.

The number of Internet Data Centers (IDC) is developing rapidly, and the large-scale load has a significant impact on the secure, economic, low-carbon, and efficient operation of the distribution network. This article proposes a joint expansion planning framework of IDC and distribution network driven by carbon neutrality goals. Firstly, considering the impact of various factors such as service level, parameters, and environment on the IDC load operation, we conducted in-depth research on the characteristics of different IDC load and established an IDC load demand response model. Secondly, in response to the uncertainty of new energy output and load, the Conditional Value-at-Risk (CVaR) theory is introduced to provide the concept and calculation method of carbon neutrality loss risk. Then, with the goal of minimizing the investment operating total cost of the distribution network, a two-stage stochastic expansion planning model of IDC and distribution network is constructed. The large M method and the polygon method are first used to solve the mixed integer nonlinear optimization model. Finally, an improved IEEE-33 node system is taken as a simulation example to verify the effectiveness and progressiveness of the proposed method. From the simulation results, it can be seen that the planning method proposed in this paper has effectively reduced the total investment cost by 23.52 % compared to the independent planning method.

To mitigate the supply-demand imbalance risk due to new energy output uncertainties and to devise an economical and secure energy supply strategy for virtual power plants (VPPs), this study introduces a robust operation optimization model. This model accounts for the uncertainties of photovoltaic (PV) output and load aggregation characteristics, integrating various methodologies including the regional climate model (Providing Regional Climate for Impact Studies, PRECIS), the BP neural network, the chance-constrained programming (CCP) algorithm, the Equivalent Thermal Parameters (ETP) model, and a two-level, two-stage optimization approach. The findings indicate that the model successfully predicts and calculates PV output and load changes, offering a solid data foundation for energy supply planning. It enables the generation of an optimal, economically viable, and robust day-ahead energy supply strategy, considering the maximization of benefits for various stakeholders within the VPP. Furthermore, by leveraging the rapid adjustment capabilities of energy storage equipment and air conditioning loads, the model corrects calculation errors in source-load forecasts, enhancing the security of the energy supply.

Proposing a flexible interconnection device, based on a back-to-back converter (BTB- VSC), to address issues of unbalanced power distribution and poor power quality arising from numerous power electronic devices connected to the power grid. This solution facilitates power flow regulation between two AC systems, thereby improving the management of power quality. Introducing the topological structure, mathematical model, and control principles of the BTB- VSC, a MATLAB/Simulink simulation verification model is created to validate the effectiveness of the proposed control strategy. The simulation outcomes demonstrate that the related control approach adeptly manages active power transmission and reactive power compensation between the two AC systems. This results in elevated power quality, augmented reliability, and enhanced economic efficiency within the distribution network's operation.

This paper presents a control strategy to improve the power quality of the grid current and PCC voltage for the grid-connected inverter by using droop control. The conventional droop control is the output average power control, and it can not preciously control the grid current. Therefore, the power quality of the grid current is sensitive to the nonlinear loads, especially rectifier loads. To improve the power quality of the grid current in the droop control, the point of common coupling (PCC) voltage is set as the feedback to compensate for the harmonic voltage at the PCC. The resonant control is to control the harmonic voltage at the PCC. If the harmonic voltage at the PCC is suppressed, the power quality of the grid current will be improved. Finally, the simulation and experimental results demonstrate the effectiveness of the control strategy.

Along with the development of renewable energy, the scale of new energy connected to the grid is increasing, and its anti-peaking characteristics bring challenges to the operation of the new power system. The electric-hydrogen coupling system can meet the peak demand of the system and support the operation of the new type of power system by taking advantage of the flexibility of hydrogen energy storage, which is an important idea to realize the goal of “dual-carbon” development of the energy and power system. In order to assess the ability of the electric-hydrogen coupling system to participate in grid peaking, a multi-objective operation optimization model of the electric-hydrogen coupling system to participate in grid peaking is proposed, which takes into account the peak-to-valley difference, economy and volatility of the system. And based on the actual scenarios to solve the arithmetic example, get the actual operation scenarios of the peaking capacity indicators, to verify the effectiveness of the proposed model. The results show that the electric-hydrogen coupling system effectively improves the economy and stability of the system while consuming new energy.

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices. This method integrates features including photovoltaic (PV) systems, energy storage coupling, varied energy roles, and energy supply and demand dynamics. The system model is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously. To strike a balance between optimality and feasibility, renewable energy resources are modeled with considerations for forecasting errors, Gaussian distribution, and penalty factors. Furthermore, this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs. Rooted in surplus theory and finite time projection, the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints. The algorithm greatly reduces the communication burden through event triggering mechanism. Finally, both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

With the development of the economy, people’s demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning.

Hardware in the loop (HIL) simulation is an important way to research and test demand side resource control strategies. Aiming at the current problems for demand side resource HIL simulation of difficulty in the controller application and relying on foreign technical products, a low code control and HIL simulation method for demand side resource based on hybrid cybernetics is proposed. First, based on the power hybrid cybernetics and the expanded activity on edge network, an event driven and adaptive general modeling of power system control strategies is carried out. Then, based on automatic differentiation technology, a configuration module is implemented for algorithms such as solving equations and optimizing models. The control model construction is decoupled from the solving algorithm, and a low code configuration method for demand side resource control strategies is established. Furthermore, based on the Java programming language, control communication programs and interfaces for simulation models are developed to achieve data exchange between Simulink simulation models and actual controllers, and a low code demand side resource HIL simulation experimental platform is built without the need for dedicated simulation machine hardware. Finally, the effectiveness of the proposed method was verified through HIL simulation examples of the photovoltaic maximum power point tracking control.

Aiming at the problems of low energy efficiency and high carbon emission, an energy cooperation optimization strategy for multi-park integrated energy system (IES) considering electrical and heating power interaction was proposed. Firstly, the cooperative operation framework and model of multi-park IES based on electrical and heating power interaction are constructed. Secondly, the energy cooperation optimization model based on Nash negotiation is constructed, and distributed solution is completed by alternate direction multiplier method(ADMM). Finally the contribution factors of electrical and heating energy are considered to redistribute the benefits of the multi-park IES alliance. The simulation results verify the effectiveness of the strategy in economy, low carbon and environmental protection.

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.

The integrated energy system at the park level, renowned for its diverse energy complementarity and environmentally friendly attributes, serves as a crucial platform for incorporating novel energy consumption methods. Nevertheless, distributed energy generation, characterized by randomness, fluctuations, and intermittency, is significantly influenced by the surrounding environment. Within the park, the output of multiple devices frequently diverges significantly from the actual demand, potentially resulting in energy waste phenomena, such as the curtailment of wind and solar power. To tackle the dual challenges of balancing energy supply and demand while reducing carbon emissions in the industrial park, this paper introduces a low-carbon integrated energy system that incorporates distributed renewable and clean energy sources. Mathematical models are formulated for the source–grid–load–storage components of this low-carbon integrated energy system. Furthermore, various operational scenarios for the park-level integrated energy system are analyzed. The ultimate goal is to devise an economically viable, low-carbon, and efficient operational strategy for the integrated energy system, aiming to satisfy the diverse objectives of various stakeholders.

近年来随着综合能源系统研究的深入,对能源设备建模的完整性和准确性提出了更高要求.针对传统研究中尚未考虑设备碳排放参数以及现有参数辨识方法的不足,文章提出一种考虑碳排放的综合能源系统抗差参数辨识方法.首先,建立了考虑碳排放强度的常见能源设备模型;其次,针对获取的系统历史运行数据,利用BP神经网络对其中的缺失值进行拟合填充,获取高可信的有效数据;最后,采用基于投票法的参数辨识方法对设备进行参数辨识,并通过旧数据遗忘与更新对设备参数进行动态辨识,通过算例对比分析了所提方法相较于现有方法的优势,说明了所提方法的必要性和有效性.

Under the dual carbon strategy, with the frequent occurrence of extreme weather and the further increase in uncertainty of multi-user behavior, it is urgent to improve the stability of the heating systems and reduce heating energy consumption. Aiming at the problem of fault-disturbance control of the multi-user heating network in an integrated energy system, this paper proposes a novel analysis method of resistance–capacitance reactance based on the circuit principle to construct a dynamic thermal-power-flow model of the whole link of the multi-user heating network and analyze the fault-disturbance propagation characteristics of the heating network by this model. It shows that the difference in disturbance characteristics of different users in a multi-user heating network mainly depends on the characteristics of the heating pipeline between the heat user and the heat source, which provides a necessary basis for formulating intelligent control strategies against fault disturbance. Finally, taking a typical daily outdoor temperature in Beijing in winter as an example, this paper compares two different heating strategies and the blocker installation methods of the multi-user heating network to obtain a better heating strategy under actual conditions. Considering the heating fault disturbance, this paper proposes a novel intelligent heating strategy whose heating temperature will decrease during the fault-disturbance time, with an energy saving of about 16.5% compared with the heating strategy under actual conditions during the same period.