This paper proposes a low-carbon optimization dispatch method for hydrogen-based multi-agent regional integrated energy system (RIES) that incorporate CET-GCT. The approach aims to coordinate the interests of all parties within the regional integrated energy system while reducing overall system carbon emissions. First, based on Stackelberg game theory, the interactions between the energy operator and agents on the supply side and demand side are fully characterized, establishing a "one main, two subordinate" multi-agent game model. Second, the model incorporates refined power-to-gas (P2G) technology to enhance system flexibility. Subsequently, a combined carbon trading-green certificate trading mechanism is introduced to effectively constrain the carbon emission behaviors of all stakeholders. Finally, an improved Ivy algorithm is integrated with the CPLEX solver to solve the proposed model. Simulation results demonstrate that while each entity maximizes its own benefits by adjusting its strategy, the system's overall carbon emissions decrease by 5.12% and total revenue increased by 11.49%, yielding significant low-carbon economic benefits. This validates the effectiveness of the proposed model and methodology.

In order to realize rapid fault recognition of residual current device (RCD) and improve power safety, a fault residual current recognition method (AVMD-DFNN) based on adaptive variational modal decomposition (AVMD) and optimal dynamic fuzzy neural network (DFNN) is proposed. The decomposition parameters of VMD are determined adaptively by empirical mode decomposition (EMD) to realize the de-noising of the residual current signal. The characteristic parameters of residual current signal are extracted and used as the classification index of DFNN to recognize the type of residual current fault after the dimensionality reduction process. The DFNN is optimized by the minimum output method to remove the redundant fuzzy rule functions, so as to realize the rapid fault recognition of RCD. The simulation results show that AVMD-DFNN has high recognition accuracy and speed, which provides a theoretical reference for the development of new adaptive residual current devices.

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits.

Against the background of rapid global industrialization and urbanization, electricity demand has increased dramatically, and the development of new energy technologies has become a focus. This study uses intelligent decision-making algorithms to construct an innovative power load forecasting model and new energy integration strategy. By comprehensively considering multi-source data such as historical load, meteorology, socio-economics, and new energy generation, a hybrid deep learning architecture combining long short-term memory network (LSTM) and convolutional neural network (CNN) is adopted to significantly improve the accuracy of power load forecasting. The experimental results show that compared with traditional forecasting methods, the mean absolute error (MAE) of the proposed model is reduced by 20 %, from the original 1.0 MW to 0.8 MW; the root mean square error (RMSE) is reduced by 25 %, from 1.5 MW to 1.125 MW, and the coefficient of determination (R²) is increased from 0.9 to 0.95. In terms of new energy integration strategy, the carefully designed multi-objective optimization algorithm has achieved an increase in the utilization rate of new energy from 70 % to 85 %, a 15 % reduction in operating costs, and a reduction in operating costs per kilowatt-hour from 0.12 yuan to 0.102 yuan, while ensuring the safe and stable operation of the power grid. This study provides strong support for the efficient planning and operation of the power system and is of great significance to promoting the green transformation of the energy structure.

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

As global energy demands continue to grow and environmental protection pressures increase, microgrids have garnered widespread attention due to their ability to effectively integrate distributed energy sources, improve energy utilization efficiency, and enhance grid stability. Due to the complexity of internal structure, variety of energy sources, and uncertainty of load demand, the optimal scheduling problem of microgrids becomes extremely complicated. Traditional optimization methods often perform poorly in complex and dynamic microgrid environments, and it is assumed that the complexity is low or that more simplification is needed, which leads to poor convergence and local optimality when dealing with uncertainty and nonlinear problems, making intelligent optimization algorithms a crucial solution to this problem. To address the shortcomings of the traditional honey badger algorithm, such as the slow convergence speed and a tendency to fall into local optima in complex microgrid optimal scheduling problems, this paper proposes a multi-strategy improved honey badger algorithm. During the population initialization phase, a combined opposition-based learning strategy is introduced to enhance the algorithm’s exploration and exploitation capabilities. Additionally, the introduction of variable spiral factors and a linearly decreasing strategy for parameters improves the overall efficiency of the algorithm and reduces the risk of local optima. To further enhance population diversity, a hunger search strategy is employed, providing stronger adaptability and global search capabilities in varying environments. The improved honey badger algorithm is then applied to solve the multi-objective optimal scheduling problem in grid-connected microgrid modes. The simulation results indicate that the improved honey badger algorithm effectively enhances the economic and environmental benefits of microgrid operations, improving system operational stability.

This paper introduces an innovative hybrid system integrating renewable biomass and geothermal energy sources to address contemporary technological and environmental challenges. The proposed system enhances geothermal power plant operations by utilizing biomass combustion products for superheating the steam turbine's inlet stream. Additionally, it incorporates a modified Kalina cycle, flash desalination, and a multi-effect desalination subsystem to efficiently utilize the geothermal plant's waste heat, enabling the simultaneous production of power, heating, cooling, and freshwater. The system's performance is assessed through a combination of thermodynamic and economic analyses. A parametric study investigates the influence of four critical decision parameters on system operations. Moreover, a multi-objective Particle Swarm Optimization algorithm, coupled with a LINMAP decision-making approach, is employed to identify the system's optimal operational state. Results indicate that the system can generate 776.3 kW of power, 237 kW of heating, 15.5 kW of cooling, and 20.35 kg/s of freshwater. This operation mode yields an exergy efficiency of 19.61 % and an economic benefit of 2.78 M$, highlighting the system's dual efficiency and profitability. The performance is significantly influenced by the effectiveness of Heat Exchanger 1. Optimal system performance, characterized by an exergy efficiency of 20.55 % and a payback period of 5.21 years, is also achieved. These findings underscore the system's potential in sustainable energy production and resource optimization.

CCHPs (Combined Cooling, Heating, and Power Systems) are capable of providing cold energy, heat, and electricity to users, allowing cascading utilization of energy and improving energy efficiency. The imbalance between cooling, heating, and electrical energy makes it difficult to accurately evaluate the performance of a CCHP system. Existing indices for evaluating the performance of the CCHP system do not account for the influence of time-sharing tariffs; therefore, the quantitative index of time-sharing is added and the time-sharing economic exergy efficiency of the radiator is established. Given that the electrical and thermal characteristics of the advanced absolutely hot compressed airheat storage system (AA-CAES) can complement the CCHP system, a model of the CCHP system with AA-CAES is established, which can be used to validate the validity of the quantitative evaluation index of time-sharing. A planetary search algorithm is proposed for solving the CCHP system model to address the multi-parameter solving characteristics of the CCHP system model and the disadvantages of the existing multi-objective optimization algorithms, which are prone to local optimality and poor optimization accuracy. Simulation validation demonstrates that the time-sharing economic exergy efficiency proposed in this paper can more accurately reflect the total energy consumption of the CCHP system than the existing evaluation indices. The performance of the CCHP system can be improved by using AA-CAES as a heat storage device.

Aiming at the teaching difficulties of signal and system course, adapting to the new situation of the information age and the engineering education of outcome-based education (OBE), adhering to the traditional classroom teaching and making full use of the network teaching platform and internet technology, a blended teaching mode of internet-engineering education (BTM-IEE) is proposed. In this mode, we adhered to the educational concept of OBE, formulated teaching objectives, optimized teaching content, and built matching teaching resources. In three teaching stages: before class, in class and after class, online-offline blended teaching was effectively organized and implemented by using internet technology, network teaching platform, independently developed a comprehensive experimental system, remote virtual experiment platform, QQ group, etc. In order to ensure a good cycle of teaching quality, a multi-dimensional evaluation system is constructed. A variety of application examples and questionnaire data showed that BTM-IEE can achieve a deep integration of in class-out of class and online-offline, improve students’ engineering application ability, autonomous learning ability, cooperation, communication ability, and cultivate innovative thinking. The horizontal and vertical comparison between the traditional and the improved teaching mode shows that the BTM-IEE can improve the teaching effect, the excellent rate is significantly increased, and the failure rate is significantly reduced. This mode provides a reference for the improvement of the teaching quality of professional courses and has important practical significance.

针对综合能源系统中主体的个体理性未被充分考虑,以及电力系统越来越重视可再生能源消纳和降低碳排放量的问题,提出了一种考虑议价能力的风-光-热电联产(combined heat and power,CHP)多主体能源系统优化运行策略.首先将综合能源系统划分为风电主体、光伏主体和热电联产(combined heat and power,CHP)主体,构建其合作运行模型,并在CHP主体中增加电制氢技术和阶梯型碳交易机制;然后,基于纳什谈判理论建立了风-光-CHP多主体合作运行模型,并由于所提合作运行模型的非凸性,将其分解为系统运行成本最小化问题(P1)和交易支付问题(P2);在P2中,提出考虑经济和环境增幅的议价能力模型,各主体根据自身议价能力来实现收益的公平分配;最后,为了保护各主体合作时的隐私,采用交替方向乘子法(alternating direction method of multipliers,ADMM),对P1和P2进行分布式求解.结果表明:考虑议价能力的风-光-CHP优化运行策略不仅可以实现合作收益的公平分配,而且能有效缓解弃风、弃光和碳排放问题.此外,验证了电制氢技术相比电转气技术更能促进系统的低碳经济运行.

The operation environment of the secondary circuit of the substation is poor, which is easy to cause grounding failure. Once the two-point grounding, there is a risk of automatic device rejection or misoperation, which needs to be eliminated as soon as possible when the single point grounding. However, the efficiency of the early search method is low, which is easy to cause the wrong action of the relay protection equipment. The AC injection method commonly used at present will cause disturbance to the secondary circuit, which is easy to be interfered by distributed capacitance. DC leakage current monitoring method has a large input and can not locate the fault point. The DC current injection method is proposed in this paper, which can overcome the influence of the charge and discharge transient of distributed capacitors. The DC constant current is used to charge distributed capacitors for a short time, and stable and continuous current is formed in the circuit where the grounding fault is located after the steady state is reached. The DC clamp ammeter is used to efficiently determine the faulty branch and quickly locate the specific grounding fault point on the branch. High security and reliability. At the same time, the method adopts constant current DC source, which can overcome the ground fault resistance fluctuation and has strong anti-interference ability.

Abstract: Power system relay protection (PSRP) is a comprehensive course in electrical engineering undergraduate stage, which has a very strong engineering application. However, due to the influence of many factors, such as the power system security, high experimental cost, limited course hours, insufficient open conditions, and so on, traditional experimental teaching combined with hardware is difficult to meet the needs of students in various scenarios anytime and anywhere. Therefore, a low-cost virtual flexible simulation experiment teaching platform (VFSETP) is developed. The platform uses Simulink to build the simulation model of power system primary system and uses graphical user interface (GUI) to design the human-computer interaction interface. Through the communication between GUI and Simulink model, the protection experiments in various scenarios are successfully simulated. The VFSETP has many advantages such as simple interface, good visualization effect, and simple operation. The teachers can easily use it for classroom demonstration, and the students can use it for verification, analysis, expansion, and exploration of experiments in a variety of application scenarios without relying on the laboratory environment. This experimental mode is very conducive to the understanding of knowledge and the cultivation of practical innovation ability. The results of the student survey show that the design method and application mode of the platform can provide a reference for similar courses.

负荷预测是电力系统调度运行的重要基础数据,短期负荷预测的样本数据既有波动性也有随机性.群体优化算法尤其是粒子群算法在负荷预测中运用非常广泛,但常规粒子群算法的惯性参数一般是固定不变的,导致后期搜索效率下降.文中采用改进的自适应粒子群算法提高搜索效率:首先用混沌初始化替代原来的随机初始化,避免了初始种群分布不均;再根据每次迭代适应度的变化更新惯性因子,可以解决后期寻优速度下降的问题;通过差分变异将适应度较差的粒子进行变异,提高了较差个体更新效率;最后利用改进后的自适应粒子群算法优化支持向量机的关键参数c和g,并进行短期负荷预测.通过测试得到改进后的自适应粒子群算法具有较好的优化效果,并且由自适应粒子群算法优化的支持向量机模型具有更好的预测效果.

It is difficult to detect the residual current of specific fault types in low-voltage distribution networks, which results in few labeled residual current samples. Thus, it is difficult to recognize the fault types of residual current. To solve this problem, a cooperative training classification model based on an improved squirrel search algorithm (ISSA) for a semi-supervised support vector machine (S3VM) and the k -nearest neighbor (KNN) is proposed (ISSA-S3VM–KNN). First, the residual current is decomposed into k intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and the characteristic parameters of the IMFs are extracted to obtain a characteristic dataset for establishing a classification model. Second, to solve the problem where it is difficult to the select parameters (such as the penalty factors, slack variables and kernel function) of a S3VM, an ISSA parameter optimization method is proposed to self-adaptively select the optimal combination of parameters for the S3VM. Finally, the KNN is used to verify the classification results of an ISSA-S3VM through cooperative training, which further improves the classification accuracy of the S3VM for unlabeled residual current samples. Classification results of measured and simulation data show that the classification accuracy of the ISSA-S3VM–KNN is higher than that of the SVM–BPNN, WE–AE–BPNN, and PSO–SVM. The ISSA-S3VM–KNN provides a certain theoretical basis for achieving fast and accurate residual current fault type recognition.

Distributed power grid integration contributes to both the reduction of greenhouse gas emissions and the protection of the environment. Nevertheless, the uncertainty and volatility associated with the production of clean renewable energy adds additional challenges to microgrid dispatch. The paper presents an adaptive mutant bird swarm algorithm and suggests a comparison mechanism based on population fitness variances and optimal values in order to overcome the shortcomings of BSA, in particular its tendency to self-correct into local optimum and slow convergence speed. First, the algorithm determines if the population is in the local optimal state. The local optimal individual is then subjected to Cauchy mutation in order to determine the optimal value again. This improves the accuracy and speed of the BSA. Based on simulation results, the improved algorithm has higher optimization accuracy and faster optimization speed, which demonstrates the effectiveness and advancement of the algorithm proposed in this research.

Aiming at the economic dispatch problem for an interconnected system with wind power integration, and in order to realize the goals of system economy and improvement of the cross-regional consumption level of wind energy, a decentralized coordination dispatch model is established in this paper. In this model, a DC tie-line is cut by the branch cutting method and used as a coupling variable. A virtual upper-level dispatch center is established, and the economic dispatch problem to be solved is decomposed into a master optimization problem for the upper-level dispatch center and subsidiary optimization problems for the lower-level dispatch centers. For solving this model, an improved Harris hawks optimization (HHO) algorithm called the chaotic mutation Harris hawks optimization (CMHHO) algorithm is proposed. In the CMHHO algorithm, tent mapping and the “DE/pbad-to-pbest/1” strategy are introduced, and a new nonlinear escape energy factor adjustment is proposed. Through an algorithm comparison experiment and a simulation experiment with two examples, the superiority of the CMHHO algorithm, the effectiveness of the proposed model and the applicability of the CMHHO algorithm to the proposed model are verified. The model proposed is of great significance for solving the economic dispatch problem for an interconnected system with wind power integration.

In order to improve the accuracy and calculating speed of load forecasting for the strong nonlinear problem of short-term load, this article proposes a Short-term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm. This method integrates the Extreme Learning Machine (ELM) algorithm into the Convolutional Neural Network (CNN): replace the fully connected layer in the original CNN network with ELM to form a CNN-ELM network. The purpose is to improve the calculation accuracy. An Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) was proposed to reduce the probability that the Fruit Fly Optimization Algorithm (FOA) would easily fall into a local optimal value. And then AMFOA is used to optimize the parameters in CNN-ELM network. The above model is used to predict the grid load of a certain area in northern China. Compared with other prediction algorithms, it is proved that the model proposed in this article has higher prediction accuracy and also proved that the model has higher calculation speed than other models.

The interconnection of multiple microgrids can effectively improve the operating efficiency of the system and reduce the cost of power generation. First, establish an economic environment optimal dispatch model based on the regional interconnected multi‐microgrids system, and formulate a dispatch strategy based on the time‐of‐use electricity price and the supply‐demand relationship between microgrids; Secondly, in order to solve this constrained optimization problem, a whale algorithm improved with five strategies including adaptive inertial weights, dynamic spiral search and generalized opposition‐based learning is proposed to obtain higher solution accuracy and anti‐premature convergence ability; Finally, combined 12 test functions to test the effectiveness of the improved algorithm, and use the improved algorithm to solve the example model containing two microgrids, and analyze it in two states of interconnection operation and non‐interconnection operation. The simulation results show that the regional interconnection operation of the microgrid under the established dispatching strategy can effectively reduce the overall operating cost of the system, and at the same time show the effectiveness of the improved algorithm to solve the optimal dispatching problem. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer’s optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm’s skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

为解决鲸鱼优化算法收敛精度低、易陷入局部最优等问题,采用多种策略对算法进行改进,提出一种自适应动态鲸鱼优化算法.引入立方混沌映射初始化提升初始解的遍历性;引入自适应惯性权重系数并对收敛因子非线性改进,平衡全局搜索与局部搜索能力;对螺旋搜索方程改进,使鲸鱼动态地调整搜索形状,提升算法的全局搜索能力以突破局部最优;为增强算法跳出局部最优的能力,引入广义反向学习机制.采用12个基准测试函数对算法检验,其结果表明,改进的鲸鱼算法有效提升了算法的收敛精度.