ABSTRACT The presence of numerous distributed power sources in distribution grids leads to a diverse array of controlled object points and significant uncertainties, thereby posing a series of challenges to the control and operation of distribution grids. Hence, this study proposes a virtual cluster partitioning model for active distribution networks using a quantum particle swarm optimization (QPSO) algorithm and sector search, aiming to achieve autonomy within clusters and coordination between clusters. First, the article proposes a sector search model that transforms the topological connections of the distribution network into mathematical expressions. This model simplifies the search for node locations and improves the algorithm's convergence speed. Building upon the traditional particle swarm optimization (PSO) algorithm, this study introduces the wave function and Schrödinger equation to enhance algorithm performance. By treating the vectors obtained from sector searches as particles, the proposed QPSO algorithm significantly improves both the search efficiency and global convergence in solving the virtual cluster partitioning model. Finally, case studies conducted on the modified PG&E 69‐node system demonstrated the proposed method's significant advantages. The method improved computational efficiency, with a cluster power supply rate over 0.6 and modularity above 0.7, ensuring balanced partitioning. The scalability and effectiveness of the proposed method were validated on an 85‐node system, achieving balanced cluster partitioning with high operational efficiency and adaptability.

Monthly centralized bidding is a key link in the transition from annual bilateral trading to spot trading, the research object of this paper is the multi-type power system (coupling system) which is integrated and coupled by thermal power and renewable energy under the same grid point, from the market point of view, this paper discusses its competitive strategy and revenue in the monthly centralized bidding market. First, an outer-level market clearing model that adapts to the participation of the coupling system is constructed to maximize the clearing in terms of social welfare. Secondly, considering the forecast error of scenery, the optimization model of the inner coupling system is established to analyze the cost of the coupling system, and the increment of the coupling system is evaluated quantitatively. Finally, a two-layer optimization model for coupling system to participate in the monthly centralized bidding market is formed, and then the optimal operation strategy of coupling system is studied. The simulation verification of the calculation example shows that participating in the monthly centralized bidding transaction in the mode of the coupling system will increase the income of each of the scenery and fire, the proposed coupling system model promotes changes in the energy structure of the power market, driven by improving the overall economic benefits, ensuring the economic benefits of traditional units and expanding the scope of the renewable energy market, so as to provide electricity to renewable energy and thermal power to improve auxiliary services. The development of the situation provides new ideas for the large-scale grid-connected consumption of new energy.

The integration and co-location of new energy sources and thermal power generation within the same grid connection point, under the management of a single market operator, provide noteworthy potential for exploration in the northern part of our nation. This method is based on the premise of allocating new energy resources primarily for generating contracted electricity, while using thermal power to offer ancillary services. This strategy aims to integrate and use the distinct benefits of both thermal power and new energy sources. The yearly bilateral trade market has significant potential for growth and development. In order to model and evaluate the income generated by the interconnected system’s involvement in the yearly bilateral trading market, this study first establishes the definition of the interconnected system and examines its research significance in engaging in annual bilateral trading. This study utilizes the theoretical framework of master-slave games to analyze the involvement of a coupled system in yearly bilateral trade. The specific case examined in this research is a wind-solar-thermal coupling system located in a particular location of Liaoning Province. This study examines the economic advantages of engaging in the electricity market by considering the marginal cost and LCOE (Levelized Cost of Electricity) perspectives. The findings suggest a notable enhancement in the advantages of the integrated system inside the yearly bilateral trade market, in contrast to conventional power sources present in the market, as well as instances when these sources function autonomously without integration.

Because of its relevance and complexity in industry, the industrial control terminal collaborative response system is popular among app developers and users, as well as the main target of malevolent criminals. The rapid spread of malware has caused significant harm to users of the industrial control terminal collaborative response system. In this study, we build a model by combining dynamic and static feature detection, compiling test software output information using Fuzz technology, and training with a method that combines dynamic and static interest-based expert recommendation model (DSIERM) algorithm with Bi-LSTM algorithm. Modeling enables specific analysis and classification of test results. Based on this, the TaintBench suite and framework are introduced to compensate for the lack of relevant content for static taint analysis of applications in industrial control terminal collaborative response systems and to increase detection efficiency.

Unlike traditional methods of electricity generation such as thermal power, renewable energy sources like wind power exhibit characteristics of randomness and volatility, leading to unstable power generation. With the continuous increase in grid-connected capacity of renewable energy sources like wind power, the demand for grid peak shaving is on the rise, highlighting the issue of inadequate peak shaving capacity in China’s power system. As a primary source for peak shaving in the country, thermal power units incur significant revenue losses when participating in deep peak shaving, and under the current mechanism, they cannot obtain sufficient compensation for deep peak shaving. In response to these issues, the paper proposes a method for compensating thermal power units for deep peak shaving. Firstly, considering the reduced efficiency of thermal power units during low-load operation, a cost model for the operation of thermal power units is established. Secondly, building upon the existing compensated peak shaving baseline, a method for compensating thermal power units for deep peak shaving is introduced. Finally, using a simulation of a local power grid in Liaoning Province as an example, an analysis of the compensation for thermal power peak shaving and overall profits under the existing mechanism and the proposed mechanism is conducted. The results obtained validate the rationality and effectiveness of the method proposed in the paper, offering insights for the development of a compensation mechanism for thermal power deep peak shaving.

The Industrial Internet of Things (IIoT) offers the manufacturing sector opportunities for transformation and upgrade but also carries significant security risks. Traditional federated learning (FL) as a potential security solution is challenging in complicated application environments with heterogeneous data, imbalanced data, and poisoning attacks. To address these challenges, we construct a clustered FL Framework for IIoT intrusion detection (CFL-IDS) based on local models’ evaluation metrics (EMs). First, we designed an intrusion detection model with a dynamic focal loss (DFL) for all edge nodes (ENs). This model’s performance is enhanced under various imbalanced data partitions by dynamically altering the focus on samples during the loss minimization training process. Second, the time series of EMs of local models to reflect the data distribution of ENs implicitly, and use clustering algorithms to facilitate knowledge sharing among those ENs with similar data distribution to co-optimize a common model for them. Finally, an intelligent cooperative model aggregation mechanism (ICMAM) adaptively adjusts each local model’s weight distribution, which substantially improves the benefits of FL and alleviate subpar models’ alleviates interference from subpar models to FL. Experiments demonstrate that CFL-IDS has stronger robustness and displays superior performance under data imbalance and non-independent and identically distributed (non-IID) situations while being effective against poisoning attacks.

The accurate prediction of electricity prices has great significance for the power system and the electricity market, regional electricity prices are difficult to predict due to congestion issues in regional transmission lines. A regional electricity price prediction framework is proposed based on an adaptive spatial–temporal convolutional network. The proposed framework is expected to better explore regional electricity prices’ spatial–temporal dynamic characteristics in the electricity spot market and improve the predictive accuracy of regional electricity prices. First, different areas of the electricity market are regarded as nodes. Then, each area’s historical electricity price data are used as the corresponding node’s characteristic information and constructed into a graph. Finally, a graph containing the spatial–temporal information on electricity prices is input to the adaptive spatial–temporal prediction framework to predict the regional electricity price. Operational data from the Australian electricity market are adopted, and the prediction results from the proposed adaptive spatial–temporal prediction framework are compared with those of existing methods. The numerical example results show that the predictive accuracy of the proposed framework is better than the existing baseline and similar methods. In the twelve-step forecast example in this paper, considering the spatial dependence of the spot electricity price can improve the forecast accuracy by at least 10.3% and up to 19.8%.

The current network security situation of industrial control systems is becoming increasingly severe, and the ICS ATT&CK framework provides a unified knowledge base for attack tactics and techniques for industrial control system network security. This paper presents a RRDD model for network security risk assessment of ICS, which consists of four parts: Risk identification (R), Risk calculation (R), Defense strategy (D), and Defense measures (D). Risk identification is based on the ICS ATT&CK to analyze the network security risk assessment indicators of the ICS, to clarify the vulnerability of the industrial control system, the importance of assets, the threats faced and the existing mitigation measures. Risk calculation is based on the analysis results of risk assessment indicators, establishing a comparative judgment matrix to analyze the relative importance of each indicator, calculating the weight of each indicator, and calculating and grading the overall risk of ICS. Furthermore, the model adopts corresponding safety control strategies and measures based on the risk calculation results, and conducts closed-loop risk assessment and elimination. It can effectively evaluate and disposal the network security risks in ICS.

With the development and construction of new technologies, the existing centralized data processing mode of ICS is limited by the lack of storage resources and computing power, and cannot meet the needs of massive data processing and frequent interaction between devices, which makes the ICS cloud-edge-end collaboration technology receive more and more attention. Based on this, this paper combs the concept of ICS cloud-edge-end collaboration, introduces the network architecture of ICS cloud-edge-end collaboration, and analyzes the interaction mode and key support technologies of ICS cloud-edge-end collaboration. By summarizing the current research situation of ICS cloud-edge-end collaboration technology, the future development is prospected.

In this paper, we propose a deep spatial transformer network (DSTN) to classify the time series data. This DSTN model can avoid the distortion that may be caused when the time series data is transformed to 2D-data, as it has an adaptive feature extraction mechanism profit and can carry out affine transformation on 2D-data. It can also avoid the influence of this distortion on the subsequent convolutional neural networks. Thus, Experimental results show that this framework can effectively classify and predict complex multivariable time series data.

Currently the flexible demand for high proportion penetration of renewable energy depends on coal-fired units (CFUs), and the large-scale phase-out of CFUs in a short time is not realistic in China. Due to urban expansion, approximately 458 Chinese coal-fired power plants (CFPPs) are now located in cities. Limited by space, urban CFUs face difficulty in becoming equipped with carbon capture and storage systems. This presents a sizeable challenge for the low-carbon transition of urban CFPPs and carbon neutral processes. Here, we present a ready-to-implement method to reduce the carbon emission of CFPPs in limited space: roof photovoltaic-assisted power generation combined with sludge co-combustion for coal-fired power generation systems (PVSCs). We also consider nonurban CFPPs with the method of roof photovoltaic-assisted power generation (PVs) only. Based on remaining life cycle analysis, we find that the PVSCs could save 28.47 Mt of coal, reduce CO2 emissions by 69.76 Mt, treat 125.70 Mt of sludge, and also generate 12.08 billion RMB worth of electricity revenue per year. In addition, our scenario analysis shows that PVSCs are more profitable when choosing an urban CFU with a remaining life of more than 12 years and while the sludge treatment subsidy is set at 100 RMB t−1. Under strict and lenient CFU decommissioning policies, CFUs with a remaining life of between 19 and 30 years and between 13 and 24 years should be selected for PVs, respectively. Thus, we conclude that PVSCs can not only generate economic benefits but also facilitate carbon reduction and solid waste treatment.

The manufacturing industry consumes electricity and natural gas to provide the power and heat required for manufacturing. Additionally, large amounts of electric energy and heat energy are used, and the electricity cost, amount of environmental pollution, and equipment maintenance cost are high. Thus, optimizing the management of equipment with new energy is important to satisfy the load demand from the system. This paper formulates the scheduling problem of these multiple energy systems as a multi-objective linear regression model (MLRM), and an energy management system is designed focusing on the economy and on greenhouse gas emissions. Furthermore, a variety of optimization objectives and constraints are proposed to make the energy management scheme more practical. Then, grey theory is combined with the common MLRM to accurately represent the uncertainty in the system and to make the model better reflect the actual situation. This paper takes load fluctuation, total grid operation cost, and environmental pollution value as reference standards to measure the effect of the gray optimization algorithm. Lastly, the model is applied to optimize the energy supply plan and its performance is demonstrated using numerical examples. The verification results meet the optimized operating conditions of the multi-energy microgrid system.

As main measure to stabilize fluctuation of power grid under renewable energy utilization, the flexible operation of thermal power unit reveals remarkable significant. To serve for peak shaving and fast load varying, a novel T-S fuzzy modeling approach based on fast adaptive moth-flame optimization (FAMFO) algorithm is proposed for dynamics investigation of supercritical unit. This modeling scheme is deemed as hierarchical process with data partition stage and parameter determination stage. Firstly, an automatic clustering strategy relying on FAMFO is fulfilled for more reasonable data partition result, which devotes to accuracy modeling without subjective interference. In this stage, the FAMFO is attained with assistance of four improvements and its merits in balancing search exploration and exploitation guarantee the width of covered operation range of supercritical unit for peak shaving. Secondly, inherent advantages of least square technics in parameter identification witness the successful implementation of a well-performed exponentially-weighted least squares algorithm in later stage of fuzzy modeling. In effectiveness verification of the proposed modeling approach, the superiorities of FAMFO in promoting search precision and speed are demonstrated via benchmark function test and Friedman test. Then, the flexible operation modeling performance is proved via extensive simulation experiments using on-site data of supercritical unit.

电热联合系统中电、热负荷在峰谷分布上存在互补特性,考虑电、热负荷的互补特性实施电热联合系统优化运行可以有效增强系统调峰灵活性,缓解电热强耦合造成的弃风.为此,研究了考虑采暖建筑用户热负荷弹性与分时电价需求侧响应协同的电热联合系统优化调度问题.阐述了电、热负荷协同促进风电消纳的机理.根据电、热负荷特性分别建立采暖建筑用户热负荷弹性模型以及分时电价需求侧响应模型,并以此为基础建立电、热负荷协同响应模型.将负荷模型纳入调度模型,构建考虑负荷协同优化的电热联合系统日前调度模型.算例分析表明,采暖建筑用户热负荷弹性与分时电价需求侧响应的协同可有效提升系统运行经济性并促进系统风电消纳.

ABS T R A C T The flexible operation of thermal power plant has become a research hotspot for its safety and validity in eliminating power grid fluctuation from large-scale integration of renewable energy. Heat-power decoupling is effective technique to promote flexibility of combined heat and power plant. To address the insufficient dynamics investigation of combined heat and power plant under different heat-power decoupling strategies, an adaptive modeling approach is proposed. This method systematically fuses the mechanism analysis and data-driven fuzzy modeling to prepare for the controller design for rapid and deep load regulation in flexible operation. Firstly, the model structures with unknown parameters are determined by mechanism analyses to reflect the effects of various decoupling techniques on the characteristics of plant. Secondly, in view of the on-site data under wide load operation conditions, corresponding model structure is adaptively matched, and a new Takagi-Sugeno fuzzy modeling scheme depend on biquantum pigeon optimization algorithm is constructed for model parameters acquisition. The effectiveness of the proposed approach is illustrated based on a 350 MW supercritical combined heat and power unit. Simulation results illustrate that the adopted method can rapidly characterize dynamics of plant under different heat-power coupling conditions. All the model identification and verification errors in wide load operation conditions are less than 3.1% plant output, which reveals high modeling precision more than 97%. Thus, the established model provides solid foundation on highly matched controller for ensuring operational flexibility.(c) 2021 Elsevier Ltd. All rights reserved.

Co-firing biomass in an existing coal combustion boiler is a promising way to mitigate carbon emission in the context of global carbon neutrality. This paper investigated the effect of biomass injection location on combustion and NOX formation characteristics in a 300-MWe tangential boiler co-firing with coal. Numerical models have been validated against experimental measurement for both pure coal firing and biomass/coal co-firing cases. Compared to pure coal firing, co-firing case with biomass injected into the highest layer can sustain a comparable temperature distribution profile along the furnace height, and generate a lower NO emission by around 20 ppm. By moving the biomass injection location downward, the temperature difference between co-firing and pure coal firing cases becomes larger, and the final NO emission increases continually from 222 to 240 ppm. When biomass is injected through the lowest layer, N element in biomass volatile is oxidized to NO directly because of the abundant oxygen; thus, NO emission turns to be the highest among all co-firing cases. Contrarily, when biomass is injected through the highest layer, the majority of N in biomass volatile is released as NH3, and it further acts as a reduction agent for NO, thus leading to the lowest NO emission.

The security situation of industrial control system is becoming increasingly severe. ATT&CK for ICS is widely used to describe and classify the attack behavior and risk identification of industrial control environment. This paper first discusses the network security risk elements of industrial control system, analyzes the vulnerability and threats faced by industrial control system, and further proposes a network security risk management model based on ATT&CK for ICS. Based on the risk identification results, the network security risk management model formulates corresponding security defense strategies and measures, and conducts risk closed-loop review and disposal, effectively improving the network security defense capability of the industrial control system.

为了更好地挖掘电网-交通网强耦合态势下电动汽车充电负荷的时空动态特征,提高充电负荷预测精度,提出了一种基于图WaveNet的电动汽车充电负荷预测框架.首先,将耦合的电网-交通网中的充电站看作充电负荷节点;然后,把充电站的充电负荷数据作为节点的特征信息,将各个节点构造成一张图,并把蕴含充电负荷空间维信息的图和充电负荷的时间维信息输入自适应图WaveNet框架中进行预测;最后,以中国某市城区内的充电站负荷数据为例,将基于自适应图WaveNet框架的预测结果与现有方法的预测结果进行对比,验证了所提方法的正确性和有效性.

北方地区普遍存在可再生能源和火力发电通过同一并网点接入电网的耦合方式.在该方式下,火电和可再生能源可作为一个整体共同参与调峰辅助服务,进一步挖掘两者的协同调度潜力.该文首先针对该类耦合方式,对耦合系统的定义进行研究,并分析耦合系统的特征和潜力;其次,根据火电机组深度调峰的运行特性,提出计及阶梯式爬坡率的火电机组运行模型,并在此基础上建立耦合系统优化调度模型;最后,以辽宁某局域电网为例的仿真结果表明,计及阶梯式爬坡率的火电机组运行模型能更精确地反映深度调峰时机组的实际运行状态,且耦合系统在现有政策下可进一步提升可再生能源和火力发电的整体控制性能和经济效益.

Big data in recent years (big data) technology has been highly concerned and valued by academia, industry and governments all over the world. Data has been widely considered to be as important as other resources. This paper first analyzes the application of big data in power grid, and then uses Hadoop technology to design a big data system suitable for power grid enterprise decision-making based on the characteristics of power platform in a province of China Data platform system. This paper constructs the operation environment and uses the big data platform to sample all kinds of power generation and user side data. Based on the platform, the behavior data of power customers are analyzed and processed, which proves that the technology of big data can better serve the company's business innovation and decision support.