Networked microgrids (NMGs) enable coordinated scheduling of multiple microgrids, maximizing the flexibility of distributed energy resources (DERs) within each microgrid. However, communication failures can hinder the availability of DERs, disrupting the coordinated operation of NMGs. This paper proposes a robust bi-level programming model for coordinated optimization of NMGs under dynamic communication uncertainty. The upper-level objective of the model employs a robust method to construct a penalty term based on power imbalance, reflecting the impact of the unavailability of DERs on system operation due to communication reliability issues. The lower-level model minimizes the total operational cost of NMGs, aiming at economic efficiency. A dynamic communication reliability model is established using a Markov chain to fully consider the influence of time-varying communication states on communication reliability during different scheduling periods. A data-driven Wasserstein ambiguity set is employed to construct a deviation uncertainty set, addressing the uncertainty in communication reliability. The effectiveness of the proposed model is verified through simulations on a modified IEEE-118 test system. Simulation results show that, compared with the traditional OPF model, the proposed method increases scheduling costs by 11% but reduces communication-induced risk by 37%. Additionally, it achieves cost reductions of 9% and 23% and risk reductions of 31% and 26% compared to SP and DRO, respectively.

The rapid development of new energy has caused a sharp increase in the stochasticity on the source side of the new power system (NPS), and extreme weather along with climate variability have also led to increased stochasticity in power demand on the load side; thus, how to achieve source-load matching and enable the load to track the source under the new situation is the key to the efficient operation of the power system. Aiming at the problem that existing load regulation potential evaluation mainly focuses on physical capacity, making it difficult to reflect users’ subjective willingness to participate as well as the dynamic changes in regulation capability under different operating scenarios, this paper proposes a two-stage dynamic profiling classification method for multi-type power user loads considering regulation willingness. First, an evaluation index system is constructed from three dimensions, physical reliability, execution reliability, and behavioral willingness, to achieve the unified characterization of the regulation capabilities of heterogeneous resources such as industrial loads and electric vehicle (EV) aggregators. Second, the DBSCAN algorithm is adopted to identify typical annual operating scenarios. Finally, the Dynamic Time Warping (DTW) distance is introduced to improve the K-Means++ algorithm, achieving the profiling classification of user regulation potential. This paper takes a certain NPS demonstration park as an example for verification, and the results show that the annual operating scenarios can be divided into 4 types of typical days; the proposed DTW-K-Means++ method has better classification performance compared with traditional Euclidean distance clustering, can effectively identify the differences and dynamic migration characteristics of user regulation potential under different operating scenarios, and stably classifies users into three types of profiles: deep regulation type, agile response type, and rigid constraint type. The research results aim to provide reliable data support for the refined dispatch of the power grid by effectively quantifying the dynamic migration patterns of heterogeneous resources under variable scenarios.

It is well known that energy management of air conditioners (ACs) is an important way of demand response (DR). The fresh air system (FAS), which is another common equipment in buildings, can effectively utilize cooling capacity of the outdoor air during the night to reduce indoor temperatures, achieving energy savings and efficiency. However, the combined scheduling of AC and FAS has received little attention. In this paper, a co-optimization scheduling method for combined AC and fresh air system (ACFAS) is proposed. Building upon the thermodynamic models of AC and FAS and considering the practical conditions, the main-power ON/OFF state, temperature set-point, and air volume level of the AC and FAS are selected as decision variables to establish a thermostatic control model. The thermostatic control model is then discretized and linearized to form a mixed-integer linear programming solvable format. Subsequently, taking into account electricity cost and comfort objectives, an optimization scheduling model is developed, allowing for the determination of the scheduling results of the main power ON/OFF state, temperature set-point, and air volume level. The testing results show that the co-optimization can utilize both AC and FAS for cooling the room temperature, and the energy efficiency can be improved.

This paper proposes a novel communication framework for a new type of power load management system, utilizing a geographic clustering approach based on the K-means algorithm to optimize network topology. Each cluster of communication nodes forms a subnet, with the cluster center serving as a routing node. Leveraging blockchain principles, each subnet is structured as a circular blockchain, starting with the routing node as the genesis block. This node initializes communication with a timestamp and computes the hash of the block, passing it to the next node. Subsequent nodes add their data and timestamps to the previous hash, computing new hashes and continuing the chain. The last node in the subnet links back to the routing node, forming a closed loop, which enables the routing node to validate the integrity of the entire subnet’s data. The same validation mechanism is extended between the routing nodes and the main station of the power load management system, ensuring robust and trustworthy communication across the network. This dual verification mechanism enhances the reliability and security of data transmission within the power load management system.

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing.

The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption. The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.

In order to address the problem that increasing number of renewables result in a loss of frequency control capacity in power system, this paper proposes a secondary frequency control strategy coordinating resources from supply, network, and demand sides. The power system is connected with an industrial power grid via VSC-HVDC link, which develops a secondary frequency control model for the interconnected system considering the VSC-HVDC power support. Based on the developed model, a distributed architecture is adopted, which involves two MPC controllers respectively in the sending and receiving systems. The two controllers calculate optimal control signals of their own control area with periodic communication and aluminum smelter loads change their power consumption to reduce the power imbalance in the industrial power grid caused by the power support. The effectiveness of the proposed strategy was verified by simulation. The results show that the proposed strategy can shorten the frequency recovery time of the receiving power system under large disturbance scenarios by 30% , while during the regulation process, the frequency deviation of the sending system is always kept within 0. 05 Hz, and the parameters of the aluminum smelter load and VSC-HVDC system are always within the safe range.

The new power load management system is a key supporting system to promote the construction of new power system. To solve the complex issue of knowledge graph construction applied in new power load management System, this paper proposed a knowledge-graph construction method of the operating fault feature library. Firstly, the principle of the top-down construction method of knowledge graph is analyzed method. Then, the proposed knowledge-graph construction method of the operating fault feature library is depicted in detail consisting of five steps, which are data collection, conceptual modeling, data preprocessing, knowledge extraction and knowledge storage. Finally, case analysis is carried out to verify the feasible performance of the proposed method, in which the Neo4j graph database is used to visualize the knowledge graph of the operating fault feature library, and the fault diagnosis of an auxiliary decision-making function is implemented.

为提高综合能源系统的能源利用效率,提出一种考虑能效的多目标优化模型与求解方法.首先,分析影响综合能源系统的能源流动环节效率的因素.然后,以运行成本最低为目标建立经济性目标函数,以能源利用效率最优为目标建立能效目标函数,采用加权系数法将多目标优化转化为单目标优化.最后,采用模型预测控制进行优化调度,设立仅考虑单一目标与考虑多目标优化多种场景.算例表明,所建立的模型能够引导综合能源系统对能源进行合理利用,通过经济性目标与能效目标的优化调度提升综合能源系统的能源利用效率.

A unified business model of energy efficiency public services is conducive to the development of energy efficiency public service projects. First, summarize the energy efficiency public service business processes. Business roles, business entities, and business activities are then extracted from the business process flows. Finally, business modeling of energy efficiency public services based on UML.

肺癌是发病率和死亡率最高的恶性肿瘤,分为非小细胞肺癌(NSCLC)和小细胞肺癌(SCLC).随着精准医疗的发展,分子靶向治疗在肿瘤领域得到了快速发展和广泛应用,尤其是在非小细胞肺癌中.表皮生长因子受体(EGFR)是NSCLC治疗中公认的有效靶点,靶向EGFR的小分子激酶抑制剂可阻断EGFR细胞内自磷酸化及下游活性信号,又称EGFR-酪氨酸激酶抑制剂(EGFR-TKIs).近年来,EGFR-TKIs靶向EGFR治疗取得了理想的临床疗效,然而由于肿瘤的异质性和基因组的不稳定性,继发性基因突变、替代信号通路的激活等继发性耐药的广泛出现大大降低了非小细胞肺癌的治愈率,与之对应的耐药问题也不断出现.在本篇综述中,作者总结了前三代EGFR-TKIs在NSCLC中的应用及临床耐药现状,并进一步探讨第四代EGFR-TKIs的研究进展,为NSCLC患者的临床用药提供参考.

Hepatitis B virus (HBV) infection remains a major global threat to human health worldwide. Recently, the Chinese medicines with antiviral properties and low toxicity have been a concern. In our previous study, Eupolyphaga sinensis Walker polysaccharide (ESPS) has been isolated and characterized, while its antiviral effect on HBV remained unclear. The anti-HBV activity of ESPS and its regulatory pathway were investigated in vitro and in vivo. The results showed that ESPS significantly inhibited the production of HBsAg, HBeAg, and HBV DNA in the supernatants of HepG2.2.15 in a dose-dependent manner; HBV RNA and core protein expression were also decreased by ESPS. The in vivo studies using HBV transgenic mice further revealed that ESPS (20 and 40 mg/kg/2 days) significantly reduced the levels HBsAg, HBeAg, and HBV DNA in the serum, as well as HBV DNA and HBV RNA in mice liver. In addition, ESPS activated the Toll-like receptor 4 (TLR4) pathway; elevated levels of IFN-β, TNF-α, and IL-6 in the serum were observed, indicating that the anti-HBV effect of ESPS was achieved by potentiating innate immunity function. In conclusion, our study shows that ESPS is a potential anti-HBV ingredient and is of great value in the development of new anti-HBV drugs.

In the context of the energy crisis and environmental deterioration, the integrated energy system (IES) based on multi-energy complementarity and cascaded utilization of energy is considered as an effective way to solve these problems. Due to the different energy forms and the various characteristics in the IES, the coupling relationships among various energy forms are complicated which enlarges the difficulty of energy efficiency evaluation of the IES. In order to flexibly analyze the energy efficiency of the IES, an operation efficiency evaluation model for the IES is established. First, energy utilization efficiency (EUE) and exergy efficiency (EXE) are proposed based on the first/second law of thermodynamics. Second, the energy efficiency models for five processes and four subsystems of the IES are formed. Lastly, an actual commercial-industrial park with integrated energy is employed to validate the proposed method.

How to perform accurate calculation of heat balance and quantitative analysis of energy efficiency for building clusters is an urgent problem to be solved to reduce building energy consumption and improve energy utilization efficiency. This article proposes a method for the heat balance calculation and energy efficiency analysis of building clusters based on enthalpy and humidity diagrams and applies it to the energy management of building clusters containing primary return air systems and heating pipe networks. Firstly, the basic structure and energy management principle of building clusters with a primary return air system and a heating pipe network were given, and the heat balance calculation and energy efficiency analysis method based on i-d diagram was proposed to realize the accurate calculation of heat load and the quantification of energy utilization. Secondly, the energy management model of the building cluster with a primary return air system and a heating pipe network was established to efficiently manage the indoor temperature and the heating schedule of ASHP, HN and HI. Finally, the proposed method was validated by calculation examples, and the results showed that the proposed method is beneficial for improving the energy economy and energy efficiency of building clusters.

随着能源市场的改革,社区综合能源系统各主体的分布式自治特征愈发明显,对传统集中式模式的计算和通信能力提出了挑战.考虑柔性负荷通过智能楼宇接入综合能源社区,形成分布式源荷互动模式,提出了基于目标级联分析理论的分布式优化调度模型.首先,将综合能源服务商和智能楼宇作为不同利益主体,以运行成本最低为目标,建立各自的优化自治模型.其次,引入目标级联分析方法,通过将购电功率等效为虚拟发电机与虚拟负荷实现不同主体间运行的解耦.最后,通过算例验证了所提方法的有效性,为综合能源社区运行提供了更为经济的运行方案.

乙型肝炎病毒(Hepatitis B Virus)是一种经体液传播的的DNA病毒,可引起急性和慢性肝炎.急性肝炎尚未有有效治疗药物,治疗目的 是保持身体舒适和营养平衡.而慢性肝炎主要服用干扰素/核苷类似物等抗病毒治疗药物,以延缓肝硬化或原发性肝癌的发生发展.但这两类药物或疗效有限、副作用明显,或需长期服用,导致病毒耐药,总之都不能彻底治愈乙肝,因此急需新型抗病毒治疗药物的出现或辅助.文章旨在对当前已上市或正在研发中的药物进展进行综述,以便优化抗乙肝病毒治疗方案,推动抗乙肝病毒新药的研发.

Increasing penetration of distributed generation (DG) has brought more uncertainty to the operation of active distribution networks (ADNs). With the reformation of the power system, increasingly more flexible loads access to distribution network through load aggregators (LAs), which becomes an effective way to solve these issues. Since LAs and ADNs are separate entities with different interests, the traditional centralized and deterministic optimization methods fail to meet the actual operational requirements of ADNs. Based on the linear power flow model, a robust optimal dispatching model of ADNs considering the influence of renewable DG’s uncertain output on voltage security constraints is established. Then, an independent optimal scheduling model for LAs is modeled based on the analysis of the composition and characteristics of flexible load in LAs. LAs and ADNs, as two different stakeholders, use a distributed modeling method to establish different economic optimization goals. The optimization problem is solved by decoupling the coupling exchanging power between LAs and ADNs into virtual controllable loads and virtual DGs. Finally, with the case study of a modified IEEE 33-bus system, the correctness and effectiveness of the proposed method are verified. The effects of the robust level and demand response incentive on the results are also analyzed.

In view of the current increasingly serious consumption of fossil fuels, new energy power generation has attracted more and more attention, and the resulting power quality problems have also attracted more research. At present, the assessment of power quality in current national standards only remains on a single index, so it is necessary to propose an effective comprehensive assessment method for power quality. Starting from a new perspective, the paper uses the Bayes discriminant analysis method to directly combine the national standards of relevant power quality indicators to establish a discriminant function for each power quality level, thereby determining the power quality level of the measurement point, and completing the comprehensive evaluation of power quality. The actual measurement data of the Dynamic Model Laboratory of Tianjin University is verified, which proves that this method can not only avoid the influence of subjective and objective factors such as the personal preference of the decision maker, but also obtain scientific and effective results conveniently and quickly.

工业园区具有经济基础好、能源消耗大、产业集聚等特点,是构建灵活多样、低碳高效的综合能源服务体系的“理想试验田”.从物理层、信息层、服务层3个层面阐述了当前工业园区综合能源服务系统架构.从促进清洁能源消纳、能源需求优化、能源效率提升以及市场效益挖掘4个方面探讨了适合园区开展的综合能源服务商业模式,并从推动市场公平竞争、开放共享发展的角度提出了相关政策建议,为探索建立园区综合能源服务商业模式提供参考.

在电力体制改革的背景下,有必要精细化挖掘用户用电特性,同时考虑售电商偏差考核控制的问题,制定套餐优化需求响应策略.首先基于自编码神经网络和模糊C均值聚类的方法对用户用电曲线进行模式分类,然后基于消费者心理学用户响应模型,对用户不同用电模式建立峰谷分时电价优化模型,在此基础上,对不同用电模式建立峰平时段叠加电价模型.研究表明,套餐制定可以有效引导用户调整用电行为,降低用电模式间差异,从偏差考核的角度看,有助于制定月购电策略.