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Abstract Background Arteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is increasingly used for limb salvage in ASO patients, but the outcomes vary. The development of prediction models using unsupervised machine learning may lead to the identification of novel subtypes to guide patient prognosis and treatment. Methods This retrospective study analyzed clinical data from 448 patients with ASOs who underwent endovascular therapy. Unsupervised machine learning algorithms were employed to identify subgroups. To validate the precision of the clustering outcomes, an analysis of the postoperative results of the clusters was conducted. A prediction model was constructed using binary logistic regression. Results Two distinct subgroups were identified by unsupervised machine learning and characterized by differing patterns of clinical features. Patients in Cluster 2 had significantly worse conditions and prognoses than those in Cluster 1. For the novel ASO subtypes, a nomogram was developed using six predictive factors, namely, platelet count, ankle brachial index, Rutherford category, operation method, hypertension, and diabetes status. The nomogram achieved excellent discrimination for predicting membership in the two identified clusters, with an area under the curve of 0.96 and 0.95 in training cohort and internal test cohort. Conclusion This study demonstrated that unsupervised machine learning can reveal novel phenotypic subgroups of patients with varying prognostic risk who underwent endovascular therapy. The prediction model developed could support clinical decision-making and risk counseling for this complex patient population. Further external validation is warranted to assess the generalizability of the findings.

This study presents a scenario-based model predictive control (MPC) approach to minimize the cotton farm microgrid operational cost under uncertainties. Uncertainties in cotton farms may come from renewable energy generation, water demand, precipitation, and evaporation, so the cotton field pumping system operation can be formulated as a stochastic MPC problem to accommodate uncertain climate conditions and real-time changes in irrigation demand. Scenario generation and reduction techniques can obtain typical scenarios and their probabilities. The typical scenarios can be used in the MPC iterative step to facilitate modelling the proposed stochastic optimization problem. This study discusses static and dynamic uncertainty modelling techniques used for MPC, and each technique is analysed separately in grid-connected and islanded microgrids through case studies. In the grid-connected dynamic scenario-based MPC, the operational cost is AU$ 18,797 over the entire irrigation period, which is AU$ 8759 lower than that of the standard MPC. Furthermore, for the islanded dynamic scenario-based MPC, the operational cost is AU$ 24,443 over the entire irrigation period, which is AU$ 6721 lower than the standard MPC.

Lower-extremity peripheral artery disease (LE-PAD) is a prevalent circulatory disorder with risks of critical limb ischemia and amputation. This study aimed to develop a prediction model for a novel LE-PAD subtype to predict the severity of the disease and guide personalized interventions. Additionally, LE-PAD pathogenesis involves altered immune microenvironment, we examined the immune differences to elucidate LE-PAD pathogenesis. A total of 460 patients with LE-PAD were enrolled and clustered using unsupervised machine learning algorithms (UMLAs). Logistic regression analyses were performed to screen and identify predictive factors for the novel subtype of LE-PAD and a prediction model was built. We performed a comparative analysis regarding neutrophil levels in different subgroups of patients and an immune cell infiltration analysis to explore the associations between neutrophil levels and LE-PAD. Through hematoxylin and eosin (H&E) staining of lower-extremity arteries, neutrophil infiltration in patients with and without LE-PAD was compared. We found that UMLAs can helped in constructing a prediction model for patients with novel LE-PAD subtypes which enabled risk stratification for patients with LE-PAD using routinely available clinical data to assist clinical decision-making and improve personalized management for patients with LE-PAD. Additionally, the results indicated the critical role of neutrophil infiltration in LE-PAD pathogenesis.

Abstract Incidents of sub‐synchronous oscillations (SSOs) reported in the Hami power network of western China indicate that the torsional dynamics of a synchronous generator (SG) can be excited by a grid‐connected wind farm and trigger torsional SSOs. The concept of open‐loop modal resonance (OLMR) reveals the excitation mechanism from the standpoint of system modal condition. This paper proposes a novel damping torque analysis (DTA) for detecting and assessing the risk of OLMR exciting torsional SSOs in a complex power system with multiple grid‐connected wind farms. The proposed analysis is modal‐computational free and can also accurately identify the grid‐connected wind farm responsible for the excitation of the torsional SSOs. Moreover, the proposed DTA provides a physical insight into the OLMR to facilitate an understanding of the essential reasons that the OLMR may excite torsional SSOs. The effectiveness of the proposed DTA is demonstrated and evaluated in an example power system with multiple grid‐connected wind farms.

Power grids are currently undergoing a significant transition to enhance operational resilience and elevate power quality issues, aiming to achieve universal access to electricity. In the last few decades, the energy sector has witnessed substantial shifts toward modernizing distribution systems by integrating innovative technologies. Among the innovations, the solid-state transformer (SST) is referred to as a promising technology due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The design of SST has combined three-stage converters, i.e., the input, isolation, and output stages. The key objective of this design is to implement a modern power distribution system to make it a more intelligent and reliable device in practice. As the power converters are used in SST, they exhibit non-linear behavior and can introduce high-frequency components, making stability more challenging for the system. Besides, the stability issue can be even more complicated by integrating the distributed energy resources into the distribution system. Thus, the stability of SST must be measured prior to /during the design. To determine stability, state-space modeling, and its controller design are important, which this paper explains in detail. Indeed, the system’s stability is measured through the controllability and observability test. Further, the stability analysis is performed using frequency and time-domain diagrams: the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot. Finally, the SST Simulink model is tested and validated through real-time digital simulation using the OPALRT simulator to show its effectiveness and applicability. The stability performance of the proposed SST is evaluated and shows the effectiveness of the controller design of each converter circuit.

Background: The abdominal aortic aneurysm (AAA) incidence is closely related to systemic lupus erythematosus (SLE). However, the common mechanisms between AAA and SLE are still unknown. The purpose of this research was to examine the main molecules and pathways involved in the immunization process that lead to the co-occurrence of AAA and SLE through the utilization of quantitative bioinformatics analysis of publicly available RNA sequencing databases. Moreover, routine blood test information was gathered from 460 patients to validate the findings. Materials and methods: Datasets of both AAA (GSE57691 and GSE205071) and SLE (GSE50772 and GSE154851) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were analyzed using bioinformatic tools. To determine the functions of the common differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia analyses were conducted. Subsequently, the hub gene was identified through cytoHubba, and its validation was carried out in GSE47472 for AAA and GSE81622 for SLE. Immune cell infiltration analysis was performed to identify the key immune cells correlated with AAA and SLE, and to evaluate the correlation between key immune cells and the hub gene. Subsequently, the routine blood test data of 460 patients were collected, and the result of the immune cell infiltration analysis was further validated by univariate and multivariate logistic regression analysis. Results: A total of 25 common DEGs were obtained, and three genes were screened by cytoHubba algorithms. Upon validation of the datasets, CXCL1 emerged as the hub gene with strong predictive capabilities, as evidenced by an area under the curve (AUC) > 0.7 for both AAA and SLE. The infiltration of immune cells was also validated, revealing a significant upregulation of neutrophils in the AAA and SLE datasets, along with a correlation between neutrophil infiltration and CXCL1 upregulation. Clinical data analysis revealed a significant increase in neutrophils in both AAA and SLE patients (p 0.7. Conclusion: This study elucidates CXCL1 as a hub gene for the co-occurrence of AAA and SLE. Neutrophil infiltration plays a central role in the development of AAA and SLE and may serve to be a potential diagnostic and therapeutic target.

Microgrid provides a promising solution to consume more distributed renewable energies. To coordinate the increasingly developed distributed renewable generators in a high flexibility and high efficiency way, distributed event-triggered mechanisms have been widely investigated in the literature to reduce the communication requirement and hence improve the control performance of microgrids. However, most of the event-triggered mechanisms mandate continuous calculation of complicated triggering conditions, which may in turn impose the computation burden of the controller and increase additional energy cost. To this end, this paper presents a distributed self-triggered control strategy for the frequency restoration in islanded microgrids with the aid of a linear clock. With this self-triggered solution, each distributed generator’s controller decides its own control and communication actions based on monitoring the linear clock, which excludes continuous calculation of any triggering conditions. Thus, the communication and computation costs can be reduced simultaneously. Moreover, Zeno behavior can be naturally excluded by the above design. The results of theoretical analysis and simulations show that the proposed distributed self-triggered control scheme can effectively coordinate distributed renewable generators with very low communication and computation requirements. Therefore, this research can improve the coordination efficiency of microgrids greatly, which is very useful for guiding the efficient operation of large-scale distributed renewable generators.

IntroductionIn China, the long-term immunogenicity and adverse effects of inactivated vaccines produced by different or the same manufacturer remain unclear. Therefore, the objective of this study was to evaluate the cellular immune responses and neutralizing antibody kinetics of homologous and heterologous administrations of an inactivated coronavirus disease 2019 (COVID-19) vaccine 240 days after the second vaccination.MethodsThis prospective, multicenter, observational, longitudinal study involved 595 participants with a negative SARS-CoV-2 polymerase chain reaction result who were serologically tested and followed for 8 months after vaccination. Neutralizing antibodies, interferon-gamma (IFN-γ), interleukin (IL)-6, CD4+ T-lymphocyte, and B-lymphocyte counts were evaluated in serum samples after stimulation with 2 μg/mL SARS-CoV-2 spike protein for 16 h at follow-up intervals of 2 months.ResultsMost participants [582/595; 146 male participants, 449 female participants; mean age 35 (26–50 years)] rapidly developed neutralizing antibodies after two doses of the vaccine administered 3-weeks apart. The positive rate of neutralizing antibodies peaked at 97.7% at 60–90 days, decreased, and stabilized at 82.9% at 181–240 days post-vaccination. Lower antibody concentrations were correlated with older age, longer duration after vaccination, non-health care workers, mixed-manufacturer vaccinations, and intervals of less than 40 days between two doses of vaccination, whereas lower IFN-γ levels and B-lymphocyte counts were associated with older age, blood type A, and non-health care workers. A higher IL-6 level was associated with older age, mixed-manufacturer vaccinations, intervals of less than 40 days between two doses of vaccination, and medical staff. Adverse reactions were mild or moderate and self-limited, with no serious events reported.DiscussionTwo doses of the Chinese inactivated vaccine induced robust and rapid antibody expression and cellular immune responses. Boosting vaccination is considered important, as antibodies and cellular immune responses were reduced in susceptible populations.

With the wide deployment of combined heat and power units, electric boilers, etc., the power system and the heating system are coupled tightly, which necessitates expansion planning in a coordinated manner. Demand response (DR) is considered an effective method for augmenting system flexibility, which would lead to a more beneficial planning strategy in the co-expansion planning strategy. Therefore, we develop a bi-level co-expansion planning model with DR constraints for the integrated electric and heating system to minimize expenses on both investment and operation. The upper level gives the optimal investment strategy of energy facilities, while the lower level is optimal operation problems with DR constraints under the given investment decision. Numerical simulation is employed in the P6H8 system to demonstrate the proposed model.

Abstract The popularisation of electric vehicles (EVs) and the development of dynamic wireless charging technology have created an emerging trend of transportation electrification, which strengthens the coupling between electrified transportation network (ETN) and power distribution network (PDN). Meanwhile, the stochastic routing behaviour of EVs and prediction error of traffic demand pose a severe challenge to the coordinated ETN‐PDN operation problem. This paper proposes a new hybrid optimisation method using stochastic user equilibrium (SUE)/information gap decision theory (IGDT) to study the impact of the unavoidable uncertainties on the coordinated ETN‐PDN operation, which consists of the following two stages. In the first stage, a collaborative optimisation model based on SUE and Dist‐Flow equations is established to deal with the stochastic EV routing behaviour. Built upon this model, the second stage continues to consider the traffic demand prediction error and establish a risk decision model using IGDT. The proposed model can provide proper road congestion tolls and local generator production schedules to lead to a minimum expected socio‐economic cost. Also, two different coordinated operation strategies, that is, risk‐seeker and risk‐averse strategies, are provided to deal with the uncertainties. Case studies are carried out to demonstrate the effectiveness of the hybrid SUE/IGDT optimisation method.

Abstract This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi‐objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57‐bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.

In this paper, a slot microring with an asymmetric grating structure is proposed. Through the coupling between the grating and the slot microring, a high free spectral range or EIT-like effects with a high quality factor can be achieved in the same device. The grating is designed as an asymmetric structure to realize the modulation of the optical signal and the control of the resonance peak by changing the grid number, and the effect of different grating periods on the output spectrum is explored. The results show that changing the grating on slot sidewalls can increase or decrease the number of resonant peaks. By selecting a specific period of the gratings on both sides of the slot, the distance between adjacent resonance peaks can be increased to achieve modulation of the free spectral range. In this paper, depending on the grating period, we obtain a quality factor of 5016 and an FSR of 137 nm, or a quality factor of 10,730 and an FSR of 92 nm. The refractive index sensing simulation is carried out for one of the periods, which can achieve a sensitivity of 370 nm/RIU. Therefore, the proposed new structure has certain advantages in different sensing applications.

Abstract Background To evaluate the accuracy and performance of the Autof MS1000 mass spectrometer in bacteria and yeast identification, 2342 isolates were obtained from microbial cultures of clinical specimens (e.g. blood, cerebrospinal fluid, respiratory tract samples, lumbar puncture fluid, wound samples, stool, and urine) collected in 2019 in Henan Provincial People’s Hospital. Repetitive strains from the same patient were excluded. We tested the Autof MS1000 and Bruker Biotyper mass spectrometry systems and the classical biochemical identification system VITEK 2/API 20C AUX. Inconsistencies in strain identification among the three systems were identified by 16S rDNA and gene sequencing. Results At the species level, the Autof MS1000 and Bruker Biotyper systems had isolate identification accuracies of 98.9 and 98.5%, respectively. At the genus level, the Autof MS1000 and Bruker Biotyper systems were 99.7 and 99.4% accurate, respectively. The instruments did not significantly differ in identification accuracy at either taxonomic level. The frequencies of unreliable identification were 1.1% (26/2342) for the Autof MS1000 and 1.5% (34/2342) for the Bruker Biotyper. In vitro experiments demonstrated that the coincidence rate of the Autof MS1000 mass spectrometer in the identification of five types of bacteria was > 93%, the identification error rate was

This paper presents a multi-objective dynamic economic dispatch model with renewable obligation requirements. The two objective functions that are presented in this paper aim to increase the level of renewable energy sources in the grid while minimising the total operating cost and respecting the spinning reserves required to maintain continuity of supply. The proposed model incorporates thermal power plants, photovoltaic and wind power plants into the grid. The paper presents a Pareto optimal solution which is a compromise between maximising the renewable energy source generation while minimising the operating costs. A renewable obligation policy is implemented in the proposed model to ensure that renewable energy source generators are utilised and any failure to attain the required renewable obligation is penalised in line with the renewable obligation framework. The proposed model is modified into a single objective optimisation problem and numerical tests are performed on the modified IEEE 24 RTS system and IEEE 118 bus system to test the effectiveness of the model. A comparative study evaluates the impact of the proposed model on the traditional dynamic economic dispatch in terms of the achieved renewable energy source penetration level and economic performance. The numerical simulations show that the proposed model is robust and can attain high renewable energy source penetration levels.

In the past decades, a huge number of large-scale photovoltaic (PV) plants have been constructed all around the world. However, the capital and operational costs can be high, which limits their widespread applications. Because the installed PV modules often operate in harsh environments (i.e. storm, high temperature, dust, hail, etc.), non-uniform aging phenomena of PV modules cannot be avoided and it impacts adversely on the performance of PV plants, especially in the middle and late periods of their service life. This paper develops an offline PV module reconfiguration strategy for the non-uniform aging PV array to mitigate this effect. In order to maximize the economic benefit, electricity price and labor cost are considered in the offline reconfiguration. The Branch-and-Bound based optimization algorithm is proposed to find the highest economic benefit. In order to verify the proposed algorithm, MATLAB software-based modelling and simulation have been performed in four case studies, in which a typical 42kW PV array with 7 × 20 connection is employed in a testing benchmark, and the manpower cost and electricity price in USA, UK, China and Turkey are considered in the case study. The results demonstrate that the economic benefit from a non-uniformly aged PV array is further enhanced with the proposed reconfiguration method.

Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies.

For plug-in hybrid electric vehicles or extended range electric vehicles, battery stored energy often cannot fully meet the travel needs, and the battery needs to stay in a charge-sustaining mode to allow for backup sources such as gasoline or diesel to power the vehicle. It is crucial to identify the optimal state-of-charge (SOC) value for the battery to maintain this charge-sustaining mode since this SOC has significant impact to battery degradation. In existing studies, Just-in-Time control proposes that this SOC should be maintained at 55% but without theoretical justification. With the help of a battery degradation model based on solid electrolyte interphase growth, this article develops a method to decide the optimal SOC value for charge-sustaining mode. Following the principle of superposition, degradation during the battery discharging process is divided as a fixed degradation caused by the drop of SOC from maximum to minimum values during charge-depleting mode, and a dynamic degradation caused by the oscillation at the charge-sustaining SOC value. Then this oscillation-caused degradation is further modeled and minimized through the investigation of the side reaction current density. The optimal SOC value obtained will be the SOC at which the side reaction current density has the slowest changing rate. This SOC value indeed relies on battery parameters, charging/discharging current and ambient temperature, and a case study shows that the best range of SOC value is 36%~38% for a 1.8Ah SONY 18650 cell. An average SOC of 37% is therefore recommended for charge-sustaining mode considering the possible errors in SOC estimation.

This study aimed to monitor severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral loads and specific serum-antibodies (immunoglobulin [Ig] G and M) among confirmed patients and asymptomatic carriers from returning healthy travelers. The throat swabs, sputum, and stool samples from 57 hospitalized coronavirus disease (COVID-19) patients and 8 asymptomatic carriers, among 170 returning healthy travelers were tested using reverse-transcription real-time polymerase chain reaction. SARS-CoV-2 IgM/IgG antibodies were detected via serum chemiluminescence assay. Sequential results showed higher viral RNA loads in the throat, sputum, and stool samples at 3–12 and 6–21 days after symptom onset among severely ill COVID-19 patients. Shorter viral habitation time (1–8 days) was observed in the oropharyngeal site and intestinal tract of asymptomatic carriers. The IgG and IgM response rates were 19/37 (51.4%) and 23/37 (62.6%) among the 29 confirmed patients and 8 asymptomatic carriers, respectively, within 66 days from symptom or detection onset. The median duration between symptom onset and positive IgG and IgM results was 30 (n=23; interquartile range [IQR]=20–66) and 23 (n=19; IQR=12–28) days, respectively. Of 170 returning healthy-travelers to China, 4.7% were asymptomatic carriers (8/170) within 2 weeks, and the IgG and IgM positivity rate was 12.8% (12/94). IgM/IgG-positivity confirmed 3 suspected SARS-CoV-2 cases, despite negative results for SARS-CoV-2 RNA. Compared with other respiratory viral infectious diseases, COVID-19 has fewer asymptomatic carriers, lower antibody response rates, and a longer antibody production duration in recovered patients and the contacted healthy population. This is an indication of the complexity of COVID-19 transmission.

ABSTRACTHere, we report the first imported case of melioidosis from Laos in central China. COMPACT VITEK2 identification system and PCR, as well as sequencing methods confirmed that the patient was infected by Burkholderia pseudomallei, a bacterial species closely related to an isolate detected in Thailand. These findings are highly valuable for an early diagnosis, treatment and to prevent the spread of this emerging infectious disease in central China.

To solve the problem of track failure in railway system, integrative design of an emergency resource forecasting-scheduling-repairing method is proposed in this paper. In the current railway repair process, resource dispatching centers dispatch transport resources to the fault point for the repair work. In order to optimize this process, a hybrid prediction method consisting of radial basis function neuron network (RBFNN) and expert prediction method to rationally predict repair resources is proposed. In order to optimize the resource transportation path and reduce the transportation cost, a bi-objective resource scheduling method is presented in this paper. A fuzzy evaluation method combining with the prospect theory to optimally select the repairing for the repair work is formulated. Finally, experiment studies are carried out and the results illustrate the effectiveness of the proposed method.

In this study, the problem of an electric vehicle (EV) aggregator participating in a three-settlement pool-based market is presented. In addition to energy procurement, it is assumed that EVs can sell electricity back to the markets. In order to obtain optimised solutions, the aggregator is considered as a price-maker agent who tries to minimise the cost of purchasing energy from the markets by offering price-energy bids in the day-ahead market and only energy bids in both adjustment and balancing markets. Since the problem is heavily constrained by equality constraints, the number of binary variables for a 24-hour market horizon is too large which leads to intractability when solved by traditional mathematical algorithms like the interior point. Therefore, an evolutionary metaheuristic algorithm based on genetic algorithms (GAs) is proposed to deal with the intractability. In this regard, first, the stochastic problem is formulated as a mixed-integer linear programming problem, and as a non-linear programming problem to be solved by CPLEX and GA, respectively. The former is used to ensure that the GA is tuned properly, and helps to avoid converging to local extremums. Furthermore, the solutions of the two formulations are compared in simulations to demonstrate GA could be faster in obtaining better results.

This study formulates a stochastic bi-level optimisation model for an aggregator participating in a two-settlement pool-based market while competing with rival aggregators. The market structure comprises a day-ahead and a balancing market. The upper level problem maximises the profit of the aggregator, while the lower level problem minimises the cost of energy procurement. The novelty of this work is that the aggregator is considered as a price-maker in both markets in which it participates in the day-ahead market by offering energy and price bids, and in the balancing market by offering only energy bids. Therefore, this study formulates an optimisation model where the price-maker economic bidding problem is considered without simplification, and serves as a reference for future studies. The proposed formulation is non-linear due to its bi-level structure and price-maker offers. The problem is then properly linearised into a single-level problem. The concept of conditional value at risk (CVaR) has been applied in the problem formulation to maximise average profit under worst scenarios. Finally, numerical results are presented through an illustrative case study to assess the performance of the proposed model. The results show that the profit of economic bidding is over 100% more than that of self-schedule in low risk cases.

This paper aims at analyzing the energy management activities for commercial buildings of a financial service company in South Africa by energy efficiency in terms of performance, operation, equipment and technology (POET). The sustainability of a general energy management program is discussed within this POET framework. As an application of this discussion to the commercial building scenario, the award winning energy management program of this financial service group company is featured from the POET perspective of energy efficiency. The case study shows that the POET based framework can not only cover all major energy management activities, but also identify further energy efficiency improvement opportunities.

With the rapid development of the new concept of energy internet, electric power systems often need to be investigated together with thermal energy systems. Additionally, to reduce pollution from gas emissions, it is very important to study the economic and emission dispatch of integrated electrical and heating systems. Hence, this paper proposes a multi-objective optimization dispatch model for a microgrid (MG) with a combined heat and power (CHP) system. This CHP-based MG system consists of a CHP unit, a wind turbine, a PV system, a fuel cell, an electric boiler, an electric storage, and a heat storage. It can exchange electricity with the distribution network and exchange heat with the district heating network. Minimum economic cost and minimum environmental cost are considered as the two objectives for the operation of this CHP-based MG system. To solve the two objective optimization problem, the multi-objective bacterial colony chemotaxis algorithm is utilized to obtain the Pareto optimal solution set, and the optimal solution is chosen by the Technique for Order of Preference by Similarity to Ideal Solution method. Finally, numerical case studies demonstrate the effectiveness of proposed model and method for the optimal economic and emission dispatch of the CHP-based MG system.

Series battery equalisation can improve battery charge and discharge reliability and extend battery life. Inductor-based battery equalisation schemes have the advantages of simple topologies and control strategies. According to the energy transfer pathway, inductor-based battery equalisation schemes can be divided into cell-to-cell and cell-to-pack equalisation schemes. The control strategies of the cell-to-cell schemes are simple; the inductor can only transfer energy between the neighbouring cells, so the equalisation speed is low. The cell-to-pack schemes are able to accomplish energy transfer between the cells and pack by charging and discharging the inductors. The equalisation speed is high, but the control strategies may be complex. In this paper, different equalisation topologies are reviewed, then a unified control strategy which is applicable to all of the inductor-based equalisation topologies is proposed. The equalisation speeds and efficiencies of these different schemes, including the newly-proposed unified control strategy, are analysed and compared. Based on the theoretical analysis, simulations, and experimental verifications, it is concluded that this unified control strategy can perform the battery equalisation process quickly and efficiently.

Security assessment is crucial for the reliable and secure operation of power systems. This paper proposes a security level classification (SLC) method to analyze the security level of power systems both qualitatively and quantitatively. In this SLC method, security levels are graded according to a comprehensive safety index (CSI), which is defined by integrating the system margin index (SMI) and load entropy. The SMI depends on the operating load and the total supply capacity (TSC) under N-1 contingency, and the load entropy reflects the heterogeneity of load distribution calculated from entropy theory. In order to calculate the TSC under N-1 contingency considering both of the computational accuracy and speed, the TSC is converted into an extended conic quadratic programming (ECQP) model. In addition, the load boundary vector (LBV) model is established to obtain the capacity limit of each load bus, and thus detect potential risks of power systems. Finally, two modified practical power systems and the IEEE 118-bus test system are studied to validate the feasibility of the proposed SLC method.

Battery equalization can increase batteries’ life cycle, utilization, and reliability. Compared with battery equalization topologies based on resistance or energy storage components, the topologies based on transformers have the advantages of high balancing current and efficiency. However, the existence of switching losses will reduce the reliability and service life span of the equalization circuit. Aiming at resolving this problem, a new battery equalization topology by fly-back transformer with an absorbing circuit is proposed in this paper. Compared with other transformer-based topologies, it can decrease switching losses because the voltage/current spike is solved by the absorbing circuit which is composed of inductance, capacitance and diode (LCD), and it can also maintain a high balancing current of about 1.8 A and high efficiency of about 89%, while the balancing current and efficiency of other topologies were usually 1.725 A/1.5 A and 80%/80.4%. The working principle of the balancing topology and the process of soft switching are analyzed and calculated in the frequency domain. Due to the addition of the LCD absorbing circuit, soft switching can be realized to reduce the switching losses while the high equalization speed and efficiency are still maintained. The corresponding control strategy of the balancing topology is also proposed and the timely balancing is achieved. The theoretical analysis is verified by simulation and experimental results.