An Optimization Model for Reliability Improvement and Cost Reduction Through EV Smart Charging

There is a general concern that the increasing penetration of electric vehicles(EVs)will result in higher aging failure probability of equipment and reduced network reliability.The electricity costs may also increase,due to the exacerbation of peak load led by uncontrolled EV charging.This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction.The objective of the proposed model is the cost minimization,including the loss of load,repair costs due to aging failures,and EV charging expenses.The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load.Considering two different test systems(a 5-bus network and the IEEE 33-bus network),this paper compares aging failure probabilities,service unavailability,expected energy not supplied,and total costs in various scenarios with and without the implementation of EV smart charging.

Scalable Distributed Optimization Combining Conic Projection and Linear Programming for Energy Community Scheduling

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection(CP) and linear programming(LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Ventilator-associated pneumonia(VAP)and pleural empyema caused by multidrug-resistant Acinetobacter baumannii in HIV and COVID 19 infected patient:A case report

We analyzed the case of a 49-year-old woman with HIV infection off-therapy with poor viro-immunological compensation,not vaccinated for SARS-COV-2,hospitalized for lobar pneumonia and severe COVID19-related respiratory failure in intensive care unit(ICU).The hospitalization was complicated by bacteraemic ventilator-associated pneumonia(VAP)caused by multidrug-resistant Acinetobacter baumannii(MDR-AB)isolated on pleural fluid culture,treated with colistin and cefiderocol for about 3 weeks.The molecular research of MDR-AB on transtracheal aspirate was negative following this therapy.The aim is to show the safety,efficacy and tolerability of colistin-based combination therapy with cefiderocol for Acinetobacter baumannii infection in HIV-infected patient.

Indices of Congested Areas and Contributions of Customers to Congestions in Radial Distribution Networks

Congestions are becoming a significant issue with an increasing number of occurrences in distribution networks due to the growing penetration of distributed generation and the expected development of electric mobility.Fair congestion management(CM)policies and prices require proper indices of congested areas and contributions of customer to congestions.This paper presents spatial and temporal indices for rapidly recognizing the seriousness of congestions from the perspectives of both magnitude violation and duration to prioritize the affected areas where CM procedures should be primarily activated.Besides,indices are presented which describe the contributions of customers to the congestions.Simulation tests on IEEE 123-bus and Australian 23-bus low-voltage distribution test feeders illustrate the calculation and capabilities of the proposed indices in balanced and unbalanced systems.

Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system.Local energy communities(LECs)are expected to play a vital role in this context.However,energy scheduling in LECs presents various challenges,including the preservation of customer privacy,adherence to distribution network constraints,and the management of computational burdens.This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method.The proposed approach uses the Limitedmemory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS)method,significantly reducing the computational effort required for solving the mixed integer programming(MIP)problem.It incorporates network constraints,evaluates energy losses,and enables community participants to provide ancillary services like a regulation reserve to the grid utility.To assess its robustness and efficiency,the proposed approach is tested on an 84-bus radial distribution network.Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.