The widespread use of distributed energy sources provides exciting potential for demand-side energy sharing and collective self-consumption schemes.Demand-side energy sharing and collective self-consumption systems ar...The widespread use of distributed energy sources provides exciting potential for demand-side energy sharing and collective self-consumption schemes.Demand-side energy sharing and collective self-consumption systems are committed to coordinating the operation of distributed generation,energy storage,and load demand.Recently,with the development of Internet technology,sharing economy is rapidly penetrating various fields.The application of sharing economy in the energy sector enables more and more end-users to participate in energy transactions.However,the deployment of energy sharing technologies poses many challenges.This paper comprehensively reviews recent developments in demand-side energy sharing and collective self-consumption schemes.The definition and classification of sharing economy are presented,with a focus on the applications in the energy sector:virtual power plants,peer-to-peer energy trading,shared energy storage,and microgrid energy sharing cloud.Challenges and future research directions are thoroughly discussed.展开更多
Recent Italian regulation rewards Local Energy Communities(LECs)through two distinct channels:an incentive for virtual shared energy and market access for distributed batteries that provide up-regulation.These incenti...Recent Italian regulation rewards Local Energy Communities(LECs)through two distinct channels:an incentive for virtual shared energy and market access for distributed batteries that provide up-regulation.These incentives often conflict,as charging batteries to maximize the shared energy limits the capacity to provide ancillary services,and vice versa.Currently,quantitative tools for effectively balancing these objectives are lacking respecting the electrical constraints of the low-voltage grid.To fill this gap,a multi-objective optimization is proposed that co-maximizes the revenue from up-regulation and the virtual shared energy reward,under the constraint that the daily energy bill does not exceed a predefined baseline.The implemented mathematical programming formulation utilizes multi-objective second-order cone programming(SOCP)with linear constraints to incorporate the network’s physical constraints.Linearization and decomposition techniques are employed to simplify the problem.By adjusting the physical constraints of the network,the impact of energy communities on the distribution network can also be evaluated with different objectives.The model allows the representation of real peer-to-peer trading,quantifying its effects on both revenue streams and voltage profiles as well as power losses.Trade-off analyses performed on an 84-bus radial distribution network,under both constant and variable prices,show that the framework adapts smoothly to market volatility,highlighting when it is advantageous to prioritize up-regulation and when it becomes preferable to maximize the virtual shared energy incentive.展开更多
As the cornerstone for the safe operation of energy systems,short-term voltage stability(STVS)has been assessed effectively with the advance of artificial intelligence(AI).However,the black-box models of traditional A...As the cornerstone for the safe operation of energy systems,short-term voltage stability(STVS)has been assessed effectively with the advance of artificial intelligence(AI).However,the black-box models of traditional AI barely identify what the specific key factors in power systems are and how they influence STVS,thus providing limited practical information for engineers in on-site dispatch centers.Enlightened by the latest explainable artificial intelligence(XAI)techniques,this paper aims to unveil the mechanism underlying the complex STVS problem.First,the ground truth for STVS is established via qualitative analysis.Based on this,an explainability score is then devised to measure the trustworthiness of different XAI techniques,among which Local Interpretable Model-agnostic Explanations(LIME)exhibits the best performance in this study.Finally,a sequential approach is proposed to extend the local interpretation of LIME to a broader scope,which is applied to enhance STVS performance before a fault occurs in distribution system load shedding,serving as an example to demonstrate the application merits of the explored mechanism.Numerical results on a modified IEEE system demonstrate that this finding facilitates the identification of the most suitable XAI technique for STVS,while also providing an interpretable mechanism for the STVS,offering accessible guidance for stability-aware dispatch.展开更多
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 optimizatio...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.展开更多
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,...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.展开更多
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...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.展开更多
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 cont...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.展开更多
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 respirator...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.展开更多
基金supported by the National Natural Science Foundation of China(No.52177087)the High-End Foreign Experts Project(No.G2022163018L).
文摘The widespread use of distributed energy sources provides exciting potential for demand-side energy sharing and collective self-consumption schemes.Demand-side energy sharing and collective self-consumption systems are committed to coordinating the operation of distributed generation,energy storage,and load demand.Recently,with the development of Internet technology,sharing economy is rapidly penetrating various fields.The application of sharing economy in the energy sector enables more and more end-users to participate in energy transactions.However,the deployment of energy sharing technologies poses many challenges.This paper comprehensively reviews recent developments in demand-side energy sharing and collective self-consumption schemes.The definition and classification of sharing economy are presented,with a focus on the applications in the energy sector:virtual power plants,peer-to-peer energy trading,shared energy storage,and microgrid energy sharing cloud.Challenges and future research directions are thoroughly discussed.
基金supported in part by the Ministry of Research,Innovation and Digitalization under Project PNRR-C9-I8-760090/23.05.2023 CF30/14.11.2022in part by Project PNRR ECS-ECOSISTER-CUP:J33C22001240001Project ECS4DRES co-funded by EU’s Horizon Europe under grant agreement 101139790.
文摘Recent Italian regulation rewards Local Energy Communities(LECs)through two distinct channels:an incentive for virtual shared energy and market access for distributed batteries that provide up-regulation.These incentives often conflict,as charging batteries to maximize the shared energy limits the capacity to provide ancillary services,and vice versa.Currently,quantitative tools for effectively balancing these objectives are lacking respecting the electrical constraints of the low-voltage grid.To fill this gap,a multi-objective optimization is proposed that co-maximizes the revenue from up-regulation and the virtual shared energy reward,under the constraint that the daily energy bill does not exceed a predefined baseline.The implemented mathematical programming formulation utilizes multi-objective second-order cone programming(SOCP)with linear constraints to incorporate the network’s physical constraints.Linearization and decomposition techniques are employed to simplify the problem.By adjusting the physical constraints of the network,the impact of energy communities on the distribution network can also be evaluated with different objectives.The model allows the representation of real peer-to-peer trading,quantifying its effects on both revenue streams and voltage profiles as well as power losses.Trade-off analyses performed on an 84-bus radial distribution network,under both constant and variable prices,show that the framework adapts smoothly to market volatility,highlighting when it is advantageous to prioritize up-regulation and when it becomes preferable to maximize the virtual shared energy incentive.
基金supported in part by the National Natural Science Foundation of China under Grant U23B6008in part by the Guangdong Basic and Applied Basic Research Foundation under Grants 2022A1515240075in part by the Italian Ministry of University and Research,Project NEST,Code PE0000021,CUP J33C22002890007.
文摘As the cornerstone for the safe operation of energy systems,short-term voltage stability(STVS)has been assessed effectively with the advance of artificial intelligence(AI).However,the black-box models of traditional AI barely identify what the specific key factors in power systems are and how they influence STVS,thus providing limited practical information for engineers in on-site dispatch centers.Enlightened by the latest explainable artificial intelligence(XAI)techniques,this paper aims to unveil the mechanism underlying the complex STVS problem.First,the ground truth for STVS is established via qualitative analysis.Based on this,an explainability score is then devised to measure the trustworthiness of different XAI techniques,among which Local Interpretable Model-agnostic Explanations(LIME)exhibits the best performance in this study.Finally,a sequential approach is proposed to extend the local interpretation of LIME to a broader scope,which is applied to enhance STVS performance before a fault occurs in distribution system load shedding,serving as an example to demonstrate the application merits of the explored mechanism.Numerical results on a modified IEEE system demonstrate that this finding facilitates the identification of the most suitable XAI technique for STVS,while also providing an interpretable mechanism for the STVS,offering accessible guidance for stability-aware dispatch.
文摘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.
文摘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.
文摘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.
基金supported in part by the Ministry of Research,Innovation and Digitalization under Project PNRR-C9-I8-760090/23.05.2023 CF30/14.11.2022.
文摘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.
文摘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.
