Response Programs Research Articles

Integrative phosphoproteomic analyses reveal hemostatic-endothelial signaling interplay

BackgroundThe vascular endothelial cell (EC) monolayer plays a crucial part in maintaining hemostasis. An extensive array of G protein-coupled receptors allows ECs to dynamically act on key hemostatic stimuli such as thrombin and histamine. The impact of these individual stimuli on EC signal transduction has been the subject of various studies, but insight into discordant and concordant EC signaling between different G protein-coupled receptors remains limited. ObjectivesTo elucidate histamine and protease-activated receptor (PAR1-4) signaling cascades in ECs, discern overlapping and diverging regulation between these stimuli and their effect on the EC monolayer. MethodsWe employed stable isotope labeling by amino acids in cell culture mass spectrometry-based phosphoproteomics on in vitro cultured blood outgrowth ECs stimulated with histamine and different PAR1 to 4 peptides. We investigated key phosphosites through immuno(fluorescence) staining and determined effects on barrier function through transendothelial resistance assays. ResultsEC histamine activation initiated an extensive (kinase) signaling network (including MAPK3, STAT3, and CTNND1). PAR1 and PAR2 receptors induced highly similar signaling cascades, whereas PAR3 and PAR4 induced minimal phospho-regulation. Integration of all applied stimuli indicated uniquely activated proteins between both stimuli, as well as a general overlapping activation of cell junction and actin cytoskeletal proteins. ConclusionWe provide an integrative phosphoproteomic analysis of histamine and PAR agonists in the endothelium that highlights the endothelial response programs that are at the basis of regulating hemostasis.

A fully decentralized demand response and prosumer peer-to-peer trading for secure and efficient energy management of community microgrid

A novel demand response program combines the advantages of price-based demand response under the time-of-use pricing and the incentive-driven demand reduction bidding strategy to manage energy demand, and maximize the net present value of both participants and the community-microgrid. It also facilitates the seller prosumers to optimally sell the surplus power in a fully decentralized peer-to-peer power trading manner. A novel mathematical demand reduction bidding model is proposed to minimize the community-microgrid cost by the prioritized selection of participants for demand reduction. This study proposed a novel decentralized three non-cooperative dynamic games strategy, incorporating Nash equilibrium approaches to achieve the optimum schedules of appliance, buying, and power trading agents to maximize the net present value of both prosumers and the community-microgrid while preserving participants’ privacy and control network congestion. The priority-based optimal selection of seller and buyer prosumers according to their utility and pricing function in the peer-to-peer power trading scheme is accomplished using a proposed color-based segmented decentralized bilateral power trading to maximize the net present value. The numerical and simulation results demonstrate the feasibility and effectiveness of the proposed strategies and increase the net present value of prosumers up to 42.4 % and community-microgrid up to 34.79 %.

Experiences and attitudes of psychiatric nurses in caring for patients with repeated non-suicidal self-injury in China: a qualitative study

BackgroundThe incidence of non-suicidal self-injury (NSSI) is high and often occurs repeatedly. Psychiatric nurses play a vital role in the care and treatment of NSSI patients, as they have the most frequent contact with patients. The experiences and attitudes of nurses has a direct affect on the quality of care they provide to patients. Negative care experiences and attitudes of patient aversion on behalf of nurses may delay the observation and treatment of changes in the patient’s condition, leading to irreversible risks. Although cross-sectional studies have investigated the attitudes of medical staff toward NSSI patients, quantitative research results cannot comprehensively reflect the emotional experiences and complex psychological changes of the study subjects. A few studies have focused on the psychiatric nurses’ care experiences and attitudes toward patients with repeated NSSI.ObjectiveThis study aimed to explore psychiatric nurses’ care experiences and attitudes toward patients during repeated NSSI.MethodsA thematic analysis qualitative study was used. Using purposive sampling, 18 psychiatric nurses were recruited from a mental health center in Chengdu, China. Semi-structured interviews were conducted and audio-recorded. Audio-recordings were transcribed verbatim and analyzed using six-phase thematic analysis.ResultsFour themes emerged from the analysis: psychiatric nurses’ care experiences, perceptions, care attitudes and coping style toward repeated NSSI patients. Psychiatric nurses have experienced negative care experiences and severe career burnout during the patient’s repeated NSSI. Nurses’ attitudes toward NSSI patients changed during repeated NSSI, from understanding to indifference to anger and resentment. At the same time, it was found that nurses’ coping style with NSSI patients could be divided into three stages, namely, active coping, neglect and perfunctory, and criticism and punishment.ConclusionsThe findings have implications for health care systems regarding interventions to improve nurses’ care experiences and attitudes toward repeated NSSI patients. These findings suggest that enhancing nurses’ understanding of NSSI, establishing standardized emergency response and intervention programs, guiding positive professional values and responsibility, and improving nurses’ caring attitudes can promote the early detection and timely intervention of NSSI.

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand

The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

As space heating accounts for 54% of annual residential electricity consumption in Quebec, demand response programs specifically target load shifting through the automated control of thermostat setpoints during peak hours. On a district scale, varied thermostat preferences and setpoint override behaviors can have an impact on the success of the demand response program. This study examines two unique occupant types (Average, Tolerant) in terms of thermostat setpoint preferences as well as three different occupant Levels-of-Detail (LoDs) and analyzes their effects on the energy flexibility provided during demand response periods. For our baseline scenario, LoD 1, a static setpoint schedule is used and there is no control of the heat pump, while LoD 2 and LoD 3 incorporate thermostat setbacks during demand response events. LoD 2 assumes the occupant is comfortable within 2°C from the setpoint while LoD 3 allows the occupant to override the DR setbacks. We estimate the flexibility services provided by a ten-house residential community through the automated control of heat pumps during a three-month winter period, and we implement and simulate our study in CityLearn using reinforcement learning-based control for district-level energy management. When comparing LoD 3 to LoD 1, electricity cost was reduced by approximately 12% and net electricity consumption was reduced by approximately 17% during demand response periods. Likewise, we find that LoD 2 could overestimate savings in net electricity consumption, cost, and peak demand by 5% compared to LoD 3. The number of hours where the indoor temperature deviated more than ▪ from the setpoint occurred for less than 5% of the timesteps on average for the 10 buildings for LoD 3 while still achieving significant net electricity consumption reductions, thus highlighting that we can provide energy flexibility services to the grid while balancing occupant thermal comfort. Finally, the agents learned optimal decision-making that reduced the number of overrides across the training episodes for both Average and Tolerant occupants. We thus present a multi-agent framework as a means for addressing various occupant setpoint preferences and override behaviors for the control of heat pump systems at the neighborhood level.

Identifying genetic variants that influence the abundance of cell states in single-cell data.

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

A blockchain-based architecture for tracking and remunerating fast frequency response

The increasing penetration of renewable sources introduces new challenges for power systems’ stability, especially for isolated systems characterized by low inertia and powered through a single diesel power plant, such as it happens in small islands. For this reason, research projects, such as the BLORIN project, have focused on the provision of energy services involving electric vehicles owners residential users to mitigate possible issues on the power system due to unpredictable generation from renewable sources. The residential users were part of a blockchain-based platform, which also the Distributors/Aggregators were accessing. This paper describes the integrated framework that was set up to verify the feasibility and effectiveness of some of the methodologies developed in the BLORIN project for fast frequency response in isolated systems characterized by low rotational inertia. The validation of the proposed methodologies for fast frequency response using Vehicle-to-Grid or Demand Response programs was indeed carried out by emulating the dynamic behavior of different power resources in a Power Hardware-in-the-Loop environment using the equipment installed at the LabZERO laboratory of Politecnico di Bari, Italy. The laboratory, hosting a physical microgrid as well as Power Hardware-in-the-Loop facilities, was integrated within the BLORIN blockchain platform. The tests were conducted by assuming renewable generation development scenarios (mainly photovoltaic) and simulating the system under the worst-case scenarios caused by reduced rotational inertia. The experiments allowed to fully simulate users’ interaction with the energy system and blockchain network reproducing realistic conditions of tracking and remuneration of users’ services. The results obtained show the effectiveness of the BLORIN platform for the provision, tracking and remuneration of grid services by electric vehicles and end users, and the benefits that are achieved in terms of reducing the number of diesel generating units that need to be powered on just to provide operational reserve due to the penetration of renewable sources, resulting in fuel savings and reduced emissions.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

The HOTspots Digital Surveillance: Conceptualisation to Clinical Deployment.

The HOTspots digital surveillance platform (HOTspots) is a critical technology of the HOTspots Surveillance and Response Program. It provides timely point-of-care access to pathology and demographic data from previously underserved regions. Co-designed with clinicians, epidemiologists, and health policy makers, the platform provides the evidence-base to empower efficient clinical management of patients with antimicrobial resistant (AMR) infections and supports national disease surveillance efforts in Australia. The pathway from conceptualisation to deployment for the HOTspots digital surveillance platform is described.

Economic management of microgrid using flexible non-linear load models based on price-based demand response strategies

Demand response programmes are used in microgrid research without considering the different price elasticity of distinct load types. To evaluate the impacts of demand response efforts, it is necessary to utilise a mix of nonlinear and linear models for creating load-responsive models in a manner that is realistic. With an aim to address this gap in research, an exhaustive technoeconomic investigation is being conducted to determine the effect that price-dependent demand response programmes have on the optimal scheduling of microgrids when linear and nonlinear load models are present. The flexible elasticity model is used to accurately describe how customers respond to changes in electricity price. Four load models, including exponential, hyperbolic, linear, and logarithmic, were constructed for each demand response programme. Rime is a newly created physics-based algorithm that has been deployed to handle the anticipated energy management problem that the microgrid may be experiencing. To evaluate the efficacy of the proposed strategy, four case studies based on grid price and participation scenarios have been carried out. The findings from our research are notably impactful, revealing an 8 % reduction in energy consumption when applying the critical peak price (CPP) load demand model. This efficiency gain translates into a notable enhancement in the load factor, increasing from 0.83 to 0.86, and a significant drop in overall generation costs from $25,463 to $21,823 under optimal conditions. These results vividly illustrate the practical value of our proposed research work, showcasing their ability to generate substantial energy savings and cost efficiencies in microgrid operations. The improvements in operational efficiency and cost-effectiveness underscore the potential of these models to refine microgrid management, delivering both economic and performance gains that are crucial for advancing sustainable energy solutions.

Оптимизация потребления сети MicroGrid с учетом потребностей на электроэнергию

Electricity grid is a product of urbanization expansion and rapid development of various infrastructures worldwide and over the past centuries. Although power companies are located in diverse regions, they typically use the same technologies to generate and distribute electricity. Proper implementation of the demand response (DR) program should be provided with some equipment to make subscribers aware of electricity price at any time and accordingly provide a proper response to the grid to reduce costs. This, in turn, reduces demand during peak hours. The intelligent grid, using the two-way communication network and the transmission of information to subscribers, and an advanced metering network provide a good structure for fully implementing DR programs. The present study applies a demand response economic model to implement DRs. The model uses price elasticity of demand, which provides subscribers a more precise consumption behavior regarding the factors influencing demand (e.g., electricity prices, bonuses, and fines). Applying the model to the microgrid, the operating cost significantly decreases in both operating modes.

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

The effect of DMSO on Saccharomyces cerevisiae yeast with different energy metabolism and antioxidant status

We studied the effect of dimethyl sulfoxide (DMSO) on the biochemical and physiological parameters of S. cerevisiae yeast cells with varied energy metabolism and antioxidant status. The wild-type cells of varied genetic backgrounds and their isogenic mutants with impaired antioxidant defences (Δsod mutants) or response to environmental stress (ESR) (Δmsn2, Δmsn4 and double Δmsn2msn4 mutants) were used. Short-term exposure to DMSO even at a wide range of concentrations (2–20%) had little effect on the metabolic activity of the yeast cells and the stability of their cell membranes, but induced free radicals production and clearly altered their proliferative activity. Cells of the Δsod1 mutant showed greater sensitivity to DMSO in these conditions. DMSO at concentrations from 4 to 10–14% (depending on the strain and genetic background) activated the ESR programme. The effects of long-term exposure to DMSO were mainly depended on the type of energy metabolism and antioxidant system efficiency. Yeast cells with reduced antioxidant system efficiency and/or aerobic respiration were more susceptible to the toxic effects of DMSO than cells with a wild-type phenotype and respiro-fermentative or fully fermentative metabolism. These studies suggest a key role of stress response programs in both the processes of cell adaptation to small doses of this xenobiotic and the processes related to its toxicity resulting from large doses or chronic exposure to DMSO.

Fostering newly resettled refugee-background adolescents’ self-efficacy and advocacy in a community-based youth program

PurposeThis study aims to explore the experiences of newly resettled refugee-background high school students participating in the Teen Response program, a community-based initiative to assist refugee-background adolescents (RBAs) in navigating educational and career opportunities in the midwestern USA.Design/methodology/approachUsing a mixed-methods approach, this study examined twenty-two RBAs through survey and focus groups.FindingsThe findings reveal significant growth and transformation in the students' self-efficacy and advocacy skills. This growth underscores the critical role of social support and community engagement in helping RBAs, empowering them to advocate for themselves and others in pursuing educational and career goals.Originality/valueThis study contributes new insights into the community-based support programs for RBAs, focusing on enhancing self-efficacy and advocacy. It offers valuable implications for developing effective support frameworks and recommends future research on these approaches.

Dynamic phosphorylation of Hcm1 promotes fitness in chronic stress.

Cell survival depends upon the ability to adapt to changing environments. Environmental stressors trigger an acute stress response program that rewires cell physiology, downregulates proliferation genes and pauses the cell cycle until the cell adapts. Here, we show that dynamic phosphorylation of the yeast cell cycle-regulatory transcription factor Hcm1 is required to maintain fitness in chronic stress. Hcm1 is activated by cyclin dependent kinase (CDK) and inactivated by the phosphatase calcineurin (CN) in response to stressors that signal through increases in cytosolic Ca2+. Expression of a constitutively active, phosphomimetic Hcm1 mutant reduces fitness in stress, suggesting Hcm1 inactivation is required. However, a comprehensive analysis of Hcm1 phosphomutants revealed that Hcm1 activity is also important to survive stress, demonstrating that Hcm1 activity must be toggled on and off to promote gene expression and fitness. These results suggest that dynamic control of cell cycle regulators is critical for survival in stressful environments.

Quantifying the operational flexibility and invocation costs of urban regional integrated energy systems for participation in demand response programs

As a typical aggregation platform, the regional integrated energy system (RIES) is required to pre-submit its flexibility for each time period when participating in demand response. However, existing studies fail to quantify the flexibility parameters necessary for RIES pre-reporting. Therefore, this paper introduces a flexibility quantification framework tailored for RIES engagement in demand response. The core principle of the framework is grounded in the RIES day-ahead dispatch model considering virtual energy storage, which transforms the flexibility quantification problem into dispatch optimization problems under baseline scenarios and flexibility invocation scenarios. In the flexibility invocation scenario, a control term is incorporated into the objective function to steer the system towards fulfilling flexibility requirements in the most economical strategy. The deviation of power and operating costs in both scenarios defines the system's flexibility and flexibility invocation cost, respectively. This framework holds broad applicability across various aggregation platforms and can measure flexibility across different time scales by adjusting the control interval of the day-ahead dispatch optimization model. Finally, the framework is validated through case studies, revealing the impact characteristics of system type and capacity on flexibility, and analyzing the sensitivity of flexibility to various uncertainty parameters.

Game Theory Applications in Smart Grid Energy Management

Diversion hypothesis has ended up a valuable way to portray and make strides complicated connections in numerous zones, such as savvy framework vitality administration. When it comes to savvy lattice operations, the truth that vitality generation, exchange, and utilize are all energetic and separated makes huge issues that are difficult to fathom with standard controlled strategies. This outline looks at how amusement hypothesis can be utilized to assist individuals make better decisions and make the leading utilize of assets in savvy framework settings. The most objective is to figure out how game-theoretic models can offer assistance with productive vitality management by looking at how diverse parties, like clients, providers, and lattice administrators, will act in numerous circumstances. Regularly, these bunches have objectives that are at chances with each other, like minimizing costs, making as much cash as possible, and ensuring the environment. Amusement hypothesis lets us see at these trades as in case they were arranged recreations, with each individual attempting to get the foremost out of the diversion by altering their claim choices and the activities of others. Key thoughts from diversion hypothesis, like Nash harmony, agreeable and non-cooperative diversions, and component plan, are utilized to come up with keen framework operations procedures that are both reasonable and proficient. Nash balance could be a key thought for a arrangement where no individual can pick up by changing their arrange on their possess. This provides security in independent decision-making. In addition, the abstract talks about a number of case studies and uses where game theory has been used successfully in smart grids. Some of these are demand response programs, energy trade markets, and making the best use of integrating green energy. By making these events into games, parties can plan for and deal with problems like grid instability, price changes, and traffic jams. The outline also talks about problems that are still being researched and where the field might go in the future. These include making game-theoretic models work on large-scale smart grid networks, using real-time data analytics to help people make better decisions, and using machine learning to make predictions more accurate.

Aberrant activation of wound healing programs within the metastatic niche facilitates lung colonization by osteosarcoma cells.

Lung metastasis is responsible for nearly all deaths caused by osteosarcoma, the most common pediatric bone tumor. How malignant bone cells coerce the lung microenvironment to support metastatic growth is unclear. The purpose of this study is to identify metastasis-specific therapeutic vulnerabilities by delineating the cellular and molecular mechanisms underlying osteosarcoma lung metastatic niche formation. Using single-cell transcriptomics (scRNA-seq), we characterized genome- and tissue-wide molecular changes induced within lung tissues by disseminated osteosarcoma cells in both immunocompetent murine models of metastasis and patient samples. We confirmed transcriptomic findings at the protein level and determined spatial relationships with multi-parameter immunofluorescence and spatial transcriptomics. Based on these findings, we evaluated the ability of nintedanib, a kinase inhibitor used to treat patients with pulmonary fibrosis, to impair metastasis progression in both immunocompetent murine osteosarcoma and immunodeficient human xenograft models. Single-nucleus and spatial transcriptomics was used to perform molecular pharmacodynamic studies that define the effects of nintedanib on tumor and non-tumor cells within the metastatic microenvironment. Osteosarcoma cells induced acute alveolar epithelial injury upon lung dissemination. scRNA-seq demonstrated that the surrounding lung stroma adopts a chronic, non-resolving wound-healing phenotype similar to that seen in other models of lung injury. Accordingly, metastasis-associated lung demonstrated marked fibrosis, likely due to the accumulation of pathogenic, pro-fibrotic, partially differentiated epithelial intermediates and macrophages. Our data demonstrated that nintedanib prevented metastatic progression in multiple murine and human xenograft models by inhibiting osteosarcoma-induced fibrosis. Fibrosis represents a targetable vulnerability to block the progression of osteosarcoma lung metastasis. Our data support a model wherein interactions between osteosarcoma cells and epithelial cells create a pro-metastatic niche by inducing tumor deposition of extracellular matrix proteins such as fibronectin that is disrupted by the anti-fibrotic TKI nintedanib. Our data shed light on the non-cell autonomous effects of TKIs on metastasis and provide a roadmap for using single-cell and spatial transcriptomics to define the mechanism of action of TKI on metastases in animal models.

Impact of a University-Led COVID-19 Case Investigation and Contact Tracing Program.

The coronavirus disease 2019 (COVID-19) pandemic devastated societies and economies worldwide. Given the major disruptions to higher education, reflection on university responses to the COVID-19 pandemic may provide insights for future outbreaks. Here, we describe the epidemiology of COVID-19 on the Emory University campus during the 2020-2021 academic year and provide an evaluation of the performance of a university-led program with the purpose of describing the effectiveness of efforts to augment the public health authority's case investigation and contact tracing efforts during a public health emergency. Evaluation of a case investigation and contact tracing program regarding operations, timeliness, and performance. We analyzed quality metrics to determine the proportion of cases and contacts interviewed and the time to completion of each step from case diagnosis to testing of contacts. During the 2020-2021 academic year, 1267 COVID-19 cases among Emory students, faculty, and staff were confirmed by polymerase chain reaction, with 1132 reported close contacts. Among cases, the median test turnaround time was 1 day (interquartile range: 1, 2). Among both cases and close contacts, 98% were successfully interviewed. The team called a majority of cases on the same day as their test result was reported to the program (87%; n =1052). Almost all (98%; n =1247) cases completed isolation or were advised to isolate during the review period. Close to half (46%; n =513) of contacts interviewed began quarantine before their interview. Among close contacts interviewed, 13% (n=145) subsequently converted to an index case. The impact and performance of Emory's program may provide useful and actionable data for future university-led infectious disease outbreak response programs. The program structure, performance metrics, and information collected via interviews provide practical implications and an organized structure to guide other programs during future outbreaks.

Eco-environmental regret-aware optimization of networked multi-energy microgrids with fully carbon elimination and electric vehicles' promotion

With the escalating concern of global warming propelled by the rise in Earth's temperature, the need for effective CO2 management has become crucial. This paper presents an innovative CO2 elimination approach, wherein a multiple integrated system of energies (MISEs) incorporating sustainable resources, including renewable resources (RENs), plug-in electric vehicles (PEVs), and demand response programs, is optimized. The proposed carbon elimination framework begins by modeling the onsite carbon capturing and recycling within each MISE. To effectively utilize the onsite carbon recycling facilities and achieve carbon neutrality, the proposed model also incorporates carbon transfer capability between MISEs, thereby enhancing the efficiency of overall carbon recycling. Furthermore, a stochastic p-robust optimization technique is proposed to effectively manage uncertainties by combining the advantages of stochastic programming and robust optimization. This uncertainty modeling approach promotes greater utilization of sustainable resources like PEVs and RENs due to their lower operational regrets from economic and environmental perspectives. Based on the simulation results, implementing the p-robust-based regret assessment technique led to the total operation cost increasing by only 2.75 %, while achieving a significant 44.5 % reduction in maximum relative regret. These results underscore the effectiveness of the proposed framework in enhancing both the economic and environmental performance of MISEs.