Flexible Utilization Research Articles

Distribution network transparency: paving the way for effective flexibility utilization in the future

Distribution network transparency: paving the way for effective flexibility utilization in the future

Enhancing extraction efficiency of carpaine in Carica papaya L. leaves: coupling acid-base extraction with surfactant-assisted micro-flotation.

Carpaine, a major alkaloid in papaya leaves, has considerable cardiovascular benefits alongside its notable effects on muscle relaxation when utilized in medicine. In this study, the coupling of acid-base extraction and flotation was developed to completely remove the use of toxic solvents. This method entails the extraction of carpaine from Carica papaya L. leaves using hot water extraction alongside ultrasound-assisted extraction followed by the condensation of the species using surfactant-assisted flotation. The acid-base extraction was applied to alter the solubility of carpaine as desired at different stages of the process. The results showed that the carpaine extraction yield using all the treatments in conjunction was significantly higher compared to the control samples in which the acid-base extraction or flotation was not applied. The TLC and GC-FID results suggested that the bubbles introduced during the flotation were highly specific toward their interactions with carpaine in its hydrophobic complex form. The quantity of carpaine extracted using our method, in comparison to the amount of carpaine obtained using a different method from a previous study that utilized ethanolic extraction, exhibited a 2.32-fold greater extraction yield. This work demonstrates the importance of flexible utilization of both surface and bulk chemistry in achieving an improved solution for a technical problem.

Genetically engineered bacterial biofilm materials enhances portable whole cell sensing

In recent years, whole-cell biosensors (WCBs) have emerged as a potent approach for environmental monitoring and on-site analyte detection. These biosensors harness the biological apparatus of microorganisms to identify specific analytes, offering advantages in sensitivity, specificity, and real-time monitoring capabilities. A critical hurdle in biosensor development lies in ensuring the robust attachment of cells to surfaces, a crucial step for practical utility. In this study, we present a comprehensive approach to tackle this challenge via engineering Escherichia coli cells for immobilization on paper through the Curli biofilm pathway. Furthermore, incorporating a cellulose-binding peptide domain to the CsgA biofilm protein enhances cell adhesion to paper surfaces, consequently boosting biosensor efficacy. To demonstrate the versatility of this platform, we developed a WCB for copper, optimized to exhibit a discernible response, even with the naked eye. To confirm its suitability for practical field use, we characterized our copper sensor under various environmental conditions—temperature, salinity, and pH—to mimic real-world scenarios. The biosensor-equipped paper discs can be freeze-dried for deployment in on-site applications, providing a practical method for long-term storage without loss of sensitivity paper discs demonstrate sustained functionality and viability even after months of storage with 5 μM limit of detection for copper with visible-to-naked-eye signal levels. Biofilm-mediated surface attachment and analyte sensing can be independently engineered, allowing for flexible utilization of this platform as required. With the implementation of copper sensing as a proof-of-concept study, we underscore the potential of WCBs as a promising avenue for the on-site detection of a multitude of analytes.

Data-free knowledge distillation via generator-free data generation for Non-IID federated learning

Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, which leads to local model drift and performance degradation. Because of the advantage of knowledge distillation, it has been explored in some recent work to refine global models. However, these approaches rely on a proxy dataset or a data generator. First, in many FL scenarios, proxy dataset do not necessarily exist on the server. Second, the quality of data generated by the generator is unstable and the generator depends on the computing resources of the server. In this work, we propose a novel data-Free knowledge distillation approach via generator-Free Data Generation for Non-IID FL, dubbed as FedF2DG. Specifically, FedF2DG requires only local models to generate pseudo datasets for each client, and can generate hard samples by adding an additional regularization term that exploit disagreements between local model and global model. Meanwhile, FedF2DG enables flexible utilization of computational resources by generating pseudo dataset locally or on the server. And to address the label distribution shift in Non-IID FL, we propose a Data Generation Principle that can adaptively control the label distribution and number of pseudo dataset based on client current state, and this allows for the extraction of more client knowledge. Then knowledge distillation is performed to transfer the knowledge in local models to the global model. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art FL methods and can serve as plugin for existing Federated Learning methds such as FedAvg, FedProx, etc, and improve their performance.

RETRACTED: Enhancing urban energy storage and optimization through advanced EV charging and vehicle-to-grid integration in a renewable-based zero-energy city

The variable natures of renewable power sources and the additional demand from EV (electric vehicle)-charging present unique challenges to electrical grids. This paper explores the potential for optimized energy synchronization at an urban scale in a city striving for net-zero energy status, utilizing wind and solar sources. It examines three scenarios for EV charging: smart, opportunistic, and V2G (vehicle-to-grid), with a specific city as the case study. The research aims to decline the discrepancy among energy supply and demand through intelligent charging and V2G strategies, formulated within the framework of quadratic programming. The analysis indicates that V2G integration, in conjunction with a balanced mix of wind and solar power, significantly enhances load matching in a zero-energy urban context. The transition from basic opportunistic charging to smart mode and then to V2G progressively improves the energy alignment. Within the ideal NZES (net-zero energy system), load matching-efficiency enhances from 67.6 % with opportunistic plan to 72.5 % with smart ones, and significantly to 83.4 % with V2G integration. Further, the full utilization of EV flexibility, particularly through an integrating a battery for EV (2400 MWh) in V2G framework as an alternating ESS (energy storage system), was equivalent to having a 1412 MWh stationary ESS in improving load-matching at an urban scale. The study also highlights the comparative effectiveness of EV batteries in a V2G configuration versus stationary ESSs in optimizing urban-scale energy-matching. These insights demonstrate the substantial potential of EVs to introduce flexibility and resilience into urban energy systems, facilitating the transition to sustainable urban living.

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience

BackgroundCurrent processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking.ObjectiveDemonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs.MethodsThe tiered framework, developed in collaboration with a Project Advisory Group and applied to breast, colorectal, and melanoma cancers, provides business rules – procedures for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour–node–metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports.FindingsThe tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. Tier 1 is suitable for all purposes, including clinical and epidemiological applications. Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired.ConclusionsThe tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

Aggregation of building predictive energy flexibility in smart microgrid

Numerous demand-side resources possess significant potential to offer substantial flexible reserves and ancillary services, thereby enhancing the economic and technical performance of power systems within the demand response domain. A novel approach to aggregating the energy flexibility of buildings based on the virtual battery model is proposed in the smart microgrid, employing predictive control techniques while ensuring consumer privacy. The individual approximation process is implemented to extract the flexible capacity of local resources while protecting information privacy. Subsequently, the aggregator collects the flexibility information to develop an optimization strategy for maximum flexibility aggregation at the upper level, enabling the broader exploitation of global flexibility. The feasibility of power disaggregation is guaranteed according to the aggregation procedure. Furthermore, the comprehensive framework accommodates temporal preferences for flexibility requests from the aggregator. Numerical simulations are conducted to perform a comparative analysis with existing state-of-the-art methods. The results demonstrate that the proposed approach exhibits improved adaptability in characterizing the aggregate flexibility from buildings, effectively managing the heterogeneity of load characteristics and the variability in the collected flexibility information. Microgrids can realize cost savings and achieve dynamic flexibility utilization. This methodology can be extended and applied to diverse types of flexible resources.

A new flexible and partially monotonic discrete choice model

The poor predictability and the misspecification arising from hand-crafted utility functions are common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve predictability through flexible utility specifications, but they do not address the misspecification issue and provide untrustworthy behavioral interpretations (e.g., biased willingness to pay estimates). Improving interpretability at the minimum loss of flexibility/predictability is the main challenge in the data-driven DCM. To this end, this study proposes a flexible and partially monotonic DCM by specifying the systematic utility using the Lattice networks (i.e., DCM-LN). DCM-LN ensures the monotonicity of the utility function relative to the selected attributes while learning attribute-specific non-linear effects through piecewise linear functions and interaction effects using multilinear interpolations in a data-driven manner. Partial monotonicity could be viewed as domain-knowledge-based regularization to prevent overfitting, consequently avoiding incorrect signs of the attribute effects. The light architecture and an automated process to write monotonicity constraints make DCM-LN scalable and translatable to practice. The proposed DCM-LN is benchmarked against deep neural network-based DCM (i.e., DCM-DNN) and a DCM with a hand-crafted utility in a simulation study. While DCM-DNN marginally outperforms DCM-LN in predictability, DCM-LN highly outperforms all considered models in interpretability, i.e., recovering willingness to pay at individual and population levels. The empirical study verifies the balanced interpretability and predictability of DCM-LN. With superior interpretability and high predictability, DCM-LN lays out new pathways to harmonize the theory-driven and data-driven paradigms.

RUBAC: Proposed Access Control for Flexible Utility–Privacy Model in Healthcare

RUBAC: Proposed Access Control for Flexible Utility–Privacy Model in Healthcare

Dynamic three-way multi-criteria decision making with basic uncertain linguistic information: A case study in product ranking

The concept of basic uncertain linguistic information (BULI) is proposed as an extension of basic uncertain information to enhance the measurement of data quality in decision-making processes by allowing for more flexible utilization of degrees of certainty. This paper seeks to investigate a new model of dynamic three-way decisions and develop an integrated framework for BULI-based multi-criteria decision-making using decision-theoretic rough sets (DTRSs). Firstly, we present the BULI-input dynamic ELECTRE-I method, which is capable of establishing an outranking relation. Then, a novel model of BULI decision-theoretic rough sets (BULIDTRSs) is proposed. The BULI similarity measure is employed to accurately estimate the conditional probability. Following this, we propose four potential approaches for combining loss functions by introducing the BULI ordered weighted averaging operator, which incorporates the consideration of decision risk. The derivation of the three-way decision rules is based on the comparison of BULI and the application of operational laws. In addition, the resolution of various issues such as the determination of criteria weights, identification of ideal solutions, and conversion of ratings to BULI is achieved through the development of a multi-criteria keyword frequency statistical method and a user credibility model. Subsequently, the algorithm for the dynamic three-way multi-criteria decision-making method, aimed at resolving product ranking issues, is presented. Eventually, we elaborate on the utilization of the proposed methodology through the illustration of a case study involving the ranking of passenger cars.

Industrial metaverse – company perspectives

The utilisation of the industrial metaverse is a topical discussion in the research community as well as within industrial companies. This study aimed to identify industry expectations towards the industrial metaverse. A questionnaire was sent to 18 companies to collect their views on potential industrial metaverse use cases and possible challenges that may occur. The companies identified use cases under eight thematic groups: remote operation and maintenance, sales and marketing, training, guidance and support, knowledge sharing and collaboration, novel services and business opportunities, and flexible utilization and workforce availability. Companies pointed out that challenges in using the metaverse may originate from technology acceptance, technology maturity, data governance, regulations and standards, cyber security and safety and cost. These findings provide company perspective on the industrial metaverse discussion and can be used when researching the topic and developing metaverse solutions further.

Optimal dispatch approach for rural multi-energy supply systems considering virtual energy storage

In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China, this study proposes an optimal dispatch approach for a rural multi-energy supply system (RMESS) considering virtual energy storage (VES). First, to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes, this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint. Subsequently, an RMESS model encompassing these two types of VES was formulated. This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations. Based on the above models, a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation. Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.

Distribution robust water-based demand side management in power transmission networks

The increasing share of renewable energy sources in the energy mix is posing new challenges for operating the power system. As a result, system operators must use the available flexibility more effectively to accommodate the surge in renewable energy. There are different ways to source flexibility including demand–response measures and sector integration. This paper investigates the utilization of flexibility from an integrated water and power network. A novel, augmented, distribution robust chance-constrained market clearing problem is proposed, assuming a centralized and fully competitive market set-up. Power generators and water pumps are modelled as flexible assets to procure flexibility from the integrated water and power network. Affine response policies and availability costs are defined to regulate the flexibility dispatch mechanism. The resulting coordination mechanism is nonlinear and non-convex. To make the problem feasible, different approximation and relaxation techniques are applied. A novel, hybrid, convex hull-based relaxation technique is proposed to convexify the friction head losses and pump energy consumption. The resulting problem is a second-order cone program which enables the power system operator to properly estimate and utilize the demand-side flexibility available in the water network. The results show that the electricity market benefits from grid flexibility obtained through the coordinated operation of controllable power generators and water pump loads. Under studied conditions, it was found that water pumps, when functioning as demand–response providers, can effectively compensate for power system imbalances, covering up to 25% of the total wind power mismatch. The sensitivity analysis emphasized the substantial impact of varying availability costs on the responses of flexible generators and water pumps. The error analysis identified the convex approximation of friction head losses as the primary source of inaccuracy in our model.

Enhanced flexibility utilization and coordinated dispatch method of energy‐intensive enterprises in power systems under time of use prices

AbstractPower system can utilize the flexibility provided by energy‐intensive enterprises (EIEs) for large‐scale renewable energy penetration. However, system operators cannot obtain the manufacture scheme of EIEs due to business privacy protection. Furthermore, highly regulated electricity prices further restrict the flexible utilization of EIEs. To overcome these issues, a coordinated dispatch framework between power system and EIEs under the intrinsic time of use (TOU) prices is designed in this paper, which requires only several boundary information for interaction. Firstly, the mathematical formulation of EIEs is given based on the inherent features analysis of typical production equipment and the optimal operation scheme of EIEs can be obtained. Then, considering uncertainties of both source and loads, the purchasing energy of EIEs from the network can be adjusted through the designed framework to track renewable energy dynamics and the clear equivalence class method is used to solve fuzzy chance constrained programming. Next, the improved Asymmetric Nash Bargaining (ANB) method is adopted to carry out the distribution of benefits. Finally, a modified IEEE‐30 test system is given to verify the feasibility of the proposed method, which can effectively improve the regional consumption level of renewable energy and ensure interests of all sides.

Data-driven aggregation of thermal dynamics within building virtual power plants

Virtual power plants (VPPs) possess the capability to aggregate flexible resources to provide grid services in the distributed network operation. The potential for flexibility utilization in building loads, particularly thermal-controlled ones, has drawn attention when aggregated into VPPs. This work proposes a data-driven approach to overcome the challenges in modeling and aggregation of thermal dynamics within clustered buildings. Specifically, we utilize piecewise linear equations to represent the impact of various input features on thermal dynamics (i.e., zone temperature variation), which is more precise in extracting the nonlinear relationship through adaptive model order adjustment and breakpoint determination. Additionally, we transform the identified thermal dynamic process into virtual storage models and aggregate them into the optimization-based system dispatch process. The proposed two-stage aggregation approach facilitates the determination of accurate dispatch decisions by iteratively updating the power bounds of flexibility regions. It ensures disaggregation feasibility simultaneously to generate individual power signals for each building. Case simulations verify the accuracy of the proposed method on modeling and aggregation of thermal dynamics within clustered buildings.

O.5.3-8 Prospects and challenges of real-world labs in physical activity promotion

Abstract Purpose Participatory health promotion uses an array of different concepts to describe project processes and effects, including empowerment, capacity building, and capabilities. In practice, however, these concepts may hamper project scalability, compatibility with state-of-the-art health concepts, and efforts to counteract social inequities. We introduce the concept of real-world labs as a potential solution for current problems in participatory health promotion and investigate its practical applicability using the case example of an existing physical activity promotion project. Methods We analyze current problems in participatory health promotion that emanate from the utilization of certain popular theoretical concepts. We then introduce real-world labs as a potential solution. Using the PArC-AVE-Project (Physical Activity-related Health Competence in Apprenticeship and Vocational Education) as a case example, we show how the approach can be used retrospectively to address the aforementioned issues. Results Real-world labs are a type of transdisciplinary approach at the intersection of research and society. They help create an environment for flexibly testing innovative solutions under changing conditions using small-scale “real-world experiments” and feedback loops. However, the approach is so far not commonly used in participatory health promotion. The example of PArC-AVE illustrates that existing participatory projects can potentially be developed towards becoming real-world labs. Relevant perspectives for this include (a) the targeted integration of different actors into the participation process, (b) more variable approaches to scaling-up interventions, (c) a more flexible utilization of different participation methods, and (d) a better recognition of concepts such as planetary health. Conclusions The real-world lab approach has the potential to increase the range of options available to participatory health and physical activity promotion. However, only further practical testing will show whether it is indeed effective in overcoming social inequities and ameliorating the high degree of context-dependence of many participatory projects.

Comparative Study of Flexibility Between Volleyball and Basketball Players of Manipur

In the present modernized society, the importance of sport is well understood and given a very high response. Flexibility is the range of motion around a joint; high flexibility helps lower the incidence of injuries in all stages of life. The statement of the problem was "Comparative study of flexibility between volleyball and basketball players of Manipur". The purpose of the study was to compare the physical fitness of volleyball and basketball players in Manipur. The researcher selected 60 players from Manipur (30 volleyball players and 30 basketball players) and 30 players each from two games selected randomly for this study. The age range is from 20 to 25 years of age. The variable for the study was flexibility (Upward-Backward movement of the arm test). The statistical analysis of the data revealed that volleyball and basketball players had no significant difference in flexibility. The insignificant result was found due to the similar nature of the game and the utility of flexibility in the upward-Backward movement of the arm test for the players of these two games. Further, the hypothesis was accepted.

The Design of a Multilayer and Planar Metamaterial with the Multi-Functions of a High-Absorptivity and Ultra-Broadband Absorber and a Narrowband Sensor

The aim of this study is to enhance the design of a multilayer and planar metamaterial that serves multiple functions, including high efficiency and ultra-broadband absorption, as well as acting as a narrowband sensor. The primary feature of this absorber is its fully planar structure, which enables the flexible utilization of two distinct absorption functionalities: ultra-broadband absorption, achieved through the application of the MgF2 layer, and narrowband absorption, achieved through the implementation of the Cu layer. To conduct the simulation analyses, COMSOL Multiphysics® simulation software (version 6.0) was employed. The initial innovation lies in the fact that upon irradiation of normal incident light on MgF2 side, the material exhibited an exceptional average absorptivity of 97.0% across an ultra-broadband range spanning from 410 to approximately 2300 nm. Moreover, when the same normal incident light was radiated on the Cu side, the material demonstrated a distinct peak at a precise wavelength of 480 nm, accompanied by an absorptivity of 95.66%. Notably, these results were obtained with the added benefit of angle insensitivity. Such characteristics arise due to the multiple excitation of diverse resonant modes facilitated by the localized surface plasmon resonance and metal–insulator–metal Fabry–Perot cavity. The second innovation focuses on demonstrating that MgF2 can serve as an effective anti-reflection layer, enhancing the absorptivity of the ultra-broadband absorber. The third innovation aims to establish that Cu is the optimal metal choice. Even substituting Cu with other metals did not diminish the absorptivity of the ultra-broadband absorber; it should be noted that alternative metals might negatively impact the absorptivity of the narrowband absorber.

Understanding barriers to utilising flexibility in operation and planning of the electricity distribution system – Classification frameworks with applications to Norway

There is an increasing need for flexibility in power systems worldwide, giving rise to European policy documents outlining how Distribution System Operators (DSOs) should procure and include flexibility in the planning and operation of their electricity distribution grids. This implies a remarkable change from today's situation where DSOs rely on investments in grid assets that they have full control of, to a new regime where DSOs would rely on flexibility provided by third parties. The objective of this work is to gain a better understanding of the barriers to and opportunities for wide-spread utilisation of flexibility in planning and operation of electricity distribution grids. Building upon a previous literature review and taxonomy for classifying and characterising power system flexibility, we propose frameworks for i) classifying flexibility resources and flexibility enablers in grid operation and planning, and ii) classifying and understanding barriers to utilising them in terms of a flexibility value chain. These theoretical frameworks are tested against empirical data collected in semi-structured in-depth interviews with a representative selection of Norwegian DSOs. Mapping the findings to the frameworks gives a systematic overview of the flexibility situation in Norway and presents both country-specific and general insights about barriers to utilisation of flexibility.

Building Energy Flexibility Assessment in Mediterranean Climatic Conditions: The Case of a Greek Office Building

The EU energy and climate policy has set quantitative goals for decarbonization based on the energy efficiency and the evolution of energy systems. The utilization of demand side flexibility can help towards this direction and achieve the target of higher levels of penetration in regard to intermittent renewable energy production and carbon emission reduction. This paper presents a simulation-based assessment of thermal flexibility in a typical office building in Greece, which is a representative Mediterranean country. The use of variable speed heat pumps coupled with hydronic terminal units was evaluated. The research focused mainly on the evaluation of energy flexibility offered by energy stored in the form of thermal energy by utilizing the building’s thermal mass. The demand response potential under hourly CO2eq intensity and energy prices was investigated. The flexibility potential was evaluated under different demand response strategies, and the effect of demand response on energy consumption, operational costs, CO2eq emissions and thermal comfort was analyzed and discussed. The results showed that both control strategies based on both the CO2eqintensity signal and spot price signal have, in some cases, the potential for cost and emission savings, and in other cases, the potential to depreciate in terms of emissions and cost the increase of energy consumption due to load shifting.