The majority of electric vehicles (EVs) are charged domestically overnight, when the precise timing of power allocation is not important to the user, thus providing a source of flexibility that can be leveraged by charging control algorithms. In this paper, we show that by keeping the EV connected to a standard domestic smart plug every night, it is possible to achieve an approximate 37% annual reduction in carbon intensity compared to uncontrolled charging (based on 2022 UK National Grid data) without compromising the EV owners' charging demand. For this purpose, we use Model Predictive Control techniques to schedule power delivery with the objective of minimizing grid-average CO2 emissions over forecast windows of multiple sessions (up to seven days ahead). We find that flexibility on the part of EV owners-both their willingness to keep the EV connected at night, and to indicate a precautionary (i.e., upper bound) estimate of their required state-of-charge in the morning-allows for significant emissions reduction and accelerated decarbonization of the mobility sector.

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios.

With the rapid acceleration of economic globalization, multinational enterprises increasingly operate across highly complex and culturally diverse environments, making effective cross-cultural management a decisive factor influencing overall organizational performance, competitive resilience, and long-term sustainability. Inherent cultural differences fundamentally shape employees' core values, interpersonal communication patterns, leadership expectations, and intrinsic motivational preferences. Consequently, these dynamics create multifaceted challenges for multinational enterprises seeking to seamlessly integrate globally dispersed workforces while maintaining operational coherence. This paper systematically explores how multinational enterprises can achieve superior cross-cultural management through the implementation of adaptive and culturally sensitive human resource management strategies. Particular emphasis is placed on the critical domains of talent recruitment, cross-cultural training programs, tailored motivation mechanisms, and comprehensive employee development frameworks. Drawing on established theoretical paradigms of cross-cultural management and international human resource management, this study adopts a rigorous qualitative, theory-informed case analysis approach, utilizing the Coca-Cola Company as a primary illustrative example. The in-depth analysis demonstrates that culturally responsive yet strategically integrated human resource practices empower multinational enterprises to effectively transform cultural diversity from a potential operational friction point into a robust source of organizational flexibility, heightened employee engagement, and sustained competitive advantage. Ultimately, this paper significantly contributes to the broader cross-cultural management literature by explicitly highlighting the strategic, indispensable role of sophisticated human resource systems in successfully balancing the dual imperatives of global integration and local responsiveness within modern multinational enterprises.

Birds control their flight by morphing their wing and tail configurations as they shift between steady glides and agile manoeuvres. Cadaveric studies have shown that birds have the capacity to adopt both stable and unstable configurations, but it remains unknown how birds exploit this ability in flight. Here, we fill this gap by studying the progression of wing and tail configurations of a free-gliding Harris's hawk (Parabuteo unicinctus) during a wing-tucking manoeuvre. Wind tunnel experiments on three-dimensional-printed models revealed that tucked configurations were statically stable, while spread configurations displayed a nonlinear relationship between pitching moment and lift. This nonlinearity allows configurations to be either stable or unstable depending on the lift state, affording a previously under-explored source of flight performance flexibility. Furthermore, we found that the hawk transitioned from an unstable, spread configuration to a stable, tucked configuration as it traversed the gap, shifting the effective static margin from -25% to 19% of the reference chord. This notable stability shift suggests that adaptive flight control allows transition between flight modes and offers insight into flight conditions where shifting stability states may be relevant. This outcome will advance novel bio-inspired, fixed-wing uncrewed aerial vehicle designs capable of rapid transitions.

This study examines how to integrate flexibility into long-term energy system models by enhancing both temporal and spatial granularity. Traditional TIMES-based models, typically using 4–12 time slices and aggregated regional structures, cannot adequately capture renewable variability and grid constraints. To address this, we developed a framework incorporating substation-level transmission capacity and 200 time slices, including 184 hourly slices for one representative summer week. A two-stage approach was applied: first, a conventional 16-slice model simulated capacity expansion to 2045; second, a 200-slice model assessed short-term dynamics in 2050. Results demonstrate that hourly granularity reproduces variable renewable generation and storage behavior, especially under stringent CO₂ reduction (90%). While this study focused on pumped storage and batteries, broader flexibility sources such as grid expansion and electrolysis are essential future extensions. The findings indicate that integrating higher-resolution temporal and spatial structures into energy system models improves representation of flexibility needs and strengthens the robustness of decarbonization pathway assessments.

Asset redeployability reflects firm ability to reallocate or sell capital assets in secondary markets. Using data from publicly listed firms, we show that asset redeployability is negatively related to managerial ability, suggesting capable managers maintain lower redeployability levels. We reveal labor efficiency as one channel through which managerial ability influences asset redeployability. Managerial ability’s negative effect on asset redeployability is stronger under low political risk, consistent with it serving as a costly form of insurance against uncertainty. Our findings imply that while asset redeployability is commonly viewed as a source of corporate flexibility, it may also reflect inefficient asset allocation.

An opaque product refers to a subset of products that only differ in some secondary attribute (such as color or style). After a customer purchases an opaque product, one of the products in the subset is then allocated to the customer. Selling opaque products has become popular on e-commerce platforms, because it is a source of flexibility that reduces demand variability and hence inventory cost. In this paper, we seek to understand how the design of opaque product configurations and the fraction of customers choosing opaque products impact the value of flexibility arising from opaque selling. The key finding from our study is the following insight: A modest opaque selling strategy where (i) the opaque options contain small subsets and (ii) only a small fraction of customers buy opaque products achieves substantial cost savings (compared with nonopaque traditional selling). Moreover, the cost savings of this modest opaque strategy is on the same order as a fully flexible scenario where all customers buy an opaque product. Our analysis relies on exploring a novel connection of multiproduct inventory management to the balls-into-bins framework along with a simple balancing policy for opaque selling. As a byproduct of our analysis, we show that the simple balancing policy is asymptotically optimal. For extensions outside of our theoretical analysis, we conduct extensive numerical experiments to show that our core insights continue to hold. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 1944428]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2019.02983 .

ABSTRACT The dynamic nature of research prioritization calls for flexibility in conservation research, yet there are many barriers to research flexibility. This article proposes that citizen science offers untapped capacity for flexibility in conservation research. Drawing on diverse examples from the literature, it explores eight common modes of research flexibility in citizen science and five underlying attributes of citizen science that generate a strong capacity for flexibility. Because these attributes are not typically viewed through a lens of flexibility; however, the capacity they confer may often be unrecognized and underdeveloped. By shining light on some common modes and sources of research flexibility in citizen science, this article establishes a shared frame of reference for researchers and practitioners in conservation and citizen science to explore specific ways that citizen science may address emerging research needs. The article closes with an overview of key considerations for actors at three levels—conservation researchers and practitioners, citizen science practitioners, and relevant professional communities—to further cultivate flexibility in conservation citizen science and to assess opportunities to leverage it for greater impact on conservation science and practice.

The sources of flexibility hosted in active distribution systems (ADSs) can contribute to transmission grid operation security through coordination mechanisms between the operators of distribution and transmission systems. Piece-wise linear cost curves of active power aggregated flexibility of an ADS at the interface with the transmission system can be calculated for one period. However, the time coupling of energy devices such as energy storage systems and flexible demand challenges drastically conceptually and computationally the calculation of cost curves in a multi-period timeframe. This paper first unveils the barriers in computing such cost curves. Then, the paper develops for the first time a methodology, with two versions, for calculating piece-wise linear cost curves of active power/energy aggregated flexibility of an ADS. The core of the methodology relies on combining tailored multi-period AC optimal power flow calculations under various objectives and sets of constraints. The value and computation efficiency of the methodology are demonstrated using a 34-bus benchmark ADS under 24 periods and a 191-bus ADS under up to 96 periods. Finally, the paper provides new insights and research directions to overcome some of the identified barriers.

Intermittent renewable electricity generation increases flexibility needs in energy systems. Demand response (DR) programs can effectively address these needs. However, their success depends on consumer willingness to participate. This study quantifies load-shifting potentials from air conditioners, water heaters, electric vehicles, and washing machines based on survey data from 1,402 Japanese households. Logistic regression analyses link willingness to engage in DR programs with socio-demographic and dwelling characteristics, yielding specific participation rates by appliance and household type. These participation rates are integrated with appliance-specific ownership data and annual energy consumption calculations to estimate flexibility potentials for Japan’s Kanto region. The study employs three distinct quantification approaches: (1) a bottom-up activity-based model, (2) a top-down average-based estimate, and (3) a hybrid method using synthetic population data. The results indicate that water heaters consistently exhibit the highest load-shifting potential across all scenarios and time frames, both per household and regionally. By 2050, electric vehicles emerge as an equally important source of flexibility due to increased adoption rates and improvements in water heater efficiency. While the top-down estimation method effectively captures overall population-level flexibility potentials, the hybrid approach is critical for accurately representing household-level variations. These findings facilitate the development of targeted DR programs and contribute to more realistic flexibility modeling within energy systems analysis. Future research should further explore behavioral uncertainties and address additional barriers to the implementation of residential demand response. • Compares methods to estimate residential appliance-level load-shifting potentials. • Novel hybrid approach offers near bottom-up performance from limited, public data. • User interest cuts appliance-level demand response potential by more than half. • Building stock explains household heterogeneity in air conditioner potentials. • Household size shapes variation in washing machine and water heater potentials.

The article proves the key role of human capital and states that it is man as a carrier of values, knowledge, experience and potential that is the basis, partner of all management, educational, technological and organizational processes. It is stated that the industry 5.0 influences the change in the requirements for the qualification of employees, new models of motivation are formed, the role of emotional intelligence, adaptability and ability to interdisciplinary interaction is increasing. The role of trade companies in the global supply chains has been proven and that the efficiency of their functioning is increasingly dependent on the quality, adaptability and innovative potential of human capital. In conditions of economic instability, hostilities, violation of supply chains, digital transformations, human capital itself is a key source of organizational flexibility, adaptability and stability of trade enterprises. The principles of formation of human capital at trade enterprises, which include strategic, investment, innovation, continuity, adaptability, flexibility, motivational synergy, digitalization are systematized. These principles are the basis for developing an effective mechanism for managing human capital of trade enterprises, which allows you to develop, motivate and maintain talented employees, ensuring the achievement of strategic goals. Modern tools for the formation of human capital at trade enterprises in industry 5.0 are generalized and offered. These include personalized learning and development, digital management mechanisms, development of emotional intelligence and Soft Skills, integration with sustainable development strategies. They are multifunctional mechanisms, the introduction of which contributes to the successful strategic development of trade enterprises with the involvement of highly skilled personnel.

Background The work evaluates the maturity of grid-scale flexibility services, essential for Europe's transition to a decarbonized economy. Conducted under the Horizon-Europe SINNOGENES project, it dives into energy storage and utility-scale flexibility technologies, examines the current national regulatory frameworks, and gathers stakeholders’ perspectives, making the point on services’ current status and future trends. Methods The methodology followed in this work comprises three phases. Firstly, the most significant grid-scale energy storage and flexibility technologies, including Battery Energy Storage Systems (BESS) and Vehicle-to-Grid (V2G) systems, are reviewed. Secondly, the regulatory framework for flexibility services at the EU transmission systems level are assessed, analysing key EU directives and national regulations from Spain, Portugal, Germany, Cyprus, and Switzerland. Thirdly, stakeholders’ perspectives are gathered through a survey conducted on forty-four stakeholders in the European energy sector and bilateral interviews. Results The study identifies three main findings. Firstly, lithium-ion batteries and pumped hydro storage stand out as the most mature grid-scale flexibility technologies (TRL 8–9), although a complementary portfolio is required to cover the full spectrum of system needs. Secondly, regulatory analysis reveals persistent barriers, including fragmented national implementation of EU directives, lengthy permitting procedures, and the lack of long-term price signals, all of which hinder investment and market integration. Thirdly, stakeholder engagement confirms a strong and growing demand for flexibility (77% of respondents already use or need it), but also highlights perceived cost–benefit imbalances, IT security concerns, and a preference for decentralised ownership models. Electrochemical storage and demand response emerge as the most relevant flexibility sources. Conclusions Overall, achieving grid-scale flexibility in Europe cannot rely on a single dominant solution but requires a complementary mix of technologies. Near-term deployment should prioritise commercially mature solutions such as lithium-ion batteries and demand response, while research, demonstration, and investment must accelerate for alternative battery chemistries, hydrogen-based systems, and long-duration storage. At the regulatory level, harmonised frameworks, streamlined authorisation processes, and explicit market recognition of storage and demand-side resources are essential to unlock investment. Stakeholder insights underscore that, alongside technology and regulation, building trust through IT security and cost-effective ownership models will be decisive for large-scale adoption.

This study investigates the asymmetric impact of Distributed Generation (DG) penetration on extreme wholesale electricity price movements in New Zealand, using the 2018 oil and gas exploration ban as a structural policy shock. By integrating benchmark fixed effects and System-GMM estimations with a Quantile Autoregressive Distributed Lag (QARDL) model, we explore how DG influences both lower-tail (ED) and upper-tail (EI) price volatility before and after the ban. Empirical results reveal that prior to the ban, higher DG penetration was significantly associated with a reduction in EI and a modest elevation in ED in the short run, helping stabilize the system by smoothing marginal price risks. However, in the long run, oversupply effects—particularly from solar—led to a rise in the frequency of ED events, suggesting unintended downward pressure during low-demand periods. After the ban, DG's regulatory role shifted: in the ED regime, DG deepened low-price suppression and volatility, while in the EI regime, it failed to mitigate upward price pressures due to fossil fuel constraints and reduced system flexibility. The system stability coefficient ρ from the QARDL-ECM specification indicates slower price mean-reversion post-ban, highlighting increased market fragility. These findings suggest that DG's effect on market stability is not static but highly state-dependent and shaped by policy context. While DG can contribute to long-term price smoothing, its short-term stabilizing capacity weakens under supply shocks and institutional constraints. To enhance system resilience, policymakers must complement DG expansion with investment in energy storage, demand-side flexibility, and diversified generation sources. Without these, DG alone may amplify rather than mitigate extreme price risks under transition pressures. • DG’s effect on extreme prices differs clearly before vs. after 2018 ban. • QARDL & GMM reveal DG’s asymmetric, policy-driven price impact. • Short-term smoothing by DG may reduce long-term storage incentives. • Policy should link DG with storage, smart grids & dynamic pricing.

Abstract The increasing integration of renewable energy sources highlights the urgent need for grid flexibility, with buildings serving as key controllable loads. In this context, accurately quantifying building flexibility is essential for enabling effective demand-side management and ensuring reliable grid operations. However, several challenges hinder this quantification. To address these issues, this study proposes a comprehensive flexibility quantification framework. First, a novel RC-Mapping model incorporating an Enumerate-Comparison Method is proposed. The RC-Mapping model can capture the thermal behavior of both the building and the air conditioning system, while the Enumerate-Comparison Method can initialize state parameters in the RC-Mapping model. Compared with the conventional approach, as validated by the experiment, the proposed method can substantially improve RMSE for indoor temperature prediction from 0.542 °C to 0.266 °C, and the MAPE for flexibility quantification from 27.58% to 10.98%. Second, the study introduces the power reduction-duration curve and temperature variation curves to characterize flexibility from both grid and building perspectives. Specifically, based on the analysis of the power reduction-duration curve, this study provides a systematic analysis of four sources of flexibility and their underlying mechanisms, including the thermal storage of the building, the thermal storage of the HVAC system, the increase of coefficient of performance (COP), and the reduction in cooling load. Finally, the study investigates the impact of uncertainties in COP and internal heat gains on flexibility quantification. According to the result, it is recommended to slightly underestimate the COP and overestimate the internal heat gain schedule to improve the accuracy of flexibility quantification.

A supply chain network design problem (SCNDP) involves making long-term, irreversible strategic decisions whose cost efficiency depends on effectively leveraging flexibility and demand information. After analyzing the interaction of these factors, we propose five distinct policies for addressing the SCNDP. Starting with a localized production model where demand is satisfied locally, the study extends to scenarios where production capacity at one location serves other nodes (Policy II). Further flexibility is introduced by enabling capacity sharing among facilities with prearranged links (Policy III). Unlike these policies, which optimize capacities as proxies for production decisions prior to demand realization, Policies IV and V defer production decisions until demand is realized. The robustness and resilience of these policies are evaluated under varying levels of demand uncertainty and risks of supply disruptions. To provide actionable insights, we develop robust two-stage optimization frameworks for the proposed policies and design efficient methods to address varying uncertainty budgets for both supply and demand risks. Our results reveal that under demand uncertainty alone: (i) Capacity-sharing links among facilities yield the highest cost savings across all uncertainty budgets due to the pooling effect and significantly reduce shortage probability (Policy III), and (ii) production postponement offers only marginal benefits, highlighting the greater importance of upstream capacity-sharing over postponing production, particularly for moderate uncertainty budgets. Under simultaneous demand and supply risks, we demonstrate that (iii) capacity-sharing retains its value, while flexibility from production postponement deteriorates performance, favoring partially flexible networks with only capacity-sharing links over fully flexible ones with both sources of flexibility. To contextualize these findings, we apply the most effective policies to a real-world case study, quantifying their impact and providing design recommendations. Finally, we extend our models to an event-wise ambiguity set and demonstrate that by (iv) leveraging the supermodular structure of the second-stage problems, one can solve instances up to eight times larger.

Economic scheduling of multi-microgrids containing distributed units and storage devices is expressed in this scheme according to the multi-objective energy management system. Microgrid operator considers the economic, security, flexibility and operation objectives. The present method minimizes the weighted sum of voltage security index, energy loss, and energy cost. Constraints consider the optimal power flow formulation, flexibility and voltage stability limits in microgrids, and mathematical formulation of sources and storages operation. Microgrid includes non-renewable and renewable units, and storage system in network are battery and compressed air storage. Unscented Transformation approach models the uncertainties of the renewables output, price of energy, and demand. Fuzzy decision approach obtains a compromise point between economic, security and operation objectives. Combining grey wolf and red panda optimizers is able to obtain an optimal solution with low value for variance of the final point. Energy management according to various technical and economic indicators in the several renewable multi-bus microgrids considering battery, compressed air storage and non-renewable unit as flexibility sources based on the Unscented Transformation model and hybrid solver are the advantage, goal and innovation of this project. According to simulation results, the energy management of the energy storage and non-renewable sources in the microgrids with renewable sources can be improved the various indicators, such as reducing the energy cost and loss as well as voltage drop about to 30–60%, 46%, 46–50%, and improving voltage security equal to 10.55% compared with power flow studies. Flexibility of 100% is also reached for microgrids thanks to the incorporation of storage equipment and non-renewable power sources.

Data centres as a source of flexibility for power systems

The growing penetration of variable renewable energy sources (RES) greatly increases uncertainty in the power system, and more flexibility is required to maintain the balance between power generation and consumption. Compared with costly investments in other resources, RES themselves can also be a promising source of flexibility, by providing balancing reserves through deloaded control. This paper proposes an economic dispatch scheme that allows variable RES to provide reserves, so that they can offer flexibility to balance the uncertainty from themselves and other variable resources. The model is versatile and considers different RES reserve control strategies and activation schemes. A data-driven distributionally robust chance-constrained (DD-DRCC) method is proposed to manage uncertainty from RES. Knowledge of variation ranges and probability distributions is captured in the data-driven uncertainty model. The adequacy, availability and deliverability of reserves in the re-dispatch stage are guaranteed by joint chance constraints. The model is transformed into a linear programming problem using conditional value-at-risk (CVaR) and solved efficiently. A case study confirms the effectiveness of the proposed method, and shows that it can achieve a balance between reliability and economy compared with existing approaches.

Simplified idealizations, such as clamped or simply supported, are commonly used in the modeling and analysis of tested aerospace structures. These simplifications are not always appropriate to address the response of nonrigid boundaries. Model modifications can better represent the test, but modifications are often not practical due to the complexities of the boundary response. Therefore, digital image correlation (DIC) and conventional instrumentation were incorporated to measure the boundary support structure response of a large-scale wing test. The additional DIC and conventional data were used to adjust the test data to remove the effects of the boundary response to improve the test and analysis correlation of a model with the idealized boundary conditions. The studied wing test had three sources of boundary flexibility identified as contributing to rigid-body rotation of the test wing. The adjusted data exhibit improved test and analysis correlation and demonstrates the benefit of using DIC along with conventional instrumentation to collect and interpret test data.

Pre-sodiation, which is capable of supplying additional active sodium sources to sodium-ion batteries (SIBs), has been widely accepted as one of the most promising approaches to address the issue of active sodium loss during initial charging and subsequent cycling. Organic materials, with their design flexibility and abundant sources, are well-suited for large-scale applications. To achieve effective organic pre-sodiation, precise control over reaction potential is essential. In view of this, molecular engineering strategies are developed to mediate the pre-sodiation potential of organic materials for efficient pre-sodiation. Nevertheless, a comprehensive review of molecular engineering in organic pre-sodiation is still lacking. This timely review aims to present the crucial role of molecular engineering in organic pre-sodiation and provide an up-to-date overview of this field. After the showcasing of fundamental details of molecular engineering in organic pre-sodiation, recent advances in modifying oxidation decomposition/reduction potentials of organic pre-sodiation materials are briefly introduced, with a focuson the structure-activity relationship between functional group modifications and pre-sodiation potential. Future challenges and directions for developing next-generation organic pre-sodiation technologies are also reviewed. The current review provides important insights into molecular engineering in organic pre-sodiation, guiding the development of next-generation technologies of SIBs.