Screen printed thermoelectrics: balancing performance, flexibility, and wearable energy harvesting

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems.

Thermally activated building systems offer significant potential for low-carbon building operation and energy flexibility by using building mass as distributed thermal storage. Recent advances in data-driven control, machine learning, and digital building infrastructure have expanded their technical capabilities. However, practical deployment remains limited. This paper addresses that gap through a scoping review of the literature on data-driven thermally activated building systems, with a focus on the conditions required for implementation, integration, and sustained operation in practice. The review examines publication patterns, realization stages, dominant realization pathways, and recurring enablers and barriers across the field. The results show that the literature is concentrated in conceptual, simulation, and pilot-stage studies, while evidence of long-term operation in occupied buildings remains scarce and evidence of scalable or transferable realization in the reviewed TABS literature remains limited. The paper proposes five realization conditions for deployment as an interpretive synthesis of the reviewed literature: operational observability, deployable model architecture, embedded digital integration, operational acceptability, and organizational handover capacity. The review reframes data-driven thermally activated building systems as a realization challenge rather than only a control problem and provides a structured analytical framework to support future research and deployment-oriented evaluation in energy informatics.

As climate change accelerates, buildings must adopt not only efficient envelopes and systems but also operational strategies that enhance sustainability. Building Energy Flexibility (BEF) becomes particularly valuable when scaled to Energy Clusters (ECs), where multiple buildings coordinate energy use. This study evaluates a cluster of 202 single-family houses in Lodz, Poland, where each building acts as a prosumer. Energy efficiency is achieved through deep thermal modernization, resulting in the full electrification of the cluster. Energy flexibility is assessed through energy and cost savings, using optimization based on Demand Side Management (DSM) techniques with weather and electricity price forecasts as key indicators. Results show that effective cluster-level management improves renewable energy utilization, lowers emissions, cuts costs, and enhances grid reliability. Results demonstrate the role of Energy Flexible Building Clusters (EFBC) in sustainable urban energy systems.

Cancer stem cells (CSCs) represent a minor but highly adaptable subpopulation within tumors that drives long-term growth, metastasis, and therapy resistance. Their ability to survive and regenerate under metabolic and therapeutic stress relies on a unique integration of energy flexibility, redox balance, and proteostatic programs. While bulk tumor cells typically favor aerobic glycolysis and high protein turnover, CSCs often exhibit elevated mitochondrial activity, fatty acid oxidation, and selective suppression of proteasome function. These metabolic features support quiescence, stress tolerance, and self-renewal. Beyond energy production, metabolic intermediates such as acetyl-CoA, succinate, and lactate serve as epigenetic cofactors, linking nutrient availability to chromatin remodeling and transcriptional plasticity. Reactive oxygen species and antioxidant responses further tune this balance, shaping the transition between glycolytic and oxidative CSC states. These intrinsic programs are continuously influenced by the tumor microenvironment, where hypoxia, cytokine-driven signaling, and metabolic coupling with stromal and immune cells modulate CSC metabolism and reinforce stemness. Despite rapid progress, major conceptual and methodological gaps still limit our understanding of CSC metabolism and this review highlights these unresolved issues and further outline key contextual factors-including tumor-intrinsic, microenvironmental, systemic, and metastatic cues-that shape CSC metabolism and help explain the divergent observations reported across studies. Understanding this network will be essential for designing combinatorial therapies that target CSC metabolism while accounting for their heterogeneity and plasticity.

Modelling the energy balance in urban areas is significantly influenced by uncertainties arising from energy production, distribution, and consumption. Renewable energy sources and their distribution, throughout the urban environment, are susceptible to unpredictable meteorological conditions, while energy consumption patterns are shaped by the stochastic nature of individual behaviours, thus the evaluation of energy flexibility strategies remains challenging. Despite the longstanding recognition of stochasticity in the energy field, its accounting in urban energy modelling remains largely confined to single buildings. Furthermore, uncertainties have typically been addressed in isolation across the energy lifecycle: stochastic optimisation is predominantly used to define uncertainties on the energy production scale, while individuals' energy consumption behaviours are usually modelled through probabilistic distributions on aggregated averages, introducing biases in simulations. This review provides an overview of existing modelling approaches, highlighting various techniques for further development, and the appropriate degree of stochasticity required for more realistic outcomes in developing energy flexibility. The analysis draws more than 200 articles, examining different stochastic approaches, focusing also on data-driven implications, as the IoT technologies’ influence continues to grow. The main findings of this review emphasise the necessity for more comprehensive stochastic models that integrate all phases of the energy lifecycle. Such models would enable more realistic simulations, support the development of flexible efficient energy strategies, and address the complexities of urban energy systems in a holistic manner. • A comprehensive review on modelling energy systems under uncertainty is presented. • Individual behaviour and occupancy patterns are integrated into the analysis. • The study bridges energy production, distribution, and consumption models. • Literature gaps and future directions for energy flexibility are identified. • Guidelines are proposed to support future modelling practices and policy design.

Electrical energy storage plays a vital role in enabling renewable energy integration and achieving decarbonization targets under the Paris Agreement. Liquid air energy storage (LAES) is a promising large-scale, long-duration storage technology due to its scalability, site flexibility, and high energy density. A crucial component of LAES performance is the cold thermal energy storage (CTES), which recovers cryogenic exergy during air regasification to recovery during the liquefaction phase, significantly improving the round trip efficiency. This paper presents a comprehensive and critical review of CTES technologies for LAES, aiming to identify optimal design approaches based on current literature and industrial practices. The review covers sensible and latent heat storage systems, hybrid and cascade configurations, and advanced geometries, assessed through thermodynamic and techno-economic performance indicators. Our analysis finds that packed beds with sensible heat materials are the most mature and cost-effective option, while phase change material-based systems offer higher efficiency potential—achieving round-trip efficiency improvements of up to 55 %—but face challenges in material cost, availability, and scalability. Hybrid and cascade configurations show promise in simulations, though experimental data remain limited. Cold storage losses are shown to impact round-trip efficiency up to seven times more than heat losses, highlighting the strategic importance of CTES optimization. The authors identify key research needs in dynamic system modeling, scalable material development, and lifecycle techno-economic assessment. Addressing these gaps will be critical to advancing CTES as a performance-enhancing and cost-effective component of next-generation LAES systems. • Cold thermal energy storage is vital to the performance of LAES systems. • A critical review identifies optimal design strategies for CTES in LAES. • CTES technologies, geometries, and materials are comparatively evaluated. • A research framework links current evidence, gaps, and future CTES pathways. • Key research priorities include dynamic modelling, materials, and lifecycle analysis.

Understanding energy flexibility in buildings: A sensitivity analysis of building characteristics and thermal storage potential

Recent progress in the energy flexibility of building clusters: A systematic review

An advanced model for energy flexibility assessment of grid-interactive phase-change thermal storage systems

Abstract As significant electricity consumers, buildings offer a notable potential for demand-side flexibility through advanced heating, ventilation, and air conditioning (HVAC) systems. A heat pump (HP), critical for building electrification and decarbonization, combined with active Thermal Energy Storage (aTES), especially using Phase Change Materials (PCM), can effectively shift electrical loads to alleviate grid stress during peak demand periods. This study evaluated an integrated HP-aTES system controlled by economic model predictive control (eMPC) via simulations using a Spawn of EnergyPlus framework across diverse climates in the United States (i.e., Atlanta, GA, Buffalo, NY, New York City, NY, and Tucson, AZ), aiming to minimize operating costs through load shifting while ensuring occupant thermal comfort. The studied HP-aTES system utilized a commercial-off-the-shelf water-to-air heat pump in parallel with a PCM-based thermal storage tank to explore their synergistic effects on cost savings, energy flexibility, and grid responsiveness through advanced controls in cooling applications. The simulation results demonstrated the HP-aTES system’s considerable potential, consistently maintaining comfort while achieving significant peak load shifting, exceeding 80% in climates such as Atlanta, GA, Buffalo, NY, and New York City, NY, with prediction horizons of 9–12 hours, and up to 70% in Tucson, AZ. Operating cost savings were highly dependent on utility tariffs, exceeding 40% in high-incentive regions such as New York City, NY, and Atlanta, GA, but remained around 12% under flat rates like those in Tucson, AZ. This was primarily achieved through load shifting rather than an absolute reduction in energy. Furthermore, this study confirms eMPC’s effectiveness for unlocking energy flexibility, emphasizing the crucial role of a sufficient controller prediction horizon and tariff design, and establishes a virtual testbed for future research into sensing, simplified controls, and validation.

Pipe-embedded walls offer a promising approach to reducing winter heating demand by mitigating envelope heat loss while maintaining indoor thermal comfort. However, most existing studies focus on single-pipe systems operating under high-flow conditions, with limited attention to low-flow operation and its implications for energy flexibility. This study investigates a parallel pipe-embedded wall system operating at low flow velocity as a flexible heating strategy. A three-dimensional CFD model was developed to analyze the coupled hydraulic and thermal behavior of the wall, including the effects of connecting columns, and was validated through experiments under identical boundary conditions. Parametric analyses examined the influence of main pipe size, branch spacing, flow velocity, water temperature, and column-induced thermal bridging. The results show that variations in flow velocity and branch spacing lead to flow distribution differences of up to 6%, while causing negligible changes in inner-surface temperature (below 0.1 °C). In contrast, increasing column size significantly intensifies thermal bridging, increasing inner-surface heat flux by approximately 21% as the column edge length increases from 200 mm to 400 mm. Overall, the results demonstrate that parallel pipe-embedded walls can enhance building energy flexibility by enabling stable thermal performance under low-flow operation.

Carbon neutrality is currently accelerating the decarbonization process in the power sector. However, China, the United States, and the European Union are taking significantly different transformation paths. This article uses an energy economics framework to link binding system constraints, policy combinations, and the overall system costs to compare the differences among the three countries. This analysis integrates evidence from international assessments and peer-reviewed studies regarding the value and flexibility of variable renewable energy (VRE). The results show that as the share of renewable energy increases, economic bottlenecks shift from generation costs to flexibility and grid transmission: the marginal market value of wind and solar energy decreases as penetration rates increase, while the value of dispatchable system services increases. The EU's total control and trading system and market integration enhance long-term scarcity expectations, but without appropriate hedging designs, they increase the risk of short-term price fluctuations. The United States relies more on technology-neutral tax credits to reduce capital costs and accelerate deployment in the absence of a national carbon price. China combines large-scale clean infrastructure construction with continuous coal supply guarantees, which makes flexibility compensation, inter-provincial transmission, and reliable emission limits key to achieving cost-effective decarbonization. For China, the policy impact lies in regarding flexibility as a decarbonization asset, strengthening market signals through improved monitoring, reporting, and verification (MRV), and reducing power outages through grid and market reforms.

As global energy systems face increasing strain from urbanization, renewable energy integration, and the push toward decarbonization, the ability to dynamically manage energy demand has become critical. Energy flexibility (EF) emerges as a key strategy to support demand-side management and enhance energy system resilience. This study presents a comprehensive systematic literature review on building energy flexibility (BEF), a key enabler of demand-side energy management and resilient urban energy systems. Employing the PRISMA methodology, Using the PRISMA methodology, 787 papers published between 2019 and 2024 were initially screened, resulting in a final set of 191 studies included for detailed analysis. The analysis reveals a dominant focus on residential buildings and electric energy systems, with load shift and demand response emerging as the most frequently implemented strategies. Grey-box models and optimization-based approaches were the most widely adopted modeling techniques, often supported by simulation tools like MATLAB and EnergyPlus. Despite growing interest in hybrid systems and real-time control, significant gaps persist in addressing flexibility at district levels, integrating non-electric energy vectors, and incorporating continuous temporal resolutions. Furthermore, the environmental and user-centric impacts remain underexplored. This review synthesizes critical insights and proposes a structured research agenda to bridge the gap between theoretical models and real-world deployment, offering strategic guidance to policymakers, energy system designers and urban planners to develop flexible, sustainable energy systems and buildings. • Reviews 191 studies on building energy flexibility from 2019–2024. • Maps modeling methods, system scales, and flexibility strategies. • Identifies dominance of grey-box models and optimization techniques. • Highlights data and methodological gaps across energy system types. • Outlines research priorities for scalable, hybrid flexibility modeling.

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.

Against the backdrop of rapidly growing global photovoltaic (PV) power generation, its inherent intermittency and volatility challenge power grid stability. This paper presents a comprehensive model for PV power forecasting and optimizing active/reactive support capabilities of distributed PV-storage stations. First, a hybrid EMD-KPCA-LSTM model is developed to address PV output variability. It employs Empirical Mode Decomposition (EMD) for multi-scale feature extraction from meteorological data, Kernel Principal Component Analysis (KPCA) for dimensionality reduction, and Long Short-Term Memory (LSTM) networks for high-precision forecasting. Experimental results show significantly improved accuracy and robustness compared to single LSTM or EMD-LSTM models, with effective quantification of prediction uncertainties. Second, a nonlinear programming model based on robust optimization and Sequential Quadratic Programming (SQP) is proposed to optimize active/reactive support. By refining energy storage charging/discharging strategies, the model maximizes grid support while mitigating PV uncertainty via robust optimization and solving efficiently with SQP. Case studies validate that optimized PV-storage stations effectively dampen PV fluctuations, providing stable and substantial active/reactive support to enhance grid flexibility, stability, and renewable energy integration. This work offers critical theoretical and practical insights for developing smarter, greener, and more reliable modern power systems.

Research on economy and energy flexibility optimization for existing buildings based on thermal and electrical energy storage

With the rapid growth of renewable energy deployment, multi-microgrid systems have become crucial for improving energy flexibility and reliability. However, challenges such as renewable intermittency and energy imbalance necessitate more advanced storage solutions. This paper proposes a shared energy storage (SES) allocation and scheduling strategy tailored for multi-microgrid environments. A bi-level optimization framework based on Mixed-Integer Linear Programming (MILP) is developed, aiming to simultaneously minimise the annual investment cost of SES and the operational costs of the microgrid cluster. The model incorporates renewable energy utilisation constraints and performs cost-benefit analysis under varying utilisation rates. Case studies demonstrate that the proposed approach significantly reduces wind and solar curtailment, enhances renewable energy consumption, and improves overall economic performance. The results validate the model's effectiveness and provide practical insights for the integrated planning and operation of SES in multi-microgrid systems.

High thermal mass radiant systems have strong potential for energy flexibility, but key parameter relationships remain unclear. This study conducts a comprehensive parameter sensitivity analysis of thermally activated building systems (TABS) and embedded surface systems (ESS) using hundreds of thousands of simulations. Two representative cases, Hangzhou, China (humid subtropical) and San Francisco, California, USA (marine), are analyzed in depth, followed by cross-climate testing in eight additional cities spanning hot to mild climates. Results show that start/stop is the primary driver of load shifting potential, while start time has little effect on cooling energy supply. For floor ESS, however, operation duration is critical: Extending operation from 8 to 24 h increases daily cooling supply by 25.9% in Hangzhou and by 35.4% in San Francisco, compared to only 5–9% in other terminal types. Using San Francisco as an example, a comparison of fixed nighttime precooling and flexible scheduling reveals key design levers, such as window-to-wall ratios and optimal orientations, that strengthen energy flexibility. Overall, the findings support a two-stage optimization: first, tuning operation timing and durations to maximize load shifting without excess cooling, and second, refining design parameters to enhance flexibility. These insights provide guidance for designing grid-interactive radiant cooling systems.

Energy flexibility in regulated users is examined as a structural property of demand, assessed through variability and disorder metrics derived from smart metering data. Using the coefficient of variation and normalized entropy, the analysis reveals stable routines during weekdays and greater heterogeneity in transitional periods such as evenings and weekends. Non-negative matrix factorization (NMF) is applied to extract latent user pro-files, which are subsequently clustered to uncover representative trajectories of consumption. Groups with bimodal or extended load distributions emerge as the most adaptable, highlighting the role of latent profiling in identifying flexibility potential. Simulations of partial load redistribution demonstrate that, while individual savings remain modest, aggregated benefits and improvements in reliability indicators (SAIDI, SAIFI, ENS) are significant. These findings confirm that flexibility is unevenly distributed across users and time, and that its quantification provides a strategic foundation for differentiated demand response schemes and the design of resilient, user-oriented energy systems.