Boosting energy flexibilities for the smart grid energy sharing network with optimal management of urban energy, charging and traffic resources

Evaluating the energy flexibility potential of residential room air conditioners from the perspective of user thermal comfort: A case study in Chongqing

Reliability and economic impacts of utilizing battery energy storage in data centers for energy flexibility services in smart grids

To address the challenges posed by increasing shares of variable renewable power generation in the electric grid, flexibility procurement platforms are being actively developed. These platforms enable prosumers to offer flexible power for use in mitigating predicted grid congestion. However, the optimal design of such flexibility markets remains unclear and requires thorough analysis. A critical parameter is the lead time between the acceptance of offered flexible power and its delivery, directly influencing flexibility availability and cost. Despite its importance, the impact of lead time on flexibility provision cost has not been evaluated in the literature. In this study, we analyze this cost effect of varying lead times on flexibility provision by simulating a 48-hour moving horizon model predictive control for multiple distributed energy systems on a market platform, delivering flexibility under different lead time scenarios. Additionally, the deliveries are analyzed under varying demand durations, electricity tariffs, daytimes, and seasons to evaluate their response to diverse influencing factors. The findings are presented using a newly developed flexibility heatmap, illustrating lead time dependent flexibility deliveries and their associated costs. The results indicate that with a lead time of 3 h, the cost of providing flexibility using current combined heat and power systems is minimized, achieving cost reductions of up to 77%. Transitioning to advanced heat pumps and battery storage technologies increases the available flexibility ninefold. However, such systems require a lead time of 16 h to deliver flexibility at minimized costs, highlighting the growing importance of lead time in flexibility provision. • Investigation of 3 types of common energy system designs based on a real company. • 48-h moving horizon model predictive control simulation. • Comprehensive analysis of different energy flexibility signal lead times. • New flexibility heatmap to visualize signal lead time dependent costs. • Increase in optimal signal lead time due to future energy system designs.

The increasing penetration of renewable energy sources (RES) into power grids necessitates innovative strategies to manage their inherent variability. Demand-side energy flexibility has emerged as a solution to ensure grid stability and maximize RES integration. However, existing methods for flexibility quantification lack generalizability across different building types, making them costly to replicate and often struggle with accuracy. This study proposes an artificial intelligence (AI) and data-driven methodology to forecast the aggregated energy flexibility in residential buildings. The methodology integrates dynamic baseline calculations, statistical feature engineering, and bidirectional long short-term memory (BiLSTM) models to predict flexibility metrics and provide insights through energy flexibility indicators (EFIs). Two distinct datasets, a residential multi-apartment building in Austria and a single residential building in Ireland were used to validate the accuracy, scalability, and adaptability of the proposed method across diverse building typologies. The flexibility predictions were further classified into peak and off-peak intervals, enabling an understanding of the energy dynamics and supporting targeted demand response (DR) strategies. Results indicate a strong predictive performance for the energy flexibility of buildings, achieving values exceeding 0.85 for energy consumption and up to 0.97 for photovoltaic (PV) production, with classification accuracy reaching 92%.

Demand response in buildings: Comparative study on energy flexibility potential of underfloor heating and air conditioning systems

Co-quantification of building energy flexibility considering the synergistic effect of HVAC and lighting systems

The urgent need for sustainable energy solutions to combat climate change and the growing global energy demand has stimulated the development of renewable energy technologies. Among these, hybrid renewable energy systems (HRES) that combine multiple energy sources promise enhanced efficiency, reliability and flexibility compared to single-source systems. This research focuses on a novel HRES configuration that integrates a concentrated solar power named solar tower with thermal energy storage (TES), photovoltaic modules and battery energy storage systems (BESS) in North Togo. The study aimed to maximize the energy produced, while reducing costs and finally evaluated the environmental aspect saved. We use parametric optimization through SAM to determine the optimum levelized cost of energy and net present value. SAM software was used for heliostat modelling. The single-owner model was used for financial analysis. The results demonstrated the project's financial viability and environmental impact, with an LCOE of $0.14kWh-1 indicating a competitive cost of energy production and an IRR of 14.87% showcasing strong investment returns. A high NPV of $9,307,001 indicated some good interest in the proposed HRES. Furthermore, the significant reduction of 25,815.7513 tons of CO2 emissions highlights the project's substantial contribution to sustainability and carbon footprint reduction.

Hydrogel electrolytes hold significant promise for flexible and wearable electronics but often suffer from limited ionic conductivity, poor mechanical integrity, and vulnerability to extreme environmental conditions. Herein, a cellulose-integrated conductive hydrogel electrolyte (MCBH-Zn) with outstanding mechanical strength and freeze resistance is reported. The MCBH-Zn hydrogel achieves a high compressive strength of 5.16 MPa and a tensile fracture stress of 312 kPa, enabled by the synergistic effects of a covalently cross-linked polyacrylamide (PAM) network, hydrogen bonding between PAM and cellulose, and coordination interactions between bentonite (BT) and cellulose. Additionally, it delivers excellent ionic conductivity of 88.9 mS cm–1 at room temperature and 27.3 mS cm–1 at −60 °C. Consequently, a flexible solid-state zinc-ion hybrid supercapacitor (MCBH-ZHSC) assembled with MCBH-Zn delivers stable electrochemical behavior across a wide temperature range and maintains a superior capacity retention of 93% after 10,000 cycles at 10.0 A g–1. Furthermore, the MCBH-Zn-based wearable device demonstrates self-sustained flexibility and efficient energy harvesting and conversion. This work offers a facile strategy for engineering high-performance hydrogel electrolytes tailored for next-generation wearable technologies.

Abstract As the adoption of renewable energy continues to grow in the building sector, the energy flexibility of air conditioning systems has become a key factor in achieving energy optimization and load shifting. This study systematically compares the performance of radiant and convective cooling systems in both continuous and intermittent operation modes, with a focus on their load shifting capabilities. A dynamic heat transfer model based on the state-space method was established to calculate load transfer indices and node temperature values for both systems under various operating conditions. The results indicate that, under intermittent cooling mode, the radiant cooling system exhibits superior load-shifting capability owing to the high thermal inertia of its radiant terminal and its distinct heat transfer pathway. It enables temporary cooling shutdowns while maintaining indoor thermal comfort for up to one hour, and effectively reshapes the cooling load profile by adjusting the chilled water supply schedule. This study offers a comprehensive comparison between radiant and convective systems, providing practical guidance for improving the adaptability of building cooling systems in future scenarios with high renewable energy penetration.

Amid rising load volatility and uncertainty, demand-side resources with regulation capabilities are increasingly engaged at scale in ancillary service markets, facilitating sustainable peak load mitigation and alleviating grid stress while reducing reliance on carbon-intensive peaking plants. This study examines the integration of electric vehicles (EVs) in peak regulation, proposing a multi-stage operational strategy framework grounded in the analysis of EV power and energy response constraints to promote both economic efficiency and environmental sustainability. The model holistically accounts for temporal charging and discharging behaviors under diverse incentive mechanisms, incorporating user response heterogeneity alongside multi-period market peak regulation demands while supporting clean transportation adoption. An optimization model is formulated to maximize aggregator revenue while enhancing grid sustainability and is solved via MATLAB(2021b) and CPLEX(20.1.0). The simulation outcomes reveal that the discharge-based demand response (DBDR) strategy elevates aggregator revenue by 42.6% and enhances peak regulation margins by 19.2% relative to the conventional charge-based demand response (CBDR). The hybridization of CBDR and DBDR yields a threefold revenue increase and a 28.7% improvement in peak regulation capacity, underscoring the efficacy of a joint-response approach in augmenting economic returns, grid flexibility, and sustainable energy management.

With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems.

The transition towards renewable energy necessitates large-scale, cost-effective energy storage solutions. Carnot Batteries (CBs), which store electricity as thermal energy, offer potential advantages for medium-to-long-duration storage, including geographical flexibility and lower energy capacity costs compared to electrochemical batteries. This article examines the evolution and current state-of-the-art of CB technologies, including Pumped Thermal Energy Storage (PTES) and Liquid Air Energy Storage (LAES), discussing their performance metrics, techno-economics, and development challenges. Concurrently, the increasing generation of biomass ash (BA) from bioenergy production presents a waste valorization challenge. This article critically evaluates the potential of using BA, particularly from woody biomass, as an ultra-low-cost thermal energy storage (TES) medium within CBs systems. We analyze BA’s typical composition (SiO2, CaO, K2O, etc.) and relevant thermal properties, highlighting significant variability. Key challenges identified include BA’s likely low thermal conductivity, which impedes heat transfer, and poor thermal stability (low ash fusion temperatures, sintering, corrosion) due to alkali and chlorine content, especially problematic for high-temperature CBs. While the low cost is attractive, these technical hurdles suggest direct use of raw BA is challenging. Potential niches in lower-temperature systems or as part of composite materials warrant further investigation, requiring detailed experimental characterization of specific ash types.

Building thermal mass offers a cost-effective solution to enhance the integration of energy supply and demand in dynamic energy systems. Thermally activated building systems (TABS), incorporating embedded heat tubes, shows strong potential for energy flexibility. However, the significant thermal inertia of TABS also imposes challenges to precise load shift and indoor climate control. This review synthesizes key research on the effective demand-side management of TABS from multiple perspectives. It examines and compares various TABS configurations, including floor, ceiling, and wall systems. Differences in heat transfer performance between heating and cooling result in distinct application preferences for each type. The integration of advanced materials, such as phase change materials (PCM), can further enhance energy flexibility. TABS flexibility is primarily activated through adjustments to indoor operative temperature, with relevant influencing factors and regulatory constraints analyzed and discussed. Key aspects of optimizing building energy flexibility, including simulation methods and control strategies for TABS, are reviewed from both theoretical and practical perspectives. The energy and economic performance of TABS under various control strategies is analyzed in detail. This review provides insights to support the optimal design and operation of TABS within dynamic energy systems and to enhance the energy flexibility of building envelopes.

This study investigates the dynamics and control of a fully electrified heat pump assisted distillation system based on the flash vapor circulation (FVC) concept. The proposed configuration enables complete electrification without auxiliary steam. Two control structures are developed and evaluated in Aspen Dynamics under ± 20% disturbances in throughput and composition. The first structure CS1 employs single-end temperature control with fixed reflux ratio and demonstrates satisfactory performance in most cases. However, it shows minor deviations in product purity under large composition changes. To address this, a second structure CS2 incorporates an additional composition controller to adjust the reflux ratio, achieving improved purity regulation and energy flexibility. The results confirm the dynamic feasibility and controllability of FVC-based distillation, supporting its integration in future sustainable and flexible separation systems. • Dynamics and control for flash vapor circulation (FVC) with full electrification. • Demonstrated stable control under ± 20% throughput and composition disturbances. • Temperature-only control structure can lead to product purity deviation. • Enhance purity control by integrating a composition controller to adjust reflux ratio. • Results confirm dynamic operability of FVC for flexible electrified distillations.

Field implementation of model-based predictive control in an all-electric school building: Impact of occupancy on energy flexibility

Abstract In order to promote the low-carbon transformation of energy systems and the interaction between electricity supply and demand, this study developed a building energy flexibility optimization framework. This study proposed a multidimensional quantitative evaluation index for building energy flexibility based on time, power, electricity, and cost, and constructed an operation optimization model and flexible optimization strategy for the building energy system. Based on the typical residential building scenario of northern China, the flexible potential and carbon reduction effect of the comprehensive utilization of building thermal mass, heat pumps, and water storage devices were optimized and analysed. The results of the investigated residential building during heating conditions show that the maximum flexible power (reduction) using the building thermal mass coupled heat pump energy storage system reaches 188.1 kW, and the flexible energy is 596.3 kWh. Compared to the benchmark operating conditions, the building carbon emissions decrease by 161.94 kg CO2, with a carbon reduction rate of approximately 20.26%. Residential buildings coupled with heat pumps and energy storage devices can effectively shift electricity load from high to low carbon emission factor periods, further releasing flexibility potential into the grid and enhancing carbon reduction benefits.

Abstract Information and Communication Technology (ICT) facilitates the flexibilization of small-scale energy technologies in households. This article investigates the resulting environmental effects from the perspective of German market players. Through qualitative interviews and a semiquantitative survey, the study identifies the perceived positive effects, such as enhanced renewable integration, and the negative effects, including increased ICT infrastructure demands and potential simultaneity effects, which might increase the need for grid infrastructure expansion. Market participants indicate that the perceived benefits outweigh the identified drawbacks, yet they signal the necessity for additional measures to enhance environmental performance and more quantitative impact analyses.

In this research work, a comprehensive incentive-based demand response (I-DR) capacity assessment tool is developed for enhancing energy flexibility and sustainability. Under I-DR, a participant’s response to load regulation activities is based on accepting appropriate financial incentives on a voluntary basis. Accordingly, the sustainability of energy and the environment can be better guaranteed through a more reasonable energy distribution among participants on a voluntary basis with clear financial incentives. It is thus meaningful to conduct an assessment for I-DR potential capacity to guide the potential of energy and environmental sustainability. This article introduces the development of an I-DR capacity assessment tool that includes three modules: a data preprocessing module, an I-DR capacity assessment module, and a visualization module, to expand the adaptability of this tool to real-world data and scenarios. A load-reduction-oriented assessment process and a carbon-reduction-oriented assessment process are also provided to expand the role of the tool to utility companies. This tool is then validated via field load data within one residential service territory of Southern Company (SOCO). Validation results demonstrate that this tool can effectively assess I-DR capacity for residential energy, which enables a user-friendly view of the quantitative relationship of the reducible load or the reducible carbon emission under the corresponding financial incentives. This tool shows promise for achieving support for decarbonization and energy sustainability.

In today's world, electricity has become the keystone for every activity undertaken. As the population increases, the electricity demand has reached unprecedented levels, putting strain on electrical grids. In many developing countries, the residential sector consumes 60% of the peak load. The negative consequences of this trend provide a pathway for frequent brownouts, which lead to enormous losses for industries as well as residential households. To date, the flexibility of energy is usually achieved on the generation side. However, an easier way to counter this would be to manage usage on the demand side. The development of smart grid facilities has enabled communication between utilities and consumers. Therefore, the demand response functionality shows greater potential to stabilize the power supply and demand for the utility and consumers, respectively. In this paper, an intelligent secure cloud service game theory-based demand response modelling algorithm is proposed to handle peak demand in the residential sector. This innovative strategy enables residential consumers to achieve mutually beneficial outcomes. Enhancing communication security between utility providers and consumers, optimizing renewable energy utilization, and improving cost-effectiveness and reliability in electricity production and delivery are vital for meeting the rising demand. The simulation results suggest that the proposed approach efficiently reduces the Peak-to-Average Ratio, leading to mutual advantages for both consumers and utility providers. This approach addresses the growing demand for electricity while promoting sustainable energy through improved energy management practices.