Electricity Demand Research Articles

Sky-Scanning for Energy: Unveiling Rural Electricity Consumption Patterns through Satellite Imagery’s Convolutional Features

The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote sensing interpretation model for feature extraction, streamlining the training process and enhancing the prediction efficiency. A random forest model is then used for electricity consumption prediction, while the SHapley Additive exPlanations (SHAP) model assesses the feature importance. To explain the human geography implications of feature maps, this research develops a feature visualization method grounded in expert knowledge. By selecting feature maps with higher interpretability, the “black-box” model based on remote sensing images is further analyzed and reveals the geographical features that affect electricity consumption. The methodology is applied to villages in Xinxing County, Guangdong Province, China, achieving high prediction accuracy with a correlation coefficient of 0.797. The study reveals a significant positive correlations between the characteristics and spatial distribution of houses and roads in the rural built environment and electricity demand. Conversely, natural landscape elements, such as farmland and forests, exhibit significant negative correlations with electricity demand predictions. These findings offer new insights into rural electricity consumption patterns and provide theoretical support for electricity planning and decision making in line with the Sustainable Development Goals.

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

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

Addressing the reliability challenge: Subsidy policies for promoting renewable electricity consumption

The intermittency and volatility of renewable electricity pose challenges to supply reliability, which is not conducive to renewable energy consumption. To ensure a reliable electricity supply, more governments implement subsidy policies to promote the adoption of innovative technologies by renewable energy producers to enhance supply reliability. We compare two types of subsidies provided by a government: investment subsidy (IS) policy, which is implemented in the deployment stage to directly reduce improvement costs, and operational subsidy (OS) policy, which is implemented in the operational stage to increase the renewable energy producer’s marginal returns. First, we show that without government intervention, customers’ low green consciousness or higher improvement costs may prevent the renewable energy producer from enhancing supply reliability. Second, through a comprehensive comparison, we find that both subsidy policies can incentivize the renewable energy producer to improve supply reliability when customers are more green-conscious, and the improvement cost is high. However, the OS policy and the IS policy operate on different mechanisms: the IS policy can directly alleviate the improvement cost burden on the renewable energy producer, while the OS policy serves a dual role of increasing the renewable energy producer’s marginal operational profit and expanding the market demand for renewable electricity. When customers’ green consciousness is low, the government can only choose whether or not to implement the OS policy. Finally, we highlight that the implementation of the IS policy by the government may not be more beneficial to both the renewable energy producer and customers compared to the OS policy. This result informs regulators that energy security should be considered when designing subsidy policies and should not be limited to promoting the interests of participants.

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

On the Different Fair Allocations of Economic Benefits for Energy Communities

Energy Communities (ECs) are aggregations of users that cooperate to achieve economic benefits by sharing energy instead of operating individually in the so-called “disagreement” case. As there is no unique notion of fairness for the cost/profit allocation of ECs, this paper aims to identify an allocation method that allows for an appropriate weighting of both the interests of an EC as a whole and those of all its members. The novelty is in comparing different optimization approaches and cooperative allocation criteria, satisfying different notions of fairness, to assess which one may be best suited for an EC. Thus, a cooperative model is used to optimize the operation of an EC that includes two consumers and two solar PV prosumers. The model is solved by the “Social Welfare” approach to maximizing the total “incremental” economic benefit (i.e., cost saving and/or profit increase) and by the “Nash Bargaining” approach to simultaneously maximize the total and individual incremental economic benefits, with respect to the “disagreement” case. Since the “Social Welfare” approach could lead to an unbalanced benefit distribution, the Shapley value and Nucleolus criteria are applied to re-distribute the total incremental economic benefit, leading to higher annual cost savings for consumers with lower electricity demand. Compared to “Social Welfare” without re-distribution, the Nash Bargaining distributes 39–49% and 9–17% higher annual cost savings to consumers with lower demand and to prosumers promoting the energy sharing within the EC, respectively. However, total annual cost savings drop by a maximum of 5.5%, which is the “Price of Fairness”.

A novel energy management of public charging stations using attention-based deep learning model

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry

Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

DESENVOLVIMENTO DE PROTÓTIPO UTILIZANDO MICROCONTROLADORES PARA CONTROLE DE EFICIÊNIA ENERGÉTICA EM RESIDÊNCIAS UNIFAMILIARES

The search for solutions that aim to improve the way we use electrical energy in our daily lives is of great importance when it comes to sustainability. Energy efficiency plays a fundamental role in the development of sustainable technologies. In a period of high demand for electricity and concerns about the environment, energy efficiency has become increasingly important, both in industrial and domestic applications. In this scenario, microcontrollers play a fundamental role in optimizing and controlling electrical energy. Among its various applications is monitoring and managing energy consumption in real time, making intelligent decisions to minimize waste. These systems, when integrated with sensors and other devices, allow the development of solutions that not only save energy, but also make environments smarter and more sustainable. Observing this context, this study aims to develop a device capable of providing accurate information about the electrical energy consumption of equipment. The developed prototype aims to provide detailed information about the energy consumption of monitored equipment, contributing to better energy management. The developed energy meter prototype demonstrated the potential to contribute significantly to energy efficiency, by providing accurate and real-time data, thus resulting in a more intelligent use of the energy consumed, as, with knowledge of real-time data, it is possible to monitor and work on energy optimization.

Shifting gears in thermal power: Displacement efficiency and environmental impact of wind and solar generation in China

China has made substantial investments in wind and solar power in response to its commitment to addressing climate change. However, the extent to which these intermittent renewable energy sources can displace the predominant thermal generation remains uncertain. Employing two-way fixed-effect panel models, this study finds that from 2011 – 2021, intermittent renewable generation displaced thermal generation by crowding out the annual operating hours of thermal power. Furthermore, the environmental impact analysis suggests that, despite the emission reduction benefits brought about by wind and solar generation through phasing out inefficient units and promoting investments in efficient units, their intermittent nature and inadequate alignment with the increasing electricity demand led to reduced emission reduction efficiency. These findings indicate a transition of thermal power from baseload to dispatchable, emphasizing the importance of tailored investment plans based on the characteristics of wind and solar and the necessity of alternative measures to enhance grid flexibility.

Environmental and Social Life Cycle Assessment of Data Centre Heat Recovery Technologies Combined with Fuel Cells for Energy Generation

The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase.

Flexibility justice: Exploring the relationship between electrical vehicle charging behaviors, demand flexibility and psychological factors

The adoption of electric vehicles (EVs) is transforming the landscape of energy consumption. While the technical and economic dimensions of EV adoption are increasingly well understood, the aspect of justice in demand flexibility remains underexplored. This study examines the complex relationship between flexibility in EV charging behaviors and the influence of socio-psychological and justice factors. We explore a range of demographic and social-psychological variables including charging anxiety, environmental concerns, perceived cost-saving perception, perceived privacy, and trust in utility providers. Our results reveal that these variables positively influence the changes in charging habits, including time-shifting and load-reduction. This study also uncovers disparities in charging behavior adjustments across various demographics groups. For instance, White respondents are more likely to charge their EVs during off-peak hours than their non-White counterparts and homeowners show a greater intention to reduce EV charging load during peak hours compared to renters. Additionally, high-income individuals exhibit a stronger willingness to shift charging times to off-peak, with White respondents within the high-income group being the most likely to reduce the amount of charging load during peak hours. Conversely, low-income White respondents are less inclined to make such adjustments. These disparities are likely tied to socioeconomic status, as more vulnerable groups often face greater constraints in adjusting their schedules. Therefore, it is imperative that policies prioritize flexibility justice by addressing the specific needs and behaviors of vulnerable groups, aiming to mitigate the additional burdens resulting from their limited flexibility.

Electric load prediction using SARIMAX

ABSTRACT Electricity demand variation is one of the prevalent phenomena in Nepal. This variation can cause excess electricity during low-demand periods and a shortage during high-demand periods. An early prediction of electricity demand allows for levelling such fluctuations by introducing dynamic tariffs based on varying demands. This study aims to use Python’s time series models to make predictions about electrical demands and propose a dynamic tariff system. The dataset of Panama City’s hourly electrical load is brought into use. Two models, namely the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Factors (SARIMAX) and the BATS model, are deemed to be the two best time series models. The selection of optimal seasonal and non-seasonal orders in SARIMAX is made by comparing Bayes Information Criterion (BIC). For the SARIMAX model, a daily dataset has been taken by summing the hourly data for each day, whereas for the BATS model, hourly data for the year 2018 has been used. The SARIMAX model resulted in the best fit with the evaluation metrics values: Root Mean Square Error (RMSE) of 975,336.88, Mean Average error (MAE) of 739.09, and R2 score of 0.75. However, SARIMAX’s limitation is that the use of daily data does not allow dynamic tariff prediction, which is possible with the BATS model.

Unsteady-state entropy generation analysis of the counter-flow dew-point evaporative coolers

The surging demand for air conditioning, especially in hot climates, underscores the urgency of strategies aimed at curbing fossil fuel reliance and fostering energy saving strategy. Despite the longstanding utility and advantages of the conventional mechanical vapor compression refrigeration system, sustainability hurdles persist owing to its pronounced electricity demand. While evaporative cooling, harnessing water evaporation, emerges as a more efficient alternative, its efficacy is contingent upon environmental conditions. Addressing this, dew-point evaporative cooling (DPEC) presents an innovative direction, showcasing superior efficiency by directing supply air into a wet channel. This study delves into the thermodynamic losses within a counter-flow configured DPEC system, employing a transient entropy generation model rooted in the second law of thermodynamics to provide a comprehensive analysis of DPEC’s performance. Key findings that emerged from this study reveal that (1) the transient entropy generation is intricately distributed across different layers of the dew point evaporative cooling (DPEC) system, including dry air, wet air, the channel plate, and the water film; (2) Parametric analyses highlight the significant impact of factors on entropy generation, inlet air temperature has a minimal effect on system efficiency, but higher temperatures increase thermal losses. Excessive humidity limits evaporative potential, while low humidity significantly increases entropy generation. The system performs optimally at a working ratio of 0.3 and lower air velocities (within the inlet air velocity range of 0.6 to 2.2 m/s). Channel length has little impact, while the system is more sensitive to channel height, with a height of 2.5–3.5 mm being conducive to better performance. Experimental validation demonstrates the model’s accuracy in predicting system performance. The research contributes to a deeper understanding of DPEC systems, offering insights for achieving optimal operating conditions and enhancing overall efficiency in the pursuit of sustainable development.

Entangled Mathematics as a Tool of Reasoning in the Mid-Twentieth-Century UK Electricity Industry

In the mid-twentieth century, the identity of those who oversaw the UK electricity grid tentatively and slowly began to shift from those who joined the electricity industry directly from secondary school to a university-educated elite with a higher level of technical education. At the same time, electricity infrastructure became increasingly centralised, leading to the creation of a national grid in 1938, meaning that control of electricity became concentrated in the hands of an ever-smaller group and increasing the stakes in debates over strategy. By using the case studies of power engineer Paul Schiller and mathematician Maurice Davies, this paper traces how the boundaries of what mathematics could or could not be used for were renegotiated between factions within the UK electricity industry. Schiller unsuccessfully attempted to use mathematics, including the authority of academic mathematicians, to reinforce controversial arguments about the strategic direction of the industry in the 1940s. Such arguments failed to resonate with a class of, as Schiller put it, “practical men” that still held considerable influence. Davies on the other hand was employed by the industry in the 1950s to improve efficiency, and was able to implement lasting measures in this effort by using the weather to forecast electricity demand, and successfully defended the usefulness of his work in the 1970s against colleagues who felt that forecasting could be done better with mathematical models without weather inputs. The contrast between these two stories shows how the influence of mathematical thinking was contingent upon existing power dynamics, and reinforce how the history of mathematics should be socially embedded.

The Catalytic Effect of Low Molecular Weight Acids on the Physicochemical and Dielectric Properties of Oil-Paper Insulation Systems.

In most industrialized countries, power transformers built several decades ago are approaching the end of their operational lifespan. The ongoing energy transition, focused on developing 100% renewable energy sources and accelerating global transportation electrification, further exacerbates these assets. Combined with rising electricity demand, there is an increasing risk of critical transformers' degradation acceleration. In this context, understanding the aging mechanisms of the insulation system inside these essential assets, which form the core of every energy network, becomes paramount for today's managers and engineers responsible for their operations. The acids generated through oil oxidation can be classified into two categories: low molecular weight acids (LMAs), which are inherently more hydrophilic and consequently have a greater impact on the degradation rate of solid insulation through hydrolysis, and high molecular weight acids (HMAs), which do not significantly contribute to the degradation of paper insulation. This study specifically addresses the impact of acids generated through oil oxidation-focusing on LMAs. New oil samples were infused with different ratios of LMAs before impregnation. The impregnated paper samples underwent thermal aging at 115 °C. Different physicochemical and dielectric properties were investigated. The investigations revealed that oils blended with formic acid exhibited more adverse effects on the insulation system compared to other LMAs. This information is essential for industry professionals seeking to mitigate the risks associated with transformer degradation and extend the lifespan of these critical assets during the energy transition.

Assessing China's solar power potential: Uncertainty quantification and economic analysis

Solar power is vital for China's future energy pathways to achieve the goal of 2060 carbon neutrality. Previous studies have suggested that China's solar energy resource potential surpass the projected nationwide power demand in 2060, yet the uncertainty quantification and cost competitiveness of such resource potential are less studied. Therefore, we applied an integrated framework to simulate China's solar photovoltaic (PV) technical potential, and incorporated potential uncertainty stemming from climate change, land use dynamics, and technological advancements. In addition, we constructed the solar energy supply curve for each province and calculated the economic potential. According to our results, approximately 78.6 % and 99.9 % of China's technical solar PV potential are priced lower than the benchmark price of coal-fired energy in pessimistic and optimistic scenario. These findings highlight the significant technical and economic potential of solar PV as a cost-effective alternative to coal-fired electricity to meet China's growing electricity demands.

Dataset on Electric Road Mobility: Historical and Evolution Scenarios until 2050

An increasing adoption of electric vehicles (EVs) is expected in the coming decades mainly due to the need to achieve carbon neutrality until 2050. However, predicting electric mobility’s future is challenging due to three main factors: technological advancements, regulatory policies, and consumer behaviour. The projections presented in this study are based on several scenarios driven mainly from reports published by public entities and consultants. It considers the evolution of electric road mobility by defined targets in the electrification of the transport sector. Therefore, the gathered data addresses different horizon times regarding EV penetration in the World, Europe, Portugal, Denmark, Greece, and Slovenia. Thus, an extensive literature review and estimating approach for EV forecast was conducted concerning EV markets, charging infrastructure, and electricity demand. Also, the dataset aims to provide a demand projection by 2050 and serving as a critical input to further work on EV mass deployment in the context of the project Electric Vehicles Management for carbon neutrality in Europe (EV4EU) and other works related to this field.

Giant Capacitive Energy Storage in High-Entropy Lead-Free Ceramics with Temperature Self-Check.

Considering the large demand for electricity in the era of artificial intelligence and big data, there is an urgent need to explore novel energy storage media with higher energy density and intelligent temperature self-check functions. High-entropy (HE) ceramic capacitors are of great significance because of their excellent energy storage efficiency and high power density (PD). However, the contradiction between configurational entropy and polarization in traditional HE systems greatly restrains the increase in energy storage density. Herein, the contradiction is effectively solved by regulating the octahedral tilt and cationic displacement in ABO3-type perovskite HE ceramics, i.e., (1-x)[0.6(Bi0.47Na0.47Yb0.03Tm0.01)TiO3-0.4(Ba0.5Sr0.5)TiO3]-xSr(Zr0.5Hf0.5)O3 (BNYTT-BST-xSZH). Combining the tape-casting process and cold isostatic pressing, the optimal BNYTT-BST-0.06SZH ceramic displays a large recoverable energy storage density (10.46 J cm-3) at 685kV cm-1 and a high PD (332.88 MW cm-3). More importantly, due to Tm/Yb codoping, abnormal fluorescent negative thermal expansion and excellent real-time temperature sensing are developed, thus the application of fault detection and warning in high-voltage transmission line systems is conceptualized. This study provides an effective strategy for enhancing the polarization of energy-storing HE ceramics and offers a promising material for overcoming the problems of insufficient capacitor density and thermal runaway in terminal communication.