Energy Management Applications Research Articles

Deep fuzzy nets approach for energy efficiency optimization in smart grids

Using smart grids has become crucial for achieving efficient and sustainable energy management. One of the main challenges in smart grids is optimizing energy efficiency by managing and controlling electricity generation, transmission, and distribution. The Deep Fuzzy Nets (DFN) approach has been proposed as a novel technique that combines the capabilities of deep learning and fuzzy login to optimize energy efficiency in smart grids. The proposed approach utilizes a deep learning architecture to learn the complex relationships between various parameters within the smart grid system. The fuzzy logic component handles uncertainties and imprecision’s in the data, making the DFN approach well-suited for real-world energy management applications. The proposed approach can provide accurate and reliable predictions and enhancing energy efficiency in a dynamic and evolving smart grid environment. The proposed deep fuzzy nets approach reached 91% sensitivity, 94.45% specificity, 92/37% prevalence threshold, and 96.54% critical success index. This approach has been tested in various energy systems and has demonstrated capabilities to improve system-level energy efficiency while still giving users control of their energy usage. As energy efficiency optimization in intelligent grids continues to be a primary focus of energy research, deep fuzzy nets could provide a powerful solution for energy optimization.

Dynamic knowledge graph applications for augmented built environments through “The World Avatar”

The proliferation of digital building models in recent years has led to a corresponding rise in specialised, non-interoperable models. These models impede sustainable developments by forming data silos that hinder cross-application data exchange and knowledge discovery processes. Although Semantic Web solutions hold promise in addressing these silos, current approaches primarily focus on developing novel ontologies, yielding similar outcomes. But it is unclear how these methodologies could support broader knowledge discovery processes and application requirements. This paper addresses these research challenges by introducing a dynamic knowledge graph as implemented within The World Avatar for interoperable building models. We demonstrate its value through two distinct applications in urban energy management and laboratory automation. The dynamic knowledge graph revolves around a comprehensive structured knowledge model constructed from ontologies and agents. Ontologies semantically annotate data and represent domain knowledge and their relationships with standardised definitions. When augmented with an agent architecture, the resulting knowledge model can align stakeholder perspectives and accommodate the dynamic and scalable nature of urban data. Moreover, the dynamic knowledge graph fosters innovative human-machine interactions through visualisation interfaces to augment knowledge discovery processes in the built environment for greater efficiencies and innovation. As the knowledge model expands, users gain access to a broader spectrum of private and public data sources and technologies, while reducing integration barriers. This is especially pertinent for smaller and less influential entities like municipal and local governments with limited resources, who can realise substantial benefits at reduced costs.

Multi-Cluster Aggregative Games: A Linearly Convergent Nash Equilibrium Seeking Algorithm and Its Applications in Energy Management

Multi-Cluster Aggregative Games: A Linearly Convergent Nash Equilibrium Seeking Algorithm and Its Applications in Energy Management

A Universal Biomineralization‐Like Interfacial‐Confined Strategy Enables Practicable, Meter‐Scale, Transmittance‐Adjustable, Highly‐Ordered, Photothermal‐Capable, 2D Conducting Polymers

AbstractConducting polymers (CPs) encompass various cutting‐edge fields owing to their combination of polymer and conducting properties. However, the mismatch between the polymer growth process and the carrier mobility design leads to poor material and device consistency, and insufficient long‐term device stability, thus hindering the practical application of CPs. Although 2D construction has been explored to address these challenges, it typically leads to materials instability and lacks methods for large‐scale synthesis, making it unsuitable for many application scenarios. Here a universal biomineralization‐like strategy relying on the joint impaction of hydrogen‐bond and gravity for large‐scale fabrication of 2D CPs is developed. As‐fabricated meter‐scale Polypyrrole films exhibit high orderliness and can be assembled into multilayers, allowing for adjustable thickness and transparency. Notably, these films demonstrate exceptional photothermal properties and remarkable long‐term stability, as their surface temperature can reach 140 °C when irradiated with an 808 nm laser at 0.7 W cm−2, with almost no attenuation after 170 days. Furthermore, under sunlight, the temperature can reach nearly 100 °C, showcasing significant potential in smart building and energy management applications. Importantly, this strategy can be extended to the 2D design of numerous light organic compounds, thus opening up possibilities for their practical applications.

Scalable Sol–Gel Permeation Assembly of Phase Change Layered Film Toward Thermal Management and Light‐Thermal Driving Applications

AbstractPhase change materials (PCMs) are pivotal in thermal energy management and conversion applications owing to their exceptional thermal energy storage and release characteristics. However, persistent challenges such as poor thermal conductivity and leakage issues have impeded their widespread adoption. While existing approaches mitigate these challenges by constructing and incorporating 3D thermal conductive networks, they are constrained by discontinuous preparation methods and mold size limitations. Herein, a scalable sol–gel permeation assembly strategy is proposed to prepare phase change layered film by in situ filling polyethylene glycol (PEG) in aramid nanofibers (ANF)/graphene nanoplates (GNP) network. Befitting from the laminated network encapsulation effect of ANF and GNP, the phase change film demonstrates leak‐free and stable phase transition behavior, even after undergoing 500 heating/cooling cycles. Moreover, the resulting PEG/ANF/GNP layered film exhibits an impressive in‐plane thermal conductivity of 23.7 W mK−1 at GNP loading of 28.1 vol.%, rendering it suitable for thermal management applications in electronic devices. The phase change layered film possesses exceptional photo‐thermal conversion properties, maintaining temperatures exceeding 90 °C under a light power density of 200 mW cm−2. Capitalizing on the thermally induced flexibility of the phase transition film and its temperature‐dependent stiffness, its utility extended to developing a light‐thermal driving gripper.

A PAM hydrogel surface-coated hydroponic bamboo evaporator with efficient thermal utilization for solar evaporation

Solar-driven interfacial water purification emerges as a sustainable technology for seawater desalination and wastewater treatment to address the challenge of water scarcity. Currently, the energy losses via radiation and convection to surrounding environment minimize its energy efficiency. Therefore, it is necessary to develop strategies to minimize the heat losses for efficient water purification. Here, a novel evaporator was developed through the in situ gelation of PAM hydrogel on the surface carbonized hydroponic bamboo (PSC) to promote energy efficiency. The inherent porous and layered network structures of bamboo, in synergy with the functional hydration capacity of PAM hydrogel, facilitated adequate water transportation, while reducing evaporation enthalpy. The PAM hydrogel firmly covered on the photothermal layer surface effectively minimized the radiation and convection heat losses, while further harvesting those thermal energy that would otherwise dissipate into the surrounding environment. The reduced thermal conductivity of PSC served as a thermal insulator as well, obstructing heat transfer to bulk water and thus diminishing conduction losses. Consequently, the rational designed PSC could efficiently convert solar energy to purified water, leading to the evaporation of 2.09 kg m−2 h−1, the energy efficiency of 87.6 % under one sun irradiation, and yielding 9.6 kg m−2 fresh water over 11 h outdoor operation. Moreover, the PSC also performs excellent salt rejection, and long-term stability at outdoor experiment. These results demonstrated high and stable solar evaporation performance could be achieved if turning heat losses into a way of extra energy extraction to further enhance the evaporation performance. This strategy appears to be a promising strategy for effective thermal energy management and practical application.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

Microgrids Efficiency Improvement for National Electricity Network Leveraging Beluga Whale Optimization

Managing of real-time energy in microgrids connected to grid is a relatively new technology that is becoming increasingly popular in the energy industry. It enables microgrids to connect with each other and with a wider electrical grid to increase efficiency and improve energy resiliency while reducing costs and emissions. It also enables the grid-connected microgrids to dynamically adjust to changing conditions, allowing for upgraded energy infrastructure and improved energy security. However, identifying an accurate and efficient approach for real-time energy management for microgrids is critical. In this regards, this paper introduces a modified metaheuristic, the Boosted Beluga Whale Optimizer (BBWO), for the application of real-time energy management to optimize battery controlling of a CM (community microgrid). This amendment involves changes to the cost function so that it better captures the battery charging/discharging operations. A function of dynamic penalty is then suggested for the sake of further improves the cost function. The suggested cost function effectiveness is determined through a real-time case study, in operational cost over a 96h time horizon. From the results, the battery's charging/discharging cycles provides lower operational expenses of $29.70 over a 96-hour time horizon. Further, the proposed innovative penalty function encourages optimal charging of the battery from RESs and the grid utility and could reduce the cost of the objective function significantly. It was also demonstrated that the battery is constantly trying to maintain full charge, which requires an expenditure of $33.14 in electricity. This cost is still less than the original cost, but it allows for high energy levels to be maintained across all time periods. Additionally, this also prevents any issues stemming from low battery levels and maximizes the life of the battery. Overall, the regularized BBWO algorithm, with the offered cost function adapted to the needs of the society, is a suitable solution for real-time energy management.

ENERGY MANAGEMENT: NEW PRIORITIES OF THE 21ST CENTURY

The development of management methodology and the concept of energy management in modern conditions of transforming the energy sector by the requirements of the carbon-free economy are considered. New qualitative features and new areas of application of energy management and energy management systems (EMS) in the implementation of the energy transition and implementation of the Smart Grid concept have been revealed. It has been established that modern energy management is a proactive, organized, and systematic coordination of the purchase, transformation, distribution, and use of energy to meet requirements, taking into account environmental and economic goals. In contrast to the formation of the EMS according to the ISO 50000 series of standards, today the construction of the EMS is becoming more relevant, which provides systemic (complex) optimization of the parameters, structure, and modes of various technical objects according to a set of technical, economic and environmental criteria. It is shown that EMS is much more than a simple technical solution for monitoring the parameters and state of energy systems. With comprehensive forecasts of energy demand and supply, this system takes energy management to a completely new level, allowing operators of facilities (enterprises, buildings, and smart networks and systems) to determine the strategic direction of energy management, implement it, and achieve both energy efficiency and energy availability and environmental (social) acceptability. The components of EMS for Smart Grid are characterized, in particular: monitoring (actual energy production, actual energy demand, state of charge of energy storage, price uncertainty); forecast (energy production, energy demand, mobile data storage, uncertainty); power quality management (loss minimization, power quality indicators, and reliability/comfort); planning/dispatch/cost (operating costs, reduction of GHG emissions, profit maximization). It is proposed that a promising direction is to consider the SEM as a system of information and communication technologies, which at the system level is combined with power energy processes used by operators of electric power systems and networks to monitor, control, and optimize the productivity of production, transmission, and consumption of energy resources in systems of various hierarchical levels. level (as UES of Ukraine, regional and local systems, on specific energy facilities or individual energy technologies, etc.). Based on the analysis of prospective areas of application of EMS at energy sector facilities, modern areas of scientific and technical research in the construction of energy management systems for energy, electric power, and electrotechnical systems, which are practical to carry out in Ukraine, have been determined.

Comparative Analysis of Ground-Based Solar Irradiance Measurements and Copernicus Satellite Observations

Solar irradiance data provided by the Copernicus program are crucial for several scientific, environmental, and energy management applications, but their validation by means of ground-based measurements may be necessary, especially if daily and hourly data resolutions are required. The validation process not only ensures that reliable information is available for solar energy resource planning, power plant performance assessment, and grid integration, but also contributes to the improvement of the Copernicus system itself. Ground-based stations offer site-specific data, allowing for comprehensive assessments of the system’s performance. This work presents a comparative statistical analysis of solar irradiance data provided by the Copernicus system and ground-based measurements on a seasonal basis at three specific Italian reference sites, showing a maximum average relative error of less than 7% for hourly horizontal global irradiance in the irradiance range defined by the IEC 61724-2.

The role of thermal cycling in micro-cracking development and flow alteration: Advanced insights from CT and lab studies

The recent interest in geothermal energy, nuclear waste management, and coal gasification has led to an in-depth examination of how granite rock behaves under subterranean reservoir conditions. Our research delves into the intricate microstructural and fluid dynamics of the Australian Harcourt granite. We emphasize its reactions to repeated temperature changes, cycling between 25 and 900 °C over 1–15 cooling episodes. To achieve this, we utilised X-ray computed tomography (CT) to visualize and reconstruct the heat-treated samples. Subsequently, we measured the porosity, identifying both total and directly interconnected spaces within these samples post-thermal treatment. Using avizo 2021.2, we created a pore network model (PNM) based on the connected porosity data to shed light on how fluids move through these heat-affected samples.The data highlighted that total porosity stayed below 0.5% up to 500 °C, with no directly connected porous regions detected. However, beyond this temperature, interconnected porosity began to appear, intensifying with increasing temperatures and more cooling cycles. The PNM allowed us to visualize and quantify specific details like pore and throat dimensions, their volumes, their interconnectedness, and the lengths of their connecting channels. Notably, these properties became more pronounced with escalating temperatures and cooling episodes, largely because of the fractures caused by the heat. Furthermore, we used AVIZO to analyse the absolute permeability of the heat-treated samples, providing a detailed understanding of their permeation characteristics. We also ran a set of rigorous lab tests to assess the impact of both confining pressure (ranging from 10 MPa to 40 MPa) and temperature (from 22 to 250 °C) on the fluid movement in the heat-treated rock. The experiments confirmed that increasing confining pressure non-linearly decreased Darcy's permeability. A rise in temperature further reduced permeability, mainly due to the narrowing of flow channels from mechanical and thermal stresses.Overall, the study provides a holistic understanding of porosity evolution and the subsequent fluid movement within thermally quenched rock samples under various reservoir conditions, providing valuable insights for energy and waste management applications.

Green, recyclable and high latent heat form-stable phase change composites supported by cellulose nanofibers for thermal energy management

Efficiently addressing the challenge of leakage is crucial in the advancement of solid-liquid phase change thermal storage composite materials; however, numerous existing preparation methods often entail complexity and high energy consumption. Herein, a straightforward blending approach was adopted to fabricate stable phase change nanocomposites capitalizing on the interaction between TEMPO-oxidized cellulose nanofibers (TOCNF) and polyethylene glycol (PEG) molecules. By adjusting the ratio of TOCNF to PEG and the molecular weights of PEG, TOCNF/PEG phase change composites (TPCC) with customizable phase transition temperature (40.3–59.1 °C) and high phase transition latent heat (126.3–172.1 J/g) were obtained. The TPCC of high-loaded PEG (80–95 wt%) ensured a leakage rate of less than 1.7 wt% after 100 heating-cooling cycles. Moreover, TPCC exhibits excellent optical properties with a transmittance of over 90 % at room temperature and up to 96 % after heating. The thermal response analysis of TPCC demonstrates exceptional thermal-induced flexibility and good thermal stability, as well as recyclability and reshaping ability. This study may inspire others to design bio-based phase change composites with potential applications in thermal energy storage and management of smart-energy buildings, photothermal response devices, and waste heat-generating electronics.

Breathers for nonlinear energy management in acoustic waveguides

Recent investigations of nonlinear metamaterials have revealed interesting wave propagation phenomena with no counterpart in linear systems. One of the appealing phenomena is traveling discrete breathers. Breathers are traveling oscillatory wavepackets with spatially localized envelopes and energy-dependent speeds. These nonlinear waves were reported in lattices with purely (or nearly purely) nonlinear neighbor interactions (possessing zero speed of sound as defined in the classical acoustics sense, and, hence, designated as sonic vacua). However, the effect of local resonators on breather propagation is elusive to date. In this work, we aim to fill this gap by studying 1D grounded strongly nonlinear lattices with linear local resonators (SNLRMs) under impulsive force excitation. Numerical simulations have demonstrated that SNLRMs support the generation of new families of breathers. These families resulted from the opening of a new optical propagation zone (PZ) in the spectrum, which is associated with the linear dynamics of the local resonators. These families are characterized by the local resonator parameters and can support multiple fast frequencies or a single frequency that belongs to either the optical or acoustical PZs. Further aspects of the nonlinear acoustics are also demonstrated using the complexification averaging method. The newly reported breathers open the venue to employ SNLRMs in passive nonlinear energy management applications. Examples are presented through acoustics waveguides and frequency filtering.

A Novel Control Application for Robust and Optimal Energy Management in a Grid-Interfaced Hybrid Renewable Energy System: AGOA-GBDT Control Approach

A Novel Control Application for Robust and Optimal Energy Management in a Grid-Interfaced Hybrid Renewable Energy System: AGOA-GBDT Control Approach

Optimizing Time-Series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction

Time-series forecasting is essential for decision-making activities, and deep learning models have shown promising results in this field. However, existing models face challenges in capturing the complex non-linear patterns and randomness of real-world time-series data. To address this issue, we propose a novel stacked deep learning framework that combines a bi-directional long-short-term memory network, convolutional 1D, and max-pooling-1D layers in each stack. We also introduce an enhanced version of adaptive moment estimation and an error correction method to improve the model's accuracy and convergence speed. We evaluate our model's performance using four real-world datasets (air quality, PM2.5, electricity price, and securities lending) and compare it with existing models based on statistical measures such as mean absolute error, mean squared error, and standard deviation. The results demonstrate that our proposed model outperforms existing models significantly, achieving a ∼ 52 % improvement in mean absolute error for air quality data, a ∼ 36 % improvement in mean squared error for the PM2.5 dataset, and a ∼ 27.5 % improvement in mean squared error for electricity price data. The enhanced adaptive moment estimation and error correction method further improve the model's accuracy and generalization capabilities, making it suitable for multi-domain data sets. Our proposed model has potential applications in various fields, including manufacturing, traffic prediction, weather forecasting, environmental monitoring, finance, and energy management.

Numerical analysis on the effect of graded porosity in closed-cell metal foams/PCM composites

The porous metal foams are potential to integrate phase change materials (PCMs) to form the shape-stabilized composited PCMs for the applications of thermal energy management and temperature regulation. However, the effect of porosity distribution of foam keeps challenging in designing such PCM-based composites. In this work, the closed-cell graded porosity foams (GPFs) involving various gradient directions and gradient differences of porosity are engineered by shrinking Voronoi tessellations to achieve precisely tunable microstructure. Then the GPF/PW composite is formed by integrating paraffin wax (PW) in each pore and its melting performance is investigated by the computational model, which is validated by the melting experiment of a specific sample. The investigations on the gradient configurations of foams reveal that the gradient direction and gradient difference of porosity have significant influences on the pore-scale melting behavior, liquid fraction variation in time and overall energy storage efficiency of PW. The positive gradient variation along the direction of heating source and the moderate gradient difference, i.e. 20% or 30%, are beneficial to improve the melting behavior of graded models. In contrast to the uniform model, the complete melting time of the graded models can maximum shorten by 5.6%, 3.8% and 3.7% for the bottom, lateral and top heating modes, respectively.

Highly Anisotropic to Isotropic Nature and Ultralow Out-of-Plane Lattice Thermal Conductivity of Layered PbClF-Type Materials.

Materials with an extreme lattice thermal conductivity (κl) are indispensable for thermal energy management applications. Layered materials provide an avenue for designing such functional materials due to their intrinsic bonding heterogeneity. Therefore, a microscopic understanding of the crystal structure, bonding, anharmonic lattice dynamics, and phonon transport properties is critically important for layered materials. Alkaline-earth halofluorides exhibit anisotropy from their layered crystal structure, which is strongly determined by axial bond(s), and it is attributed to the large axial ratio (c/a > 2) for CaBrF, CaIF, and SrIF, in which Br/I acts as a rattler, as evidenced from potential energy curves and phonon density of states. The low axial (c/a) ratio leads to relatively isotropic κl values in the BaXF (X = Cl, Br, I) series. MXF (M = Ca, Sr, Ba) compounds exhibit highly anisotropic (a large phonon transport anisotropy ratio of 10.95 for CaIF) to isotropic (a small phonon transport anisotropy ratio of 1.49 for BaBrF) κl values despite their iso-structure. Moreover, ultralow κl (<1 W/m K) values have been predicted for CaBrF, CaIF, and SrIF in the out-of-plane direction due to weak van der Waals (vdWs) bonding. Overall, this comprehensive study on MXF compounds provides insights into designing low κl layered materials with a large axial ratio by fine-tuning out-of-plane bonding from ionic to vdWs bonding.

THEORETICAL FOUNDATIONS OF RISK MANAGEMENT FOR ENERGY SECTOR ENTERPRISES

In a rapidly evolving energy environment, effective risk management is paramount to ensuring the stability and success of businesses. This article examines a systematic approach to risk management in the energy sector, emphasizing the development of strategies adapted to a specific enterprise.The study delves into the various models used in the energy sector to manage risk. Energy balance models, energy network models, economic-mathematical models, demand and production forecasting models, risk management system models, renewable energy sources (RES) models, and system dynamic models are discussed. The study proposes a comprehensive risk management system for the energy sector, including SWOT analysis, Delphi method, sensitivity analysis and simulation modeling. The approach involves a detailed analysis of internal and external factors, expert assessments and scenario testing for the formulation of adaptive risk management strategies, taking into account regional characteristics, prioritizing resistance to change, aligning them with modern industry challenges. Given the competitive and dynamic nature of the energy sector, the study uses SWOT analysis and the Delphi method as key methodologies. The results of the study emphasize the need for a comprehensive risk management system in energy. A systematic approach combining various analytical methods is proposed, aimed at maximizing sustainability and competitiveness. The decision support system, which can be built on the basis of the developed methodology, will be automatically adapted to specific enterprises, and will facilitate prompt response to changes and optimize risk management strategies. The developed concept of a systemic approach to consideration and response to risks in the energy sector offers a clear path for enterprise risk management. The integrated system proved to be innovative and effective, making a significant contribution to the improvement of risk management methodology in the energy sector. This research provides valuable information for practical applications in energy and risk management.

Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted.

Polypyrrole-modified flax fiber sponge impregnated with fatty acids as bio-based form-stable phase change materials for enhanced thermal energy storage and conversion

In this study, we integrated organic phase change materials, decanoic acid (DA) and palmitic acid (PA), into bio-based samples made from sodium alginate (SA) and flax fiber (FF). We also synthesized conductive polypyrrole (PPy) coating through in situ polymerization to create two thermal energy storage and conversion systems with distinct phase change temperatures (32 °C and 63 °C). The PPy coating exhibited uniform polymerization, resulting in electrical conductivity (1.52 ± 0.07 S/m) and thermal conductivity (0.453 ± 0.02 W/mK). The conductive sponge, similar to the plain sponge, had a porous microstructure (85 % porosity) that allowed successful impregnation of 60 % and 74 % of DA and PA, resulting in outstanding energy storage and conversion properties. Specifically, the conductive sponges with DA and PA achieved phase change enthalpies of 100.98 J/g and 154.52 J/g, respectively. DA/PPy@Sponge demonstrated exceptional durability after thermal cycling tests. Furthermore, the conductive sample efficiently converted electrical energy into thermal energy along the conductive path, making it suitable for storing generated heat as both sensible and latent heat, as well as capturing and storing solar energy. Overall, the developed conductive phase change materials offer shape stabilization, high thermal energy storage capacity, and efficient electric/photo-to-thermal conversion properties. These attributes make them promising candidates for diverse applications in energy harvesting, storage, and management in electronics, buildings, transportation, and other relevant fields.