Efficient Utilization Of Renewable Energy Research Articles

Renewable Energy System Optimization: Mppt Inverter Integration, Energy Storage Systems, And Its Impact on Sustainability and Efficiency Use Of Energy

Abstract: This study endeavors to enhance the utilization of solar panel energy by employing MPPT inverters and integrating energy storage systems. It will be conducted at the Renewable Energy Laboratory, Institute of Technology Sampling, spanning one academic year. Following an experimental research format with a single control group, measurements will be taken pre and post integration of MPPT inverters and energy storage systems to assess energy efficiency and supply stability. Primary data will be gathered from direct measurements of solar panels with integrated MPPT inverters and energy storage systems, encompassing energy production, supply fluctuations, and system stability. Data collection methods include direct measurements with state-of-the-art energy measurement equipment and interviews with renewable energy experts and technicians. Control of variables such as weather conditions and energy consumption will ensure information validity, while external validity will be bolstered by comparing findings with existing literature. Statistical analysis using advanced software will identify significant differences pre and post integration. Additionally, qualitative observations will be conducted to evaluate system stability and the impact of optimization on the sustainability and efficiency of renewable energy utilization.

Short-term wind power prediction method based on multivariate signal decomposition and RIME optimization algorithm

Accurate characterization of the dynamics of wind power systems under highly unstable conditions is essential for short-term wind power forecasting. Multivariate factors, such as wind speed, wind direction and climatic factors at different heights, as well as the relationships between them, need to be fully considered. To this end, after the Pearson correlation coefficient is used to identify the main influencing variables, the empirical modal decomposition (MEMD) method is utilized to decompose the data in order to capture the deep coupling relationship and the spatio-temporal pattern of change among the variables. Combined with a bidirectional long and short-term memory network (BiLSTM) optimized through the RIME algorithm, a short-term wind power prediction model was constructed. Data analysis of two wind farms in Xinjiang and Gansu, China, shows that the model has higher prediction accuracy compared to the baseline model. The effectiveness of RIME and MEMD is verified through component ablation experiments and comparison experiments with other optimization algorithms and signal decomposition algorithms. To cope with large-scale data, dimensionality reduction of IMF features using Elastic Net (EN) is explored to reduce computational requirements and maintain good prediction performance. The method provides strong support for power system scheduling and efficient utilization of renewable energy.

Interface-rich porous Fe-doped hcp-PtBi/fcc-Pt heterostructured nanoplates enhanced the C[sbnd]C bond cleavage of C3 alcohols electrooxidation

Efficient CC bond cleavage and the complete oxidation of alcohols are key to improving the efficiency of renewable energy utilization. Herein, we successfully prepare porous Fe-doped hexagonal close-packed (hcp)-PtBi/face-centered cubic (fcc)-Pt heterostructured nanoplates with abundant grain/phase interfaces (h-PtBi/f-Pt@Fe1.7 PNPs) via a simple solvothermal method. The open porous structure, abundant grain/phase interface and stacking fault defects, and the synergistic effect between intermetallic hcp-PtBi and fcc-Pt make h-PtBi/f-Pt@Fe1.7 PNPs an effective electrocatalyst for the glycerol oxidation reaction (GOR) in direct glycerol fuel cells (DGFCs). Notably, the h-PtBi/f-Pt@Fe1.7 PNPs exhibit an excellent mass activity of 7.6 A mgPt−1 for GOR, 4.75-fold higher than that of commercial Pt black in an alkaline medium. Moreover, the h-PtBi/f-Pt@Fe1.7 PNPs achieve higher power density (125.8 mW cm−2) than commercial Pt/C (81.8 mW cm−2) in a single DGFC. The h-PtBi/f-Pt@Fe1.7 PNPs can also effectively catalyze the electrochemical oxidation of 1-propanol (17.1 A mgPt−1), 1,2-propanediol (7.2 A mgPt−1), and 1,3-propanediol (5.2 A mgPt−1). The in-situ Fourier-transform infrared spectra further reveal that the CC bond of glycerol, 1-propanol, 1,2-propanediol, and 1,3-propanediol was dissociated for the complete oxidation by the h-PtBi/f-Pt@Fe1.7 PNPs. This study provides a new class of porous Pt-based heterostructure nanoplates and insight into the intrinsic activity of different C3 alcohols.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

Optimization of hydrogen production in multi-Electrolyzer systems: A novel control strategy for enhanced renewable energy utilization and Electrolyzer lifespan

In large-scale water electrolytic hydrogen production system based on renewable energy, the allocation strategy of hydrogen production power among multi-electrolyzers plays a critical role in the efficiency of renewable energy utilization and the quantity of hydrogen production, given a fixed power output from renewable energy sources. This paper considers the minimum hydrogen production power constraint of electrolyzer and proposes a novel control strategy for multi-electrolyzers. The operational phase of multi-electrolyzers is divided into two distinct phases: 1) the rapid start-up phase; 2) the equal power allocation phase. During the rapid start-up phase, by reducing the power of electrolyzer that has reached its rated power to a predetermined level and using this saved power to start another electrolyzer, this strategy ensures that electrolyzers can proactively and quickly exceed the minimum operational power threshold. Upon the activation of all electrolyzers, the system enters second phase, wherein the power is evenly allocated among all electrolyzers. Besides, this strategy employs a priority scheduling method to ensure the uniformity of operating times across all electrolyzers. Compared to the existing three control strategies, this proposed strategy demonstrates significant advantages in minimizing unused wind energy and optimizing the lifespan of electrolyzers. The effectiveness of proposed strategy is validated by the simulation result.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Numerical analysis of stress and deformation characteristics of compressed air energy storage chambers developed from a modified coal mine tunnel

The use of abandoned coal mine tunnels as underground compressed air energy storage (CAES) facilities has garnered significant attention given that it effectively repurposes unused underground space and enhances the efficiency of renewable energy utilization. However, existing coal mine tunnels, without optimization and retrofitting, may not meet the stability requirements of compressed air energy storage repositories. Sectional transformation is a retrofitting method with excellent optimization effects. In this study, numerical simulation based on Midas software was employed to create a three-dimensional model of straight-wall arched mine tunnels. The stress and displacement deformation of the tunnel sections were simulated under internal pressure of 10 MPa, and scenarios with no modification and circular-section backfilling were compared. The outcomes of the study are (1) For deeply buried soft rock mines, the existing structure without modification may not meet the stability requirements of compressed air storage facility due to low rock strength. (2) In the unmodified scenario, stress concentration effects and excessive floor tensile stress occur near the tunnel closure. (3) Backfilling straight-wall arched tunnels into circular shapes effectively reduces stress and displacement along the tunnel walls, preventing stress concentration near the tunnel closure. This study provides a retrofitting approach for repurposing abandoned mines into compressed air energy storage facilities, providing guidance for future projects facing complex geological conditions.

Design and hardware verification of photovoltaic converter based on adaptive P&O with snubber and BJT circuits

The efficient utilization of renewable energy is a key technology area of high domestic and international concern, and the research and development of DC–DC photovoltaic converters with high conversion efficiency is of great significance for improving performance and reducing the cost of solar power generation systems. In order to improve the conversion efficiency of solar energy, this paper proposes the design of a high-efficiency photovoltaic DC–DC converter with a non-isolated DC–DC converter as the object of the study. Solar power conversion is accomplished by designing a simple and reliable snubber circuit and a triode auxiliary circuit and tracking the maximum power point by combining the perturbation observation method with variable step size. The snubber circuit can effectively suppress the problems of pulse spikes and oscillations, and the triode auxiliary circuit prevents the reverse current and improves the switching speed, which reduces the switching loss and combines with the perturbation observation method with variable step size for fast and stable tracking of the maximum power point. In order to verify the feasibility of the converter, a 600 W prototype is designed. The experimental results show that using the snubber circuit reduces the pulse spike by 4.2 V and the overshoot is reduced by 4.3%. The maximum conversion efficiency is increased by 0.88% with the use of the transistor-assisted circuit, and the tracking efficiency of the MPPT is still stable under cloudy conditions. The maximum conversion efficiency of the prototype is finally measured to be up to 98.12%.

Research on Capacity Configuration for Green Power Substitution in an Isolated Grid Containing Electrolytic Aluminum

The deployment of a green power alternative within an isolated network, powered by renewable energy sources, in the “Three North” region of China can facilitate the substitution of high-energy-consuming industrial loads with green power. However, an inadequate power supply configuration may lead to economic and reliability issues. To address this problem, effective capacity allocation within the green power alternative isolated network is proposed. The capacity allocation process starts with the design of a network structure that aligns with local conditions. Subsequently, a capacity allocation model is developed, considering economic factors, renewable energy utilization efficiency, and system reliability. The gray wolf optimizer is enhanced to establish a capacity allocation method for the green power alternative isolated network. This method is then employed to simulate and assess the performance of the network. The results indicate that the green alternative isolated grid can successfully facilitate green power substitution, satisfying the energy requirements of the loads.

Cooperative operation of multiple low-carbon microgrids: An optimization study addressing gaming fraud and multiple uncertainties

To achieve efficient utilization of renewable energy and facilitate the development of a more equitable and harmonious power market, an optimization study is conducted focusing on peer-to-peer (P2P) power sharing within multiple low-carbon microgrids (LCMGs). Firstly, the coordinated operation mechanism of a LCMG is proposed involving a step-type carbon trading scheme as well as flexible supply and demand. Then, an optimization model of multi-LCMG cooperative operation is formulated based on the Nash bargaining theory. The original Nash bargaining problem is equivalently transformed into a subproblem of maximizing alliance benefits and a subproblem of maximizing the benefits of power trading. In the first subproblem, a combination of opportunity constraints and robust optimization method is employed to address these multiple uncertainties from renewable generation and electricity prices. The second subproblem addresses the issue of gaming fraud among LCMGs with a third-party intermediary model applied to achieve a fraud equilibrium. Further, to solve these problems efficiently, an improved adaptive parametric alternating direction method of multipliers (AP-ADMM) algorithm is used to protect the privacy of each party. Finally, a case study was conducted to validate the efficacy of the proposed method in enhancing the stability and cost-effectiveness of cooperative optimization within multi-LCMG systems.

A cooperative interprovincial management model under renewable portfolio standard policy accounting for employment factors

The vigorous development of renewable energy (RE) is the key to mitigating climate change, realizing energy transformation and thereby guaranteeing energy security. China formally implemented the renewable portfolio standard (RPS) in 2019 and has made certain advances, however, the spatial reverse distribution of RE supply and power consumption center and interprovincial barriers hinder the further development of the RPS. Therefore, this study considers the quota completion cost and employment factors comprehensively to construct a cooperative interprovincial management model (CIMM) of the RPS and address the interprovincial barriers while giving full play to provincial advantages. The minimum cost residual saving method is used to apportion the cooperation cost reasonably. Beijing, Hebei, Inner Mongolia and Shandong are taken as case provinces to verify the model. The result shows that, compared to the territorial management model, the CIMM increases RE generation by 19.58% and employment creation by 19.19% and lowers quota completion costs by 3.84%. A sensitivity analysis verified the robustness of the model. This study provides a scientific interprovincial cooperation model and cost distribution method for the central government to realize the efficient utilization of RE, and promote the further development of RE. Finally, policy recommendations to promote RE cooperation are proposed.

Wearable All-Fabric Hybrid Energy Harvester to Simultaneously Harvest Radiofrequency and Triboelectric Energy.

Distributed micro-energy harvesting devices offer the flexibility, sustainability, and multi-scenario applicability that will be critical to wearable electronic products in the Internet of Things. The radiofrequency and triboelectric (RF-TE) hybrid energy harvester (HEH) concept and prototype is presented for the first time, to simultaneously capture the energy from ambient electromagnetic waves and biological motions. The proposed hybrid energy harvesting system consists of a wearable rectenna, a triboelectric nanogenerator (TENG), and a power management circuit (PMC). Among them, the all-fabric rectenna exhibits good impedance matching characteristics in the ISM frequency. The flexible TENG unit can generate a maximum power density of 0.024µWcm-2. The designed multifunctional fabric-based PMC can considerably enhance the controllability of harvested hybrid energy. Additionally, a normalizable fabric circuit board quasi surface mount technology (FCB-SMT) is proposed to integrate all modules on the same fabric substrate in one step, making the entire system superior mechanical robustness. The proposed wearable fabric-based RF-TE hybrid energy harvester is capable of successfully driving consumer electronics (such as sensors, watches, etc.). It provides a new energy solution strategy for self-powered wearable electronic devices and is anticipated to encourage the efficient utilization of renewable energy.

Optimizing Electric Vehicle Integration with Vehicle-to-Grid Technology: The Influence of Price Difference and Battery Costs on Adoption, Profits, and Green Energy Utilization

Over the past decade, the widespread adoption of global green energy has emerged as a predominant trend. However, renewable energy sources, such as wind and solar power, face significant wastage due to challenges in energy storage. Electric vehicles (EVs) are considered an effective solution to address the energy storage dilemma. “Vehicle-to-grid” (V2G) technology, allowing vehicles to feed electricity into the grid, enhances the efficiency of renewable energy utilization. This paper proposes an electric vehicle game model that comprehensively considers user choices, corporate profits, and green energy utilization. The model, based on difference in prices, electricity rates, and fuel prices, establishes user utility models to determine optimal driving distances and driving decisions. It separately formulates the maximum profit functions for selling conventional electric cars and V2G electric cars, deriving optimal pricing for enterprises and user adoption rates. The research findings indicate that when price difference can offset V2G battery costs, increasing price difference and reducing battery costs effectively enhance electric vehicle adoption rates, increase corporate profits, and improve green energy utilization. Moreover, compared to conventional electric vehicles, V2G electric vehicles demonstrate a comparative advantage, with the implementation of V2G expanding corporate profits and green energy utilization. Validation using Chinese data reveals that when price difference can offset V2G battery costs, drivers are more inclined to choose V2G electric vehicles. Both battery electric vehicles (BEVs) and V2G electric vehicles exhibit adoption rates that can increase by over 35%. This study provides theoretical and model support for the future development of V2G and policy formulation, underscoring the comparative advantages of V2G in enhancing green energy utilization efficiency. Additionally, this study offers valuable insights as a reference for business models in the V2G electric vehicle industry.

Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization

A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training Algorithm (FTTA) is proposed according to the training method of the football team, which simulates the three stages of the training session: Collective Training, Group Training and Individual Extra Training. By the test on two groups of test functions, CEC2005 and CEC2020, the proposed optimization algorithm (FTTA) achieves the best results, which far exceeds the traditional Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) algorithms and so on. In the engineering application, a new hybrid wind speed prediction system is proposed based on FTTA. The FTTA is used to optimize variational mode decomposition (VMD) to improve the effect of data denoising. At the same time, based on unconstrained weighting algorithm, FTTA and combination prediction model build a new hybrid prediction strategy. Through the experiments on four groups of wind speed data in Dalian, the accuracy, stability, advancement, and CPU running speed of the system are verified. It is obvious that the practical application ability of the system is much better than previous methods, which can effectively improve the utilization efficiency of renewable energy.

Social-Aware Learning-Based Online Energy Scheduling for 5G Integrated Smart Distribution Power Grid

A 5G integrated smart distribution power grid brings a new paradigm shift to realize base station (BS) operation cost reduction, efficient renewable energy utilization, and stable energy supply. However, energy scheduling still faces some major challenges, such as coupling between energy sharing and energy trading, dimensionality curse, and intertwinement of social network attributes and BS load. To tackle these challenges, we propose a social-aware learning-based online energy scheduling (SNES) algorithm, which minimizes BS operation cost minimization under the constraints of energy supply stability. SNES leverages a deep neural network (DNN) to learn the action-state value of energy scheduling and intelligently adjusts purchased, sold, and shared energy based on only casual information. Moreover, SNES achieves social awareness by approximating the nonlinear interconnection between energy scheduling and quality of service (QoS) requirements of social network services. Simulation results verify the superior performance of SNES compared with state-of-the-art energy scheduling algorithms.

Monolithic Self-Charging Storage Device with Stable 3 V Operation

Monolithic self-charging storage device with Stable 3 V Operation The energy paradigm has been changing from the traditional power plant system to micro-gride system based on renewable energy. However, renewable energy including wind, solar, and vibration, is intermittent and influenced by time, weather, and location. Thus, the suitable storage device is required for efficient utilization of renewable energy. The representative combination of renewable energy and energy storage device is an energy storage system (EES) consisting of photovoltaic systems and Lithium-ion batteries. The EES is mainly applied to social infrastructure system that generate large amounts of energy. As the Fourth Industrial Revolution progresses, the rechargeable power supply including Lithium-ion batteries is widely applied to wireless system such as sensor network, wearable device, and electric vehicles. However, the batteries have only storage function, so periodic charging is required. Thus, it is necessary to develop technologies for portable and small energy generation/storage as standalone power sources that can continuously supply power to various electronic devices. Large-scale energy production/storage system has entered the maturity stage, but the development of portable and small energy generation/storage systems based on IoT is in its early stages. To date, research on this system is conducted by simply connecting energy harvesting and energy storage device to external wires after manufacturing them independently. However, the introduction of additional wires is complicated in system design and manufacturing and has difficulty in miniaturization. These problems have motivated the integration of both functions into one device. The integrated device is fabricated by sharing one electrode, called a common electrode between the energy harvesting and storage components. Currently, research on a monolithic photo-charging storage device that integrates solar cells and energy storage devices into one device has been developed. This structure can miniaturization and flexible design, thereby it is suitable for the wearable and smart device. Although the importance of an integrated device is emphasized, the current research of monolithic device is limited to the fabrication of an integrated device without considering their practicability. In other words, most studies focus on simply connecting two devices to achieve the highest overall efficiency. In this case, since the open circuit voltage of single solar cell is under 1 V, the operation of photo-charging storage device is limited to 1 V. Thus, we proposed the monolithic photo-charging storage device operating at 3 V. We fabricated the multiple series-connected perovskite solar cell to increase the operating voltage. Since the secondary batteries are difficult to be charged by the high current density of 20 mA cm−2 solar cell owing to their storage mechanism, we choose the supercapacitor with the rapid response to an applied electric field change. We select the silver paste as the common electrode owing to its strong adhesion, high electric conductivity, and lack of chemical/mechanical damage to the solar cell and supercapacitor. Based on density functional theory (DFT) calculation, we prove the common electrode exhibits good compatibility with our supercapacitor and solar cell. For this reason, the proposed photo-charging storage device exhibits an ultra-fast charging time of less than 3 sec under AM 1.5 G illumination and high overall efficiency of 13.17 % at 1 mA cm−2 and 9.87 % at 20 mA cm−2. For electric double layer supercapacitors integrated with a solar cell, higher discharging current densities that 20 mA cm−2 have not been reported so far. We firstly demonstrate the behavior of photo-charging storage device under light/dark state by the impedance analysis. By the impedance analysis, we demonstrated that the solar cell provides a stable power supply to the supercapacitor under light condition. Further, the additional encapsulation ensures the long-term stability of photo-charging storage device and demonstrate the indoor cycling performance. We prove the photo-charging storage device has high potential as an IoT sensor power source. Figure 1

An Integrated Neutrosophic Approach for the Urban Energy Internet Assessment under Sustainability Dimensions


 The urgent issue of climate change has resulted in an increasing preoccupation with the transition to low-carbon energy. The urban energy internet (UEI) enables the efficient utilization of renewable energy via the integration of contemporary energy grids, smart energy services, and cyber-physical systems. Consequently, the assessment of urban energy is vital for the construction of low-carbon cities. This study intends to provide an integrated decision-making approach for addressing assessment challenges related to UEI, with a focus on sustainability. The introduced approach adopts the opinions of three experts to express their opinions through semantic terms in evaluating sustainability factors and evaluating UEI projects. Determining the most suitable project for sustainable UEI is a difficult task. In addition, there are multiple sustainability dimensions, including: economic, social, technical, environmental and resource dimension. This paper presents an integrated approach through which experts can use semantic terms to express their opinions to evaluate the priorities of sustainability dimensions that affect the sustainability of UEI projects. Therefore, this study applies a hybrid multi-criteria decision making (MCDM) approach that takes into account multiple dimensions. Also, the uncertainty in the study is handled by type 2 neutrosophic numbers (T2NNs). Initially, the CRiteria Importance through Inter-criteria Correlation (CRITIC) method was used to determine the relative importance of the sustainability dimensions used in the study. Also, the evaluation based on distance from average solution (EDAS) method was used to rank the selected alternatives. The results indicate that the technical dimension is the most important with a weight of 0.384. Finally, a sensitivity analysis was conducted to prove the validity and reliability of the developed framework with differences in weights.

Numerical Research on Thermodynamic Properties of a Thermocline in Thermal Energy Storage Tank Based on Modified One-Dimensional Dimensionless Model

The application of thermal energy storage (TES) has been proved effective to improve the energy utilization efficiency of renewable energy and industrial waste heat energy. In this paper, a modified one-dimensional dimensionless model for the thermocline thermal energy storage tank is derived to simulate the system more accurately. An adaptive strategy for solving region compartmentalization is proposed for reducing computing time. Based on the proposed model, the effects of three different parameters on the performance of the thermocline tank are studied. The results show that increasing the inlet velocity can reduce the thickness of the thermocline and improve the system efficiency. Increasing the temperature difference between hot and cold water leads to a thicker thermocline, but the thermal energy stored in the tank increases. Increasing the tank height has no effect on the motion characteristic of thermocline, but the system efficiency can be increased.

Simplicity is the ultimate sophistication: One-step forming for thermosensitive solid–solid phase change thermal energy harvesting, storage, and utilization

In light of the growing recognition of low-carbon initiatives, renewable energy is gradually assuming a dominant role within the energy framework. Phase change materials can enhance the efficiency of renewable energy utilization by energy peak shaving and time shifting. However, leakage and inherent rigidity signify that a significant amount of energy input is required for scale-up applications of phase change materials to enhance shape stability and processability during industrial installation. In conventional enhancement strategies, high-quality encapsulation means more intricate processes and higher energy consumption. Here we propose a simplified thermally-initiated encapsulation and molding strategy, which involves grafting the phase change materials onto thermosetting polymer backbones by covalently integrated, to construct self-supported phase change materials with complex three-dimensional geometry. The encapsulation process is macroscopically manifested as a transformation from solid-liquid to solid–solid phase change. Therefore, by imposing restrictions on liquid materials, any ultra-high-precision complex structure can be molded, and mechanical properties are imparted through curing to achieve shape stability, ultimately enables the integration of phase change material encapsulation and casting. The complete process requires no curing agents or organic solvents, and no pollution emissions, making it highly maneuverable and environmentally friendly. Furthermore, we demonstrate the universal high value applications of new materials in different fields. In the ancient thermotherapy experiment, the sample exhibited sustained heat release for 8 min. In the advanced chip cooling simulation, the chip exhibits superior heat dissipation performance, as evidenced by its operating temperature being 50 °C lower than that of natural cooling within 20 min. It confirms the remarkable potential of phase change materials constructed through this approach for thermal energy applications.

Thermodynamic analysis of a hybrid energy system coupling solar organic Rankine cycle and ground source heat pump: Exploring heat cascade utilization

The demand for efficient utilization of renewable energy is growing and the hybrid energy system driven by solar and geothermal energy is a significant way to achieve carbon neutrality. This paper presents a hybrid energy system coupling solar organic Rankine cycle (S-ORC) with ground source heat pump (GSHP) for heating and power supply to explore the heat cascade utilization. In the hybrid energy system, the ground heat exchanger (GHE) is shared by the S-ORC and GSHP systems and the waste heat from the condenser of S-ORC is recovered to enhance the coefficient of performance (COP) of GSHP. A mathematical model is optimized to analyze the parametric effects on thermodynamic performance. The results indicate that the COP of the hybrid energy system can be improved up to 6.5 which shows the obvious advantage compared to 2.8 of the stand-alone GSHP system. Furthermore, the hybrid energy system can obtain optimum exergy efficiency by matching ground source temperature and condensation temperature in the ORC unit. For a building of 3000 m2 with heating and power supply, multi-objective optimization is carried out to obtain the optimal design performance that the net power output, thermal efficiency and exergy efficiency are 180.0 kW, 27.5%, and 9.2% respectively.