Transportation is vital to meeting China's carbon neutrality target by 2060. Nevertheless, the question of how to reach it remains unclear. Here, we employ a bottom-up energy system optimization model to investigate carbon dioxide emission trends using two sets of scenarios. The first relies solely on the efforts of the transportation sector, employing the avoid-shift-improve approach. In contrast, the second set of scenarios involves collaborative collaboration from the transportation, power and hydrogen sectors. The results reveal that achieving carbon neutrality solely through the efforts of the transportation sector is a challenging task. However, integrating negative emission technologies from the power and hydrogen sectors makes it feasible for the transportation sector to achieve carbon neutrality. Our findings suggest that in order to meet the carbon neutrality target, the energy structure of the transportation sector will undergo a fundamental transformation, with a significant increase in the use of electricity and hydrogen by 2060. Meanwhile, the power and hydrogen sectors will need to rely heavily on renewable energy sources and implement carbon capture and storage technologies to achieve substantial emissions reductions and offset the residual emissions from transportation. This study puts forward a comprehensive pathway that integrates the transportation sector with the power and hydrogen supply sectors, aiming to achieve carbon neutrality by 2060.

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand.In this study, a real-world electricity system was modeled rather than modeling hypothetical future electric power systems where the existing electricity infrastructure are neglected. In addition, instead of modeling the general requirements of storage in terms of cost and performance, an existing thermal energy storage concept with estimated capital cost that are sufficiently low to enable large-scale deployment in the electric power system were modeled. The storage unit is coupled with a photovoltaic (PV) system and were modeled with different storage capacities, whereas each storage unit had various discharge capacities.The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO2 emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO2 reduction scenarios, as the storage unit is utilized differently. When CO2 emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit.The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.

Demand side response (DSR) according to the time-of-use (ToU) tariff can bring a number of benefits to consumers and networks. However, for microgrids with high penetrations of renewable energy and flexible load, the sudden and drastic variations of loads triggered by the discontinuity of the ToU tariff could be fatal to the frequency stability of this low inertia system. This paper proposes a new frequency-based DSR (FB-DSR) strategy through a two-stage model with the frequency dynamics expressed analytically as the exchange power. The first stage optimizes the day-ahead DSR strategy based on the ToU tariff and equipment degradation cost. At the second stage, frequency response is constrained through the whole process of inertia support, frequency nadir and quasi-steady-state by integrating time-domain optimization and frequency-domain control. Numerical results demonstrate that the proposed strategy can effectively reduce the volatility of flexible load when responding to the ToU tariff, and thus significantly lowering the cost of maintaining the system frequency stability. At the same time, the negative impact of this strategy on the economic benefits of DSR is negligible. The overall operating cost of the test system is reduced by 7.34% even considering the increased degradation cost.

The effectiveness of continuous synthesis of petroleum coke-based potassium ion battery anodes by chemical activation agent cycle was systematically studied. The potassium ions storage performance in the synthesized carbon anode was measured. High‑sulfur petroleum coke, as a natural sulfur-doped hard carbon skeleton, can provide an excellent active site for the storage of potassium ions. Ultrasonic intensification significantly reduces the elution time required for neutralization and eliminates the need for a forced increase in elution temperature and rate to improve elution efficiency. In the continuous synthesis process, the recycled chemical activation agent not only does not destroy the activation effect but also has stable activation performance and a good driving force for the growth of activated carbon quality (the specific surface area is 1000 ∼ m2 g−1). Moreover, the sulfur content of the prepared hard carbon anode gradually decreases, whereas its electrical conductivity gradually increases, resulting in an increase in its potassium ions storage capacity. The carbon anode prepared by chemical activation agent after four cycles has a high reversible capacity of 290 mAh g−1 at 0.5 A g−1 and excellent cycling stability (99.5% hold rate at 0.5 A g−1 after 400 cycles). The galvanostatic intermittent titration technique results show that appropriate sulfur content and perfect carbon skeleton structure can promote potassium ions intercalation kinetics. In conclusion, the chemical activation agent cycling strategy can be used to achieve the continuous synthesis of petroleum coke-based potassium ion battery anode.

Polyethylene terephthalate (PET) is widely used as packaging and textile materials. Although PET bottles recycling is mature in many countries, steam gasification could be a solution to recover valuable products from end-of-life PET. CaO has been investigated as an absorbent to capture CO2 and improve H2 production in gasification but mostly it was analyzed as an individual effect. As the main novelty, this work studied not only the individual effect of temperature, steam/PET ratio and CaO/PET ratio on gas products, tars, and char but also the combined interaction of them on gas yields using response surface methodology in PET steam gasification with CaO. The experimental work was conducted in a bubbling fluidized bed gasifier and mathematical models were fitted with considering all significant terms. The results showed that H2 yield was doubled at 800 °C but increasing by 44% at 750 °C when the CaO/PET ratio raised from 0 to 2.0. Thus, temperature, CaO, and their interaction had significant effect on H2 yield, which was also reflected by the P-values calculated from the coefficients of the mathematical models. Tar analysis showed that benzene accounted for 80 wt% in tar products and adding CaO can reduce benzene by 34%. However, CO2 increased with adding CaO at temperatures of 700 °C – 800 °C implying that CaO mainly functioned as a catalyst instead of an absorbent. The models fitted well in R2 and model validations with non-model-fitting points. Therefore, the models can be applied for the prediction of gas product yield in the studied range.

For an automotive proton exchange membrane fuel cell engine, the efficient water content management is critical to its overall efficiency and lifetime. The fuel cell stack designed with internal humidifying effect is a promising solution for enhanced performance and compact system integration. In this work, a novel internal humidifying fuel cell stack design is proposed which utilizes the water and heat transfer through membrane in the triangular gas feed areas. Validated by the experimental test, a coupled three-dimensional model is developed to compare the fuel cell performance with three different feed area functions. The active feed area design performs the worst with the lowest reaction uniformity, while the humidifying feed area design presents the best performance with greatly improved water content distributions. The internal humidifying stack design is suitable for operations under dry reactants inflow conditions with more performance improvement and more evenly distributed reaction, which is beneficial for the compact fuel cell system integration without external humidifiers. To further improve the internal humidification effects of the stack, the asymmetric inlet/outlet feed areas with specific flow channels will be studied in future work.

Co-gasification technology provides a promising solution for the energetic valorization of various biomass feedstocks, especially those not directly applicable for gasification owing to their low-calorific values or high ash content, but the complexity of co-gasification technology has put forward challenges to model formulation when using numerical methods to evaluate the performance of the gasifier. In the present work, the Multiple Thermally Thick Particle (MTTP) model is developed and validated for fixed-bed co-gasification process. The sub-phase treatment that extends the conventional Eulerian-Eulerian framework allows calculating the inhomogeneity in the solid phase, and the intra- and inter-particle heat and mass transfer sub-models enable quantitative evaluation of the conversion of different solid fuels with nonnegligible intraparticle gradients. Parametric analyses of moisture content and particle size were performed to evaluate how the degree of difference between each fuel would influence the reaction processes, gasification performances and steady-state operating conditions. Upon changing the moisture content of the fuel mixtures (biomass and MSW) from both 9.0 wt% to 9.0 wt% for MSW and 27.0 wt% for biomass, the maximum temperature difference between the solid particle surfaces of different fuels was nearly 150 K, and the regions of different conversion processes became highly overlapped. When increasing the moisture content of biomass from 9.0 wt% to 27.0 wt% under 1:1 mixing ratio, the cold gas efficiency, volume fraction of H2 and higher heating value of syngas increased from 65.26%, 12.03%, and 6.29 MJ/Nm3 to 68.39%, 15.62%, and 6.44 MJ/Nm3, respectively. Therefore, the quality and efficiency can be improved when increasing the moisture content in a certain range although the overall capacity of the gasifier will decrease. The intraparticle temperature profiles calculated by the MTTP model also revealed that decreasing the particle size from centimeter- to millimeter-level will significantly reduce the temperature difference between the surface and center of the fuel particles, thus accelerating the in-bed reaction rates and increasing the overall capacity of the gasifier. The MTTP model is flexible for various working conditions and solid fuel mixtures, which is a versatile and convenient tool to optimize the complicated co-gasification process.

The traditional fuel locomotive is the primary type of locomotive currently in operation on non-electrified railways; however, it presents certain disadvantages including a low efficiency and high fuel consumption. Therefore, in this study, a multimode hybrid locomotive configuration scheme is designed to improve the system efficiency and reduce fuel consumption during locomotive operation; further, the power flow state under different modes is analyzed, the mathematical model of the hybrid locomotive is established, and a system optimal efficiency calculation method is developed. Moreover, an energy management strategy based on the optimal system efficiency and with a hierarchical architecture is proposed. In the upper layer of this proposed energy management strategy, the optimal efficiency of all operating points and operational state of each component are determined offline using the optimal efficiency calculation method. The lower layer selects and coordinates the mode online by identifying the condition of the wheel and distributes the torque of each power source and the state of each transmission system component. The simulation results indicate that the fuel economy of the proposed energy management strategy is improved by 27.22% compared with that of the traditional fuel locomotive, and the fuel economy is only 7.21% lower than the global optimization result obtained via dynamic programming. In addition, no frequent clutch switching is observed under this strategy, and the electric motor does not operate under non-rated conditions for prolonged periods; these advantages ensure the rationality of control and the reliability of component operation. Finally, the results of a hardware-in-the-loop simulation test confirm that the proposed energy management strategy demonstrates good real-time performance.

The “shoeboxing” algorithm is a simplification approach capable of converting a building of any shape into a representative shoebox, with the aim of speeding-up Urban Building Energy Modeling simulations. The procedure works towards accurately predicting both annual energy needs and hourly thermal loads, going beyond the preliminary assessment of buildings' thermal performance at city-scale. Furthermore, the simplification has been particularly developed to work with buildings of complex geometry considering adjacencies and obstructions. After a first validation performed for stand-alone buildings, in this paper, the capabilities of the algorithm are evaluated at district-level on fictional parametrically generated layouts and in different climatic conditions. As a whole, the shoeboxing algorithm properly predicted both heating and cooling needs at building-level in all the considered climatic conditions, yielding annual differences within ±10% and ± 20%, respectively for cooling and heating. Moreover, both annual and hourly deviations showed to be similar in the different climates considered, suggesting that the simplification can be reliably employed worldwide. Finally, the thermal simulation time has been reduced up to 36 times.

Collaboration between transmission and distribution system operators (TSOs and DSOs) is crucial to improve power supply reliability during a massive blackout. However, these two systems usually operate separately in reality. To coordinate them, a new fully parallel decentralized TSO + DSO service restoration (D-TDSR) scheme is proposed in this paper. Based on analytical target cascading (ATC) method, the proposed D-TDSR is able to formulate the local restoration model for each TSO and DSO by decomposing shared boundary information in an iterative process. By introducing the diagonal quadratic approximation in ATC, all local restoration models are solved in a fully parallel manner with no need for a central coordinator. This parallel implementation increases the computation efficiency of decentralized restoration procedure. Furthermore, to enhance the ability of TSO and DSOs to support each other, the potential benefits of flexible regulation in DSO, including soft open points and network reconfiguration, are fully exploited, thus improving critical load restoration speed, and reducing voltage deviations. The effectiveness of the proposed D-TDSR is validated using an IEEE standard test system.

Intelligent design and operation optimization allow energy systems to take advantage of the flexibility that multi-generation provides. This study proposes a basic solar-driven system integrated with thermal energy storage for round-the-clock energy harvesting. A modified configuration is then designed incorporating innovative multi-heat recovery approaches to increase the capacity and product diversity of the basic system. The modified system is able to cover vital urban utilities such as electricity, fresh water, cooling, and hydrogen throughout the day. To overcome the time-consuming procedure of dynamic techno-economic simulation as well as the limitation of commercial engineering equation solvers for tri-objective optimization, a deep learning approach is developed to reduce the computational complexity and improve the analysis accuracy. In this regard, the trained neural networks play an intermediary role in coupling the developed code with the MATLAB optimization toolbox. A comparison between the modified and conventional configurations indicates that implementing the multi-heat recovery approach results in a 29% increase in power generation while only increasing the overall system cost by 1.97%. From an economic perspective, the Sankey diagram depicts that the storage unit with a cost rate of 12.25 $/h accounts for 6.77% of the plant's cost rate, which enables the system to operate continuously. According to the sensitivity analysis and contour plots, the number of collectors significantly affects the total cost rate and fresh water production capacity while it has no tangible effect on the exergy efficiency.

The aggregator can be introduced to the community microgrid to facilitate small-scale prosumers participating in peer-to-peer (P2P) energy trading. However, when the aggregator performs price anticipation for profit with its market power, market trading fairness and efficiency of the P2P market may be harmed. To analyze the formation and negative effect of market power, this paper proposes a market power modeling and restraint method of aggregated prosumers with a game-theoretic approach. First, a framework for the P2P energy trading market with the aggregator is established, and the price impact factor is defined and introduced to characterize the impact of the aggregator's trading behavior on the market price. Then, the competition between the aggregator and individual prosumers is modeled as a non-cooperative game, and the impacts of market power on price formation, trading profit, and overall benefit are analyzed, respectively. Finally, the tentative offers penalty is introduced in the market to restrain the market power of the aggregator. Numerical results show that the aggregator can take advantage of its market power to manipulate the market price for profit when the value of the price impact factor is well perceived, which may harm the fair distribution of welfare among prosumers and the overall benefit of the microgrid. Meanwhile, the results also prove that the involvement of the tentative offers penalty can restrict the market power perception ability of the aggregator and further restrain the impact of market power on the benefit of the market.

Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ∼ 980 to 2,290 kWh in a single-family home with a floor area of ∼ 220 m2 compared with an IECC 2018 baseline. The energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate.

Precise wind speed forecasting contributes to wind power consumption and power grid schedule as well as promotes the implementation of global carbon neutrality policy. However, in existing research, the negative impact of outliers on forecasting models is ignored and the inherent shortcomings of the single predictors have not been taken seriously. Moreover, the intrinsic parameters of predictors are set by manual and empirical methods in some research, leading to difficulties in achieving optimal forecasting performance. To solve the shortcomings of existing research, a multi-step short-term wind speed forecasting framework is proposed by incorporating boxplot-medcouple (MC), variational mode decomposition (VMD), phase space reconstruction (PSR), weight-based stacked generalization with enhanced differential evolution slime mold algorithm (DESMA). Firstly, boxplot-MC is employed to achieve outlier detection and correction for preprocessing original wind speed data by analyzing values and trends. Then, the modified data is further adaptively decomposed into multiple subsequences by VMD, after which each subsequence is constructed into feature matrices through PSR. Subsequently, weight-based multi-model fusion strategy in layer-1 of stacked generalization is proposed to integrate the predicting values acquired by three primary learners, of which the weight coefficients are calculated with the error between actual values and predicting values. After that, kernel extreme learning machine (KELM) in layer-2 of stacked generalization is applied to predict the fusion result to obtain forecasting value corresponding to each subsequence. Meanwhile, an enhanced DESMA based on slime mold algorithm (SMA) and differential evolution (DE) is proposed to calibrate the parameters of KELM. Eventually, the final wind speed forecasting results are attained by summing the prediction values of all subsequences. Furthermore, comparative experiments from different aspects are undertaken on real datasets to ascertain the availability of the proposed framework. The experimental results are clarified as follows: (1) outlier detection and correction employing boxplot-MC is dedicated to analyzing values and trends effectively, with which the negative impact of outliers can be weakened while retaining valid data significantly; (2) VMD can prominently reduce the non-smoothness and volatility of wind speed data; (3) weight-based stacked generalization is conducive to exploiting the advantages of individual primary learners, contributing to compensating for instability; (4) DESMA enhances prediction accuracy by optimizing the parameters of KELM. Additionally, the code has been made available at http://github.com/fyc233/a-multi-step-short-term-wind-speed-forecasting-framework.git.

Local energy generation and energy use flexibility is becoming increasingly relevant for planning energy systems with a high level of renewables, electrification, and decentralization. Although current energy system models represent various flexibility options, capturing demand-side flexibility and prosumption remains challenging. In light of these challenges, this study presents a national-scale energy system optimization model covering the residential and commercial sectors of Belgium and considering the interplay between local energy use flexibility from electric vehicle (EV) charging, battery storage, rooftop photovoltaic (PV) systems and distribution grids. To adequately represent energy use flexibility, the model adopts a high temporal resolution, consisting of three representative weeks at an hourly time scale. The role of prosumption is analysed by introducing a prosumer potential reflecting the extent to which local energy production can be used to meet local demand ‘behind the meter’. The results show that flexibility significantly reduces total system costs with reduced distribution grid capacity investment by 48% by 2050. Furthermore, local energy use flexibility enables a significant uptake of rooftop PV (+6.7 GW), but only under a high prosumer potential condition. Finally, a curtailment of around 5–7% of PV production is cost-effective to sustain a more continuous use of the distribution grid at low peak capacity. To increase the robustness of the results, future research should consider expanding the model's sector coverage, adding additional flexibility sources, improving the representation of prosumption, soft-linking with distribution grid models and a better representation of mobility patterns.

Although the utilization of solar photothermal gain (SPG) (0.28μm–2.5 μm) or infrared radiation cooling (IRC) (2.5 μm–25 μm) enables the thermochromic smart window to play a role in decreasing building energy consumption, the fragmented current research on these properties unfortunately leads to a low efficacy. Herein, based on the simulation of SPG and IRC properties on building energy consumption, a tailored hydrogel is employed in a reversible glass mold with anisotropic infrared radiation emission (ε) in the different surfaces, successfully integrating excellent SPG parameters (phase transition temperature (τC), 28°C, post-phase solar transmittance (Tsol−HT), 3.52%) and IRC parameters (switchable infrared emissivity (Δε), 0.75–0.90). By utilizing summer and winter climate data from Wenzhou, as well as annual climate data from Singapore and Helsinki, it is determined that this gel window may save up to 64.68 MJ/m2, 11.31 MJ/m2, 201.84 MJ/m2, and 60.16 MJ/m2 in the respective locations, when compared to regular glass using the same model in EnergyPlus software. With the exceptional pre-phase visible light transmittance (Tlum−LT=84.86%) and building energy saving performance, the all-day and all-region smart window deserves attention, further investigation, and discourse for alleviating the energy crisis and mitigating greenhouse emissions.

This paper presents a novel wide-bandwidth triboelectric energy harvester (WBTEH) that takes advantage of impact nonlinearity and multi-resonance. The harvester features a triboelectric transducer that operates in contact and separation mode, with two cantilever beams of different resonant frequencies connected to it. By exploiting the relative motion of the beams, the harvester achieves a broad bandwidth through the resonance shift caused by the impact and multi-resonance. A WBTEH prototype with a 3 mm gap between the triboelectric pair shows a total bandwidth of 4.3 Hz even at a low base excitation of 3 m/s2. The matched peaks and bandwidth in the frequency up-sweep and down-sweep tests demonstrate the excellent stability of the WBTEH. The frequency locking phenomenon with strong resonance occurs in the WBTEH when the displacement amplitude/gap ratio exceeds 1.48, which is beneficial for obtaining a continuous bandwidth and a high power output. An electromechanical model is formulated for parametric studies that investigate the effects of contact stiffness and damping on the performance of WBTEH. It is found that large impact stiffness and small damping can cause quasi-periodic motion, leading to a non-constant voltage output that should be prevented in the harvester design. The WBTEH is capable of powering wireless sensors, making it a potential candidate for Internet of Things (IoT) applications.

Antibiotic wastewater has caused serious environmental pollution and is hard to utilize. Thus, it is urgent to develop a green and efficient technology for treating antibiotic wastewater and effective utilization. The reported fuel cell technologies can degrade antibiotics and generate electricity simultaneously, but their cell performance is very bad and difficult application. Herein, we reported a new environment-friendly and low-cost redox flow fuel cell (RFFC) based on the photothermal synergistic effect to convert antibiotic wastewater into electricity directly and efficiently at low temperature. The developed RFFC can output the maximum power density of 98.2 mW cm−2 which is 545 times of the reported microbial fuel cells and 270 times of the reported photocatalytic fuel cells. And the photothermal degradation method is better than the thermal degradation and photo degradation. Furthermore, it can discharge stably >2.5 h at high current density of 2 A cm−2 and successfully power a small electrical fan (1.5 V). The reaction mechanism is studied by the density functional theory (DFT) calculation, and the results show that FeCl3 molecules as photocatalyst and electron carriers of the RFFC can complex with antibiotics to greatly reduce the energy gap between HOMO and LUMO of antibiotics, which make the antibiotic molecules easy to be excited to unstable excited state by visible and UV light (λ < 733 nm) and greatly beneficial for their photothermal degradation. Besides, when using cefuroxime sodium wastewater as the model wastewater, HS-GC–MS results show that cefuroxime sodium can be completely degraded into non-toxic micro molecules after generating electricity. This work shows promising potential application for high-value utilization of antibiotic wastewater and generating clean electricity.

Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and extend battery service life. The existing battery models used for SOC estimation inadequately capture the dynamic characteristics of a battery in a wide temperature over the full SOC range, leading to significant inaccuracies in SOC estimation, especially in low temperature and low SOC. A novel SOC estimation approach is developed based on a fusion of neural network model and equivalent circuit model. Firstly, the weight-SOC-temperature relationship is established by obtaining the weights of the equivalent circuit model and the neural network model offline using the standard deviation weight assignment method. Following that, an online adaptive weight correction approach is implemented to update the weight-SOC-temperature relationship. Finally, a novel multi-algorithm fusion technique is utilized to achieve SOC estimation accuracy within 1%. The results clearly demonstrate that the developed approach achieves twice the accuracy of the existing approach, highlighting its superior effectiveness.

The gas production from marine gas hydrate has always been plagued by low productivity; the complex geological environment poses a significant obstacle to its commercialization. Horizontal wells are thus getting increasing attention due to their advantages in facilitating pressure propagation. Here a pre-embedded dual horizontal well was used to probe its effects in the local temperature distribution and gas production behavior. By doubling the number of boreholes, the flow channels of gas were increased enlarging the decomposition zone. Specifically, this was found to raise the reservoir temperature from −1 °C in a single well to approximately 0 °C. The temperature decline was more moderate due to a weakened Joule-Thomson effect. Consequently, almost 90% of the cumulative gas yield was produced before the lowest temperature occurred, compared to ∼30% in the single vertical well case. This indicates that the gas production from a dual well case was proceeding at a relatively higher temperature, potentially benefitting an enhanced gas production efficiency. An enlarged depressurization region was thus suggested in the field test for a controlled temperature decline to make more use of the sensible heat of the reservoir.