Unit Energy Research Articles

Robust optimization scheduling of microgrid considering a tiered carbon trading mechanism and flexible loads

Abstract Considering the comprehensive demand response, stepwise carbon trading, and uncertainty of wind power output for cogeneration microgrids, relevant optimization scheduling research will be carried out. The electric heating load is divided into non-dispatchable electric heating load and dispatchable electric heating load. By introducing electric heating demand and tiered carbon trading, the economic and low-carbon operation of microgrids can be achieved. The fluctuation of the output of new energy units is reflected using probability scenario sets in statistics, and a robust optimization model with related distributions is constructed using the C&CG algorithm to calculate and solve the established model. The simulation and comparison of numerical examples show that the microgrid energy management scheme formulated by the model established in this paper has good economic and environmental friendliness, and it also simultaneously validated the superiority of distributed robust optimization methods over traditional optimization methods

Leachate treatment via electrocoagulation–coal‐based powdered activated carbon process: Efficiencies, mechanisms, kinetics, and costs

AbstractThis study aims to improve COD, NH3‐N, and turbidity removal from Bingöl's leachate using a single‐reactor integrated electrocoagulation (EC)–coal‐based powdered activated carbon (CBPAC) process under various experimental conditions. In the EC‐CBPAC process, three stainless‐steel cathodes and three aluminum electrodes were connected to the negative and positive terminals of the power supply, respectively. The initial concentrations in the leachate were 1044 mg O2/L for COD, 204 mg/L for NH3‐N, and 57 NTU (or 71.25‐mg (NH2)2H2SO4/L) for turbidity, respectively. After a 40‐min EC‐CBPAC process, with a CBPAC dosage of 5 g/L and pH of 5 for COD and turbidity, and 9.5 for NH3‐N, the optimum removal efficiencies for COD, NH3‐N, and turbidity were achieved at 92%, 40%, and 91%, respectively. When the EC process was applied without CBPAC under the same experimental conditions, the removal efficiencies of COD, NH3‐N, and turbidity were 87%, 28%, and 54%, respectively. Before and after the EC‐CBPAC process, the Brunauer–Emmett–Teller (BET) surface area, pore volume, and mean pore diameter of the CBPAC were found to be (888 m2/g, 0.498 cm3/g, and 22.28 Å) and (173 m2/g, 0.18 cm3/g, and 42.8 Å), respectively. The optimum pseudo‐first‐order (PFO) rate constants for COD, turbidity, and NH3‐N were determined to be 3.15 × 10−2, 4.77 × 10−2, and 8.8 × 10−3 min−1, respectively. With the current density increasing from 15 to 25 mA/cm2, energy consumption, unit energy consumption, and total cost increased from 68.7 to 122.4 kWh/m3, 6.948 to 15.226 kWh/kg COD, and 0.85 to 1.838 $/kg COD, respectively.Practitioner points EC‐CBPAC process has greater COD, NH3‐N, and turbidity removal efficiency than EC process. COD and turbidity achieved their optimum disposal efficiencies at 92% and 91%, respectively, at pH 5 The most efficient disposal efficiency for NH3‐N was observed to be 40% at pH 9.5. EC‐CBPAC process increased removal efficiencies for COD, NH3‐N, and turbidity by 20%, 19%, and 38%, respectively, compared with EC alone. The turbidity, NH3‐N, and COD disposal fitted PSO model due to high correlation values (R2 0.94–0.99).

Low-maintenance anti-scaling of nanofiltration pretreated by bipolar-induced electrolysis for decentralized water supply

Nanofiltration (NF) offers an effective solution to ensure a decentralized water supply. However, its reliance on dosing for pre-precipitation and anti-scaling severely complicates its maintenance, adding to its cost and limiting its application, especially in rural areas. In this study, a bipolar-induced electrolysis (BIE) pretreatment method was developed to reduce the NF membrane scaling potential without the need for additional reagents. The water flux of the BIE-NF increased by 24.2 ± 6.5 % compared to traditional NF. This benefits from the synergistic effects of electrocoagulation (EC) and electro-precipitation (EP) of BIE to remove hardness ions (36.8 ± 0.7 g CaCO3 m−2 h−1), 26.2 ± 2.6 % and 16.1 ± 4.7 % faster than that of traditional EP and EC, respectively, exhibiting robust resistance to the interfering ions and organics. The unit energy consumption of BIE was only 18.9 ± 1.0 kWh/(kg CaCO3), 11.8 ± 1.9 % and 67.0 ± 1.4 % lower than that of EP and EC, respectively. After BIE electrolysis, the Ca, humic acid, and Mg contents were dramatically reduced, and a uniformly loose fouling layer formed on the NF membrane, demonstrating alleviated membrane scaling. Remarkably, BIE softening demonstrated its stability and feasibility in sustaining 120.0 m3/d of drinking water to rural populations without requiring additional reagents or manual maintenance, minimizing the operation and maintenance cost to 5.1 US cents/m3 water, 29.0 % lower than the cost of conventional dosing pretreatment. This study offers a low-cost and sustainable method for water supply through decentralized membrane purification.

Semi-submersible non-axial current generators based on improving current energy conversion efficiency

Abstract With the development and utilization of ocean current energy, how to improve the efficiency of ocean current energy utilization has become the focus of attention in many countries. In this paper, the optimal design of the structure of the ocean current generator is mainly used to improve the energy capture power and the conversion efficiency of the ocean current energy unit under the condition of low flow rate. The effect of peripheral fluid growth on the generator is analyzed using finite element analysis. The three-dimensional finite element model of the semi-submersible axial-current generator is established by using the ANSYS finite element method, and the calculation and analysis of the peripheral fairing are simplified. Through finite element calculation and analysis, the velocity cloud diagram and velocity linear vector diagram are obtained, respectively. The results show that the semi-submersible axial current generator has the ability to start and generate electricity at low velocity and can use the current resources for power supply in situ. Its application can maximize the current capture and greatly improve the utilization rate of current energy.

Enhancing CO2 capture efficiency: Computational fluid dynamics investigation of gas-liquid vortex reactor configurations for process intensification

This study employs non-thermal Computational Fluid Dynamics (CFD) simulations to explore the efficacy of a gas–liquid vortex reactor (GLVR) for intensifying CO2 capture. The investigation concentrates on the multiphase flow and mass transfer behavior in diverse GLVR experimental units. Employing a multiphase Euler-Euler CFD model integrated with a Population Balance Model (CFD-PBM), a mass transfer model, and reaction kinetics, allows us to accurately simulate the reactive absorption of CO2 into aqueous Monoethanolamine (MEA). Experiments are conducted using a 30 wt% MEA solution for CO2 absorption, serving the purpose of model validation. This comprehensive approach enables a simulation of complex dynamics within the GLVR, emphasizing bubble breakage, coalescence, and reactive mass transfer processes. Examining bubble size distribution, pressure drop, CO2 absorption efficiency, and energy input systematically across various reactor geometries and operational conditions, our findings demonstrate that an optimized GLVR configuration significantly enhances CO2 absorption compared to the original design. Furthermore, the optimized GLVR outperforms state-of-the-art process intensification equipment in terms of CO2 absorption rate per unit reactor volume and energy efficiency.

A two-tier bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism for coupled electricity and carbon markets

To facilitate the coupling of electricity and carbon markets, a two-layer bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism is proposed. The upper layer models the bidding strategies of renewable energy units and thermal power generating units, and the lower layer is the market clearing layer. A multi-stage offer‑carbon joint incentive clearing mechanism is proposed. In the pre-clearing stage, the environmental benefit factor of the carbon market is introduced to adjust the bidding strategy of the units, the carbon emission intensity enters the market in the form of price, the environmental benefit of the generating units is fully considered, and the carbon quota allocation model of units is established; in the second stage, carbon trading cost is added based on the pre-clearing model to further optimize the unit output; in the third stage, the clearing model with the minimum cost of electricity energy is established for tracking the clearing price. Under this mechanism, the carbon trading cost is traded on the market and unit offers are combined with carbon element to participate in market trading. Three bidding models with different clearing mechanisms are set up, and the results of the arithmetic example show that the proposed model can realize the balance between the economic and low-carbon goals of market clearing, and can achieve the maximum carbon emission reduction based on no increase in the total cost of electric-carbon coupling. As the share of renewable energy increases, the clearing price will decrease. A reasonable offer from renewable energy sources can avoid their interests being jeopardized. Concurrently, increasing the initial carbon price can effectively guide the electricity market to develop in a cleaner direction.

Reliability modeling and assessment of a community microgrid with electric vehicle charging station as a critical load

Microgrids are constituted of interconnected loads and dispersed energy resources. They increase the reliability of the power supply, decrease the cost of the per unit energy and support the green environment concept. Microgrids have gained importance due to their ability to deliver reliable energy and operate in both grid-connected and off-grid/island modes. Renewable energy sources such as photovoltaic (PV) and wind can be connected to the microgrid system to generate green energy. An electric vehicle charging station (EVCS) can be deployed in microgrids powered by renewable energy sources. An effective way to handle the rapidly rising load demand of electric vehicles (EVs) is to integrate EV recharge stations into microgrids. Due to the intermittent nature of solar PV and EV load's reliability, an evaluation of microgrids becomes challenging. Assessment of the components’ failure and their impact on the system reliability is proposed using different methods. Reliability Block Diagram (RBD), Markov Reliability Modeling (MRM) and Fault Tree Analysis (FTA) - Monte Carlo simulation are used for the Reliability Modeling of a community microgrid considering EVCS as a critical load. The mean time to failure (MTTF) is computed for the entire microgrid which includes PV, wind and fuel cell systems integrated into the grid with associated power electronic devices, transmission lines, transformers and point of common coupling (PCC). MATLAB software was used to program and study the reliability aspects of the microgrid. Using the FTA MCS technique and grid functions for 45 years with 20 % reliability, significant results were obtained in terms of a MTTF rate of 26.16 years.

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Optimal production decision of hybrid power generation enterprises in multi-quota policy coupled markets

At present, China is adopting both Carbon Emission Trading (CET) and Tradable Green Certificate (TGC) markets to optimize the resource allocation of its power industry. To mitigate the impacts of the above dual markets, more and more power generation enterprises are adopting hybrid production mode, generating electricity from thermal and new energy units simultaneously. In these complex production and market conditions, enterprises are difficult to arrange their generation activities. This research describes the production decision process of hybrid power generation enterprises with different market positions, obtains their optimal yield, and then offers market equilibrium results. In addition, the consequences of enterprise collusion behaviors and government interventions are also measured. Numerical experiment results confirm the correctness of the production decision model and reveal the following facts: 1) CET and TGC markets effectively control the carbon emission level and promote the consumption of new energy, but magnify the failure of electricity market to a certain extent. 2) CET and TGC markets promote each other in function. The existence of one market can help the other to achieve the anticipated goal. 3) Enterprises first offset the impact of market condition changes by adjusting the share of new energy generation, and then digest the residual influence by adjusting the total power generation. 4) Enterprise collusion behaviors aggravate the welfare loss of electricity consumer and then lower the expected effects of CET and TGC markets.

Multi-objective optimization for generative morphological design using energy and comfort models with a practical design of new rural community in China

In recent years, the development of new rural communities has emerged as a pivotal new trend in china, aimed at enhancing energy efficiency and residential comfort in rural regions. This study introduces a multi-objective optimization design framework for the morphological design of residential area, with a focus on performance objectives such as building energy use intensity, summer thermal comfort, and winter thermal comfort. A case study of a new rural community design project in Hongnan Village, Yancheng City, was conducted. Utilizing the Rhino-Grasshopper parametric platform, this study undertook parametric modeling, performance simulation, and multi-objective optimization to receive the Pareto-optimal solutions. ParetoResults indicate that in the Pareto-optimal morphology scheme for the Hongnan Village community, open spaces are predominantly located on the upwind side with respect to the prevailing summer winds of the site. The number of duplex houses is often 28, and the building orientations are predominantly aligned at 23°, mirroring the direction of the surrounding structures. There is a significant correlation between residential unit configuration, building spacing, and energy use intensity, with correlation coefficients of 0.53 and −0.43, respectively. This study would like to offer valuable insights into the energy-efficient design of new rural communities, thereby contributing to the creation of a more sustainable and habitable rural living environment.

Resource management for computational offload in MEC networks with energy harvesting and relay assistance

In mobile edge computing (MEC) networks, the integrated application of energy harvesting (EH) and relay cooperation is a promising technology, which ensures the real-time performance of the system and computing power supply. Relay and EH cooperation also expand communication range and extend the battery life of energy-constrained devices. However, in wireless powered and relay-assisted MEC networks, it is challenging to jointly dispatch energy, and computing resources to coordinate heterogeneous performance requirements. To fill this gap, this paper examines the fundamental compromise between utility energy efficiency (UEE) and stable queue length in wireless powered and relay-assisted MEC network systems, and the amount of data transferred for each unit of energy is called UEE. The data queue and energy queue models are introduced and the constraints of stability of two queues in long term are included in the formulated problem. To tackle the fractional and nonconvex optimization problems, the Dinkelsbach method is utilized. Since optimization problems change from time to time and the long-term averaging queue stability is considered, the Lyapunov optimization theory is introduced to convert the non-convex problems into the solvable one. An online resource offloading allocation algorithm is proposed to determine the solution. In addition, the algorithm implements the control parameter V to achieve the compromise between UEE and stable queue length, while the impact of various parameters are also revealed on system performance.

An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details.

Review of the development of carbon-based battery

In todays society, people are increasingly aware of the importance of protecting the environment. In the past, some batteries and power generation methods were extremely harmful to the environment. So people began to seek sustainable energy sources, such as hydropower, solar power, and wind power. Among them, how to efficiently and sustainably store electric energy for these power generation methods while using the most environmentally friendly materials has become an important research direction. Carbon-based batteries have become the preferred energy storage device due to their many advantages, such as easy material acquisition, low cost, environmental protection and degradability. This paper mainly summarizes the advantages of carbon-based batteries such as simple and relatively mature preparation process, high stability and strong sustainability, as well as unit energy from the aspects of carbon-based supercapacitors, solar cells, solid-state batteries and bio-carbon-based batteries. This paper finds that shortcomings of carbon-based batteries such as low density and poor safety in special environments require further innovation in materials and processes. If the above shortcomings can be made up, carbon-based batteries will have huge application space.

Process simulation and optimization for post-combustion CO2 capture pilot plant using high CO2 concentration flue gas as a source

This work mainly focuses on process simulation and optimization of a high-concentrated CO2 chemical absorption process using aqueous amine solutions based on both pilot plant data and process modeling. To optimize operating parameters for reducing energy consumptions, this study explores the influence of process structure and operational parameters on the product purity, capture rate, and energy consumption, which would provide theoretical and technical support for optimizing high-concentration CO2 capture. At first, the rate-based absorption/desorption models are established and well-verified by a total of 25 sets of pilot plant data, which shows high accuracy for reliable model predictions. Under an annual absorption capacity of 50 tons at a 90% capture rate and 95% product purity, the optimal case using Monoethanolamine (MEA) solution shows an increased CO2 capture rate of 12.37% and a decreased unit energy consumption of 22.5%. After flowsheet optimization, a new system of blended MEA, Methyl diethanolamine (MDEA), and 2-Amino-2-methyl-1-propanol (AMP) solution is designed to capturing high-concentrated CO2 under the same conditions. Compared with the MEA system, its specified energy consumption is reduced by 10.35% and its CO2 capture capacity increases by 11.6 tons annually.

ASSESSING THE SUSTAINABILITY OF ADVANCED AND DEVELOPING COUNTRIES: A DIFFERENT PERSPECTIVE

Purpose – The research aim of the article is the analysis of advanced/developed and developing countries in the context of sustainable development, using invariant coordinate system in energy units, as well as the impact of structural changes in the economy affect the possibility of transition to sustainable development and creation post-industrial society. Research methodology – In order to achieve the aim were applied the method of socio-economic systems power changes analyzing in the frame of structural economic Kaldor model of GDP and the energy flows models. Finding – The advanced countries have seen a trend of “zero growth” and a decrease in useful energy production in the last 10 years. These countries have high labor productivity, technological level and diminishing potential for sustainable development. China is in the stage of “growth and development” and has great potential. Data calculated by the authors of the article correlates with the findings of the Kaldor model. Research limitation – The initial interpretation of the calculated data of France, Germany, Japan, USA, European Union and China was made. Countries have been analyzed in the period from 1990 to 2019. The data of the Central Statistical Office of EU and the World Bank were used. Practical implications – The results of the study can be used to further planning and design of transition to sustainable development for advanced and developing countries. Originality/Value – within the framework of the approach, an invariant coordinates system in energy units is proposed and the main parameters for assessing the potential of growth and sustainable development for advanced countries were formed.

Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading

Promoting renewable energy and developing low-carbon integrated energy systems are noteworthy in the energy sector. However, in existing works on the integrated energy system, the coupling of green certificate and carbon trading mechanism under diversified utilization of hydrogen energy has not been fully considered to provide an incentive effect for uncertain renewable energy resources. Based on these considerations, a multi-timescale optimization of an integrated energy system with diversified utilization of hydrogen energy is studied under the coupling of green certificate and carbon trading mechanism. First, the coupled interaction mechanism of green certificate trading-stepped carbon trading is proposed by linking stepped carbon trading to green certificate trading through green certificates. Furthermore, the establishment of the diversified utilization unit of hydrogen energy addresses carbon emissions from the perspective of energy application and offers fresh concepts for a low-carbon economy. Then, an optimized scheduling model for multiple time scales of day-ahead/intraday/real-time is developed to reduce the impact of renewable energy prediction errors on system operation. Results acquired from case studies demonstrate the effectiveness in terms of achieving economic and low carbon. The accommodation level of renewable energy with a coupling mechanism improves by 3.08% compared to that without considering coupling. In addition, the diversified utilization of the hydrogen energy model enables the reduction in total cost and carbon emission by 5.14% and 2.4927 tonnes in comparison to the traditional power-to-gas mode.

The effect of current collectors on polymer-based energy-generating units inspired by the electric organs of eels

Bioinspired energy generation systems, particularly focusing on replicating the electrocytes, which are the cells of electric discharging organs of electric eels, have attracted much attention for battery cell development. Motivated by this trend, this study explores the effects of different current collectors (Au, Cu, Ni foils, and multilayer graphene-coated Ni foam) on the cell performance and open-circuit potentials of bioinspired energy-generating units. The results indicate that asymmetric current collectors, particularly the Cu-cell-Al configurations, yielded higher open circuit voltages and more stable power outputs over extended cycles. A polymer-based stacked energy unit between nickel current collectors achieved a high open circuit voltage of 230 mV and had a 75 % capacity decrease after 500 cycles. The developed devices, especially with asymmetric current collectors, provided a promising energy density (6541μWh.cm−2) and power density (50 mW.cm−2) while maintaining stability over thousands of cycles. In conclusion, flexible energy-generating units with potential applications in long-term energy needs were proposed in this study by incorporating electrochemical interactions to enhance the power output.

A bilevel fast-convergent optimizer via high-fidelity convex models: Application on optimal operation of all-parallel heterogeneous chiller-pump systems

To cope with buildings’ growing and diverse cooling demand, heterogeneous multi-chiller systems with non-dedicated pumps are commonly applied in large-scale centralized chiller plants. All-parallel heterogeneous systems offer high operation flexibility but pose challenges to optimal operation planning. For the deficiencies in convexity tractability, the classical neural networks (NNs) achieved great success in system identification yet were restricted in model-based optimization. In this study, an optimization-oriented convex modeling approach based on input convex neural networks (ICNN) to identify energy consumption models for energy units of chiller-pump systems was presented, which provides convex input–output mappings while leveraging the high-fidelity capability of NNs. Using the convex models, the energy minimization issue subjected to multiple constraints was formulated, the optimality of which was mathematically proven. A bilevel deterministic optimizer was developed to determine the global optimal. Comprehensive data experiments were conducted using practical and simulated operation data to evaluate the modeling and optimization performances of the proposed methods compared with conventional candidates. Numerical results suggest that the ICNN-based convex models exhibit superior modeling performances than physics-based models. A better overall energy saving ratio of 8.86 % and faster convergence time of 3.53 s for average achieved by the proposed bilevel optimizer compared with rule-based and meta-heuristic optimizers under identical external conditions.

Greenhouse gas reduction in a medium-duty compression ignition engine with optimization for B20

Soy-based biodiesel can reduce well-to-wheels greenhouse gas (GHG) emissions per unit energy (i.e., gCO2e/MJ) by 66%–72% as compared to the petroleum-based diesel fuel with currently adopted agricultural and industrial practices. Biodiesel can reduce particulate matter and carbon monoxide emissions with a manageable degree of increase in NOx emissions. From the perspective of GHG emissions reduction per unit travelling distance (i.e., gCO2e/mile), the application of B20 in compression ignition engines without the adjustment in engine control unit (ECU) settings will not extract the best carbon emissions reduction that B20 could achieve. Optimizing the engine control settings permits re-calibration to achieve the maximum brake fuel conversion efficiency (BFE) based on comprehensive understanding on the impact of both “fuel” and “ECU calibration” on BFE and other criteria pollutant emissions. The maximum GHG emissions reduction with B20 application is experimentally measured with the optimized ECU calibration, thus providing the understanding of the combined impact of biodiesel fuel and calibrations on engine performance and emissions. Six steady operating modes were considered, that can be combined to estimate the US federal test procedure BFE and emissions over the Federal Test Protocol (FTP) 75 cycle. Combined with the weight factors to simulate the EPA FTP 75 cycle from these 6 “mini-map” test points, 0.53% improvement in the energy requirement per unit traveling distance (i.e., MJ/mile) is achieved for B20 with the final ECU calibration, in addition to the degree of GHG emissions reduction on a “gCO2e/MJ” basis from the use of B20 blend of soy biodiesel of ∼12.5% reduction in gCO2e/MJ, for a total GHG emissions reduction of 13%.

Life cycle assessment of lithium-sulfur batteries with carbon nanotube hosts: Insights from lab experiments

Lithium‑sulfur (Li-S) batteries are candidates for next-generation batteries. It can be produced sustainably using sulfur instead of controversial cathode minerals like cobalt. However, Li-S batteries must improve their energy density and cycle life at their current state of development. Hosting the sulfur on carbon nanotubes (CNT) is a potential solution. This study investigated the environmental performance of a CNT-based Li-S battery based on original experiment data. The CNT in the batteries functioned as binders, conductive fillers, and current collectors, reducing auxiliary materials and simplifying manufacturing processes. We conducted a cradle-to-gate life cycle assessment (LCA) to clarify the environmental performance. We showed that the global warming potential (GWP) of the Li-S battery pack in a conventional battery structure was 105 kg CO2e/kWh. By partially substituting current collectors, increasing lithium anode efficiency, and reducing solvent usage, we could achieve 87, 82, and 78 kg CO2e, respectively. The lowest GWP of Li-S battery was 20 % lower when compared to a graphite‑nickel manganese cobalt oxides (NMC) battery. In addition, the other environmental indicators, including human toxicity, ecotoxicity, and acidification, were 26 % to 79 % lower, showing no major environmental burden shift. One exception was the mineral resource depletion, which was 2 % higher because lithium metal, instead of synthetic graphite, was used as a high-capacity anode pair. This study also found that modifying the cathode production process by using a less energy-intensive filtration before evaporating the remaining solvent could significantly reduce the energy required, thus mitigating the previously identified environmental hotspot. Our finding highlighted that a prospective LCA collaborating with battery experimentalists could lead to novel discoveries for environmentally sustainable battery production. Overall, we showed that Li-S batteries are environmentally preferred at the production stage, as supported by advanced experiments. Further research into improving the cycle life of Li-S batteries is crucial when considering the use stage to reduce the environmental impact per functional unit of energy delivered over the battery lifetime.