[en] Highlights: • Biomethane produced from organic fraction of municipal solid waste(OFMSW) • 151.4 kg CH₄ and 355.6 kg negative emissions are obtained from 1 ton dry OFMSW. • The process reach energy efficiency of 0.40 and cost of 6.74 MJ to store 1 kg of CO_2. • Operating expenses calculation result indicates a positive net profit. The use of bioenergy with carbon capture and storage (BECCS) is vital to reaching the desired climate goals. This study proposed a novel process combining anaerobic digestion, pyrolysis, catalytic reforming and methanation (APRM) to produce biomethane and to capture carbon emission from the organic fraction of municipal solid waste (OFMSW). The evaluation of the process was conducted by using modelling software and techno-economic analysis. The process modelling and evaluation result showed that 151.4 kg CH₄ and 355.64 kg stored carbon emission can be produced from 1 ton dry matter of OFMSW with an energy efficiency of 0.40. 6.74 MJ electricity was required to capture 1 kg of CO₂ via the proposed process. The energy balance of the pyrolysis reaction was investigated. The sensitivities of the pyrolysis temperatures, dewatering technologies and conversion of catalytic reforming on the process performance were analyzed. The result also indicated a positive net profit when using the APRM process to treat the OFMSW based on the calculation of operating expenses and revenue, when the CO₂ negativity can be sold as commodity.

[en] Highlights: • High solar fraction boosts the economic and ecological efficiency of cooling systems. • PVT cooling system exhibits better performance in almost all climates. • Solar thermal cooling systems have the best performance in arid climate conditions. • PV and PVT cooling systems are the most cost-effective given their low LCOC. • Solar adsorption cooling system is the most eco-friendly given its low LCCP. Solar cooling systems are gaining more attention as eco-friendly cooling technology, which could be an alternative to the conventional vapor compression cooling one to meet the growing demand of space cooling. This paper proposes the investigation of different solar cooling systems, namely: solar absorption, solar adsorption, photovoltaic and photovoltaic thermal cooling systems on the basis of performance, economic and environmental aspects. The main objective is to identify the most favorable system depending on climate condition and solar fraction. Moreover, a numerical simulation is performed on EnergyPlus software to determine the cooling loads of a typical office building for air-conditioning purpose. Performance, economic and environmental analyses are carried out in terms of key indicators, such as solar coefficient of performance (SCOP), levelized cost of cooling (LCOC), discounted payback period (DPP), and life cycle climate performance (LCCP). The results showed that the increase of solar fraction impacts positively the economic and environmental indicators, since this could reduce the LCOC, DPP and LCCP values. Besides, the photovoltaic thermal cooling system exhibits the better performance in all climate conditions with SCOP values ranging from 36% to 52%, depending on climate. Furthermore, the economic findings revealed that the lowest values of LCOC are acquired by using photovoltaic cooling system; varying within the range of 0.056–0.25 €/kWh_c, depending on climate. While the lowest DPP values are observed in regions with high solar irradiation using photovoltaic cooling system. Moreover, solar adsorption cooling system was found to be the most environmentally-friendly since it exhibits the lowest LCCP value. In addition, it was found that the increase of ambient temperature and solar radiation results in higher SCOP and lower LCOC and DPP values, especially for solar absorption and solar adsorption cooling systems. The current work is expected to serve as a reference for engineers and designers to select the suitable solar cooling system to be installed according to the target location and solar fraction.

[en] Highlights: • Two stage HTL-AP fermentation boosted biogas production by 25.5% from single stage. • Two stage co-fermentation with crude glycerol further enhanced biogas yield by 85%. • Initial pH value affected intermediate metabolism and fermentation efficiency. • Organic removal and energy generation were enhanced by 48.6% and 84.9%. • Synergism of two stage, glycerol addition, and pH control improved energy recovery. Hydrothermal liquefaction (HTL) is a promising method to convert wet biomass into biocrude oil which can further be upgraded into transportation fuel. Approximately 20–40% of the total energy still remains in the aqueous phase after the HTL process. While conventional anaerobic digestion has demonstrated a limited conversion efficiency, two stage co-fermentation with crude glycerol was developed in this study to process HTL aqueous phase (HTL-AP) into hydrogen and methane, aiming to enhance biogas generation and energy recovery. Compared with single stage operation, two stage HTL-AP fermentation improved the biogas production by 25.5%. Subsequently, the addition of co-substrate crude glycerol helped relieve the acidic stress, adjusted the nutrient supply, and diluted the toxic concentration of chemicals in HTL-AP within the reactors. The biogas production was further enhanced by 1.85 times from single stage when the HTL-AP to crude glycerol ratio was 1:1. The initial pH value of the two stage operation was also controlled to optimize the metabolic pathways during the first stage of hydrogen production and to provide desirable intermediates for methanogenesis. Results showed that an initial pH of 5.5 resulted in the highest hydrogen production in this study. Accompanied with the enhanced biogas yield, the organic conversion, energy generation, and energy recovery from two stage co-fermentation were improved by 48.6%, 84.9%, and 40.1% compared to single stage fermentation, respectively. The enhanced biogas production, especially the hydrogen generation, provided a promising direction for wet biomass conversion. Specifically, downstream two stage treatment of HTL-AP could be integrated with upstream HTL by utilizing the produced hydrogen for upgrading biocrude oil via hydrocracking, and the methane could be used as a heating source for the HTL process.

[en] Highlights: • Hybrid solar, wind, biogas with vanadium redox flow battery-based system is studied. • Cost and life cycle emissions are optimised by multi-objective genetic algorithm. • Multi-objective provides better environmental benefits than single objective one. • Evolutionary algorithm offers cost-effective solutions than the software tool. • Electricity cost is comparable with the grid supply at the cost of reliability. Renewable hybrid energy systems are well-proven to be capable of supplying reliable power in the remote areas, where grid extension is not viable due to geographical constraints, but not absolutely emissions free. The present study investigates a hybrid energy system that entails photovoltaic module, wind turbine, biogas generator, and vanadium redox flow battery for supplying stable power to a remote Island, Saint Martin, Bangladesh. Two well-known multi-objective optimisation techniques such as non-dominated sorting genetic algorithm II and infeasibility driven evolutionary algorithm are applied to size the hybrid system components based on the cost of energy ($/kWh) and life cycle emissions (kg CO₂-eq/yr) under a certain reliability. In addition, a fuzzy decision-making technique is applied to find the optimal solution. A comparative analysis of using single objective function is compared with the multi-objective one. In addition, results from the non-dominated sorting genetic algorithm II optimisation technique is compared with the widely utilized software hybrid optimisation of multiple energy resources tool and the infeasibility driven evolutionary algorithm. Although the cost of energy is relatively comparable between the objective functions considered, the multi-objective approach provides better environmental benefits than the single objective optimisation system. The analyzed results also indicate that the intelligent techniques are the superior to the hybrid optimisation of multiple energy resources software tool in terms of costs and environmental point of view. Furthermore, the unit electricity cost of the proposed hybrid system configuration is comparable with the grid electricity supply at the loss of power supply probability of over 8% with significantly lower life cycle emissions.

[en] Highlights: • Energy efficiency of hydraulic manipulators is limited due to complex couplings. • The dynamic programming algorithm was adopted. • The energy consumption models were formulated. • Circuits throttling and load losses were both decreased. • Effectiveness was validated in CP, LS, and ELS systems. Open-loop controlled hydraulic manipulators are still dominating in construction sites and forest fields thanks to little reliance on the expertise of human operators in manipulator control, which however exist large inlet/outlet and other energy losses during working cycles. And the situation further deteriorates with the increasing number of joints. This paper discusses the problem of global energy optimization of three-degrees-of-freedom (3-DOF) hydraulic manipulator in case of plane motion where exists an exceeding DOF for motion redundancy. Different from conventional energy optimization methods applied to electric driven manipulators, the proposed global energy-optimized dynamic programming (DP) algorithm models the whole system at the hydraulic level, which contains the dynamic characteristics of pressure-flow coupling between cylinders. The energy consumption models of constant pressure (CP), load-sensing (LS) and electrohydraulic load-sensing (ELS) systems are built in terms of cost functions formulated in the proposed algorithm together with penalty function containing physical actuator constraints at position, velocity and acceleration level. The DP algorithm is tested in various cases, and the convergence is proved consistent. To highlight the effectiveness of the proposed algorithm, the gradient projection (GP) method and the actuator velocity minimum norm (MA) method are introduced as a contrast. The results of the numerical examples of end-effector paths using the mechanical-hydraulic coupling AMESim model demonstrate that the DP algorithm saves around 10.49% hydraulic energy consumption in the CP system while saving around 76.55% in the LS system. Experiments performed on a typical-structured hydraulic manipulator further validate the effectiveness of the proposed algorithm, with approximately 4.61% energy saving in the CP system and 29.89% in the LS system. The energy-saving ratio is even larger in the ELS system, with around 80.03% comparing the max value. These contribute to the energy optimization resolution of redundant hydraulic manipulators to a degree.

[en] Highlights: • Combined X-ray CT with Neutron imaging to characterize compression effect. • PEFC with medium compressed metal foam achieves the best performance. • Compression process decreases the pore size and narrows the PSD of metal foams. • Compression process facilitates effective water removal due to high pressure drop. • Medium compressed cell shows moderate water removal ability and parasitic power. The mechanical compression of metal foam flow-field based polymer electrolyte fuel cells (PEFCs) is critical in determining the interfacial contact resistance with gas diffusion layers (GDLs), reactant flow and water management. The distinct scale between the pore structure of metal foams and the entire flow-field warrant a multi-length scale characterization that combines ex-situ tests of compressed metal foam samples and in-operando analysis of operating PEFCs using X-ray computed tomography (CT) and neutron radiography. An optimal ‘medium’ compression was found to deliver a peak power density of 853 mW cm⁻². The X-ray CT data indicates that the compression process significantly decreases the mean pore size and narrows the pore size distribution of metal foams. Simulation results suggest compressing metal foam increases the pressure drop and gas velocity, improving the convective liquid water removal. This is in agreement with the neutron imaging results that demonstrates an increase in the mass of accumulated liquid water with minimum compression compared to the medium and maximum compression cases. The results show that a balance between Ohmic resistance, water removal capacity and parasitic power is imperative for the optimal performance of metal foam based PEFCs.

[en] Highlights: • An HEV coupled with a calibrated linear model of the ORC-WHR system is developed. • An applicability analysis of WHR system is conducted under diverse road conditions. • An “optimization strategy” based on road condition recognition is proposed to optimize the vehicle performance. The challenges of energy conservation and environmental protection are becoming severe. Therefore, hybrid electric vehicles (HEVs), owing to their low fuel consumption and low emissions, are being considered as the ideal transition models between conventional fuel vehicles and pure electric vehicles. The growing demand for increasing vehicle efficiency has motivated the introduction of waste heat recovery (WHR) technology in the automotive industry. The organic Rankine cycle (ORC), with its advantages of great flexibility, high safety, low cost, and low maintenance requirements, is considered to be a potential WHR method. Currently, only a few studies have been conducted on coupling the HEV with ORC-WHR systems, which focus on the exploration of hybrid powertrain strategies under a certain type of road condition, but lack the applicability analysis of WHR technology and strategy exploration for HEVs operating under diverse road conditions. To analyze whether the WHR system has considerable and valuable energy savings potential under various road conditions, an applicability analysis is conducted under nine types of standard driving cycles. Based on these, for better adaptability to the complex and changing road conditions, an optimization strategy for the HEV-WHR integrated system is proposed based on road condition recognition technology. The results reveal that the WHR system is not suitable for urban road conditions, but is well adapted to suburban and highway conditions. For example, under the NYCC (New York City Cycle) and the Artemis Urban Driving Cycle, the WHR system even increases energy consumption by 0.18% and 0.12%, respectively, while under suburban and highway road conditions, the overall impact of coupling the WHR system reduces energy consumption by 3.36%–10.60%. Meanwhile, in HEV-WHR system coupling with the above proposed optimization strategy, the state of charge (SOC) of the battery is more stable, the start and stop times of the WHR system decrease, the engine thermal efficiency and average motor efficiency are much higher than the efficiencies obtained without the optimization strategy, and the ultimate energy savings potentials are calculated as 3.19%, 3.59%, and 4.16% under the CLTC (China Light Vehicle Test Cycle), NEDC (New European Driving Cycle), and WLTC (Worldwide Light Vehicle Test Cycle) driving cycles.

[en] Highlights: • A modular operational optimization framework for energy aggregators is presented. • Several flexibility options are considered using a generic component interface. • The operation on sequential balancing, day-ahead and intraday markets is simulated. • Consideration of multiple markets significantly improves economic efficiency. Distributed flexible energy consumption, production and storage technologies are an option to increase the flexibility of electricity systems and foster the integration of variable renewable energy sources. Aggregation business models, providing residential customers access to different electricity markets, can activate and utilize this untapped flexibility potential. However, economic feasibility for both aggregator and customers is a prerequisite for the adoption of these business models. In a European electricity market design with sequential markets, participation on multiple markets is supposed to further increase the economic benefits of aggregated demand response. In this work, a modular and extensible operational optimization and simulation framework based on mixed interger linear programming is developed to investigate different business models for aggregation of residential flexibility options on multiple markets. Simulation results of a specific case study show that considering day-ahead, balancing and intraday markets with adequate risk management in the optimization can significantly improve economic benefits compared to single-market optimization. Battery storages contribute most to these benefits. Business models on multiple markets are complex in terms of business model design and optimization, but they are economical for both aggregator and customers. Moreover they provide additional flexibility options to electricity systems. Thus, barriers for their implementation should be mitigated.

[en] Highlights: • A modified transcritical CO₂ heat pump system is proposed for space heating. • A new series water flow configuration using three heat exchangers is proposed. • The modified system achieves up to 6.5% energy saving in whole heating season. • The annual life cycle cost of the modified system can be reduced by up to 3.7%. Utilizing air-source heat pumps for residential space heating in cold climates is an important measure for environmental protection and energy conservation. In this paper, a new water flow configuration for water-heating process is proposed for transcritical CO₂ heat pump combined with a dedicated mechanical subcooling (DMS) subsystem. In the modified system, the conventional parallel water flow configuration is changed to a new series water flow configuration, in which the CO₂ gas cooler is separated into two parts with the condenser of DMS subsystem in the middle. The water is sequentially heated by CO₂ gas cooler 1, condenser of DMS subsystem and CO₂ gas cooler 2. Through three-stage heating, the uniformity of refrigerant-water temperature difference field is remarkably improved. The evaluations are conducted from energetic, exergetic and economic perspectives. Firstly, the impact of heat transfer area allocation among the three water-heating heat exchangers on system performance is numerically investigated. The results show that the COP of modified system is 1.6%–7.6% higher than that of the existing system. The greater the temperature difference between supply and return water and the higher the ambient temperature are, the more significantly the COP increases. By evaluating heating seasonal performance in three typical cities, energy efficiency of the modified system is increased by 2.4%–6.5%. Besides, the exergy efficiency is also improved by 2.1%–6.6%. As for life cycle cost, the proposed system can save up to 3.7% investment in terms of annual operation.

[en] This paper proposes a multi-objective predictive energy management strategy based on machine learning technique for residential grid-connected hybrid energy systems. The hybrid system considered in this study comprise three principal components: a photovoltaic array as a renewable energy source, a battery bank as an energy storage system, and residential building as an electric load. The proposed strategy comprises three levels of controls: a logical level to manage the computational load and accuracy, a dual prediction model based on residual causal dilated convolutional networks for energy production and electric load on system, and a multi-objective optimization for efficient trade of energy with the utility grid by battery charge scheduling. The prediction model used in this study can provide one-step ahead photovoltaic energy production and load forecast with sufficient accuracy using a sliding window training technique and can be implemented on an average personal computer. The energy management problem comprises multiple objectives that include minimization of energy bought from utility grid, maximization the battery bank’s state-of-charge and reduction of carbon dioxide emission. The optimization problem is constrained to the maximum allowed carbon dioxide production and battery bank’s state-of-charge limits. The proposed strategy is tested for static and dynamic electricity prices using hourly energy and load data. Simulation results show a high coefficient of determination of 93.08% for energy production predictions and 97.25% for electric load predictions using proposed dual prediction model. The proposed prediction model is benchmarked against naïve prediction, support vector machine and artificial neural network models using several metrics and shows noticeable improvements in prediction accuracy. Not only the proposed strategy combined with the proposed prediction model can handle over 50% of the total yearly load requirement but also shows a significant decrease in electricity bill and carbon dioxide compared to residential buildings without hybrid energy systems and hybrid energy system without energy management strategy.