Applied Energy最新文献

Typically, solar power is offered for price bids at the level of their near zero marginal costs to electricity markets. While aggregate effects of this behaviour on prices (merit-order effect) and profits (cannibalisation effect) have been studied extensively, potential deviations from this strategy still lack an understanding. We observe a group of firms to offer solar power for prices larger than zero to the Iberian electricity day-ahead market. Based on a literature review and analysing incentives set for solar power by the Spanish electricity market design, we suggest these price bids to result from revenue opportunities in sequential markets. Results of our regression analyses confirm that the observed group of firms is more likely to conduct arbitrage. This motive also allows for explaining the level of a case-study firm’s price bids.

While identifying the drivers of global CO₂ emission is crucial for climate change mitigation, the heterogeneous technology-related drivers (e.g., technological change and efficiency of energy and CO₂ emission) were ignored to a large extent at a global scale, hindering to formulate heterogenous climate policies. Moreover, the projection of CO₂ emission was also not well compared for countries. Here, the study investigated the heterogeneous technology-related drivers of CO₂ emissions in time and space simultaneously in 42 major emitter countries over 1998–2020 by extending the spatiotemporal production-theoretical decomposition models, and compared the different performances for forecasting CO₂ emission by traditional time-series models and several machine learning models. Key findings as follows: first, drivers of CO₂ emissions exhibit significant heterogeneity across countries where the effects of energy usage technology gap and CO₂ emission technology gap were negative drivers for USA, South Korea, and the Czech Republic and potential energy intensity effect was the negative driver in countries like China, Russia, Japan, and India. Second, the effects of within-GDP per capita and within- population size were the important drivers affecting global CO₂ emission difference. Third, general regression neural network achieved the best forecasting performance on average compared with other models in the study. The study highlights the importance of formulating climate policies based on heterogeneous technology and emission forecast modeling.

A highly efficient and clean biomass energy-based calcium carbide-acetylene production system, including low-carbon energy supply, solid waste recycling and cascade utilization of waste heat, has been developed for the conventional energy-intensive and highly polluting fossil fuel-dependent calcium carbide industry. This system supplies the carbide-acetylene production plant with energy from the gasification of biomass and converts the plant's solid waste carbide slag into the calcium feedstock required by the plant. The waste heat from the plant's high-temperature exhaust gases is recycled and used via the multi-stage heat exchange, so that the energy cascade conversion and utilization is achieved. A simulation model of the plant is created in Aspen Plus, and a mathematical model for the biomass gasification process and cycle compensation of the calcium source through the Fortran language is written and embedded. When calculating the carbon consumption and CO₂e emissions of the system, it was found that the carbon consumption and CO₂e emissions of the conventional process were 5.43 t Coal·t⁻¹C₂H₂ and 2.25 t CO₂e·t⁻¹C₂H₂, respectively. However, the carbon consumption of the new process was reduced by 65.19%, and carbon emissions by 27.24% in comparison. The energy analysis shows that the energy efficiency of the system is 36.21% for the conventional process and 44.82% for the new processes. The exergy analysis of the effective energy shows that the exergy efficiency of the new process is 73.20%, which is 52.98% better than that of the conventional process. Introduction of an index, the levelized income of acetylene product (LIOA), to characterize the product income of the system. When the price of acetylene is between 2.23 $/kg and 4.19 $/kg, the LIOA for the conventional and the new processes are 1.41 $/kg to 3.38 $/kg and − 1.28 $/kg to 0.68 $/kg, respectively. It is worth noting that the critical price (${\mathrm{PC}}_{C_2{H}_2}$) for products generating net revenue from the new process is 3.17 $/kg. This study is of great importance for the development of a low-carbon biomass-coupled calcium carbide-acetylene process.

The tremendous energy usage from buildings leads to research studies on their improvement, among which advanced building control plays an important role. In advanced building controls, data-driven predictive control (DDPC), differentiable predictive control (DPC), and reinforcement learning (RL) have shown advantages, but their comparison often lacks in existing studies. The simulation-based prior comparison studies have inconsistent results due to different assumptions and simplifications. Therefore, to comprehensively compare the three advanced strategies for real-time building HVAC controls, we implemented DDPC, specifically, hierarchical DDPC (HDDPC), DPC, and RL in a real building testbed for more than 5 months. The results show that all three advanced controls maintained the indoor environmental quality (IEQ) cost-effectively. Overall, HDDPC outperformed the baseline control with more than 50% energy savings, followed by RL with 48%, and DPC with 30.6%. Most control failures were related to API communication issues. Besides, the information gaps between room and system level controllers and non-optimal control decisions will degrade HDDPC's performance. Such degradation did not happen in DPC and RL, which led to better performance of agent-based control over HDDPC. Moreover, HDDPC needs minutes to make control decisions whereas DPC and RL need milliseconds, indicating higher online computing resources required by HDDPC. For agent training, DPC is faster than RL, as DPC training needs minutes and RL needs hours, but its performance is not as good as RL. This study provides a comprehensive understanding and assessment of the pros and cons of advanced building controls and sheds light on future research on building controls.

Most methods for creating an indoor thermal environment are based on controlling heating, ventilation, and air conditioning (HVAC) systems and do not consider the various needs of individuals in a multiperson space. Personal comfort systems (PCS) and personal comfort models (PCM) are popular technologies for achieving personal thermal comfort. This paper presents a thermal environmental collaborative control system (TECCS) that regulates environments at different spatial scales by leveraging the advantages of the HVAC system, PCS, PCM, and PCM-based automatic control to address the issue of individual differences in thermal demand in multiperson environments. The TECCS predicts thermal sensation votes (TSV) by combining facial skin temperature data obtained by an infrared sensor with environmental parameters. Subsequently, it performs the corresponding PCS control and adjusts the air conditioner according to the operating state of the PCS. This study proposes a collaborative control strategy with PCS at the core, enabling communication between thermal state recognition, HVAC system, and PCS. Twenty-eight adult males participated in the experiments testing the TECCS's performance. The results indicate that the TECCS can automatically regulate environments at different spatial scales based on thermal sensation prediction and that the operating state of the PCS can effectively guide air conditioning operations. Compared with constant setpoint control, the TECCS offers the advantage of improving thermal comfort. This paper also proposes future optimization directions based on the research results, focusing on recognition, equipment, and control.

This paper presents an improved optimization algorithm for the energy management of a renewable energy solar/wind microgrid with multiple diesel generators applied to off-grid remote communities. The main objective aims to solve the economic emission dispatch problem with a price penalty factor to minimize the energy cost and the emission level. An enhanced metaheuristic optimization algorithm, Lévy arithmetic algorithm, is applied to improve the searchability for optimal solution compared to the conventional arithmetic algorithm. The Lévy arithmetic method is used for the management of the microgrid and compared to other metaheuristic optimization algorithms for the same application. Comparative analysis demonstrates good cost savings using the Lévy arithmetic algorithm, compared to other optimization algorithms such as the arithmetic algorithm, crow search algorithm, hybrid modified grey wolf algorithm, interior search algorithm, cuckoo search algorithm, particle swarm algorithm, colony algorithm, and genetic algorithm.

Simulated H₂-rich reactant gases containing one or more impurity gases, (CO, CO₂, and CH₄) were used as the hydrogen source to investigate the performance of Ni-Mo/TiO₂-Al₂O₃ catalyst for the hydrodesulfurization (HDS) of shale oil. The simulated gas (45% H₂, 20% CO, 20% CO₂, and 15% CH₄) was on-line synthesized under the optimal conditions of a reaction temperature of 380 °C, a pressure of 4.0 MPa, a gas/oil (v/v) ratio of 600: 1, and an LHSV of 4.0 h⁻¹. The sulfur content in the upgraded shale oil reached a steady-state value of about 4300 ppm, which met the requirement of national standard marine fuel oil (GB17411–2015, China) that requires <0.5 wt% in sulfur content. Meanwhile, the outlet gas showed a higher heating value than the inlet gas, which had almost no effect on its use as a fuel gas. This work provides a novel idea for the high-value utilization of H₂-rich industrial vents and a fresh approach for reducing the costs of hydrogen supply during low-rank oil upgrading.

Maintaining a battery cell at an optimal temperature improves both its performance and lifespan. This study proposes a cold plate equipped with hybrid manifold channels, positioned at the bottom of a high-capacity 280 Ah LiFeO₄ battery pack. Based on the developed whole battery pack model, the response surface method elucidates the functional relationship between design parameters (i.e., the width of parallel channels, the width of manifold channels, the height of parallel channels, and the inlet velocity) and responses (i.e., the flow pressure drop, the temperature difference of the entire battery modules, and the temperature difference of the cold plate). Multi-objective optimization of design parameters is performed to search the Pareto front to maximize thermal performance and minimize flow pressure drop, employing the NSGA-II algorithm. Results reveal that the maximum battery temperature can be limited to 30.73–33.78 °C with a coolant pressure drop ranging from 7.66 kPa to 1.76 kPa, at a heating power of 10 kW/m³ for the battery cell. The optimal design configuration, identified through TOPSIS, limits the maximum battery temperature to an acceptable temperature of 45 °C at a discharging rate of 3C, with a pressure drop below 4.2 kPa. Compared to the 280 Ah LiFeO₄ battery with natural air cooling and forced flow immersion cooling systems, the maximum battery temperature with a discharging rate of 1C is reduced by 17.6 °C and 11.7 °C, respectively.

Wastewater treatment plants (WWTPs) have been striving to recover energy and resources, targeting water and carbon near zero emissions. This study aims to develop a water-energy-tailored model for such a proposal. On one hand, this model will unveil the potential for resource and energy recovery by analyzing the energy flow and mass balance of the WWTP. On the other hand, it explores in-situ energy generation by calculating photovoltaic power generation at a specific location using high spatial-temporal resolution data. The model is employed in a typical town-level WWTP with a capacity of 4000 m³/d located in China. The potentials for carbon emission reduction and associated cost-benefit were analyzed under four different power supply paradigms from the perspective of life cycle assessment. Key findings are as follows: firstly, there is untapped chemical energy (1.65 kWh/m³) and thermal energy (2.32 kWh/m³ for heating) potential within wastewater. It is necessary to recover energy from it and enable water reuse to achieve near-zero wastewater discharge. Secondly, it is hard to balance operation energy consumption and in-situ solar energy recovery along with water-borne energy in the WWTP. The tipping point is identified at a scale of 10,000 m³/d, when constructing a photovoltaic and energy storage system within all available space on the plant premises, with a capacity potential of 95 kWh/(m²$\bullet$a). Thirdly, under this condition, the cost of the photovoltaic and energy storage system is at least 73% of the electricity cost from the grid over the assessed 25-year period. The economic viability of WWTPs throughout the entire lifecycle remains a challenge. Therefore, caution is warranted in claiming the feasibility of constructing near-zero carbon WWTPs. Policy implications are also carefully discussed, targeting to achieve a balance among technology, economy, and environment while making the model work in real.

Accurately predicting effective thermal conductivity is of great importance for the design and performance evaluation of emerging composites. In this paper, an efficient and implementation-friendly lattice Boltzmann (LB) scheme for predicting the effective thermal conductivity of 3D complex structures is proposed. The key innovation is that the optimum convergence parameter of the 3D thermal LB method is found, which enables the LB equation to converge to steady heat conduction equation with the fastest speed and without losing any accuracy. To deal with the thermal contact resistance between different components, an interface treatment scheme is derived. In comparison with the existing schemes, the present scheme enjoys several hundred times higher computational efficiency. By virtue of this LB scheme, the effective thermal conductivity of the reinforced composites with different dimensional fillers are systematically calculated, and a comprehensive machine learning model is developed. This work provides a powerful numerical tool for high-throughput simulations of the 3D representative volume elements with high thermal conductivity ratios and large grid numbers. It may facilitate the application of data-driven techniques in study of the thermal transport properties of emerging composite materials and structures.