With the development trend of carbon neutrality and emission reduction in the aviation industry, hybrid-electric propulsion system (HEPS) is one of the feasible solutions that can significantly reduce fuel consumption, carbon emission and maintenance cost. HEPS with multiple power sources requires the reasonable energy management strategy (EMS) to monitor and regulate the working status of each component in real time. Equivalent consumption minimum strategy (ECMS) can distribute the output power of the engine and battery effectively, but it cannot maintain the state of charge (SOC) within permitted range. Therefore, combining the advantages of robustness and adaptability in fuzzy logic control (FLC), we proposed the fuzzy logic control-equivalent consumption minimum strategy (FLC-ECMS) for series hybrid Unmanned Aerial Vehicle (UAV). Under the cruise flight mission profile, the simulation results showed that hybrid UAV with the ECMS and FLC-ECMS can reduce fuel consumption and CO2 emissions by 22% versus the engine-only UAV. Moreover, compared to ECMS, FLC-ECMS can decrease fuel and emissions while maintaining the battery SOC in the tolerance interval, and keeping the smaller front-to-back difference of SOC, which verifies the effectiveness of this strategy.
The energy mixes, which describe energy consumption structure by fuel type, are complex compositional data that cannot directly apply the multivariate statistical methods. Consequently, we apply a special compositional linear regression model to study how the country-level energy mix is influenced. The main findings of 45 countries by income group over 1990-2019 are: 1) The dependence of energy use on energy resource for each fuel type is confirmed in upper-middle-income countries. 2) Non-fossil use is driven by hydroelectricity resources and economic level in high-come and upper-middle-income countries, and by oil resources in lower-middle-income and low-income countries; 3) The energy transition as economy grows presents two typical patterns: high-income and upper-middle-income countries shift to non-fossils and natural gas, while the lower-middle-income and low-income shift to coal and oil.
Reducing transportation CO2 emissions and addressing population characteristic changes are two major challenges facing China, involving various requirements for sustainable economic development. This paper decomposed the population characteristics into population growth, population distribution, population quality, population living standard and age structure, by using STIRPAT model and panel data from 2000 to 2019 to explore the impact of population characteristics on China’s transportation CO2 emissions. Further, we analyzed the impact mechanism and emission effect of population aging on transportation CO2 emissions. Results show that during 2000-2019: (1) population aging and population quality restrain the transportation CO2 emissions, but the negative impact of population aging is indirectly produced by economic growth and transportation demand. And with the aggravation of population aging, the impact on transportation CO2 emissions changes and presents a U-shaped. (2) Population living standard on transportation CO2 emissions exhibits an urban – rural difference, and urban living standard was dominant in transportation CO2 emissions. Additionally, population growth has a weakly positive effect on transportation CO2 emissions, while population distribution has no significant effect on transportation CO2 emissions. (3) At the regional level, the effect of population aging on transportation CO2 emissions shows regional differences.
This work constructs an innovative dynamic energy efficiency optimization model of methane hydrate dissociation by thermal stimulation method base on artificial intelligence predictive control of heat injection strategy. Model can divided into two parts, firstly, the prediction of hydrate decomposition rate of each time step is realized via the supervised learning neural network prediction part of the model. The optimization of the energy consumption of hydrate decomposition by thermal stimulation under different gas recovery situations is realized by the deep reinforcement learning-based policy optimization part of the model. Take the lowest injection/recovery energy consumption ratio as the optimization objective, take the injection temperature and heat injection rate (per unit time step) as optimized variables. Implement evaluation and execution for each time step, updating and correcting the prediction errors of successive time steps to achieve dynamic optimization of energy efficiency. The application results of the model showed that under the premise of the same deposits conditions and the same injected heat, the recovery time of the model optimization group decreased by 38% compared with the control group; while under the same deposits conditions and recovery time, the energy consumption of the model optimization group decreased by 40% compared with the control group.
This paper proposes a power system load forecasting method based on generative adversarial network and convolutional neural network (GAN-CNN), and applies it to the short-term load prediction. In this model, the generation layer and the discrimination layer form a maximum-minimum game and finally reach a Nash equilibrium. The data feature extraction method is integrated with the CNN convolution operation and variational mode decomposition (VMD) technology, which improves the quality of sample generation and reduces the prediction error. This paper provides a new and effective method for selecting similar days and forming the input matrix of the model. Finally, the real data were used to demonstrate the superior performance of the proposed method.
In recent years, the advancement of trading technology and the acceleration of information transmission have intensified the intraday volatility of the oil market. To identify the volatility and risk of the intraday market accurately and effectively, this paper proposes a method for intraday risk prediction based on generalized heterogeneity autoregressive for high-frequency spot volatility modeling. First, use the threshold kernel variation method to separate jumps and characterize the spot volatility, then redefine the heterogeneity characteristics of the high-frequency intraday market to construct the optimal generalized heterogeneity autoregressive model, and finally predict and assess the intraday market risk. The results show that the intraday jumps of the high-frequency crude oil futures mostly occur in the event window of geopolitical news and EIA announcements, and there are short-term jump aggregations. Separating the jumping components can establish a more accurate prediction model for the fluctuation process. The model proposed takes into account the characteristics of intraday heterogeneity and finds that weekly fluctuations have no information contribution to high-frequency traders. Compared with the ARMA and GARCH models, it ensures the validity and accuracy of the results. With easy operation and scalability, it is an effective risk management tool for crude oil intraday market transactions.
Accurate prediction of the heat-side load of a central heating system is of great importance to meet the thermal comfort of users, while saving energy and reducing emissions. Most of the current research reports on load models rarely consider the difference and time-varying of actual user demand room temperature. In this paper, user room temperature is introduced into the heat load model, and a hybrid mechanistic and data-driven approach is used to construct a heat load model for a building complex, including base load, cumulative temperature effects and determination of model parameters, which is applied to two practical engineering cases. The results show that: the relative deviations of the simulated values compared with the actual are all no more than 3% for annual cumulative loads, and no more than 25%, 20%, and 18% for daily, three-days, and weekly loads, respectively. The heat load model in this paper can reflect the demand loads of a building complex at different target room temperatures. By setting the
target room temperature values with reference to the design specifications, it is found that both cases have great energy-saving potential, with the annual cumulative load being reduced by 32.2% for case 1 and 62.7% for case 2
Various technologies for renewable energy need to be employed for sustainability and the hydrogen production through water electrolysis (WE) is one of the green energy approaches. In this study, solar energy was used for splitting sodium chloride solution into hydrogen gas using an experimental electrolyzer, while the hydrogen production and the performance of the PV cell were evaluated. The incoming solar radiation ranged from 810.1 W/m2 to 637.8 W/m2 whereas the respective atmospheric temperature increased, and humidity subsequently decreased. The efficiency of WE (ȠF) was 61.8% and the PV cell efficiency (ȠSP; at temperature; 25.5oC, humidity; 42.2% and IR; 810.1 810.1 W/m2) was 12.7%. The H2 production was coupled with voltage drop whose minimization needs to be addressed in future research along with improvement of efficiency and cost-effectiveness.
A dual-purpose underground thermal battery (DPUTB) was proposed for Grid-interactive Efficient Buildings. It integrates underground thermal energy storage with a shallow-buried ground heat exchanger (less than 6 m deep). The charging and discharging performance of a lab-scale DPUTB were experimentally investigated. The test results show that the lab-scale (1:125 in volume) DPUTB can provide 34 W cooling continuously for 3.7 h with a supply water temperature below 14°C. The water temperature rise of the inner tank was slowed down during the discharging process due to the phase change of the phase change material (PCM). Thermal storage capacity was increased by 156% using the PCM that only occupied 19% volume of the inner tank. The heat lost from the inner tank was recovered in the outer tank and led to the efficiency improvement of a ground source heat pump.
The Decarbonisation of Heat Theme within the UK Centre of Research in Energy Demand Solution (CREDS) has been a three-year programme of work, which has sought to explore possible strategic directions for heat decarbonisation through a dialogue with stakeholders using system architecture concepts and tools. In September 2020, a Concept Evaluation workshop was held with key stakeholders, with the aim of capturing their views on expectations, requirements, and possible architectures for a future decarbonised system. The evaluation and discussion of options were structured using the Pugh Matrix Method: a pair-wise comparison of technology options against system criteria. This paper presents and analyses the results of this evaluation exercise. Pugh Score sheets returned by individuals showed that the Hybrid Heat Pump Option was positively valued over other systems but was neither deeply explored nor exhaustively articulated in group discussion. The analysis of the discussions showed that the stakeholders were both challenged and stimulated by the way that discussion was structured. Their responses to the concept evaluation exercise revealed the dynamics and combinations of technological options under consideration for fulfilling the joint goal of decarbonising heat and of building a system that is robust enough to withstand short term stresses and shocks, while having the capacity to evolve under changing conditions in the medium-to-long term. Implications for policy and modelling practices are briefly discussed.