Abstract

A novel two-phase model was proposed for fluidized bed methanation reactor. In this model, the distribution of gaseous feedstock between bubble phase and emulsion phase due to the effect of the circulation flow was provided. Furthermore, the influence of bubble size on mass transfer was also considered. The proposed model was validated by experimental data of the fluidized bed reactor with methanation. The simulation results of the novel model was compared with the simulation results of the classic Kunii and Levenspiel (K-L) model. To evaluate the role of the two key parameters, two simplified models were presented and the models are solved. Simplified model 1 only considered the influence of bubble size on mass transfer coefficient. Simplified model 2 only considered the influence of circulation flow on the distribution of the gaseous feedstock between bubble and emulsion phase. Finally, the simulation results are compared and results indicate that the role of the circulation flow on the distribution of the gaseous feedstock between bubble and emulsion phase is more important.

Abstract

Liquid chemical looping technology is an innovation of chemical looping gasification technology. The use of liquid metal oxides as oxygen carriers in the gasification process could extend the service life of oxygen carriers and improve the gasification efficiency of carbon-based fuels. The combination of liquid chemical looping technology and bituminous coal traditional gasification technology can avoid the above problems, and can also improve the coal gasification efficiency and achieve almost zero greenhouse gas emissions. In this paper, based on the Gibbs minimum free energy principle, the computer simulation calculation of the chemical looping gasification technology of bituminous coal is carried out. The results indicated that increasing the temperature of the chemical looping gasification is conducive to the improvement of bituminous coal gasification efficiency. When the gasification temperature is 900~1000℃, the content of CO and H2 is the highest. When the molar ratio of the oxygen carrier to the gasification raw material is 0.3, the CO output is the largest. The addition of water vapor is conducive to the increase of the H2/CO molar ratio in the system.

Abstract

With the widespread application of renewable energy, how to improve energy efficiency has become a focal issue. Grid-connected power generation and energy storage have always been key issues in photovoltaic(PV) power generation technology. This research uses deep reinforcement learning(DRL) methods to make grid connection and energy storage more efficient. Deep reinforcement learning (DRL) combines the characteristics of deep learning(DL) and reinforcement learning(RL). It has both the perception ability of deep learning and the decision-making ability of reinforcement learning. Maximum Power Point Tracking (MPPT) is a technical condition commonly used in photovoltaic (PV) power generation systems to maximize power extraction under all conditions. In this paper, Deep Q-Learning(DQN) algorithm and Deep Deterministic Policy Gradient(DDPG) algorithm in deep reinforcement learning(DRL) are used to obtain the maximum power(MPP) of photovoltaic power generation, use Markov Decision Process (MDP) to complete the distribution and dispatch control of electric energy. These methods are simulated and analyzed in MATLAB/Simulink. Experiments were conducted on various input and output conditions. Judging from the results, these methods are efficient and reliable. This study provides good technical conditions for the intelligentization of power grids in the future.

Abstract

With the widespread application of renewable energy, how to improve energy efficiency has become a focal issue. Grid-connected power generation and energy storage have always been key issues in photovoltaic(PV) power generation technology. This research uses deep reinforcement learning(DRL) methods to make grid connection and energy storage more efficient. Deep reinforcement learning (DRL) combines the characteristics of deep learning(DL) and reinforcement learning(RL). It has both the perception ability of deep learning and the decision-making ability of reinforcement learning. Maximum Power Point Tracking (MPPT) is a technical condition commonly used in photovoltaic (PV) power generation systems to maximize power extraction under all conditions. In this paper, Deep Q-Learning(DQN) algorithm and Deep Deterministic Policy Gradient(DDPG) algorithm in deep reinforcement learning(DRL) are used to obtain the maximum power(MPP) of photovoltaic power generation, use Markov Decision Process (MDP) to complete the distribution and dispatch control of electric energy. These methods are simulated and analyzed in MATLAB/Simulink. Experiments were conducted on various input and output conditions. Judging from the results, these methods are efficient and reliable. This study provides good technical conditions for the intelligentization of power grids in the future.

Abstract

The high energy consumption of the steel industry has been a major challenge for the sustainability and environmental protection. Steel companies, the backbone of heavy industry in China take a vital position in climate change mitigation. To pursue sustainability and decrease emissions, an increasing number of steel companies tend to seek help from renewable energy. Integrating solar photovoltaics (PV) at steel plants is promising to reach the target. This paper investigates the potential capacity, potential output and economic performance of PV technology of 228 steel plants in China. The results indicate that the huge potential capacity and output are up to 6.96×106 KW and 9.71×109 kWh, respectively. Moreover, the ROI and profit under all self-consumption scenarios and all feed-into grid scenarios are evaluated, which shows integrating PV into the steel industry is also profitable. Among two scenarios, the all self-consumption scenario earns a higher ROI and profit compared with the all feed-into grid scenario.

Abstract

Electrification is taking place to reduce the emissions from waterborne transport by substituting ships’ auxiliary engine generation with shore power, therefore the power demand at berths increases dramatically. In this paper, we take Yangshan Port as an example to evaluates the feasibility of applying photovoltaic (PV) technology to suffice its potential shore power demand based on geographic information system. The results show the potential shore power demand at Yangshan port is 288.8 MW, which can be sufficed by deploying PV either on available land or on water area. The further techno-economic comparison between conventional grounding PV and floating PV reveals that although the system cost of floating PV is higher than grounding PV, the higher efficiency of floating PV due to the cooling effect of water, in turn, results in a lower levelized cost of electricity (LCOE). The LCOE of All types of PV system is much lower than the retail electricity rate at Yangshan Port, which indicates that with the assistance of PV technology, high cost, the biggest hindrance of prompting shore power at Yangshan port can be overcome, and integrating PV technology at ports is a promising and practical solution to cope with electrification trend in waterborne transport sector.

Abstract

The virtual synchronous generator (VSG) control strategy is beneficial to improve the damping characteristics of the system. However, the traditional VSG control method based on droop characteristics can lead to static frequency deviation for the frequency modulation. A kind of enhanced VSG control strategy is described in detail in this paper. The static frequency deviation can be eliminated and the secondary frequency modulation can be realized within 0.5 seconds. A Matlab model is used to simulate the operation of working load fluctuation under isolated island operation mode. The simulation result shows that the design is feasible.

Abstract

The model research of regional energy internet is of great significance to enhance the flexible coupling transformation of smart city multi-energy system, improve the reliability of energy supply and realize the preferential consumption of renewable energy. Based on the energy self-organization and energy community, from the perspective of complex network to carry out the wisdom of the city region energy complex dynamical network model research, through summarizing the many challenges facing the complex dynamic network institute, puts forward the study area energy Internet time-delay research idea and method of complex dynamic network model, to establish scientific value and practical significance of complex dynamic network model with time-delay system research theory and method.

Abstract

Usually the energy matching between building load demand and PV generation is rigid for photovoltaic direct-driven air conditioners (PVAC). The utilization of adaptive thermal comfort temperatures can improve the energy flexibility of building loads to increase the dynamic energy matching for PVACs. This study aims to propose a dynamic zero energy evaluation method combined with the adaptive thermal comfort temperatures for PVACs. The interaction between the flexible building load and rigid PV generation are investigated by different machine learning models with 1-minute time resolution. The indoor temperatures conditioned by PVACs are simulated under actual operations. Indicators such as hourly self-consumption (SC), hourly self-sufficiency (SS), hourly zero energy times (ZET) and real-time zero energy probability (RZEP) are used for evaluating the dynamic energy performance of PVACs in different seasons. With fixed indoor setting temperature selected from standard, the RZEP is only 27.87% in summer and 34.88% in winter for hot-summer and cold-winter zone. While taking the adaptive thermal comfort temperature into account, the corresponding RZEP for PVACs reaches 51.31% and 79.55% based on 100% SC of PV generation, respectively. Moreover, the zero energy points always appear at the large cooling demand times which can reduce the burden of the utility grid. An optimization for PV capacity is also conducted, the increasing of PV capacity helps to raise RZEP but brings the excessive energy output. The dynamic zero energy evaluation method with adaptive thermal comfort temperatures is useful to evaluate the zero energy potential and design a more flexible PVACs.

Abstract

The multi-layered electrode is expected to exhibit improved flow battery performance due to its variable flow and electrochemical properties that can be locally optimized in different layers. The porosity combination and the thickness of layers with different porosities will affect the reaction rate and the uniformity of the reactant distribution. Although a variety of models have been developed to guide the design of the electrode microstructure, how to mathematically optimize the properties of the electrode with multi-layered carbon paper sheets, remains unknown. In the present study, the authors devise a 3D, multi-physical vanadium redox flow battery model with the multi-layered electrode, and put forward a 1D analytical model for optimizing porosity distribution of multi-layered electrode in the redox flow cell. The optimization results based on the 3D simulations are presented.