Industrial demand response plays a key role in mitigating the operational challenges of smart grid brought by massive proliferation of distributed energy resources. However, industrial plants have complex and intertwined processes, which provides barriers for their participation in industrial demand response programs. This is in part due to the complexity and uncertainties of approximating systems models. More recently, reinforcement learning has emerged as a data-driven control technique for sequential decision-making under uncertainty. This emergence is strongly coupled with the abundance of data offered by advanced information technologies. The potential of applying reinforcement learning in industrial demand response is identified in this work by comparing pivotal aspects of reinforcement learning with the requirements of industrial demand response schemes.
The calorific value of biomass fuels is affected by the moisture content. In this study, the moisture content in corn straw, wheat straw, and rice straw was measured and predicted based on near-infrared spectroscopy (NIR) data. The prediction performance of the partial least squares (PLS) model was the best when first-order derivative preprocessing and the stochastic method of dataset division were used at the same time. The correlation coefficient of calibration (R_c^2), the root mean square error of calibration (RMSEC), and the root mean square error of prediction (RMSEP) were 0.937, 1.984 and 3.411 respectively. The results showed that PLS model based on NIR has the potential to rapidly characterize the moisture content of biomass fuel.
The development of shale reservoirs relies on hydraulic fractures. The development process often faces the shortcomings of fast decreasing production rate and insufficient formation energy. The high permeability of gas makes gas injection into shale reservoirs an effective development strategy. In complex fractured reservoirs, reasonable timing of gas injection will significantly improve the development of the reservoir and effectively prevent the risk of gas intrusion. Currently, the shale reservoirs in Changqing Oilfield in the Ordos Basin of China face problems such as low reservoir pressure coefficients, poor physical properties, and obvious non-Darcy flow, which lead to low initial development production of the reservoirs. In this paper, we propose a new development strategy named advanced gas injection, where gas is injected in advance before the production of the production wells to improve the initial formation pressure and fluid physical properties and to realize the improvement of the fluid flow capacity. The advantages of the advanced gas injection strategy in shale reservoirs are analyzed using a shale numerical simulator, the model adopts the embedded discrete fracture model (EDFM) method to realize the fracture modeling, and the components adopt the results of the analysis of the actual extracted fluid components in the field. The simulation results show that advanced gas injection can significantly increase the oil production rate in the early stage of development, and the average oil production rate in the initial stage (three months) is increased by 10-35% compared with the lagging injection. However, the decline rate of production is faster, and this yield difference will be reduced in the subsequent production; further design of the follow-up development strategy is necessary.
Some of the beddings in lamellar shale oil reservoir are opened and intersected with the hydraulic fractures, result in orthogonal fracture networks, after hydraulic fracturing. Which make it difficult to interpret properties of fractured reservoir, and affecting the accuracy of production prediction. In this work, a new semi-analytical model is developed specifically for modeling oil-water-gas three-phase production during flowback and early-time production for lamellar shale oil reservoirs. Two flow regions are assumed: opened beddings and matrix, which is considered as dual-medium model, including shale matrix and unopened beddings. A semi-analytical solution method based on dynamic drainage area (DDA) concept is used to solve the mathematical model, in order to improve the accuracy of initial time steps. Stress-dependent and saturation-dependent properties of fractures and matrix are handled in the solution. The robustness of the innovative model is tested through comparison with rigorous numerical model. Based on the proposed model, the influencing factors of three-phase flowback performance for multi-fractured horizontal wells in lamellar shale oil reservoir are clarified. The model in this study provides a foundation for efficient automatic history matching.
Peer-to-Peer (P2P) multi-energy trading is crucial to improve the utilities and the local energy resource utilization of end-side prosumers. However, the benefits balance between the network operator (e.g., the integrated community energy system (ICES) operator) and the prosumers are not well addressed. In this context, this paper proposes a novel coordinated optimal operation method and an optimal network charge pricing strategy for the ICES operator to multiple prosumers with P2P behaviors. The interaction between the ICES operator and prosumers is modeled as a bi-level optimization problem. At the upper level, the ICES operator optimizes the electricity/heating network charge prices to maximize its network charge revenue, while considering the network constraints. At the lower level, prosumers optimize the P2P multi-energy trading schedules to minimize the operational cost. Moreover, to protect the information privacy of the ICES operator and prosumers, a distributed optimization method based on alternating direction method of multipliers (ADMM) is applied to solve this bi-level optimization problem. Finally, case studies demonstrate that the proposed method can effectively benefit both the ICES operator and prosumers in P2P multi-energy trading.
Lithium-ion batteries have played a significant role in industries such as new energy vehicles. However, the performance of lithium-ion batteries is seriously affected by low temperatures. Alternating current (AC) self-heating is a feasible method to eliminate the negative effects of low temperatures on lithium-ion batteries. Nevertheless, the capacity degradation of batteries can be generated if the amplitude and frequency of AC are not adopted properly. In this study, the boundaries of temperature rise and capacity loss are calculated by an electrochemical-thermal coupled (ETC) model verified by experiments, and the range of AC parameters that satisfies the temperature rise requirement without capacity loss is determined. This range of AC parameters results from a combination of high amplitude and high frequency. Using AC in the range can heat the battery from -20 Â°C to above 0 Â°C within 5 minutes without any capacity loss.
In regions of southern China characterized by low winter temperatures and high relative humidity, frost-related challenges are frequently encountered by air source heat pumps. Traditional defrosting methods have been found to be inefficient and to yield unstable results. Therefore, an innovative frost-free air source heat pump system integrated with a recirculated regenerated desiccant wheel was proposed in this study. The impact of environmental temperature, humidity, and return water temperature on system performance were numerically investigated. Key performance indices, including inlet air humidity, system COP (Coefficient of Performance), and compressor output, were investigated. It was found that, when compared to conventional air source heat pump (ASHP) systems, a 40.3% increase in COP was achieved by the integration of the recirculated regenerative desiccant wheel. The system compressor output was significantly influenced by the ambient humidity (Ï†amb). When absolute humidity surpassed 4.4 gÂ·kg-1, the compressor output decreased with increasing Ï†amb. Significantly, the system performance improvement was more pronounced in conditions of higher ambient humidity. A 10% increase in relative humidity resulted in a significant 3.7% increase in COP. In summary, this study substantiates the system’s reliability across diverse operating conditions, affirming its practical viability.
Achieving environmental governance and energy poverty alleviation requires high-efficiency and environmental-friendly thermal management systems for electric vehicles. This study developed and tested an integrated R290 (propane) vapor-injection (INJ) heat pump system with waste heat recovery in a cold climate. Dual secondary loops are adopted considering the flammability of the R290 refrigerant. Experimental results reveal that the INJ system outperforms the basic (BAS) system by 12.1% in coefficient of performance (COP) and 36.3% in heating capacity at âˆ’20 Â°C/20 Â°C (out-cabin/in-cabin) ambient temperature. The INJ mode with 1 kW waste heat recovery can provide a 29.8% improvement in heat capacity and a 7.4% increase in COP compared to that with no waste heat at âˆ’20 Â°C/20 Â°C. This study provides experimental support for achieving efficient operation of automotive heat pump systems under a wide operation temperature range.
The high mitigation cost of clean innovations, warrants policy support for increased uptake. This study applies optimization techniques to investigate the impact of market-based policies in generating sufficient demand pull to trigger cost reduction under uncertainty. A novel Stochastic Market Potential Optimization model (SMPOM) is developed to maximize the cost difference between the initial cost of a technology and the new cost using a market-based policy. The model is applied to a case study of carbon capture and storage (CCS) in 32 integrated steel plants in Europe. Results show policy induced demand pull can reduce the mitigation cost of CCS.
Lithium-ion batteries (LIBs) experiences a significant loss of initial capacity due to the formation of solid electrolyte interface (SEI) layer for the initial charge-discharge cycle, and weaken the advantages of LIBs. Pre-lithiation (Pr-Li) has emerged as an effective strategy to compensate for such lithiuim loss in the initial cycles. We employed in-situ research techniques to investigate the direct contact Pr-Li process in graphite anode, and utilizing ultrathin lithium foil structures. Results show that the specific evolution process of direct contact Pr-Li in the graphite anode. Notably, when the lithiation state is 50%, the lithiated graphite electrode undergoes a distinct color transformation from the initial black-gray to dark blue color (LiC18). It is investigated the Li transport pathway primarily involves convert into Li+ followed by diffusion within the electrolyte. This intricate process eventually results in the lithiated graphite electrode was obtained. By employing ultra-thin lithium foil structure as the lithium source and exploring with in-situ techniques, this study reveals the complex pre-lithiation dynamics inside the graphite anode using the direct contact Pr-Li method. This study contributes to more deeply understanding the mechanism of the direct contact Pr-Li, and have potential implications for the in-depth study and application of pre-lithiation technology in LIBs.