Rapid humidity regulation is key to maintaining good fuel cell performance, so the design of a suitable humidification system and control strategy is important for the polymer electrolyte membrane fuel cell test system. In this study, a humidification system with dry and wet gas mixing is proposed, and PID control is used to track a predetermined relative humidity set point. The results show that the humidity can be quickly and precisely controlled and adjusted by dry and wet gas distribution control, and that the change in wet gas inlet flow is consistent with the actual change in relative humidity.
Thermal energy storage (TES) plays a crucial role in waste heat recovery and decarbonisation of the industrial sector and energy efficiency improvement. The combination of two energy storage technologies makes TES a promising asset for managing different types of energy in a single system; however, such an opportunity has not received attention so far. To overcome the traditional view of TES based on a single approach only, this study investigates the techno-economic value of using hybrid TES (HTES) as a multi-tech energy storage asset for the provision of energy streams either in low or high energy density for the industrial applications. The system is envisioned to consist of sub-sections which to provide fast-response TES as well as longer-duration TES. The selected case study is a hybrid water-latent heat system. The former accept steam while the latter can support the former and optionally receive energy from an additional source. The study discusses the technical characteristics and the interaction of the compartments. Compared to a conventional TES, the proposed HTES provides a relevant 20-30% increase in overall storage capacity based on fixed equipment size. The economic analysis revealed that the potential reduction in investment cost and O
Economic consequences have been felt around the world as a result of COVID-19, among which have been changes in electricity demand. In this project, we use a convolutional neural network (CNN) to investigate whether there was a change in electricity demand in the state of Texas, located within the United States, during the pandemic, as compared to before it. Training the model on electricity demand and weather data, we were able to achieve a relative RMSE, relative MAE, and R2 of 0.049, 0.041, and 0.92, respectively, on a testing set that represented a normal, pre-pandemic year. The CNN showed better performance, as compared to a plain artificial neural network (ANN). Based on the predictions of the CNN and the actual demand in 2020–2021, we find that the hypothesis that demand decreased during the pandemic was partially supported. A larger decrease was present due to extreme weather events; therefore, we recommend that Texas fortify its electricity generation facilities against such events.
As one of the most important supervisory control functionalities, the energy management strategy (EMS) of a hybrid electrified vehicle (HEV) optimizes the use of onboard energy resources for energy conservation and emission mitigation. Engine Start-up proved to have great contribution to fuel consumption and emission. A deep reinforcement learning based EMS is proposed for a power-split HEV to reduce the energy consumption and emission by recognizing start-up conditions and decreasing the start-up frequency. The EMSs based on Proximal Policy Optimization (PPO) and Twin-delayed Deep Deterministic Policy Gradient (TD3) are also compared in transient working condition frequency. Simulation study is conducted to demonstrate the advantage of the proposed energy management method. The EMS considering fuel consumption minimization and irrational actions avoidance is optimized by running the vehicle model under the WLTC condition repeatedly. PPO can get 9.02% lower fuel consumption, 25.6% lower start-up times and 8.2% transient working condition percentage than TD3. PPO is more suitable in the EMS domain.
Lithium-ion batteries are the main power source of electric vehicles (EVs). Prediction of battery State-of-Charge (SoC) for EV is important but challenging because battery SoC cannot be directly measured through onboard sensors. This paper proposes a surrogate model
for battery SoC evaluation based on a Pseudo 2-Dimensional (P2D) model, offering increased physical insight and predictability than the conventional Resistance-Capacitor (RC) model in a computationally efficient way. By simulating battery performance under different cycles using COMSOL, the proposed P2D model demonstrates its strong representation capability quantified by Root Mean Square Error (RMSE), which can be controlled below 0.03 under all studied conditions while providing physical and analytical characteristics in
battery operation. Furthermore, based on the simulated data from the P2D model, the proposed surrogate modeling using Gradient Boosting Machines (GBMs) is proposed to build the recurrent model for the voltage and SoC prediction using previous voltages. The results from GBR with Root Mean Square Error (RMSE) 0.0387 are close to training data with RMSE 0.0258.
Detailed Computational Fluid Dynamics (CFD) simulation model of a particular designed shell-and-tube supercritical CO2 gas heater in a biomass-CO2 power generation system has been developed based on actual heat exchanger structural data. The model has been validated with both manufacturer data and empirical correlations. It is thus applied to investigate and predict the performance of the heat exchanger and its associated system at various operating conditions. Based on the CFD simulation results, the performance improvement strategies for the heat exchanger designs and system controls are found and recommended.
In this paper, a chemical looping fixed-bed reactor driven by concentrating solar energy was built, and the chemical looping cycle process of the solar syngas production was studied experimentally, which is the key reaction of the liquid sunshine production process. N2 is used as a heat transfer medium to flow through the heat collecting tube and enter the reactor to provide heat for the reduction reaction. NiO is put into the reactor as an oxygen carrier, and methane is introduced as an oxygen carrier as fuel gas. The results show that with the development of the reaction process, the main reaction in the reactor has gradually changed from complete oxidation of methane to partial oxidation of methane. In this process, the methane conversion rate and the outlet syngas concentration are affected by the reaction temperature. Under the direct normal irradiation of over 860W/m2, the methane conversion rate can reach up to 90%, and the outlet syngas concentration can be maintained at 50%. This paper also studied the chemical looping cycle reaction of methane under different irradiation intensities. The results show that when the DNI reaches 920W/m2, the efficiency of solar energy to chemical energy can reach over 55%.
This study proposes a modified energy planning model that considers a broad range of future uncertainties. Modifications to hybrid stochastic robust optimization and robust optimization methodology allow for the introduction of multi-objective functions that reflect the various dimensions of energy planning including cost, emission, and social impact. Changing the priorities of the objective functions generates different energy policies, which are then compared. Data envelopment analysis is applied to measure the energy efficiency of each optimal energy policy produced by the energy planning model. Energy efficiency is defined as the ability to satisfactory address five main aspects—cost, emissions, social impact, employment, and security. An updated power development plan for Thailand is used as an illustrative case study. Empirical analysis indicates that a policy that prioritize the environment first, followed by social impact and cost, is the most efficient among the five alternatives considered. Results from the case study offer quantitative support for policy makers seeking to devise an efficient energy policy that meets extensive requirements while still dealing with the bounds of uncertain future projections.
This analysis investigates the business case of a virtually aggregated unit with PV and power-to-gas, outlining the added value of enhanced operation modes for the integration of distributed energy resources. Such an aggregated unit can not only leverage the internal benefits of acting as a single unit, for example, by reducing imbalance errors and respective payments but also by offering a larger variety of products and services to the system than each unit could offer individually. Based on empirical generation and market data, the presented analysis outlines the added benefit of the so-called value stacking implementing the balance of forecast errors, the exploitation of short-term arbitrage opportunities, and the provision of secondary and tertiary frequency reserve. A multi-stage and multi-period optimization approach is presented to generate an aggregated bidding strategy on multiple energy and ancillary service markets. On the one hand, the results highlight the value of individual operation modes for the plant and, on the other hand, the aggregated benefit of value stacking with multiple combined operating modes. The provided empirical insights are beneficial for both potential investing parties that want to evaluate the potential value of combined plants and policymakers that consider further regulatory amendments to open markets and enable further integration of new energy sources.
Today, threats arise in many respects due to the interruption of the carbon cycle, and these threats endanger the future of human and earth. The major reason is the fossil fuels consumption in energy production and the inefficient use of energy and sources. In this study, in addition to supporting the integration of renewable energy sources (RES), which will be increased for global sustainable development and targets of net-zero emissions, especially the 2060 carbon neutral targets of China which is the production center of the world, the main motivations are reducing the carbon emissions that occur in energy production and industrial productions, increasing the efficiency of energy consumption and to put the carbon cycle on track and to eliminate global disasters especially climate change and extreme weathers.
In this study, it is aimed to achieve carbon neutralization by adapting industrial productions to demand side management (DSM) applications in the determined industry with fully autonomous dynamic production lines equipped with industrial internet of things (IIoT) and automation technologies in accordance with Industry 4.0 standards, while making full use of RES and carbon capture, utilization, and storage (CCUS) technologies.