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Interest in the highly efficient energy hub (EH) model has been growing despite the high computational requirements of planning for a multi-energy, multi-device operation. To address both the device size limitation and the multi-scenario issue, we propose a new solution methodology for solving the EH planning problem. In the method, the decision variables are device sizes. First, a dimension reduction technique is proposed to address the curse of dimensionality based on the correlation of unknown variables such as the capacities of different devices in an EH. Second, to avoid local convergence, a solution method called the variable-sized unimodal searching (VUS) approach is proposed to assure a global optimal planning scheme for the one-dimensional non-convex optimization model obtained from the preceding dimension reduction process. The case study indicates that the proposed approach has a higher computing efficiency than the Benders decomposition (BD) algorithm to deal with a scenario-based stochastic planning problem with a large number of scenarios. Thus, the effectiveness of the EH planning approach is verified.

Control methods based on global positioning systems (GPS) are reported in multiple literatures recently, which achieve a fixed frequency operation of the microgrid and therefore has the tremendous benefit that any problems related to frequency instability are eliminated. However, the possible interruption of GPS timing signals may cause current circulating and finally leads to instability of the microgrid, yet it is largely neglected in literatures. This paper presents an angle synchronizing mechanism which utilizes the timing signal from GPS satellites and an auxiliary frequency droop loop to ensure synchronization during GPS offline events. Smooth transfer is guaranteed between primary and auxiliary control loops with discrete control architecture. Also, to allow microgrid using GPS-based control to work in tandem with the bulk power system or another frequency droop microgrid, an extra synchronization algorithm is proposed. The viability and performance of the proposed control structure is validated by case studies.

Serum palmitic acid (PA), a type of saturated fatty acid, causes lipid accumulation and induces toxicity in hepatocytes. Ethanol (EtOH) is metabolized by the liver and induces hepatic injury and inflammation. Herein, we analyzed the effects of EtOH on PA-induced lipotoxicity in the liver. Our results indicated that EtOH aggravated PA-induced apoptosis and lipid accumulation in primary rat hepatocytes in dose-dependent manner. EtOH intensified PA-caused endoplasmic reticulum (ER) stress response in vitro and in vivo, and the expressions of CHOP, ATF4, and XBP-1 in nucleus were significantly increased. EtOH also increased PA-caused cleaved caspase-3 in cytoplasm. In wild type and CHOP(-/-) mice treated with EtOH and high fat diet (HFD), EtOH worsened the HFD-induced liver injury and dyslipidemia, while CHOP knockout blocked toxic effects of EtOH and PA. Our study suggested that targeting UPR-signaling pathways is a promising, novel approach to reducing EtOH and saturated fatty acid-induced metabolic complications.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

An incentive-compatible demand response (DR) strategy is proposed for engaging spatially-coupled Internet data center (IDC) and their spatial load regulation potentials in electricity markets. First, an optimal power flow (OPF) model which considers IDC (referred to as IOPF) is proposed to coordinate IDC DRs and power system operations, in which the formulated IDC load model is compatible with the conventional OPF model. Second, the IOPF-based locational marginal price (LMP) (referred to as ILMP) is derived, which is used to analyze the impact of spatially-coupled DR options on ILMPs and the IDC DR’s clearing price. Third, IDC DR activation strategy is proposed as an extension of Net Benefit Test (NBT), where the risk of negative benefit to IDCs is derived. An IDC DR’s activation strategy is proposed based on NBT to determine whether non-zero IDC DR dispatches in IOPF are cost-effective. Last, an extra benefit redistribution mechanism is proposed to achieve the incentive-compatibility between social welfare and IDC benefit. The proposed approach is based on the Vickrey–Clarke–Groves mechanism and the contribution factor theory, where the market’s revenue adequacy is maintained, and benefits to IDCs and other customers are guaranteed. Simulation results verify the efficiency of the proposed method, implying that the proposed spatially-coupled DRs are compatible with and can enhance existing DR mechanisms.

Interest in electricity-gas demand response (EGDR) programs has been growing, especially with respect to Energy Hub (EH) systems, which are typically large industrial multi-energy users involving electric, heating and cooling loads. A key component in modeling EGDR is the electricity-shifting curve (ESC), which is usually obtained empirically. However, rigorous and systematic approaches to obtaining and understanding the ESC are lacking. In this article, a quantitative modeling approach is proposed based on the aggregated utility curve of multi-energy demands such as electricity, heating and cooling loads. First, an ESC based on consumer psychology and behavior is adopted. Second, utility curves of different loads (electricity, heating and cooling) are aggregated to a single utility curve, which is then combined with the theory of consumer choice to develop the ESC with regard to the price ratio of different energy sources. Third, various factors affecting the shifting curves are identified and then analyzed. Based on the case studies, generalized guidelines for improving the ESC are provided. Additionally, a multi-energy user with a higher heating-to-electricity ratio (HER) is verified as having better performance in obtaining a broader shifting area, and a multi-energy user with lower HER has a larger shifting amount in common range of price ratio. In summary, this work presents a quantitative and systematic approach to modeling the ESC in an EH and to understanding EGDR behaviors which can be used to improve EH response flexibility.

Abstract Effective thermal conductivity (ETC) is an important parameter in packed-bed systems and is affected significantly by packing structure. Heat flow through a packed bed can be divided into three parallel paths: the solid-fluid-solid path, the solid-solid path and the fluid path. The models to evaluate ETC in packed beds for the first two paths were previously developed. In the present work, a comprehensive model to consider the heat transfer through the voids of a randomly-packed bed is further developed using the Delaunay tessellation. The results show that heat conduction through the fluid-filled voids becomes significant when the solid-to-fluid conductivity ratio decreases to a certain level (

Abstract Cetyl palmitate (CP) was thermally synthesized using binary 1-hexadecanol (HD) and palmitic acid (PA) mixture as the starting materials in nitrogen with the absence of any catalyst. The molecule structure, thermal property, thermal stability and reliability of as-prepared cetyl palmitate was studied by 1H nuclear magnetic resonance (1H NMR), Fourier transformation infrared (FT-IR) spectrometer, differential scanning calorimetry (DSC) and thermogravimetric analyzer (TGA), respectively. 1HNMR and FT-IR confirmed the as-prepared product under 350 °C for 30 min was CP with high purity. According to the DSC measurements, the onset melting temperature and latent heat of fusion for the as-prepared CP was 52.3 °C and 180.9 J/g, respectively. Results showed that the synthesized cetyl palmitate was favorable used as phase change material (PCM) owing to its desirable thermal properties, excellent thermal stability and reliability. Due to the low thermal conductivity of CP, it was impregnating into nickel (Ni) foams with different pore sizes to make CP/Ni foam composite PCMs (CPCMs). Compared with pure CP, thermal conductivity of CP/Ni foam (70, 90, and 110PPI) CPCMs were increased by 1.88, 2.02 and 4.86 times, respectively. The CP/Ni foam CPCMs displayed appropriate thermal properties for latent heat energy storage applications.

With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.