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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.

More and more renewable energy sources are integrated into power grids, leading to a power electronic-based low-inertia power system. The grid-forming (GFM) inverter is an effective method for improving the inertia of the system. However, with the increased GFM inverters in the system, how the multiple control parameters affect the frequency response is still not clear. In this study, first, the power-phase model of the power grid is established; then, a small-signal distributed frequency model of the GFM inverter-based power system is established associating with the power-phase model of the power grid and the power-frequency model of the GFM inverter. Based on the proposed model, the influence of the multiple parameters to the frequency response is analyzed. It is concluded that both the inertia and damping coefficient affect the settling time, overshoot, and oscillation of the frequency. Finally, the simulation results verify the proposed model and the conclusion.

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency (EE) of a multiuser multiple-input multiple-output-aided distributed antenna system. The fronthaul link’s power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints, some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the EE optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Second, a triple-layer iterative algorithm is proposed for solving the approximated EE optimization problem, which is proved to converge to the Karush–Kuhn–Tucker conditions of the smoothened EE optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms are capable of achieving an order of magnitude higher EE than its conventional counterpart operating without considering link selection.

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

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.

In a milling process, proper selection of cutting parameters can significantly reduce the electrical energy consumption. Many researchers have conducted cutting parameter optimization of the milling process for electrical energy saving during the past several years. However, in the milling process, a large amount of auxiliary materials such as cutting tools and cutting fluids are consumed. The production process of these materials is energy-intensive and a lot of energy are consumed. Optimizing cutting parameters considering both the electrical energy consumption and embodied energy consumption of auxiliary materials can further reduce the environmental impact of the milling process. In this paper, an approach of cutting parameter optimization is proposed to maximize energy efficiency and machining efficiency for milling operation. Firstly, an energy consumption model of milling operation considering both the electrical energy consumption and embodied energy consumption of cutting tools and cutting fluids is proposed. Then a multi-objective optimization model is established to achieve maximizing energy efficiency and machining efficiency. Finally, to verify the proposed multi-objective model, case studies are carried out and the results indicate that (i) the optimum cutting parameters of milling process vary with the energy boundaries whether considering the embodied energy of the auxiliary materials or not; (ii) the optimum cutting parameter schemes for maximum machining efficiency do not ensure maximum energy efficiency; (iii) multi-objective optimization is an effective method to address the conflicts of the two objectives.