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As distributed renewable energy (DRE) generation technologies advance and transportation electrification deepens, electric vehicle charging stations (EVCSs) equipped with DRE will quickly proliferate in the near future. This paper, thus, proposes a distributed energy trading framework for EVCSs equipped with distributed photovoltaic (PV) system considering transportation network (TN). The framework contains the electric vehicle allocation problem (EAP) in TN and the distributed energy trading (DET) problem among EVCSs. The target of EAP is minimizing the whole time cost of TN. Therefore, the EV quantities in each EVCS can be obtained after solving the EAP problem by introducing the time cost coefficient. As for DET, this paper develops a distributed trading method among EVCSs derived from the Nash bargaining (NB) theory. The method considers not only the benefit of each participant itself, but also the fair distribution of the mutual profits among the participants at the same time. Numerical simulations are performed to validate the effectiveness and viability of the mentioned framework and models. The numerical results show that the EAP model can reasonably allocate the electric vehicles (EVs) that need to be charged. The operating profits of each EVCS can be significantly promoted by the DET among EVCSs. In addition, the proposed distributed solution methodology can efficiently address DET problems and protect the business privacy of participants.

The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.

Multi-energy microgrid (MEMG) is a typical realization of multi-energy system. Its day-ahead dispatch is crucial for the economic operation of MEMG. However, previous research usually considered the efficiency of multi-energy coupling equipment in MEMG as a constant value neglecting the non-linear relationship of heterogeneous energy conversion. With this, day-ahead dispatch would result in inappropriate plans and further impair safe operation of MEMG. To deal with this challenge, this paper studies a novel day-ahead dispatch model for MEMG by considering variable operating conditions of multi-energy coupling equipment. Specifically, first, a novel day-ahead dispatch model of MEMG is proposed. The day-ahead dispatch model incorporates refined energy conversion constraints, and these constraints can capture variable operating conditions of multi-energy coupling equipment. Then, due to the non-linear terms with respect to variable operating conditions, a piecewise linearization method is utilized to transform the proposed model into a mixed-integer linear programming formulation. Finally, simulation results show that our approach can obtain a more reasonable day-ahead dispatch scheme with multiple advantages, such as improving the overall operating economy, avoiding multi-energy power curtailment, and enhancing the potential of demand response.

As the need for clean energy increases, massive distributed energy resources are deployed, strengthening the interdependence of multi-carrier energy systems. This has raised concerns on the electricity-heat system’s co-operation for lower operation costs, higher energy efficiency, and higher flexibility. This paper discusses the co-operation of integrated electricity–heat system. In the proposed model, network constraints in both systems are considered to guarantee system operations’ security: the branch flow model is utilized to describe the electricity network, while a convexified model considering variable mass flow and temperature dynamics is adopted to describe the heat network. Additionally, novel models for heat pumps and the stratified water tank are proposed to represent the physical system more accurately. Finally, to preserve the information privacy of separate systems, a distributed algorithm is proposed based on the alternating direction method of multipliers (ADMM). Numerical studies show that the co-operation could provide a more economical and reliable solution than the decoupled operation of the heat network and electricity network. Moreover, the ADMM-based algorithm could derive solutions very close to the optimum provided by centralized optimization.

The experimental and numerical investigations of the heat transfer of supercritical pressure n-decane flowing through a pipe at various rotational speeds, mass flow rates, heat fluxes, and pressures, are presented. This pipe is 2 mm in diameter, 200 mm in length, with a radius of 0.328 m, and is parallel to the rotating axis. The wall temperature was measured at four positions around the periphery of the pipe at each of the five selected cross section along the pipe's length. Maximum convective heat transfer was observed at the outer edge of the horizontal section, while its corresponding minimum was observed at the inner edge. The heat transfers at the two sides of the channel were observed to be similar. The density and pressure differences between the outer and inner edges increased at increasing rotating speeds. However, the temperature difference between the outer and inner edges decreased with increased rotational speed mainly because of the increase of secondary flows in the section. The section's average convective heat transfer coefficient increased with an increase in the rotational speed, and its value at 1000 rpm was approximately twice than that at static conditions. The phenomenon of oscillation was observed near the exit of the horizontal section, and was caused by the flow and considerable property changes near the pseudo critical temperature. A computational fluid dynamics (CFD) model was developed using the real gas thermal properties and was coupled with the heat transferred owing to fuel flow. The predicted fuel and wall temperatures were in good agreement with the experimental data. A new local Nusselt number correlation of the heat transfer of n-decane in a rotating horizontal section was proposed. [ABSTRACT FROM AUTHOR]

Abstract Background A malignancy of the liver, hepatocellular carcinoma (HCC) is among the most common and second-leading causes of cancer-related deaths worldwide. A reliable prognosis model for guidance in choosing HCC therapies has yet to be established. Methods A consensus clustering approach was used to determine the number of immune clusters in the Cancer Genome Atlas and Liver Cancer-RIKEN, JP (LIRI_JP) datasets. The differentially expressed genes (DEGs) among these groups were identified based on RNA sequencing data. Then, to identify hub genes among signature genes, a co-expression network was constructed. The prognostic value and clinical characteristics of the immune clusters were also explored. Finally, the potential key genes for the immune clusters were determined. Results After conducting survival and correlation analyses of the DEGs, three immune clusters (C1, C2, and C3) were identified. Patients in C2 showed the longest survival time with the greatest abundance of tumor microenvironment (TME) cell populations. MGene mutations in Ffibroblast growth factor-19 (FGF19) and catenin (cadherin-associated protein),β1(CTNNB1) were mostly observed in C2 and C3, respectively. The signature genes of C1, C2, and C3 were primarily enriched in 5, 23, and 26 pathways, respectively. Conclusions This study sought to construct an immune-stratification model for the prognosis of HCC by dividing the expression profiles of patients from public datasets into three clusters and discovering the unique molecular characteristics of each. This stratification model provides insights into the immune and clinical characteristics of HCC subtypes, which is beneficial for the prognosis of HCC.