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Abstract Using energy storage to solve the multiperiod OPF problem for renewable energy fluctuation is an effective way to increase operation safety and reduce the cost of power systems. However, in solving this OPF problem, model-based methods cannot accurately model uncertain scenarios, while traditional RL methods cannot satisfy the constraints well, and both methods have limitations. Therefore, we propose an RL method, ERL-HC, that does not require scene modelling and can handle general forms of physical constraints. First, a constraint policy network (CPN) is proposed that corrects the output of a neural network on the basis of the inequality generalized reduced gradient (GRG) method; the outputs of this network satisfy all constraints, and it can be trained in an end-to-end manner. Second, the critic network is improved based on the IM method to increase the sample learning efficiency by improving the agent's understanding of state interdependencies. Finally, the adaptive-tuning Lagrange multiplier method is applied in the AC framework to reduce the number of iterations of the inequality GRG in the CPN and efficiently train ERL-HC. ERL-HC was tested on two systems of different sizes. The results show that ERL-HC has a better learning ability than general safe RL algorithms, overcomes the limitations of mainstream safe RL methods in handling equality constraints, and addresses the poor generalization issues of RL methods that can handle equality constraints.

Abstract Background CITED4 belongs to the CBP/p300‐interacting transactivator with glutamic acid and aspartic acid‐rich tail (CITED) family which is induced by various cytokines and participates in cytokine‐induced proliferation and differentiation. CITED4 is induced by HB‐EGF in lung cancer cells. However, it is unclear whether and how CITED4 contributes to the invasion and metastasis of lung adenocarcinoma (ADC). Methods CITED4 expression in lung adenocarcinoma and its association with disease‐free survival (DFS) and overall survival were analyzed based on a cohort of 261 patients. The roles of CITED4 were validated via loss‐of‐function and gain‐of‐function experiments. The relationship between CITED4 and CLDN3 was validated by immunohistochemistry, Western blotting, and luciferase reporter assays. The function of the CITED4‐CTNNB1‐CLDN3 complex was fully validated and described. Results CITED4 expression was significantly upregulated in ADC tissues and cells and a predictor for DFS. Downregulation of CITED4 attenuated the proliferation and invasion, whereas CITED4 overexpression enhanced these effects. Overexpression and knockdown of CITED4 resulted in the upregulation and downregulation of CLDN3, respectively. Moreover, CITED4 downregulation suppressed CLDN3‐mediated ADC cell metastasis in vivo. CITED4 was highly expressed and positively correlated with CLDN3. Mechanistically, CITED4 interacted with CTNNB1 and functioned synergistically to enhance CLDN3 transcription. Importantly, CITED4 induced ADC invasion via a CLDN3‐dependent pathway. CITED4 determined the level of CLDN3, which in turn affected the sensitivity of tumors to Clostridium perfringens enterotoxin treatment. Conclusions The CITED4‐CTNNB1‐CLDN3 axis plays a key role in the invasion and metastasis of ADC and provides a novel therapeutic target for lung cancer treatment.

Background To assess the potential prognostic value of the albumin to alkaline phosphatase ratio (AAPR) in patients with non‐small cell lung cancer (NSCLC) after surgery. Methods The log‐rank and Kaplan‐Meier analyses were performed to detect differences in survival levels between different groups. A model of Cox proportional hazards was used to perform univariate and multivariate survival analyses. Comparisons of receiver operating characteristic (ROC) curves and the likelihood ratio test (LRT) were also utilized to compare the prognostic abilities of different systems for overall survival (OS) prediction. Results The optimal cut‐off value of the preoperative AAPR was 0.64. A decreased AAPR was associated with several clinicopathological and clinicolaboratory variables related to cancer progression. The preoperative AAPR of patients was positively correlated with the poor prognosis of NSCLC. In multivariate analyses, the preoperative AAPR was identified as an independent prognostic factor for disease‐free survival (DFS; P = 0.001) and overall survival (OS; P = 0.003). The LRT showed that the AAPR tumor‐node‐metastasis (TNM) system presented a significantly larger χ2 value (112.4 vs. 89.2, respectively, P

In this paper, we propose a bi-level, real-time economic dispatch method of a virtual power plant (VPP), including various distributed energy resources (DERs). Considering the different interests of VPPs and a system operator, the real-time economic dispatch of VPPs is described as a bi-level programming problem, where a system operator dispatches VPPs based on the price incentive mechanism on the upper level, and the VPPs provide response according to the optimal control of their DERs on the lower level. Considering the uncertainties of DERs and loads, the decision risks of a system operator on the upper level and VPPs on the lower level are further dealt with by the fuzzy chance constrained programming, such that they can make reasonable decisions according to their own preferred risks. The mapping method and the bi-level optimization method are also presented as the solutions for the proposed model. In this way, the fuzzy chance constraints and objective functions of both levels are transformed into deterministic forms and, then, are calculated dispersedly. As a result, the calculation burden of a system operator and the information privacies of VPPs all can be treated availably. The case studies verify the effectiveness of the proposed method in the end.

Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.

A novel strategy for optimal scheduling of short-term electric power generation of cascaded hydroelectric plants based on particle swarm optimization (PSO) and chance-constrained programming is presented to maximize the expected profit at a given risk level in this paper. Based on chance-constrained programming, in which some specified probability are given to simulate some uncertainties, such as water inflows, electricity prices, unit status, and so on. This paper proposes a model for short-term scheduling optimization of cascaded hydro plants, which includes uncertainties, spatial-temporal constraints among cascaded reservoirs, etc. A hybrid particle swarm optimization (HPSO), which is embedded with evolutionary algorithms, is presented to use for the solution of global optimization problems. Catastrophe theory, which is concerned with natural evolutionary or survival-of-the-fittest, is utilized as an indication of the premature converge of PSO, and the positions of particles are further adjusted in the search space according to chaos optimization. In this way, each particle competes and cooperates with its neighbors. The proof shows that HPSO is guaranteed to converge to the global optimization solution with probability one. The model presented is solved by a combination method of HPSO and Monte Carlo simulation. Finally, a numerical example is served for demonstrating the feasibility of the method developed. Index Terms--Cascaded hydroelectric plants, chance-constrained programming, optimal scheduling, particle swarm optimization, short-term electric power generation.

This study proposes a decentralised methodology to deal with the optimal energy flow (OEF) problem of the electricity-natural gas system (EGS). The OEF problem of EGS is formulated as a multi-stage dynamic programming (DP) process, and the approximate DP algorithm is used to decompose it into subproblems. The independent decision of each subproblem can be made by solving Bellman's equation, requiring just a moderate interchange of information among different subproblems. Such that the information privacy and dispatch independency of subsystems (including electricity and natural gas (NG) subsystems) can be ensured. Different from most existing decentralised algorithms, the impact of one subproblem's decision on other subproblems can be estimated in the proposed algorithm, and the parameters of the proposed algorithm are not required for tuning, which makes more sense in real applications. Furthermore, an improved linear cut technique is proposed to handle the non-convexity of the NG network model, so that the tightness of all gas flow constraints can be ensured. Case studies containing small, large and complicated EGSs validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, the use of interconnected multi-area systems has increased significantly. To operate these systems efficiently, a proper coordination approach among different areas is required. With this context, a novel decentralised optimal dispatch algorithm is proposed in this study. The centralised economic dispatch model is decoupled into a series of subproblems for different areas, allowing the independent decision of each area without a central operator. The detailed information of each area is not required for information exchange. To better estimate the impact of each area's decision on other areas, the approximate value functions of the tie-line powers are added into the local objective function of the sub-problem. This technique makes the decentralised solution of the proposed algorithm more explicit and rational compared with most existing distributed algorithms. Besides, the proposed technique does not require parameter tuning and has good convergence performance. Numerical simulations on several test systems and a real power system demonstrate that the proposed algorithm is favourable in terms of accuracy, adaptability, and computational efficiency. [ABSTRACT FROM AUTHOR]

The reactive power optimization (RPO) problem of power systems is a mixed-integer nonlinear programming problem, which can be computationally expensive. This paper applies the alternating direction method of multipliers (ADMM) to solve it. By duplicating discrete control variables with one copy allowed to vary continuously, the RPO model is formulated as a modified model in which the objective function and constraints have a separable structure with respect to the continuous and discrete variables, except for the coupling constraint. We then applied the ADMM to solve this model with the separable structure, and hence the original problem is converted into two optimization sub-problems, which are nonlinear programming (NLP) and mixed-integer quadratic programming (MIQP) problems, respectively. To improve the convergence of the algorithm, we proposed an extended ADMM by adding the upper and lower limits on state variables into the MIQP sub-problem based on the sensitivities obtained in the NLP sub-problem. We also established a mechanism to filter out inactive inequality constraints in the MIQP sub-problem to improve computational speed. Moreover, numerical results tested on IEEE test systems and a real 739-bus system have shown the correctness of the proposed method and its adaptability for power systems with different sizes and configurations. [ABSTRACT FROM AUTHOR]

The integration of distributed wind farm leads to additional fluctuation of power flow in a high-voltage distribution network, which has become a major concern in automatic voltage control. This study proposes a slack optimal control method to determine the tolerance band of gateway reactive power (GRP) which takes into account voltage constraints, energy loss and excessive operations of control devices. Analysis of network characteristics shows that the GRP control band should be set according to both load levels and the outputs of distributed wind farm. Based on the analysis, slack optimal active loss difference strategy is proposed to set the slack optimal band of GRP. A slack optimal band matrix is defined and a partitioning intersection strategy is proposed to implement the differential setting of the slack optimal control band parameters of GRP. Simulation studies are conducted on a practical distribution network in China and the results show that the proposed method performs well in energy saving, control device regulating, voltage profile keeping, and reactive power balancing in a simple and practical way. [ABSTRACT FROM AUTHOR]

We demonstrate that loss of succinate dehydrogenase 5 (SDH5) expression initiates epithelial-mesenchymal transition (EMT), which is visualized by the repression of E-cadherin and up-regulation of vimentin in lung cancer cell lines and clinical lung cancer specimens. In SDH5 knock-out mice, lung epithelial cells exhibited elevated mesenchymal markers, which is characteristic of EMT. Using a human lung xenograft-mouse model, we observed that knocking down endogenous SDH5 in human carcinoma cells leads to the development of multiple lymph node metastases. Moreover, our data indicate that SDH5 functions as a critical protein in regulating EMT by modulating the glycogen synthase kinase (GSK)-3β-β-catenin signaling pathway. These results reveal a critical role for SDH5 in EMT and suggest that SDH5 may be a prognostic biomarker and potential therapeutic target for lung cancer metastasis. [ABSTRACT FROM AUTHOR]