Abstract
Context. Clouds are ubiquitous in exoplanet atmospheres and they represent a challenge for the model interpretation of their spectra. When generating a large number of model spectra, complex cloud models often prove too costly numerically, whereas more efficient models may be overly simplified. Aims. We aim to constrain the atmospheric properties of the directly imaged planet HR 8799e with a free retrieval approach. Methods. We used our radiative transfer code petitRADTRANS for generating the spectra, which we coupled to the PyMultiNest tool. We added the effect of multiple scattering which is important for treating clouds. Two cloud model parameterizations are tested: the first incorporates the mixing and settling of condensates, the second simply parameterizes the functional form of the opacity. Results. In mock retrievals, using an inadequate cloud model may result in atmospheres that are more isothermal and less cloudy than the input. Applying our framework on observations of HR 8799e made with the GPI, SPHERE, and GRAVITY, we find a cloudy atmosphere governed by disequilibrium chemistry, confirming previous analyses. We retrieve that C/O = 0.60(-0.08)(+0.07). Other models have not yet produced a well constrained C/O value for this planet. The retrieved C/O values of both cloud models are consistent, while leading to different atmospheric structures: either cloudy or more isothermal and less cloudy. Fitting the observations with the self-consistent Exo-REM model leads to comparable results, without constraining C/O. Conclusions. With data from the most sensitive instruments, retrieval analyses of directly imaged planets are possible. The inferred C/O ratio of HR 8799e is independent of the cloud model and thus appears to be a robust. This C/O is consistent with stellar, which could indicate that the HR 8799e formed outside the CO2 or CO iceline. As it is the innermost planet of the system, this constraint could apply to all HR 8799 planets.

Abstract
We study the computational power of parsing expression grammars (PEGs). We begin by constructing PEGs with unexpected behaviour, and surprising new examples of languages with PEGs, including the language of palindromes whose length is a power of two, and a binary-counting language. We then propose a new computational model, the scaffolding automaton, and prove that it exactly characterises the computational power of parsing expression grammars (PEGs). Several consequences will follow from this characterisation: (1) we show that PEGs are computationally "universal", in a certain sense, which implies the existence of a PEG for a P-complete language; (2) we show that there can be no pumping lemma for PEGs; and (3) we show that PEGs are strictly more powerful than online Turing machines which do o(n/(logn)(2)) steps of computation per input symbol. (C) 2020 Published by Elsevier Inc.

Abstract
Economic generation scheduling (EGS) is a non-convex optimization problem for allocating optimal generation among the committed units that can meet given real-world practical limits such as ramp rate limits, prohibited operating zones, valve loading effects, multi-fuel options, spinning reserve and transmission system losses at the minimum fuel cost. Moreover, considering environmental issues results in an environmental/economic generation scheduling (EEGS) problem that is a multiobjective optimization model with two non-commensurable and contradictory objectives. In this paper, a novel method has been presented in order to minimize production cost and emission of the steam power plants in short term periods. The obtained results showed that the proposed method can be used in short-term decision making of steam power plants which will be absolutely effective in long-term emission target oriented strategies. A framework is proposed for solving single objective EGS and multiobjective EEGS problems considering the aforementioned constraints. The problem is solved by a new meta-heuristic optimization called Ray Optimization (RO) to determine the optimal power generation. The performance of the proposed algorithm is investigated by applying it to solve diverse test systems having nonconvex solution spaces. Numerical results have been comprehensively compared with some of the most recently published research works in the area in order to validate the results and confirm the potential of the proposed approach. The obtained results show the application of the proposed framework and effectiveness of the solutions.

Abstract
Large-scale compressed air energy storage (CAES) is conventionally used in power systems. However, application of CAESs at the distribution level is limited because of differences in design and efficiency. On the other hand, application of electrical batteries suited for distribution networks (DNs) faces also challenges from high investment cost and significant degradation. In this regard, this paper presents the participation of an active distribution system equipped with a small-scale CAES (SCAES) in the day-ahead wholesale market. To make CAES applicable to DNs, thermal-electrical setting design of the SCAES coupled with a packed-bed heat exchanger is adopted in the operation of the grid, where SCAES performs as an energy storage for DNs to surpass existing deficiencies of battery banks. The electrical/thermal conversion rate has been modeled for the SCAES operation. Moreover, the operation strategy of the SCAES is optimally coordinated with an electric vehicle charging station (EVCS) as an alternative energy storage technology in deregulated DNs. To make EVCS simulation more realistic, Gaussian Copula probability distribution function is used to model the behavior of the EVCS. The results obtained from different case studies confirm the value of SCAES as a reliable energy storage technology for DNs.

Abstract

Abstract
Autonomous inspection Unmanned Aerial Vehicle systems are an essential research area, including power line distribution inspection. Considerable efforts to solve the demanding presented in the autonomous UAV inspection process are present in technical and scientific research. One of these challenges is the precise positioning and fly control of the UAV around the energy structures, which is vital to assure the security of the operation. The most common techniques to achieve precise positioning in UAV fly are Global Positioning Systems with Real-Time Kinematic. This technique demands a proper satellite signal receiving to work appropriately, sometimes hard to achieve. The present work proposes a complementary position data system based on augmented reality tags (AR Tags) to increase the reliability of the UAV fly positioning system. The system application is proposed for energy power tower inspections as an example of use. The adaptation to other inspection tasks is possible whit some small changes. Experimental results have shown that an increase in the position accuracy is accomplished with the use of this schema.