Abstract
A new decentralised demand response (DR) model relying on bi-directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q-learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets' viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation-based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

Abstract
This paper develops a novel short-term load forecasting technique to predict the demanding power for the next hour. In this study, a linear equation-error Auto Regressive Auto Regressive Moving Average Exogenous (ARARMAX) model is trained to specify power consumption as a function of a few past hours. The parameters of the candidate mathematical model are estimated by using two least squares-based iterative algorithms. The main difference with these algorithms is the total number of past data involved in the modeling. Whereas practical data are always subject to noise and un-accurate measuring, a wavelet de-noising technique is utilized to reduce the effect of noise on forecasting which leads to more precise predictions. The superiority of the proposed approach is validated by utilizing practical data from a power utility in Canada in January 1995. The first three days' data are utilized to train the selected model and the fourth-day data are dedicated to test the prediction of the provided model. The L-2 and L-infinity norms error and MAPE, MAE, and RMSE are selected as criteria to show the merits of the proposed approach.

Abstract
Phasor Measurement Unit (PMU) provides beneficial information for dynamic power system stability, analysis and control. One main application of such useful information is data-driven control. This paper is devoted to presenting an approach for optimal signal selection in PMU-based power system stabilizer (PSS) design. In this paper, for selecting the optimal input and output signals for PSS, an algorithm is suggested in which the combination of clustering the generators and the buses of the system with ICA, modal analysis and PCA techniques is used. The solution for optimal PSS input-output selection is found to increase the observability and damping of the power system. This method is simulated on a 68 buses system with 16 machines. To compare the results with the previous methods, the system is simulated and the results of two previously-developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which lowers the number of required PMUs while the system damping is not deteriorated.

Abstract
Transformer buildings are at the heart of the effective operation of distribution systems, and heating problems of transformers under severe operational conditions are among the main factors affecting the lifetime, efficiency, technical losses, etc., of such important power system assets. It is crucial that the inside temperature of transformer buildings is higher than the outside temperature due to the operation of the transformer and the effect of ambient conditions. This issue may cause several problems such as additional transformer aging, losses, and moisture. The main purpose of this study is to decrease the inside temperature of transformer buildings; in other words, to prevent the inside temperature from being higher than the outside temperature. To realize this, it is recommended to apply a combined heat reduction technique by covering the outer surface with a reflective surface and use a low-emitting material on the inner surface. The relevant results of the practical evidence in this manner are presented in detail at a distribution system in Turkey with different climate and loading conditions in the summertime.

Abstract
This article presents a robust chance-constrained optimization framework for the optimal operation management of an energy hub (EH) in the presence of electrical, heating, and cooling demands, and renewable power generation. The proposed strategy can be used for optimal decision making of operators of EHs or energy providers. The electrical energy storage device in the studied EH can handle the fluctuations in operating points raised by such uncertainties. In order to model the hourly demands and renewable power generation uncertainties, a robust chance-constrained close-to-real-time model is adopted in this article. The considered EH in this study follows a centralized framework and the EH operator is responsible for the optimal operation of the hub assets based on the day-ahead scheduling. A thorough analysis of energy flows with different carriers is presented. In addition, a numerical stability test regarding the selection of the time step size is performed to guarantee the solution's time resolution independence, occurring in previous studies.

Abstract
The need for flexibility in power system operation gradually increases regarding more renewable energy integration, load growth, etc., and the system operators already invest in this manner to enhance the power system operation. Besides, the power system has thermally sensitive assets such as lines, transformers, etc. that are normally operated under highly conservative static ratings. There is a growing trend in this regard to use the actual capacity of such assets dynamically under varying operating conditions leading to a dynamic thermal rating concept which is referred as dynamic line rating (DLR) approach specifically for lines. This study provides a comprehensive overview of existing perspectives on DLR and combination with other flexibility options from an operational point of view. Apart from the existing review studies more focused on implementation category of DLR concept, the concentration on more operational stage from the power system operation point of view leads the difference of this study compared to the mentioned studies. A categorization of the DLR implementation for either being sole or combined usage as a flexibility option is further realized. Besides, a geographically categorized analysis on existing practical evidence on DLR concept and implementations is also presented in this study.