Abstract
A viable option for electrification of remote areas far from power grids is to set up microgrids and feed them with local generation. Such microgrids are referred to as isolated microgrids and due to the lack of possibility of power exchange with the grid, their operation is different from grid-connected microgrids. Isolated microgrids, similar to grid-connected microgrids are equipped with energy management systems including unit commitment and economic dispatch modules. In this paper, the aim is to formulate the dynamic economic load dispatch (DELD) in isolated microgrids, while curtailment of responsive loads and curtailment of renewable power is allowed and load shedding is used as the last resort for balancing generation and demand. The generated power of dispatchable distributed generators (DGs), curtailed power of renewable DGs, curtailed demand and shed power are determined for each time period. The formulated DELD problem is solved with the well-established particle swarm optimisation (PSO) algorithm. The results for a microgrid with four dispatchable DGs and two renewable DGs show the performance of PSO over grey wolf optimisation (GWO) and also indicate the significant effect of demand response in reducing the operation cost of isolated microgrids.

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
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 tendency to use renewable energies in DC microgrids (MGs) has been increased in the past decades. Due to the unpredictable behavior of renewable resources, it is vital to utilize energy storage resources in the MG structure. The generation sources and storages in DC MGs should be chosen in order to meet the maximum demand in both grid-connected and islanded mode. Also, penetration of power electronic based devices is essential to connect these resources to the network. The control of these devices are another challenge in this regard. So, a proper configuration along with an efficient control approach is needed for development of DC MGs. In this paper, a new structure for DC MG is presented which includes solar photovoltaic (PV) as generation sources and supercapacitor and battery as storages. Furthermore, an innovative control method based on voltage variations is introduced for the proposed structure. It is shown that simultaneous usage of battery and supercapacitor improves the performance of the MG in handling the abrupt load changes in the both grid-connected and islanded mode operations. To evaluate the performance of the proposed structure and control algorithm, different conditions are simulated in MATLAB/Simulink software and the results are presented. The results confirm a high degree of performance for proposed structure and control method.

Abstract
Increasing the penetration of renewable resources has aggravated the operational flexibility at distribution level. In this study, a flexibility-oriented scheduling of microgrids (MGs) is suggested to reduce the power fluctuations in distribution feeders caused by the high penetration of wind turbines (WTs) in MGs. A flexibility constraint as viable and practical solution is used in MG scheduling to address this challenge. The presented scheduling model, implemented using mixed integer linear programming (MILP) and a stochastic framework, exercises risk constraints to capture the uncertainties associated with wind turbines, loads and market prices. The effectiveness of the model is investigated on a MG with high penetration of WTs in the presence of demand response (DR) and energy storage systems (ESSs). Numerical studies show the influence of risk parameters' changing on operation costs. In addition, the flexibility constraint mitigates the sharp variation of the net load at distribution level, which improves the flexibility of the distribution system.