Abstract
Unpredictable system component contingencies have imposed vulnerability on power systems, which are under high renewables penetration nowadays. Intermittent nature of renewable energy sources has made this unpredictability even worse than before and calls for excellent adaptability. This study proposes a flexible security-constrained structure to meet the superior flexibility by coordination of generation and demand sides. In the suggested model, demand-side flexibility is enabled via an optimum real-time (RT) pricing program, while the commitment of conventional units through providing up and down operational reserves improves the flexibility of supply-side. The behaviour of two types of customers is characterised to define an accurate model of demand response and the effect of customers' preferences on the optimal operation of power networks. Conclusively, the proposed model optimises RT prices in the face of contingency events as well as wind power penetration. System operators together with customers could benefit from the proposed method to schedule generation and consumption units reliably.

Abstract
In this paper, we propose an active fault tolerant control (FTC) to regulate the active and reactive output powers of a voltage source converter (VSC) in the case of actuator failure. The active fault tolerant controller of the VSC which connects a distributed energy resource to the distribution power grid is achieved through the fault diagnostic and controller reconfiguration units. The diagnostic unit reveals the actuator failure by comparing the known inputs and measured outputs of VSC with those of the faultless model of the system and testing their consistency. In the case of actuator failure, the reconfiguration unit adapts the controller to the faulty system which enables the VSC to track the desired active and reactive output powers. The reconfiguration unit is designed using the virtual actuator which does not interfere with the regular controller of the VSC. The effectiveness of the proposed active FTC is evaluated by the numerical simulation of a VSC connected to the AC distribution grid.

Abstract
As a result of Demand Response (DR) programs implementation in the industrial sector, varying electricity prices based on Time-of-Use (ToU) rates are becoming more common, replacing traditional flate-rates per unit of energy consumption. On the other hand, increased automation of industrial facilities is gaining interest due to their reliability, flexibility, and robustness. However, it is necessary to determine a suitable task schedule in order to ensure their cost-efficiency and maximize profits. In this study, a Market-Based approach is considered to solve the Multi-Agent Task Allocation (MATA) problem for a group of homogeneous agents and tasks. While most previous studies model the problem considering flate-rates for electricity consumption, the main contribution of this study is accounting for the implementation of a DR program with varying ToU rates. The effects of optimizing the task allocation process on the costs incurred are investigated and compared to the effects of random assignment. Four different case studies are analyzed considering different-sized maps and number of tasks. The results show the computational efficiency of the proposed algorithm and its ability to massively decrease the electrical charging costs.

Abstract
In this paper, a risk-constrained optimal scheduling framework is proposed for an economic and reliable operation of microgrids. The framework is developed based on a scenario-based optimization technique, to schedule the microgrid operation both in normal and islanding modes. The prevailing uncertainties of islanding duration as well as prediction errors of loads, market prices and renewable power generation are addressed in the scheduling problem. The effect of participation of customers in demand response (DR) programs is investigated on economic-reliable operating solutions. Also, the uncertainties associated with wind power, loads and electricity prices as well as the uncertainties of islanding duration events of the microgrid are modeled, properly. The optimal scheduling carried out through a unit commitment algorithm and an AC power flow procedure by considering system's objectives and constraints. Moreover, to adequately handle the uncertainties of the problem, conditional value-at-risk (CVaR) metric is incorporated into the optimization model to evaluate the profit risk associated with operator's decisions in different conditions. With the proposed model, the impacts of DR actions, in terms of economy and reliability, are investigated with a 400 V microgrid system.

Abstract
This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required.

Abstract
A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs' energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.