Abstract
This paper presents a dynamic model to improve the resilience of the distribution network during contingent events. In this model, when an event occurs, the system operator maximizes power supply by changing the network topology as well as utilizing the direct load control (DLC) program. The model is implemented on a modified IEEE 69-bus distribution system and includes three types of residential, commercial and industrial loads. First, numerous scenarios are generated based on weather forecasting, and then the problem is solved for high-probability scenarios. It is noteworthy that industrial loads are considered as vital loads and the priority of load supply is for industrial, residential and commercial loads, respectively. The final problem is formulated as mixed-integer linear programming (MILP) problem and solved by CPLEX solver in GAMS software. The effect of dynamic topology on load supply has been investigated. In addition, the impact of using the DLC program and electrical energy storage systems (EES) systems on load supply been studied in detail.

Abstract
With the significant increase in wind speed usage as a clean source of energy, an accurate wind speed forecasting system is a must for more effective utilization of this energy. Failure to consider the inherent spatio-temporal features of wind speed time series leads to the lack of generalization capacity for current wind speed forecasting approaches. This paper proposes an end-to-end deep neural network framework, i.e., convolutional rough long short-term memory (ConvRLSTM), to extract spatio-temporal wind correlations and mitigate the inherent uncertainties in wind time series by incorporating the Rough set theory into a combination of convolution neural network (CNN) and LSTM units. Our proposed model receives the historical data of wind speed for a 20x20 array of wind turbines in North Carolina, US. Several ConvRLSTM layers extract the most relevant features for the forecasting task, and finally, fully connected layers predict 400 wind speed values using the spatial features obtained by the CNN and temporal features computed by the LSTM. Through analyzing the numerical forecasting results, it can be inferred that the proposed approach outperforms the mainstream and recently published forecasting strategies in terms of the RMSE metric.

Abstract
In this paper, a flexibility oriented stochastic scheduling framework is presented to evaluate short-term reliability and economic of islanded microgrids (MGs) under different incentive-based DR (IBDR) programs. A multi-period islanding constraint is considered to prepare the MG for a resilient response once a disturbance occurs in the main grid. Also, a multi-segment optimal power flow (OPF) approach is used to model the IBDR actions and reserve resources. Moreover, uncertainties associated with electricity prices, loads, renewable generation, calls for reserve as well as uncertainties of islanding duration of the MG are considered. The ultimate goal of the MG operator is to maximize its expected profit under a certain level of security and reliability in conjunction with the minimization of energy procurement costs of customers. The MG's economy and reliability indices are studied considering normal operation and resilient condition based on appliances characteristics, customers' and operator's behaviors. The proposed model can effectively manage MGs operation in both normal and resilient conditions in order to improve economic and reliability indices. Numerical results demonstrate that by implementing IBDR, in cases of normal and resilient operation, the expected profit of the MG operator increases about 4% and 2.7% and reliability indicator improved 60% and 56%, respectively.

Abstract
In this paper, a stochastic optimization model is developed for optimal operation of the active distribution networks. The proposed model is investigated on the transactive energy market in the presence of active consumers, local photovoltaic power generations and storage devices. The stochastic behavior of photovoltaic panel power generation units and load consumptions have been modeled using scenario generations and scenario reduction technique. Besides, the stochastic nature of the demand power as well as rooftop photovoltaic panels have been investigated in this paper. In the transactive energy market model, the distribution system operator is the main responsible for the market-clearing mechanisms and controlling the net power exchange between the distribution network and upstream grid. The proposed model is tested and verified on a radial medium voltage distribution network with 16 buses.

Abstract
This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration.

Abstract
This study describes a computationally efficient model for the optimal sizing and siting of Electrical Energy Storage Devices (EESDs) in Smart Grids (SG), accounting for the presence of time-varying electricity tariffs due to Demand Response Program (DRP) participation. The joint planning and operation problem for optimal siting and sizing of the EESD is proposed in a two-stage optimization problem. In this regard, the long-term decision variables deal were the size and location of the EESDs and have been considered at the master level while the operating point of the generation units and EESDs is determined by the slave stage of the model utilizing a standard mixed-integer linear programming model. To examine the effectiveness of the model in the slave sub- problem, the operation model is solved for different working days of different seasons. Binary Particle Swarm Optimization (BPSO) and Binary Genetic Algorithm (BGA) have been used at the master level to propose different scenarios for investment in the planning stage. The slave problem optimizes the model in terms of the short-term horizon (day-ahead). Additionally, the slave problem determines the optimal schedule for an SG considering the presence of EESD (with sizes and locations provided by the upper level). The electricity price fluctuates throughout the day, according to a Time-of-Use (ToU) DRP pricing scheme. Moreover, the impacts of DRPs have been addressed in the slave stage. The proposed model is examined on a modified IEEE 24-Bus test system