Abstract
Energy hub systems improve energy efficiency and reduce emissions due to the coordinated operation of different infrastructures. Given that these systems meet the needs of customers for different energies, their optimal design and operation is one of the main challenges in the field of energy supply. Hence, this paper presents a two-stage stochastic model for the integrated design and operation of an energy hub in the presence of electrical and thermal energy storage systems. As the electrical, heating, and cooling loads, besides the wind turbine's (WT's) output power, are associated with severe uncertainties, their impacts are addressed in the proposed model. Besides, demand response (DR) and integrated demand response (IDR) programs have been incorporated in the model. Furthermore, the real-coded genetic algorithm (RCGA), and binary-coded genetic algorithm (BCGA) are deployed to tackle the problem through continuous and discrete methods, respectively. The simulation results show that considering the uncertainties leads to the installation of larger capacities for assets and thus a 8.07% increase in investment cost. The results also indicate that the implementation of shiftable IDR program modifies the demand curve of electrical, cooling and heating loads, thereby reducing operating cost by 15.1%. Finally, the results substantiate that storage systems with discharge during peak hours not only increase system flexibility but also reduce operating cost.

Abstract
The problem of reconfiguration for active distribution systems is formulated as a stochastic mixed-integer second-order conic programming (MISOCP) model that simultaneously considers the minimization of energy power losses and CO2 emissions. The solution of the model determines the optimal radial topology, the operation of switchable capacitor banks, and the operation of dispatchable and non - dispatchable distributed generators. A stochastic scenario-based model is considered to handle uncertainties in load behavior, solar irradiation, and energy prices. The optimal solution of this model can be reached with a commercial solver; however, this is not computationally efficient. To tackle this issue a novel methodology which explores the efficiency of classical optimization techniques and heuristic based on neighborhood structures, referred as matheuristic algorithm is proposed. In this algorithm. the neighborhood search is carried out using the solution of reduced MISOCP models that are obtained from the original formulation of the problem. Numerical experiments are performed using several systems to compare the performance of the proposed matheuristic against the direct solution by the commercial solver CPLEX. Results demonstrate the superiority of the proposed methodology solving the problem for large-scale systems.

Abstract
Photovoltaic (PV) as one of the most promising energy alternatives brings a set of serious challenges in the operation of the power systems including PV system protection. Accordingly, it has become even more vital to provide reliable protection for the PV generations. To this end, this paper proposes two-stage data-driven methods. In the first stage, a feature selection method, namely t-distributed stochastic neighbor embedding (t-SNE) is implemented to select the optimal features. Then, the output of t-SNE is directly fed into the strong data-driven classification algorithm, namely robust soft learning vector quantization (RSLVQ) to detect PV array fault and identify the fault types in the second stage. The proposed method is able to detect the two different line-to-line faults (in strings and out of strings) and open circuit fault and fault type considering partial shedding effects. The results have been discussed based on simulation results and have been demonstrated the high accuracy and reliability of the proposed two-stage method in detection and fault type identification based on confusion matrix values.

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
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.