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
The conflicting issues of growing demand for electrical energy versus the environmental concerns have left the energy industries practically with one choice: to turn into renewable energies. This duality has also highlighted the role of power transmission systems as energy delivery links in two ways, considering the increased demand of load centers, and the integration of large-scale renewable generation units connected to the transmission system such as wind power generation. Accordingly, it has become even more vital to provide reliable protection for the power transmission links. The present protection methods are associated with deficiencies e.g., acting based on a predefined threshold, low speed, and the requirement of costly devices. A two-stage data-driven-based methodology has been introduced in this paper to deal with such defects, considering wind power generation. The proposed approach utilizes a powerful feature extraction technique, namely the t-distributed stochastic neighbor embedding (t-SNE) in the first stage. In the second stage, the extracted features are fed to a robust soft learning vector quantization (RSLVQ) classifier to detect and locate transmission line faults. The WSCC 9-bus system is used to evaluate the performance of the proposed data-driven method during various system operating conditions. The obtained results verify the promising capability of the proposed approach in detecting and locating transmission line faults.

Abstract
Nowadays, recent advances in information technology and communication facilitates using networked controlled systems in different industrial plants. Whereas data is transferred among different components of the networked systems, they are vulnerable to various types of attacks. This important issue in nowadays industrial plants should be treated logically and reasonable protection mechanisms to mitigate such attacks should be provided. This paper considers delay attack impacts on frequency regulation of an electric vehicle aggregator (EVA) system. The command control action is received by the EVA through an imperfect channel containing uncertainties subject to the time-delay attack. A systematic approach based on a direct search algorithm (DSA) is developed to design a resilient proportional-integral (PI) controller for mitigating such attacks. The proposed DSA provides low-conservative results, explores the design space to find a feasible solution, and computes the PI controller gains to assure the stability of the EVA system in the presence of the delay attack. Stability analysis and numerical simulations for a typical attacked EVA frequency regulation are given to show the effectiveness of the developed controller.

Abstract
This paper presents a strategy based on mixedinteger linear programing (MILP) model to improve the resilience in electric distribution systems (EDSs). The restoration process considers operational resources such as the optimal coordination of dynamic switching operations, islanding operation of distributed generation (DG) units, and displacement of mobile emergency generation (MEG) units. In addition, the benefits of considering a demand response (DR) program to improve the recoverability of the system are also studied. The switching operations aim to separate the in-service from the out-of-service part of the system keeping the radiality of the grid. The proposed MILP model is formulated as a stochastic scenario-based problem where the uncertainties are associated with PV-based power generation and demand consumption. The objective function minimizes the amount of energy load shedding after fault, and the generation curtailment of the PV-based DG. To validate the proposed strategy, a 33-bus EDS is analyzed under different test cases. Results show the benefits of coordinating the dynamic switching operations, the optimal scheduling of MEG units, and a demand response program during the restoration process.

Abstract
There is an urgent need to reduce the combustion of fossil fuels and replace these sources with renewable energy sources. The two major renewable energy resources, solar PV and wind generation, are variable. This variability makes balancing the electrical system more difficult. One way to manage this volatile system is to use markets for ancillary services to ensure that the electrical grid can operate in a safe, efficient and reliable manner. This paper proposes a methodology for a group of smaller consumers to be aggregated together so that they can effectively bid into markets for ancillary services. The methodology is tested on the Portuguese reserve regulation market and the financial viability of such aggregation is explored. Results show that aggregating consumer bids for downward regulation services can be financially viable in the Portuguese market. Reducing the minimum bid size increased the participation of the consumers thus increasing revenues.