Abstract
ABSTR A C T Since energy hubs meet the needs of customers for different energies, their construction rate has increased in recent years. The annual growth of load demand on the one hand and the declining efficiency of hub converters on the other hand have posed many challenges for hub designers. Therefore, this study develops a multi-objective model for the design of hub considering converters' variable efficiency, degradation of equipment and annual growth of the load and energy prices. The proposed hub is equipped by a power-to-gas (P2G) technology and its consumers participate in an integrated demand response (IDR) program. The problem is formulated in mixed-integer non-linear programming (MINLP) format and is solved via DICOPT in GAMS environment. The simu-lation results substantiate that dynamic framework has led to the much more accurate determination of equipment capacity. Besides, the results indicate that the P2G technology reduces CO2 emissions by 9.89% through consuming CO2 emitted from the CHP and boiler. The results also illustrate that P2G increases the ef-ficiency of gas-fired converters by injecting hydrogen into them, thus reducing losses by 9.2%.

Abstract
This article presents a restoration approach for improving the resilience of electric distribution systems (EDSs) by taking advantage of several operational resources. In the proposed approach, the restoration process combines dynamic network reconfiguration, islanding operation of dispatchable distributed generation units, and the prepositioning and displacement of mobile emergency generation (MEG) units. The benefit of exploring a demand response (DR) program to improve the recoverability of the system is also taken into account. The proposed approach aims to separate the in-service and out-of-service parts of the system while maintaining the radiality of the grid. To assist the distribution system planner, the problem is formulated as a stochastic-scenario-based mixed-integer linear programming model, where uncertainties associated with PV-based generation and demand are captured. The objective function of the problem minimizes the amount of energy load shedding after a fault event as well as PV-based generating curtailment. To validate the proposed approach, adapted 33-bus and 83-bus EDSs are analyzed under different test conditions. Numerical results demonstrate the benefits of coordinating the dynamic network reconfiguration, the prepositioning and displacement of MEG units, and a DR program to improve the restoration process.

Abstract
The issue of a state estimation-based fault-tolerant controller for direct current (dc) microgrids (MGs) is studied in this article. It is considered that the dc MG contains nonlinear constant power load (CPL) and is subjected to actuator faults. Current sensors are not installed and the voltages of the dc MG are measured in the presence of noise and sensor faults. To estimate the system states, a novel dual-Extended Kalman filter is proposed, which simultaneously estimates the states and faults. The fault- and noise-free estimations are then deployed in a nonlinear Takagi-Sugeno fuzzy predictive controller to regulate the dc MG. The proposed method outperforms the exiting results, being robust against faults and noise. Also, the predictive scheme makes it robust against system uncertainties and forces the system states to converge the desired values, precisely. The accuracy and robustness of the developed method are evaluated and compared to advanced state-of-the-art techniques for a typical dc MG with a resistive load, CPL, and energy storage unit.

Abstract
This paper investigates prosumers' flexibility provision for the optimal operation of active distribution networks in a transactive energy (TE) market. From a prosumer point of view, flexibility can be provided to operators using renewable energy resources (RES) and demand response (DR) through home appliances with the ability to modify their consumption profiles. In the TE market model, the distribution system operator (DSO) is responsible for market-clearing mechanisms and controlling the net power exchange between the distribution network and the upstream grid. The contribution of this work is the enhancement of a strategy to reduce operational costs of an active distribution network by using prosumers' flexibility provision through an aggregator or a smart building coordinator. To this end, a TE market for both energy and flexibility trading at distribution networks is presented, demonstrating the possibility to fulfill DSO requirements through the flexibility contributions in the day-ahead (DA) and real-time (RT) markets.

Abstract
Today's power grid is in a transitional stage to cater to the needs of energy efficiency, climate change, and environmental targets. In the process of designing the future power grid, one of the most fundamental models to be utilized is AC optimal power flow (AC-OPF). Since the feasible space of AC-OPF is non-convex, the optimization models developed using it often result in multiple local minima. To avoid such computational challenges in solving optimization models, various relaxation methods have been developed in the past. In the literature, these relaxation methods are mainly tested on specific networks. However, the scalability of relaxation techniques on branch-flow-based AC-OPF is yet to be explored. In this context, this paper compares the performance of different relaxation methods with the well-established MATPOWER AC-OPF solver in terms of the mean square error (MSE), maximum squared error, minimum and maximum values of voltage magnitude, and the average simulation time. In addition, the scalability of these models is tested on various radial and mesh networks with nodes ranging from 33 to 6655 nodes and 9 to 6515 nodes, respectively. In this manner, the trade-off between computational complexity and solution accuracy is demonstrated and analyzed in depth. This provides an enhanced understanding of the suitability and efficiency of the compared relaxation methods, helping, in turn, the efficiency of optimization models for varying sizes and types (i.e., radial or meshed) of networks.

Abstract
Nowadays, decentralized microgrids (DC-MGs) have become a popular topic due to the effectiveness and the less complexity. In fact, DC-MGs resist to share their internal information with the distribution system operator (DSO) to protect their privacy and compete in the electricity market. Further, lack of information sharing among MGs in normal operation conditions leads to form a competitive market. However, in emergency operation conditions, it results numerous challenges in managing network outages. Therefore, this paper presents a hierarchical model consisting of three stages to enhance the resilience of DC-MGs. In all stages, the network outage management is performed considering the reported data of MGs. In the first stage, proactive actions are performed with the aim of increasing the network readiness against the upcoming windstorm. In the second stage, generation scheduling, allocation of mobile units and distribution feeder reconfiguration (DFR) are operated by DSO to minimize operating costs. In the final stage, the repair crew is allocated to minimize the energy not served (ENS). Un-certainties of load demand, wind speed and solar radiation are considered, and the effectiveness of the proposed model is investigated by integrating to the 118-bus distribution network. Finally, the results of the simulation indicate that DFR and proactive actions decrease the ENS by 19,124 kWh and 4101 kWh, respectively. Further, the sharing of information among MGs leads to a 48.16% growth in the supply service level to critical loads, and consequently a 3.47% increase in the resilience index.