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

Abstract
Consumption patterns of electric power systems are important for distribution companies, because of their significant impact on energy losses amount. Therefore, some incentives are suggested by distribution companies to energy consumers for correcting their consumption manner. For a specific load pattern, distribution system reconfiguration (DSR) is an effective way to mitigate energy losses. Hence, some research works have included load variations in the DSR problem to show the importance of consumption patterns in reconfiguration decisions. However, some of the specialized literature has ignored load changes in their reconfiguration models due to the high computational burden and processing time. On the other hand, the energy losses are calculated inaccurately if the consumption pattern is neglected. Consequently, the main goal of this article is to investigate load pattern impact on switching sequences to find out how much is load profile important for minimization of energy losses in DSR. The evaluations were carried out for three well-known distribution systems using a classic optimization tool, the A Mathematical Programming Language.

Abstract
The decrease of short-circuit current in islanded mode due to the existence of resources with low inertial, topology changing, multi-directional current, and telecommunication are the most critical issues encountered by micro-grid protection. The central adaptive protection schemes are widely used for micro-grids, but due to the lack of reliability of this schemes, they are not perfect methods. In this paper, a hybrid scheme of adaptive and multi-agent protection for micro-grid is discussed, which will be able to provide safety protection at several layers and levels, using the equipment in the micro-grid with distributed generation including renewable and nonrenewable energy resources. The proposed scheme calculates relays pickup current with a formula that uses the superposition theorem. To demonstrate the proposed scheme performance, it is implemented and simulated on a sample micro-grid in MATLAB/Simulink, and its results have been analyzed and discussed. Simulations and numerical results show that for 96% of simulated topologies in single and multi-event faults, the relay settings are updated correctly and detect subsequent faults at the right time. Also, due to the use of offline calculations in the equipment layer, the time delay due to sending information to higher layers is minimized for single-event faults in the micro-grid. (C)& nbsp;2022 Published by Elsevier Ltd.

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.