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For providing required load in n coastal and island regions, tidal barrage can be integrated in microgrids. To produce electricity from tides, in tidal barrage, water is moved between sea and reservoir through sluices containing turbines to generate electricity. In operation phase, produced power of tidal barrages depends on number of turbines, sluices and hydro-pumps. Thus, to maximize generated energy of tidal barrage, optimum number of turbines, sluices and hydro-pumps can be obtained through heuristic optimization techniques. Because of tidal level variation, generated power of tidal barrages changes over time. Thus, for load supplying, other renewable resources such as photovoltaic units, batteries, fuel-based generation units and grid-connected mode of microgrid are utilized. In this research, two-stage optimal operation of microgrids composed of tidal barrage, photovoltaic units, batteries and fuel-based generation units is done. In first stage, optimum number of turbines, sluices and hydro-pumps related to tidal barrage is determined for maximizing produced energy of tidal unit during time horizon of the study. In second stage, remaining load of microgrid is provided by photovoltaic units, batteries, fuel-based generation units and main network. To this end, generated power of fuel-based plants and power exchanged between microgrid and main grid are determined for minimizing operating cost of microgrid. The operating cost including operating cost of fuel-based generation units, cost of exchanged power between main grid and microgrid and penalties of load curtailment is optimized using particle swarm optimization method. Numerical results presents among different optimization algorithms, particle swarm method has performed best in operation studies of tidal barrage. For understudied microgrid, maximum generated energy of tidal barrage is 25.052 MWh, and minimum operating cost of the microgrid is 39868 $.

Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity of photovoltaic (PV) arrays, several cells are arranged in parallel or in series to form a panel in order to obtain the expected power. Short circuit (SC) and open circuit (OC) faults in the solar PV systems are the main factors that reduce the amount of solar power generation, which has different types. Partial shadow, cable rot, un-achieved maximum power point tracking (MPPT) and ground faults are some of these malfunctions that should be detected and located as soon as possible. Therefore, effective fault detection strategy is very essential to maintain the proper performance of PV systems to minimize network interruptions. The detection method must also be able to detect, locate and differentiate between SC and OC modules in irradiated PV arrays and non-uniform temperature distributions. In this paper, based on artificial intelligence (AI) and neural networks (NN), neutrons can be utilized, as they have been trained in machine learning process, to detect various types of faults in PV networks. The proposed technique is faster than other artificial neural networks (ANN) methods, since it uses an additional hidden layer that can also increase processing accuracy. The output results prove the superiority of this claim.

The argument of power systems planning in home microgrids has become one of the burning topics in optimization studies today among the researchers. Since the installation and use of high-capacity energy sources in power systems have many limitations and constraints, so part of the perspective of power systems studies tends to operate residential microgrids. For this purpose, in this paper, operation planning is based on a residential microgrid consisting of combined heat and power (CHP), heat storage tank and boiler, and when possible, surplus electricity is sold to the upstream network to generate revenue. One of the innovations of this paper is the use of the exergy function to complete the optimization and, in practice, combine energy with economics. Other objective functions of this paper are to discuss the reduction of carbon dioxide in the air and the cost of operation. Energy management and planning in this home microgrid is tested with different capacities and types of CHPs, so that the home operator can choose the best mode to use. The multi-stage decision based dynamic programing (MSD-DP) optimization approach is used to minimize the operation costs of proposed framework. The most important innovation of this paper is the use of exergy function for energy management in a residential complex where CHP can also be used to generate electricity and heat simultaneously. Therefore, determining the capacity of CHP and the possibility of exchanging electricity with the upstream network can be mentioned as other innovations of this research.

Distribution Static Synchronous Compensator (D-STATCOM) is a shunt compensator in the distribution systems that one of the most important tasks is to improve the imbalance, reduce and eliminate the nonlinear loads harmonics. Distributed generation resources such as photovoltaic (PV) array can be used as the DC input of D-STATCOM. In this paper, PV array and DC/DC boost convertor are used to stabilize the DC link voltage of D-STATCOM. The main advantage of the proposed method is that for all time provides continuous compensation. Another power quality issues are neutral current in four-wire systems that are created due to the harmonics and system imbalance. Zig-Zag transformer is one of the ways for compensating neutral current and providing isolation between the convertor and flow of the fundamental zero sequence component which contains harmonic neutral current. Role and performance of the distribution shunt compensator in the improvement of power quality indices depends on the performance of its control system. In this paper, the control scheme of synchronous reference frame theory based on fuzzy controller is used for D-STATCOM based on PV and Zig-Zag transformer. Performance and behaviour of the proposed system examined using Matlab/Simulink software and the results will be presented.

Distributed generation (DG) sources are modeled using an ideal DC voltage source connected to the microgrid via voltage source converters (VSCs). Model predictive control presents a distinct method for energy processing.  In this method, the electric power converter is considered a power amplifier with a discrete and nonlinear structure. Therefore, unlike linear control methods, the discrete and nonlinear nature of the converter is considered in this method. In this paper, the distributed model predictive controller was selected from among different methods of load allocation among DG sources due to its more advantages compared to the linear quadratic regulator (LQR) controller. It has been Proposed that we could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. In this research, all the simulations have been performed in the MATLABSimpower environment of MATLAB software. Moreover, to demonstrate the superior performance of the model predictive controller compared to the LQR controller, both performance modes of the microgrid, namely the grid-connected and islanding modes, have been considered.

A reward and penalty scheme (RPS) is used for setting up the service quality which exposes the distribution companies to financial benefits caused by demand for the reliability of customers. In this paper, an algorithm for the optimal switch number and placement in distribution networks in the presence of RPS is presented. The primary objective is reliability improvement and minimization of the cost of sectionalizing switches (SS) and tie switches (TS) for a given regulatory period considering acceptable financial risk. In this algorithm, the uncertainty in the reliability is appeared as financial risk. A genetic algorithm is adopted to solve the optimization problem. The number and location of SS and TS is found while financial incentives of RPS, capital investment and annual operation and maintenance costs are considered. The performance of the proposed approach is assessed and illustrated on a real distribution network. The results show the efficiency of the proposed algorithm.