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Microgrid (MG) is not only undergoing rapid upsurge and an effective combination of distributed generations (DGs)-based renewable energy sources, but it also fulfills a significant function in upgrading grid infrastructure. It is necessary for DGs, during and after any disturbances in the voltage, to stay connected in order to enhance the voltage, most importantly guaranteeing the stability of the power system. In this present context, the behavior of the distributed generations control systems has a significant impact on the MG performance. This paper investigates the effect of different control schemes of the inverter-based DG unit on the stable operation of MG after the occurrence of faults that lead to islanding events. In this paper, an appropriate control strategy is proposed to enhance the fault ride-through of MG. Within the scope of the study, the MG consists of a conventional diesel generator and an inverter-based DG unit. The MG also contains several load types; RLC load as a static load and induction motor as a dynamic load. The MATLAB Simulink is used to model the MG and its inverter control schemes. In order to assure MG stability, the variations of the voltage and frequency are possessed as key references. Results indicate that the control strategies of the inverter have a strong influence on the stability of MG. Moreover, the MG configuration with the proposed FRT resists fault effects for longer durations and this enhances the stability. [ABSTRACT FROM AUTHOR]

Power system utilities need to replace the conventional measuring and monitoring devices to the phasor measurement units (PMUs)-based wide area monitoring system (WAMS) to be able to move from conventional to smart grids. The challenge of this action is the cost of WAMS. Many researchers have tried to deal with this challenge by optimal allocation of PMUs to find the minimum number that fulfils the full observability of the system. The WAMS is not only PMUs but also it comprises communication infrastructures (CI) such as communication links, switches and phasor data concentrators. This paper proposes an optimization algorithm to solve the problem based on two strategies. In the first strategy, the objective function is to find the minimum number of PMUs, i.e. conventional objective function. While in the second strategy, the objective function is the minimum cost of the WAMS, i.e. cost of PMUs and their CI. The objective function in the two strategies is solved under normal operating condition and under contingencies such as failure of PMU and outage of network lines. The proposed artificial electric field algorithm (AEFA) is applied using two strategies on IEEE standard networks: 14-bus, 30-bus, 57-bus and 118-bus. The results show that the number of PMUs using two strategies is not the same and this means that the second strategy should be chosen to solve this problem. The results are also compared with other optimization algorithms, and they depict that the AEFA has the efficiency and superiority to find minimum cost of WAMS and lesser distance path of establishing communication links with higher observability. [ABSTRACT FROM AUTHOR]

The demand-side management (DSM) has a great significance as it covers economical activities through decreasing demand of electricity during high consumption hours. Meanwhile, the multi-agent system (MAS) is a computational intelligence technique that has been applied to reduce the overall consumers' cost as well as total loads. This paper proposes a hybrid technique based on DSM and MAS to manage the energy in residential, commercial, and industrial microgrids. First, the DSM mechanism based on load shifting technique for managing and coordinating load demand is implemented. Then, according to the resulting scheduled load curve, the MAS is utilized in order to optimally coordinate different renewable sources to cover the demand and allowable energy to exchange between the main grid and microgrids. The problem is formulated as an optimization problem and solved by a powerful Antlion optimizer algorithm with an objective of maximization of load factor and minimization of overall consumers' cost. The validation of the proposed algorithm is achieved via comparative studies with other published works. The results show the prosperity of the proposed technique and its capability of saving energy cost and reducing peak demands. [ABSTRACT FROM AUTHOR]