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This paper gives a novel hybrid optimization method to find optimal sitting and operation of an autonomous MG at the same time. The operation is optimized via finding the optimal droop gain parameters of DGs. The optimization problem is formulated as a multi-objective problem where the objectives are applied to minimize the fuel consumption of DGs and to improve the voltage profile and stability of MG subject to operational and security constraints. A hybrid algorithm, named HS-GA, is developed to solve the paper optimization problem. A new formulation of power flow is derived to run the proposed algorithm where the steady state frequency of system, reference frequency, reference voltage and droop coefficients of DGs, based on a droop controller, are considered as optimization variables. The performance of the paper approach is compared with other optimization and non-optimization methods in MG with 33and 69 buses using MATLAB. The performance of the proposed method is compared with a method that the parameters of DGs are pre-determined without conducting any optimization process. The results show, which optimized droop parameters improves the operation of the MG. [ABSTRACT FROM AUTHOR]

Environmentally friendly energy sources with high power quality or reliability and low costs are regarded as an effective solution for energy supply problems arising from use of conventional methods. Presented in this paper, gives an optimal management strategy of PV/wind/diesel independent hybrid systems for supplying required energy in autonomous microgrids. A new optimization problem is formulated for minimizing the capital investment and fuel costs of the system. To solve the proposed optimization problem a novel algorithm, named Guaranteed convergence Particle Swarm Optimization with Gaussian Mutation (GPSO-GM), is developed. Two operators, namely mutation and guaranteed convergence, are added to PSO in order to help finding more accurate results and increasing the speed of calculations. The performance of the proposed strategy is evaluated in two case studies. [ABSTRACT FROM AUTHOR]

An increase in the number of Plug-in Hybrid Electric Vehicles (PHEVs) might negatively affect the performance of Microgrid (MG) such as, reducing power quality and efficiency, increasing power losses and voltage variations, and increasing customers' energy prices. An effective selection of the capacity and location of charging stations helps in reducing such negative impact. This paper proposes a method for finding the optimal location and capacity of PHEVs charging stations along with the optimal location of renewable energy sources (RESs). A multi-objective function including charging/discharging costs, losses, and voltage profile is adopted to formulate the paper problem. Differential Evolution algorithm is used to solve the proposed objective function. By applying the proposed method into a 69-bus network its performance is assessed. The simulation results support the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Recently, microgrids have attracted considerable attention as a high-quality and reliable source of electricity. In this work energy management in microgrids is addressed in light of economic and environmental restrictions through (a) development of an operational strategy for energy management in microgrids and (b) determination of type and capacity of distributed generation (DG) sources as well as the capacity of storage devices (SD) based on optimization. Net present value is used as an economic indicator for justification of investment in microgrids. The proposed NPV-based objective function accounts for the expenses including the initial investment costs, operational strategy costs, purchase of electricity from the utility, maintenance and operational costs, as well as revenues including those associated with reduction in non-delivered energy, the credit for reduction in levels of environmental pollution, and sales of electricity back to the utility. The optimal solution maximizing the objective function is obtained using a hybrid optimization method which combines the quadratic programming (QP) and the particle swarm optimization (PSO) algorithms to determine the optimum capacity of the sources as well as the appropriate operational strategy for the microgrid. The fuzzy set theory is employed to account for the uncertainties associated with electrical power price. Application of the proposed method under different operational scenarios serves to demonstrate the efficiency of the proposed scheme. [Copyright &y& Elsevier]

In an islanded microgrid mode the use of conventional power flow analysis is not effective as the voltage of slack bus and the frequency of the microgrid are assumed to be constant. Such assumption fails to consider the real characteristics of the island microgrid as all DGs are involved in providing the demand of active and reactive power as well as in maintaining the frequency of the microgrid. In this paper, a novel algorithm, named GPSO-GM (Guaranteed convergence Particle Swarm Optimization with Gaussian Mutation), for the power flow analysis problem in an islanded microgrid is proposed. The problem is modeled without any slack bus by considering steady state frequency as one of the power flow variables. To model different control modes of DGs, such as droop, PV and PQ, in an islanded microgrid, a new formula for power flow equations is developed. PSO is adopted to minimize the mismatch of total active and reactive power. Two operators, mutation and guaranteed convergence, are added to PSO in order to help in finding an optimal solution and to assist in increasing the speed of the proposed algorithm as well as the accuracy of the results. The performance of proposed load flow based on GPSO-GM is compared with the PSO, Newton-trust and time domain methods. The results provide support for the validity of GPSO-GM. [ABSTRACT FROM AUTHOR]