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Summary: Microgrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs. [ABSTRACT FROM AUTHOR]

The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized by GA, and then these optimum values are used in ANN. The simulation results show that the ANN-GA in comparison with the conventional algorithms with high accuracy can track the peak power point under different insolation conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Proposing optimal day-ahead scheduling of MGs. • Considering renewable energy resources in the presence of Electric Vehicles. • Presenting Modified Shuffled Frog Leaping Algorithm considering Control Strategies. • Reporting superior solutions in scenario-based stochastic optimization. Microgrids (MGs) have turned into vital components of the modern power system with the capability of efficiently accommodating renewable energies and electric vehicles (EVs) with high flexibility. MGs can contribute to mitigating the operating cost and environmental emissions of power systems. Accordingly, the optimal operation of such systems is of very high significance. In this relation, the problem of optimal day-ahead scheduling of MGs is studied in this paper in the presence of renewable power generation, EVs, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem, characterized using the Monte-Carlo simulation (MCS) method. The developed framework includes one objective function, defined as the total operating cost minimization and the presented single-objective optimization problem is tackled using an effective optimization technique, named "modified shuffled frog leaping algorithm (MSFLA)". The suggested optimization framework takes into consideration various charging/discharging patterns of EVs. Finally, the problem is simulated on a test MG and the obtained results are compared to those derived by other algorithms to verify the performance of the MSFLA algorithm. [ABSTRACT FROM AUTHOR]

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Electric Vehicles • Presenting Modified Harmony Search Algorithm for optimizing Micro-grid • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management Modern power systems are seeking for alternative energy sources will less emission to conventional fossil-fuel power plant to mitigate the global concerns on environmental issues. On one hand, renewable energies have turned to the first choice of system operators. On the other hand, the system should provide the required infrastructure to appropriately accommodate such energy sources. In this respect, microgrids (MG) would provide the needed conditions for integrating renewable energy sources (RESs). Thus, the optimal energy management of such systems in the presence of highly uncertain renewable power generation is of great importance. Accordingly, this paper provides a stochastic programming framework for the optimal scheduling of an MG equipped with RESs and plug-in electric vehicles (PEVs). The power sources considered include a wind energy system in the form of wind turbine (WT), a solar photovoltaic (PV) system, a fuel cell (FC), a microturbine (MT), besides a battery storage system (BSS). The mentioned problem is formulated as a single-objective optimization problem aimed at minimizing the total operating cost over the scheduling period. The MG is considered in the grid-connected mode where it can transact power with the upstream system. The uncertainty of the problem is due to the intermittent power output of the wind energy system and the PV unit, as well as uncertain behavior of the EV owners in charging/discharging their vehicles. The proposed stochastic optimization problem is the solved using an effective and efficient optimization algorithm named "modified harmony search (MHS) algorithm". Finally, the simulation results are discussed and the superior performance of the suggested algorithm is verified through making a comprehensive comparison with some well-known methods. [ABSTRACT FROM AUTHOR]

• Considering renewable energy resources in the presence electric vehicle. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. • Proposing an economic and environmental model for optimal energy management. • Considering demand side management base microgird. This paper suggests an optimal management for a microgrid containing renewable sources and electrical vehicles (EVs) with responsive loads for minimizing operation costs and emissions. The role of EVs is coping with peak load conditions while responsive loads can deal with uncertainties of renewable-based sources. A two-stage model is implemented for clearing the energy and reserve markets. The costs of generation and reserve power are minimized first, and then the costs of variations in the management owing to the changes in wind turbine and photovoltaic (PV) are optimized. To optimize the objective function, an improved shuffled frog leaping algorithm (ISFLA) is used. Simulation results are provided for different case studies in a microgrid and the outcomes confirm the superiority of the recommended algorithm in comparison with other conventional approaches. The use of EVs and responsive loads provides advantages such as minimizing the operational cost, emissions and the changing behavior of the PV and wind turbines. [ABSTRACT FROM AUTHOR]

The penetration level of intermittent power generation into power systems has been substantial during recent years, which in turn highlights the need for installing storage systems. Renewable energy sources have been widely integrated into distribution systems and microgrids. One effective solution would be utilizing battery energy storage systems, which can provide the system with various merits like ancillary services and enhanced power quality, mainly due to their high power density and fast response. Accordingly, the problem of resource scheduling of microgrids with volatile power generation and storage systems needs to be further studied, and an effective model should be presented. In this respect, this paper investigates the problem of day-ahead operation of a grid-connected MG, integrated with distributed generation units and storage systems. The problem has been modeled an optimization problem while the objective has been assigned to the model as the total cost minimization, subject to different constraints, both system constraints and assets' constraints. Such constraints further complicate the original problem and an efficient solution method is required to tackle the problem as a large-scale optimization one. Thus, θ-modified krill herd approach is employed to solve the problem and provide the decision maker with an efficient solution. The simulation has also been conducted using a test MG and the results, obtained have been validated by comparing the results obtained from the presented method and those ones, derived from some well-known optimization algorithms. [ABSTRACT FROM AUTHOR]