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Microgrid operation is challenging because the amount of electricity that is produced from renewables is uncertain and the inertia of distributed generation resources is very small. Energy storage systems can regulate energy, improve the reliability of the power systemand enhance the transient stability. This paper determines the optimal capacities of energy storage systems in an islanded microgrid that is composed of wind-turbine generators, photovoltaic arrays, and micro-turbine generators. The energy storage system can enhance the reliability of the microgrid and eliminate the unnecessary load shedding when a severe transient (such as a generator outage) occurs in the islanded microgrid. The studied problem is expressed as a multi-objective programming formulation, which is solved using an immunity-based algorithm. Four objective functions are optimized: minimum of energy storage capacity, minimum of load shedding, maximum of the lowest swing frequency, and minimum of the Customer Average Interruption Duration Index (CAIDI). These four objective functions are subject to both steady-state constraints and the transient-state equality constraint. The steady-state constraints include the total shed load limit, the feasible range of energy storage capacities while the transient-state equality constraint is expressed by the dynamic equation. The Pareto optimums are explored and optimality of the problem is investigated. The simulation results based on an islanded 15-bus microgrid show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Underfrequency load shedding maintains the power system in a stable condition when a severe disturbance occurs. The underfrequency relay protects the power system against a blackout when the system frequency declines to predetermined settings. This paper determines the shed load at each stage for the underfrequency relay (81L) in a stand-alone microgrid considering multiple scenarios. In addition, the probabilities of all scenarios are defined based on historical meteorological data and the Markov two-state model. The shed load is minimized and the lowest swing frequency is maximized using the genetic algorithms. Due to many inequality constraints in the studied problem, an efficient tree-encoding chromosome and the penalty functions are presented as well. Moreover, applicability of the proposed method is demonstrated using a microgrid test bed with the gas-turbine, wind-turbine generator, and solar photovoltaic. [ABSTRACT FROM PUBLISHER]

The installation of renewable energy generation resources (such as photovoltaic arrays and wind-turbine generators) in a microgrid is important because a microgrid can increase the penetration of renewable energies in a smart grid. A community may be a grid-tied microgrid in which an energy management system may dispatch elastic loads (such as air conditioning systems). This paper investigates the optimal sizing of renewable energy generation resources in a community microgrid. The cost of renewables and community welfare are optimized while the comfort zone of indoor temperature in all homes is maintained using air conditioning systems. Community welfare is ensured by minimizing the purchased power from and maximizing the sold power to the utility grid with different time-of-use electricity tariffs. Since the problem of interest involves a large number of variables and chronological constraints, Markov models of photovoltaic power generation, wind generation, load and temperature are utilized to reduce the numbers of variables and constraints. The Markov-based optimization problem is then solved using the interior-point algorithm. The simulation results, based on a smart community of 50 homes, reveal the applicability of the proposed method. [ABSTRACT FROM AUTHOR]