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During the power system restoration there is a high possibility that the load demand is greater than the normal system operation for thermostatic loads. This demand increment, called Cold Load Pick-up (CLPU) has been a critical concern to utilities and will slow down the restoration process. Thus, it is imperative to predict the CLPU demand to perform restoration process more precisely. The use of new loads such as electric vehicles (EVs) has made new problems. In this paper, first the negative impact of EVs after an outage, on CLPU demand is investigated and then it is shown that the behavior of EVs during restoration service is the same as CLPU phenomenon. In doing so, the proposed approach uses Monte Carlo simulation method to predict EVs recharging demand. Since there will be some other new loads in the future that may have CLPU performance, a new approach is proposed to predict the CLPU demand of any load, without the need to have its exact model. ANFIS method is used to estimate CLPU demand based on previous outage cases in the feeder. The proposed approach is implemented in MATLAB® and simulation results confirm its ability in load prediction for restoration service. [ABSTRACT FROM AUTHOR]

The use of storage systems has recently been increased to cope with the uncertainties related to renewable energy sources and also to increase the flexibility required for applying energy management system. Energy management system is considered as a key factor to enhance the effective performance of Microgrids regarding energy demand boosting. Since any parking lot with electric vehicles can participate in distribution systems as a storage unit, this paper employs a new technique for parking management system to develop competition between electric vehicles owners inside the parking lot. Parking lot is responsible to support energy management system to decrease the operation cost and to increase the reliability in grid-connected and islanding modes of Microgrid. The new method considers the stochastic behaviors of electric vehicles, including their arrivals and departures with their bids. The strategy of energy management system is based on determining the output power of generation units and the operation mode of the parking lot. This system also provides the demand response programs to consumers for load shifting. A multi-objective optimization problem is used to optimize both the operation cost and the reliability of the Microgrid. The capability of the proposed algorithm is verified by simulation results. • A stochastic intelligent parking management system is proposed in this paper. • A competition arena is provided inside the parking for EVs. • Participation of EVs in parking energy price determination is taken into account. • Two stochastic approaches are proposed for parking energy price determination. • The capability of proposed approaches is investigated by energy management system. [ABSTRACT FROM AUTHOR]