This paper proposes a hierarchical outage management scheme to enhance the resilience of a smart distribution system comprised of multi-microgrids against unexpected disaster events. In this regard, after identifying the main features and requirements for a resilient outage management scheme, an appropriate framework is devised and the roles and tasks of different management entities in a multi-microgrids system are introduced. Based on this framework, the microgrids schedule their available resources in the first stage using a novel model predictive control-based algorithm. In the second stage, distribution system operator coordinates the possible power transfers among the microgrids and utilizes the unused capacities of microgrids' resources for feeding the unserved loads in stage I. The general optimization model that needs to be run is formulated as a mixed integer linear programming problem and a novel index is presented to quantify the performance of the proposed method. The developed scheme is implemented on a test system and its effectiveness in confronting with different outage events is demonstrated through realistic case studies.

This paper concentrates on degradation of electric vehicle (EV) lithium-ion batteries in vehicle-to-grid (V2G) programs and proposes a practical wear cost model for EVs charge scheduling applications. As the first step, all the factors affecting the cycle life of lithium-ion batteries are identified and their impacts on degradation process are investigated. Subsequently, a general model for battery loss of cycle life is devised incorporating all the pertinent factors associated with charging and discharging activities in V2G applications. Modeling the battery wear cost as a series of equal-payments over the cycle life, a mechanism for calculating the cost incurred by EV users due to participation in V2G programs is developed. Taking into account the developed battery degradation cost model, EVs charge scheduling problem is revisited and it is formulated as a mixed integer linear programming problem. As the actual battery degradation cost and adopted charging strategy are mutually dependent, a novel iterative method is proposed to efficiently obtain the optimal solution to charge scheduling problem and calculate the associated wear price. Several case studies are presented to demonstrate the effectiveness and applicability of the proposed method in integrating the degradation cost of lithium-ion batteries into charge scheduling of V2G-capable EVs.

This paper presents a stochastic framework for day-ahead scheduling of microgrid energy storage systems in the context of multi-objective (MO) optimization. Operation cost of microgrid in normal conditions and load curtailment index in case of unscheduled islanding events (initiated by disturbances in the main grid) are chosen as main criteria of the proposed scheme. In practice, duration of disconnection from the upstream network is unknown in unscheduled islanding incidents and cannot be predicted with certainty. To properly handle the uncertainties associated with time and duration of such events as well as microgrid load and renewable power generation, stochastic models are involved in the MO scheduling framework and they are formulated as mixed integer linear programming problems. The non-dominated sorting genetic algorithm II is employed to effectively cope with the MO optimization problem and a fuzzy decision making approach is employed for appropriate representation of microgrid operator's preferences in compromising between the two objectives. The proposed scheme is implemented on a test microgrid and the obtained results demonstrate the applicability and efficiency of this framework in dealing with conflicting requirements of microgrid security and economic operation.

This paper develops a general framework for reliability assessment of multi-microgrid (MMG) distribution systems. It also investigates reliability impacts of coordinated outage management strategies in a MMG distribution network. According to the proposed reliability evaluation framework, which is based on sequential Monte Carlo simulation method, distribution system is divided into smaller sections/microgrids based on protection system configuration and operating measures are efficiently simulated considering different operation modes. In order to demonstrate the role of outage management strategy in reliability performance of MMG distribution systems, at first, the required features of an outage management strategy are identified. Then, suitable centralized and hierarchical schemes are introduced for operation of such systems during outage events. The proposed schemes, which are based on model predictive control approach, minimize total load curtailments in the system. Moreover, they are flexible and can effectively deal with multiple contingencies as well as uncertainties of outage duration. The developed reliability assessment framework is applied to a test system and performance of the presented outage management schemes are explored through extensive case studies. Obtained results suggest that implementation of an appropriate coordinated scheme is crucial to reliable operation of MMG distribution systems.

This paper deals with energy management of microgrids during unscheduled islanding events, initiated by disturbances in the main grid. In these situations, the main challenge is uncertainty about duration of disconnection from the main grid. In order to tackle this issue, a stochastic framework is proposed for optimal scheduling of microgrid resources over this period. The presented framework addresses the prevailing uncertainties of islanding duration as well as prediction errors of demand and renewable power generation. According to this framework, the probability distribution of islanding duration needs to be estimated, instead of predicting its exact value. The objective is to minimize the expected value of operation cost over the estimated islanding interval, while restricting the load loss risk imposed by uncertain parameters within an acceptable level. Moreover, the impact of microgrid scheduling on the subsequent grid-connected operation is considered via a simple method. The associated stochastic optimization problem is formulated as a mixed integer linear programming model. The developed framework is implemented on a test microgrid and various case studies are presented to demonstrate its effectiveness.

This paper investigates the role of parking lots in improving the reliability of renewable-based distribution systems, through provision of vehicle-to-grid (V2G) programs during outage events. In this regard, municipal parking decks are viewed as distributed energy storage systems and the amount of available energy from these units in random contingency events is probabilistically modeled for reliability studies. Appropriate stochastic models are also introduced to capture the volatility and intermittency of renewable sources. Moreover, it is discussed that how the adopted outage management scheme (OMS) in contingency events can affect the reliability level of customers. In response, several OMSs on the basis of bankruptcy problem are proposed to fairly distribute the available resources among different microgrids/sections, once a failure occurs in the system. Subsequently, a general framework based on the non-sequential Monte Carlo simulation method is developed to evaluate different customers and system reliability indices in presence of emergency V2G programs. The effectiveness of the proposed framework is validated through implementation on a test system and several case studies are presented. The results suggest that realization of V2G programs offered by EV parking lots, accompanied by an appropriate OMS, can significantly enhance the reliability of supply in modern distribution networks.