Distribution Systems
S. Panjeie; A. Fakharian; M. Sedighizadeh; A. Sheikhi fini
Abstract
Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration ...
Read More
Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration (DFR) and scheduling of the distributed energy resources (DERs). Later happening a line fault, the radial network topology is determined by the proposed model using an evaluation of the inci-dence matrix. The presented work handles the uncertain behavior of non-dispatchable DERs and the electrical loads which model by the robust optimization approach. To expand the flexibility of the proposed model, the demand response program (DRP) is treated as the curtailed demand. The aim of optimization is the minimization of the total cost for dispatchable DER operation and electrical load decrease. The recommended robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. Applying the suggested model in the 69-bus unbalanced test system demonstrate that the proposed model averagely decreases total operation cost and execution time by 10.62% and 22.23% on all scenarios in comparison with the de-terministic model.
H. Salmani; A. Rezazadeh; M. Sedighizadeh
Abstract
Fossil-fueled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using a massive unexploited ...
Read More
Fossil-fueled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using a massive unexploited capability of the batteries in too many EVs makes these challenges opportunities. This unused capacity can be employed for the grid ancillary services and trading peer-to-peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EV bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid. Considering the likings of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimization problem aiming at optimal managing SOC of EV battery and electrical energy placement of several facilities considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real-world data collected from Tehran city are used as input data for simulation. Numerical results demonstrate the ability of the presented model. Simulation results display that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy-planning model such that it could balance the charging cost. Moreover, this advanced user-based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid. Simulation results show that the yearly cost of optimal electrical charging on normal trips, light trips, and heavy trips is reduced by 32.6%, 51.2%, and 34.8% compared to non-optimal ones, respectively.
