چكيده به لاتين
Thanks to the recent innovations in modernization of power system, distributed generation is now crucial in supplying the system demand in different conditions. In this regard, in addition to conventional distributed generations, an alternative option is introduced as the source of power supply for improved operation of smart grid technologies. This alternative option is transportation electrification with the concept of electric vehicles. Due to the potential of electric vehicle’s parking lots to exchange energy with electric power system, they can be considered as the kind of distributed energy resources. Therefore, in the near feature, electric vehicles can play a significant role in supplying system loads. Although, the electric vehicles consume electric power and act as a consumer, deployment of vehicle to grid technologies allows the electric vehicles to exchange energy with power grid. All of the abovementioned technologies, are important cross-functional solutions that accelerate the integration of electric vehicles and help the distribution companies in optimizing operation costs. In addition to these technologies, there are more attractive and affordable alternatives which make today’s power systems smarter than traditional networks. One of these alternatives is the distribution network reconfiguration. The distribution network reconfiguration is defined as the process of changing the status of normally open/closed switches of distribution network to reach a configuration that optimizes desired objectives while satisfying all operational planning constraints of network without isolating any network node(s). In this thesis, an optimal coordination model is proposed for optimally coordinating the distribution system reconfiguration and output of distributed generations and electric vehicle’s parking lots. Regarding this, a mixed integer non-linear programming (MINLP) model is introduced for optimal hourly configuration of system along with optimal power output of distributed generation units and optimal number of electric vehicles in the network at each hour. The model is solved in three case studies namely, (a) the proposed model with reconfiguration, distributed generation and electric vehicle’s parking lots, (b) the proposed model without distributed generation units and electric vehicle’s parking lots to study the effect of DGs and PLs, and (c) the proposed model without reconfiguration to study the effect of hourly reconfiguration on results. For each case study, optimal result is obtained and the interconnection between various technologies is investigated. To validate the performance of proposed methodology, it is implemented on the well-known IEEE 33-bus distribution test system, and solved by general algebraic modeling system (GAMS) software package. The simulation results, validate the feasibility and effectiveness of the proposed approach.
