| The development of smart grids has become a focus of modern power industry in recent years. A smart grid must be able to support large quantities of intermittent renewable generations. Wind energy and solar energy has some complementary characteristics, hybrid wind/solar energy generation can compensate the unstable generation when there is only wind power generation or only PV (Photovoltaic) generation. Wind and solar power outputs are influenced by natural climate, and hence are random, intermittent, and volatile. Electric Vehicles (EVs) are drawing attention by governments, automobile manufacturers and widespread concern of energy enterprises. EV can inject energy back to the grid (Vehicle to Grid, V2G). As a special kind of load in a power system, EV has three dimensions of uncertainty of time (charging time is uncertain)-space (charging position is uncertainty)-behavior (driving behavior, charging/discharging behavior is uncertain). When a large number of uncoordinated EVs is distributed, it will impact the safety and economic operation of the power system.The aforementioned uncertainty brings great difficulties to power system planning and optimization which is a large system with multitudinous variables. Wind and solar power output uncertainty will bring risk to the planning of wind and solar generation system, a large number of uncontrolled EVs in power system will increase the peak load, reduce the load utilization hours, decrease the ratio of network investment and benefit, decrease the efficiency of electric power system.Specifically, the contents of this dissertation are as follows:1) DG siting and sizing optimization considering several objectives with cloud theory adapted GA. Several technical indexes involving energy loss, voltage quality and stability, line loadability are considered to optimize DGs’(distributed generators) capacity and location. It is an optimization problem both discrete and continuous variables are involved, cloud theory adapted genetic algorithm (CAGA) is employed to help GA avoid a local optimum and improve its convergence speed. The weights of the indexes are decided by judgment matrix. DGs in a28-node distribution system is optimized quickly to demonstrate the CAGA method developed in this thesis. Meanwhile the optimization results are compared with traditional genetic algorithm.2) Wind Power Generation (WPG) system capacity optimization with EV based on reliability/cost Evaluation. This part proposes a reliability cost evaluation model to decide the optimized wind generation capacity in an isolated distribution network with EV. Randomness of both EV charging/discharging process and wind power generation is considered. EV is used for storage of energy that can be discharged to the distribution network when demand exceeds generation. The total cost, which includes customer interruption cost and annual generation cost is optimized. Monte Carlo simulation is employed to simulate wind speed and wind turbine/load points outage and EV numbers charging/discharging, etc. The distribution system for IEEE-RBTS (the Roy Billinton Test System) is employed to demonstrate the mathematical model proposed in this thesis. The least total cost and wind power generation capacity varies under different circumstances.3) Probabilistic load flow in power systems with wind/photovoltaic generation and EVs. A probabilistic load flow model is first developed, and is able to accommodate the stochastic features of the wind power generation and PV generation as well as EVs. Based on meteorological data, wind speeds and solar radiations are first simulated for different seasons and different weather conditions. The three-point estimation method (3PEM) is employed to solve the probabilistic load flow model, and the statistical features of the load flow calculation results could then be obtained. Finally, a140-node distribution system is employed to demonstrate the developed model and method. The results obtained by the presented method are compared with those by the well-established Monte Carlo simulations to show the accuracy of the former one. In addition, different results of the probabilistic load flow in a distribution system with wind/PV generation and EVs in various seasons and various hours are examined and analyzed.4) The installed capacities of wind and PV generation in a distribution system is optimized. First, the modeling issues for wind power generation and PV generation and BESS (battery energy storage system) charging and discharging power are addressed based on meteorological data, and the output power randomness of both wind power and PV generations are taken into consideration. On this basis, the charging/discharging power of BESS is optimized by the well-established dynamic programming approach. A mathematical model is presented for determining the optimal installed capacities of wind power and PV generations subject to the constraint of power supply reliability, and solved by the traversal searching technology. Finally, the revised IEEE-RBTS-Bus4distribution system is employed to demonstrate the feasibility and effectiveness of the proposed method through many test scenarios.Finally, several conclusions are obtained based on the research outcomes, and directions for future research indicated. |
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