Renewable Energy Planning Considering The Operational Flexibility Of Distribution Network

With the rapid and steady growth of the global economy and the over-exploitation and utilization of fossil energy(oil,coal,et al.),mankind is living under the dual pressure of energy crisis and environmental problems.In recent years,the large-scale integration of renewable energy into the distribution network is gradually becoming the main development mode of the distribution network in the future,but the randomness and intermittency of Renewable-energy Distributed Generation(RDG)has brought many challenges to the operation and planning of the distribution network.How to absorb these renewable energy sources as much as possible and reduce the impact of their volatility on the power system has become a concern of all parties.By reasonably planning RDG,energy storage and other regulating resources,not only the revenue of the distribution network operator can be increased,but also the adverse impact of RDG on the distribution system can be reduced,and the flexibility of the system can be improved.In this context,this thesis has carried out research on the collaborative planning of RDG,energy storage system and micro gas turbines.The main contents of this thesis are as follows:(1)Define the comprehensive flexibility index of the distribution network.Firstly,by referring to the connotation of flexibility and the evaluation index of the flexibility of the transmission network,defining the flexibility of the distribution network.Secondly,by referring to the reliability index,the flexibility resource supply index,the flexibility demand index and the flexibility margin index at every moment are quantified,and the flexibility of the distribution network after planning is quantitatively evaluated.Finally,an example is given to verify the validity and rationality of the flexibility index in this thesis.(2)Improvement of particle swarm optimization algorithm.In this thesis,the optimization problem is a mixed integer nonlinear planning problem,and particle swarm algorithm based on bacterial foraging is designed to solve it.Drawing lessons from the motion experience of Helicobacter pylori,based on the traditional particle swarm optimization algorithm,this thesis improves the defect of the original algorithm,which is easy to fall into local optimum due to the decrease of population diversity in searching solution space.Through the calculation of Rastrigin test function,it is verified that the particle swarm optimization based on bacterial foraging algorithm can achieve a good balance between local and global search,and reduce the error between the calculated result and the ideal value.(3)A source-storage bi-level planning model for flexibly regulating resource under the constraint of flexibility balance of distribution network system is established.Combining with the economic and technical characteristics of RDG and flexible resources,a source-storage bi-level planning model is established from the perspectives of equipment investment and system operation.From the perspective of the distribution network operator,comprehensively considering electricity sales income,RDG operation and maintenance costs,network loss costs and other costs,the upper-level planning aims at maximizing revenue,and optimizes the location and capacity of RDG,micro gas turbines and energy storage system,and transfers them to the lower-level model.On the basis of determining the position and capacity of devices,by simulating the operation of multiple scenarios at different timings,the lower-level model aims at minimizing the costs of wind abandonment,photovoltaic abandonment,and load shedding,and obtains RDG actual output,micro gas turbine output,charging and discharging strategy of energy storage system,cutting load and flexibility index,and transfers them to the upper-level model.The upper and lower levels iterate continuously to get the optimal planning scheme.Then,the improved IEEE 33 nodes system is used for case study,and particle swarm optimization based on bacterial foraging algorithm and traditional particle swarm optimization algorithm are respectively used to verify the validity of the source-storage bi-level planning model.