Distribution Network Planning Considering Multiple Resources Coordinated Utilization

With the increasing shortage of fossil fuels and the increasing environmental pollution,it is highly valued by the nations of the world that how to make the most of new energies and the complementary property among multiple energy sources.Distributed generations(DG)are integrated into distribution network and many projects of the demand side response(DSR)are implemented for distribution network.The number of regional integrated energy system(RIES)is gradually increased in the initial phase of forming the energy internet.A series of changes will take place in the composition and functions of the distribution network,so traditional distribution network planning methods are no longer effective.New planning methods considering resources coordinated utilization are urgently needed,to realize the economy,safety,reliability and environmental protection of distribution network planning and operation in the meet load demand requirements.In this,firstly,the improved genetic membrane algorithm(IGMA)is studied to effectively solve the complex planning model of the distribution network with DG.Then,the model of the planning problem is focused.Based on resources coordinated utilization for distribution network,the coordinated planning of distribution network considering benefits of the source,grid and load is studied.And the distribution network expansion planning considering optimal operation of the RIES is studied.The core work of this paper is as follows:(1)For medium and long term planning of distribution network,a multi-stage planning method is proposed to complete the sizing and siting of DG and the network expansion.The model of distribution network multi-stage planning based on the minimum of the life cycle cost present value is presented.The genetic membrane algorithm is improved from the mathematical properties of the planning problem.An IGMA based on arccotangent-exponential self-adaptive cross transposition rule,arctangent-exponential self-adaptive mutation rewrite rule and exchange recombination rule is proposed.Besides,the adjacency matrix in graph theory is used to filter infeasible solutions.Finally,a numerical simulation shows that IGMA has the fast convergence speed and the strong capability of global search as well as stability.(2)For the distribution network which contains DG and demand side resources,a tri-level planning model considerrng interests of distribution company,DG operators and users is proposed.DGs are sited and sized considering the best benefit of DG operators in “source” level.The distribution network expansion planning is made to minimize construction and operation costs in “grid” level.Users participate in the DSR to maximize their benefits in “load” level.The DSR includes the price-based and the incentive-based demand response.A parallel genetic membrane algorithm(PGMA)combined with support vector regression power flow calculation is used to solve the model.Finally,a numerical simulation shows that source,grid and load can share benefits based on the tri-level planning model.(3)For the increasing number of RIES in distribution network,a method of distribution network expansion planning considering optimal operation of the RIES is proposed to meet the increasing power demand and promote the optimal use of resources.Firstly,a model for RIES is established.Secondly,a tri-level interactive structure including supervisor level,region level and device level is established based on the multi-agent system(MAS).Then,a bi-level optimization model is proposed for the distribution network including residential RIES,commercial RIES and industrial RIES.The distribution network expansion planning is made in the upper level,and the optimal operation of the RIES is made in the lower level.The genetic membrane algorithm and the sequence quadratic programming are used to alternately solve this model.Finally,a numerical simulation shows that the proposed planning method can reduce the total cost of the planning program,and improve the energy utilization efficiency and the new energy accommodation.