Research On Large Scale Global Optimization For Energy Management Of Energy Internet System

With the rapid development of renewable energies such as wind and solar,the way of energy utilization and consumption has changed significantly.However,the deficiencies of renewable energies such as the geographical dispersion,low capacity density and the stochastic nature,create great challenge on the use of renewable energies.Energy Internet(EI)which integrates advanced concepts and technologies of both energy and internet network appeared.The EI takes the traditional high-voltage transmission network as foundation,and focuses on the upgrade of medium/low voltage transmission and distribution network.It aims to realize the point-to-point interaction for both energy flow and information flow.Moreover,through the fusion of physical and information systems,the EI can make full use of the large-scale distributed renewable energies,such as in-field use,the nearest consumptive,cross-layer interaction and transmission and plug and play.The internal and external collaborative optimization and the joint scheduling of different levels and regions based on Energy Local Area Networks(ELANs)is a new and hot research topic in the field of energy management for EI system.The task is to,by adjusting the interactive relationship of schedulable units,such as internal generation-load-storage units and external adjacent energy local area network as well as the utility grid,to maximize the use of renewable energies,to minimize the total operation cost and keep the balance of supply and demand.In order to achieve this aim,this study first proposes the hierarchical structure of energy management for EI.Second,the optimal scheduling of energy flow within an ELAN and across multiple ELANs is studied,respectively.Moreover,since the scheduling model involves of large-scale variables optimization,advanced evolutionary algorithms are proposed.Overall,main contributions are as follows.(1)The proposal of the hierarchical energy management structure.First,based on the classification and interaction rules of different EI nodes(i.e.,schedulable resources),the EI system is divide into local supply and demand control layer,regional centralized scheduling layer and global optimal matching layer.The corresponding construction,function and operational modes are respectively introduced.This has also taken the strategy of “regionalization and subarea,intranet autonomy,network synergy” and the difference of structural composition and functions into account.Second,the hierarchical energy management decision frame based on the large scale global optimization is designed.Lastly,the whole solution of the energy management is provided.(2)The proposal of improved differential grouping method for large-scale global optimization encountered in the energy management of EI system.The variable grouping method under the cooperative co-evolution framework is systematically analyzed.In order to examine the grouping accuracy,the improved differential grouping method is embedded in the cooperative co-evolutionary algorithm framework,resulting in an advanced optimization algorithm.First,it is identified that the inaccuracy of the standard differential grouping method comes from the calculation error due to the storage length of a computer.Second,the separable and non-separable variables are identified and are divided into corresponding sub-groups.Third,since there are large-scale sub-groups after grouping,these sub-groups with larger dimensions are further divided.Lastly,the grouping results produced by the improved differential grouping method are used by a cooperative co-evolutionary algorithm.Experimental results show that the improved differential grouping is effective which helps improve the algorithm performance,resulting in better solutions.(3)The proposal of an effective model and algorithm for energy management within an ELAN considering the cooperative optimization of resources,load and battery energy storage system.First the operation conditions of various resources and their interactions for the management of ELAN are introduced.Second,an energy management model is proposed,which aims to maximize the utilization of renewable energies and minimize the overall system cost.Meanwhile,the optimal scheduling strategies are provided for either case of productive capacity and shortage.Lastly,three different optimization schemes considering demand-response effect,direct interaction effect,are proposed in which the Lagrangian multiplier method plus the evolutionary large-scale global optimization algorithm(the self-adaptive neighborhood search differential evolution),are used to find the optimal solution.Experimental results show that the proposed model and algorithm is effective and efficient which can realize the goal of “intranet autonomy,network synergy and global optimization”.(4)The proposal of an effective model and scheduling strategy for energy management within multiple ELANs.First,the energy management problem for multiple ELANs is discussed and its six typical scenarios are given.Second,considering the difference and interaction relationships among all schedulable resources,the energy management and optimization models for each layer of EI are given.Third,the optimization method for multiple ELANs is proposed,which are verified by nine classical constraint optimization problems.Finally,an example with 18 ELANs is adopted to verify the model and scheduling strategy proposed in this paper.Experimental results show that the method proposed in this paper can quickly,accurately and cost-effectively achieve the joint scheduling of multiple energy local area network and output the reasonable energy distribution plan.