Study On Capacity Co-Optimization Methods And Models For Wind-Hydrogen Coupling Programme

Suppressing volatility of wind power generation and avoiding wind curtailment as well,the combination of wind power and hydrogen could promote large-scale production of wind power connected to the grid,and thus promote decarbonization process of the electricity sector.Besides,the green,high purity hydrogen gas generated by electrolysis could also be applied to transformation and industry sectors to facilitate their deep decarbonization processes.The above-mentioned benefits by wind-hydrogen generation projects could substantially assist process of greener transformation and industrial structure revolution,and thus the realization of "30.60" target.While so far,the scale effect of wind-hydrogen projects is to be emerged,resulting in humble economic performance and difficulties on the wide applications.The wind-hydrogen couplingprogramme(WHCP)could handle the demerits of individual projects and promote incremental gains by the coconfiguration and co-operation of separated projects.As the very prerequisite of project construction and operation,the co-configuration optimization could fully explore the potential of various resources in the programme,and consequently enhance system flexibility and sustainability of the programme.But it is the complicated large-scale system structure with considerable decision variables and constraints that dramatically increase the difficulty of co-configuration optimization for the programme.Therefore,this paper analyzes the mechanisms of programme configuration optimization and conclude research framework in the first place,based on the optimization models and methods for the coconfiguration of WHCP,as well as current research background and policy instructions.Then,the location layout and optimization for WHCP is introduced balancing resource endowment and environmental impact.Static and dynamic co-configuration models are constructed aiming at short-term and long-term planning respectively.This research aims at the theorical and technical support for a smooth decarbonization transformation of energy structure in China.(1)Concerning the various types of methods with distinctive fields and scopes of application involved in the modeling,co-configuration optimization,and decision-making of WHCP,this paper implements a comparative analysis on the information processing,weight determination of criteria,and ranking techniques in the optimization and decisionmaking process to illustrate the scope,advantages and disadvantages of each technique.Then,the model solution methods are analyzed for capacity co-optimization,in order to conclude the suitability of optimization and heuristic algorithms for single objective and multi-objective models.Next,the techniques measuring uncertain factors in the optimization process of wind-hydrogen coupling programme are discussed,including fuzzy evaluation,stochastic optimization,robust optimization and so on.The former analysis provides support for ranking,capacity optimization and decision-making methodology under uncertain environments of WHCP.(2)Concerning the vague co-operation relationship among individual projects,influential factors and unclear optimization path,this paper analyzes the WHCP system structure,device characteristics and power modeling,and thus clarify the coordination mechanism.Then the key power patterns are obtained according to coordination operation mode among individual projects and various types of devices.The typical generation scenarios are concluded by Gaussian Mixture Model,Latin hypercube sampling,and KMediods clustering algorithm.Further discussion on the principals and technical difficulties of capacity co-optimization is put forward,and key factors affecting WHCP modeling and input are recognized.Based on the former analysis,optimization path of WHCP coordinated configuration is illustrated.The optimization path is designed by analyzing the inter-relationship among the main contents and data interaction.WHCP layout optimization,static capacity co-optimization and dynamic capacity co-optimization are included in the research context.The capacity co-optimization mechanism of WHCP provides theoretical support for the research in the following chapters.(3)Concerning the multi-dimensional influential factors and high complexity during WHCP layout optimization,this paper identifies the key factors that could affect WHCP layout from various dimensions including natural,environmental,economic,and social aspects.The identified factors are divided into veto and evaluation criteria according to policy instruction and industrial norms,and they constitute the WHCP layout criteria system.Then,data collection and processing are carried out as decision information,including geological data and IT2F data.Geological data is utilized in GIS platform to exclude restricted areas and provide potential location with quantitative results measuring suitability.IT2F data are utilized to determine weights and address the fuzzy and uncertain factors in the ranking process,and then to evaluate the potential alternatives with high suitability.The former research introduces a comprehensive WHCP evaluation methodology based on GIS and IT2F-PROMETHEE model and carries a two-stage decision-making procedure.Finally,an experimental analysis on the Zhangjiakou city,Hebei province is carried out to illuminate the layout of wind and hydrogen subsystem,which provides guidance for capacity co-optimization in the following chapters.(4)Concerning difficulties including the complicated operation situations,various objectives,tremendous decision variables and constraints,this paper constructs a static coordination optimization model by bi-level strategy.Firstly,the iterative optimization procedure through outer configuration and inner operation is clarified.And the model assumptions for model construction are also put forward.Next,the outer model is analyzed from objective function,decision variables and other key modeling factors,clarifying relevant constraints measuring inter-relationship of energy flow and device output.Based on the former research,a static capacity co-optimization model is proposed containing dual levels.The model balances WHCP benefits from multi-dimensional aspects,and maximize resource values within the programme to determine the capacity results based on optimal operation conditions.Further considering the structure of the inner and outer models,an improved non-dominant sorting genetic algorithm,primal and dual simplex algorithms are applied to solve the model.The applied methods are constructed as an integrated solution procedure and finally,a case study is carried out to validate the rationality and effectiveness of the proposed model and the solution procedure.(5)Concerning the long-term uncertainty in the project lifecycle including demand increase,price fluctuation and cost change,this paper carries out the dynamic capacity optimization of WHCP through several phases and node configuration.Firstly,the basic concepts on dynamic coordinated configuration are discussed,which provides clear elements and optimization ideas in the configuration process.Next,the long-term uncertain factors,namely demand,benefits,and cost,are measured by uncertainty set,Geometric Brownian motion and dual-factor learning curves respectively.Then,decision variables and objective functions in the optimal configuration and operation are proposed.Their variation,cohesion and transition are also illustrated to provide the model evolution in different stages,with necessary assumptions of the model.Based on the former research,the dynamic WHCP capacity co-optimization considering multi-dimensional uncertainty and optimal configuration nodes is constructed.KKT condition is applied to convert the bi-level model into single layer,and big-M method is utilized to linearize the relaxed model.The inner model is converted as a set of linear constraints which are supplemented in the original outer model.And the optimal configuration nodes and corresponding capacity could be obtained by optimization algorithms.And finally a case study is carried out to validate and analyze the proposed model.