| Among the multiple batteries,the vanadium redox battery(VRB)has recently drawn great attention due to its unique characteristics,such as long life cycle,safety,independent energy capacity and power output,low full-life cycle cost.It is especially suitable for large-scale storage system.The policy “Energy technology innovation action plan(2016-2030)” and “the roadmap for key innovation action of energy technology revolution” pointed out that it is necessary to demonstrate the 100 MW vanadium redox battery energy storage system.The policy “guideline on promoting the development of energy storage industry and technology” also points out that applying 100 MW vanadium redox battery energy storage power station is one of their key tasks.According to the above,it is urgent to build a MW VRB energy storage power station.How to make it meet the requirements of different scheduling instructions and maximize the commercial value under the premise of ensuring safety,control and efficiency is one of the key issues to be solved.Therefore,the coordinated control of MW VRB energy storage system is researched in this paper.By coordinating and optimizing the operation control of VRB energy storage system,the operation efficiency of the whole system can be improved,which has important theoretical and practical significance for accelerating the commercialization and application process of VRB energy storage system.The main works of this paper are as follows:(1)The model of MW VRB energy storage system is established.Firstly,the configuration and hierarchical control structure of MW VRB energy storage system are given.Then,the state-space model of a single VRB is established,and the dominant factors affecting the VRB external characteristics are found through frequency domain sensitivity and trajectory sensitivity analysis.Finally,a model which is suitable for the coordinated control of MW VRB energy storage system is derived: equivalent circuit and structure diagram.(2)Two methods for estimating the single VRB state of charge are proposed.(1)An estimation method is proposed which is based on the combination of the recursive least squares(RLS)algorithm and extended Kalman filter(EKF)algorithm.The RLS algorithm is used to estimate the battery model parameters,and the EKF algorithm is used to estimate the battery SOC.The battery SOC is estimated accurately by the combination of the RLS and EKF when the battery parameters are changed.The results show that the algorithm can estimate the SOC of vanadium redox battery accurately.(2)The convergence of EKF method is influenced by the uncertain initial SOC,thus an improved extended Kalman filter(IEKF)method to estimate SOC for VRB is proposed.The IEKF method is validated and compared with EKF against unknown initial value through the experiments.The results have shown that IEKF method is superior to EKF in terms of accuracy,convergence speed and robustness.The distributed computing scheme of VRB energy storage system SOC is presented based on the structure of MW VRB energy storage system and the accurate estimation of single VRB SOC.(3)A local layer control strategy of VRB energy storage system is proposed which is based on the nonlinear quantitative CMAC(Cerebella Model Articulation Controller)neural network.Firstly,the nonlinear quantitative CMAC controller which uses the power function,Gaussian function and piecewise function to quantify is designed,and the applicability of different nonlinear quantization methods is given.Then the piecewise nonlinear quantization CMAC controller is combined with the proportional controller to form the composite controller.Finally,the composite controller is applied to the local layer control of VRB energy storage system and compared with PI controller.The results show that the composite controller can effectively improve the VRB energy storage system's power tracking speed,which charging response time,discharge response time,charge-discharge conversion time and discharge-charge conversion time are all less than PI controller,and better than the time specified in the standard.(4)A power distribution strategy based on P-AWPSO algorithm(priority-adaptive weight particle swarm optimization)is proposed.Firstly,a multi-objective optimization model including loss cost,loss rate and SOC balancing is proposed,as well as four indexes to evaluate the power distribution of VRB energy storage system.Then,a PAWPSO algorithm is adopted to solve the problem.This algorithm uses the idea of "selecting the unit first and then distributing the power",that is,the energy storage unit which can participate in the power distribution is selected firstly according to the priority,and then the power distribution is carried out in the selected energy storage unit.Finally,the algorithm is used in the simulation of two examples and compared with the traditional power allocation algorithm.The simulation results show that the strategy can realize the power distribution of VRB energy storage system,reducing the charge and discharge times of the battery.It can also reduce the running cost of the battery and improve the working efficiency.(5)The key technology verification platform of VRB energy storage system is built,including a semi-physical simulation platform of MW VRB energy storage system and a demonstration platform of 5k W/30 k Wh VRB energy storage system.The semi-physical simulation platform is used to verify the effectiveness of the coordination control algorithm,and the empirical platform is used to verify the accuracy of VRB model and SOC estimation. |
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