Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the d...A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.展开更多
For the design of the hybrid electric vehicles,the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge.If handled sequentially,it is defined as sub-optim...For the design of the hybrid electric vehicles,the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge.If handled sequentially,it is defined as sub-optimal.In order to obtain the optimal design of the system,it is necessary to integrate the physical system and its controller.Taking component parameters and energy management strategy as research objects,this paper elaborates an integrated optimization approach for a series–parallel hybrid electric vehicle.Firstly,a rule-based control strategy that can be applied online is designed according to various driving modes of the hybrid electric vehicle.Then,considering the coupling between component parameters and control strategies,a dual-layer optimization framework with genetic algorithm and double dynamic programming is proposed to optimize fuel economy and battery life.Among them,parameters of component size and control for the upper-layer of the framework are selected as preparative optimization parameters.In order to get rid of the influences of energy management strategies and obtain the optimal upper-layer parameters,the lower-layer of the framework adopts the global optimization algorithm to calculate the optimal energy distribution ratio for each driving mode.The results indicate that,while ensuring the good working condition of the battery,the fuel economy has improved by 7.79%under the selected driving cycle after optimization.The optimized upper-layer parameters combined with the proposed control rules can be applied online.展开更多
The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demand...The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.展开更多
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
基金This work was supportedbytheNationalNaturalScienceFoundationofChina(No.60474051),theProgramforNewCenturyExcellentTalentsinUniversityofChina(NCET),andtheSpecializedResearchFundfortheDoctoralProgramofHigherEducationofChina(No.20020248028).
文摘A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.
文摘For the design of the hybrid electric vehicles,the strong coupling between plant parameters and controller parameters turns the problem into a multi-layered challenge.If handled sequentially,it is defined as sub-optimal.In order to obtain the optimal design of the system,it is necessary to integrate the physical system and its controller.Taking component parameters and energy management strategy as research objects,this paper elaborates an integrated optimization approach for a series–parallel hybrid electric vehicle.Firstly,a rule-based control strategy that can be applied online is designed according to various driving modes of the hybrid electric vehicle.Then,considering the coupling between component parameters and control strategies,a dual-layer optimization framework with genetic algorithm and double dynamic programming is proposed to optimize fuel economy and battery life.Among them,parameters of component size and control for the upper-layer of the framework are selected as preparative optimization parameters.In order to get rid of the influences of energy management strategies and obtain the optimal upper-layer parameters,the lower-layer of the framework adopts the global optimization algorithm to calculate the optimal energy distribution ratio for each driving mode.The results indicate that,while ensuring the good working condition of the battery,the fuel economy has improved by 7.79%under the selected driving cycle after optimization.The optimized upper-layer parameters combined with the proposed control rules can be applied online.
基金National Natural Sciences Foundation of China,Grant/Award Numbers:62125302,62203087Sci-Tech Talent Innovation Support Program of Dalian,Grant/Award Number:2022RG03+1 种基金Liaoning Revitalization Talents Program,Grant/Award Number:XLYC2002087Young Elite Scientist Sponsorship Program by CAST,Grant/Award Number:YESS20220018。
文摘The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.
