Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solva...Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.展开更多
Convex relaxations and approximations of the optimal power flow(OPF)problem have gained significant research and industrial interest for planning and operations in electric power networks.One approach for reducing the...Convex relaxations and approximations of the optimal power flow(OPF)problem have gained significant research and industrial interest for planning and operations in electric power networks.One approach for reducing their solve times is presolving which eliminates constraints from the problem definition,thereby reducing the burden of the underlying optimization algorithm.To this end,we propose a presolving framework for convexified optimal power flow(C-OPF)problems,which uses a novel deep learning-based architecture called𝙼MoGE(Mixture of Gradient Experts).In this framework,problem size is reduced by learning the mapping between C-OPF parameters and optimal dual variables(the latter being representable as gradients),which is then used to screen constraints that are non-binding at optimum.The validity of using this presolve framework across arbitrary families of C-OPF problems is theoretically demonstrated.We characterize generalization in𝙼𝚘𝙶𝙴and develop a post-solve recovery procedure to mitigate possible constraint classification errors.Using two different C-OPF models,we show via simulations that our framework reduces solve times by upto 34%across multiple PGLIB and MATPOWER test cases,while providing an identical solution as the full problem.展开更多
基金This work was supported by the Dalian Key Field Innovation Team Project(2020RT04)Airport Terminal Wisdom Environment Security and Energy Saving Laboratory of Guangdong Airport Baiyun Information Technology Co.,Ltd.in China.
文摘Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.
基金supported by the 2023 CITRIS Interdisciplinary Innovation Program(I2P),USA and the UCSC Dissertation-Year Fellowship,USA.
文摘Convex relaxations and approximations of the optimal power flow(OPF)problem have gained significant research and industrial interest for planning and operations in electric power networks.One approach for reducing their solve times is presolving which eliminates constraints from the problem definition,thereby reducing the burden of the underlying optimization algorithm.To this end,we propose a presolving framework for convexified optimal power flow(C-OPF)problems,which uses a novel deep learning-based architecture called𝙼MoGE(Mixture of Gradient Experts).In this framework,problem size is reduced by learning the mapping between C-OPF parameters and optimal dual variables(the latter being representable as gradients),which is then used to screen constraints that are non-binding at optimum.The validity of using this presolve framework across arbitrary families of C-OPF problems is theoretically demonstrated.We characterize generalization in𝙼𝚘𝙶𝙴and develop a post-solve recovery procedure to mitigate possible constraint classification errors.Using two different C-OPF models,we show via simulations that our framework reduces solve times by upto 34%across multiple PGLIB and MATPOWER test cases,while providing an identical solution as the full problem.
