The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform

Prashant G Mehta - One of the best experts on this subject based on the ideXlab platform.

  • model predictive control of Central chiller Plant with thermal energy storage via dynamic programming and mixed integer linear programming
    IEEE Transactions on Automation Science and Engineering, 2015
    Co-Authors: Kun Deng, Sisi Li, Yan Lu, Jack Brouwer, Prashant G Mehta, Mengchu Zhou, Amit Chakraborty
    Abstract:

    This work considers the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the Central Plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

  • optimal scheduling of chiller Plant with thermal energy storage using mixed integer linear programming
    American Control Conference, 2013
    Co-Authors: Kun Deng, Yan Lu, Jack Brouwer, Amit Chakraborty, Prashant G Mehta
    Abstract:

    In this paper, we consider the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.

Amit Chakraborty - One of the best experts on this subject based on the ideXlab platform.

  • model predictive control of Central chiller Plant with thermal energy storage via dynamic programming and mixed integer linear programming
    IEEE Transactions on Automation Science and Engineering, 2015
    Co-Authors: Kun Deng, Sisi Li, Yan Lu, Jack Brouwer, Prashant G Mehta, Mengchu Zhou, Amit Chakraborty
    Abstract:

    This work considers the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the Central Plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

  • optimal scheduling of chiller Plant with thermal energy storage using mixed integer linear programming
    American Control Conference, 2013
    Co-Authors: Kun Deng, Yan Lu, Jack Brouwer, Amit Chakraborty, Prashant G Mehta
    Abstract:

    In this paper, we consider the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.

Kun Deng - One of the best experts on this subject based on the ideXlab platform.

  • model predictive control of Central chiller Plant with thermal energy storage via dynamic programming and mixed integer linear programming
    IEEE Transactions on Automation Science and Engineering, 2015
    Co-Authors: Kun Deng, Sisi Li, Yan Lu, Jack Brouwer, Prashant G Mehta, Mengchu Zhou, Amit Chakraborty
    Abstract:

    This work considers the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the Central Plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

  • optimal scheduling of chiller Plant with thermal energy storage using mixed integer linear programming
    American Control Conference, 2013
    Co-Authors: Kun Deng, Yan Lu, Jack Brouwer, Amit Chakraborty, Prashant G Mehta
    Abstract:

    In this paper, we consider the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.

Yan Lu - One of the best experts on this subject based on the ideXlab platform.

  • model predictive control of Central chiller Plant with thermal energy storage via dynamic programming and mixed integer linear programming
    IEEE Transactions on Automation Science and Engineering, 2015
    Co-Authors: Kun Deng, Sisi Li, Yan Lu, Jack Brouwer, Prashant G Mehta, Mengchu Zhou, Amit Chakraborty
    Abstract:

    This work considers the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the Central Plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

  • optimal scheduling of chiller Plant with thermal energy storage using mixed integer linear programming
    American Control Conference, 2013
    Co-Authors: Kun Deng, Yan Lu, Jack Brouwer, Amit Chakraborty, Prashant G Mehta
    Abstract:

    In this paper, we consider the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.

Jack Brouwer - One of the best experts on this subject based on the ideXlab platform.

  • model predictive control of Central chiller Plant with thermal energy storage via dynamic programming and mixed integer linear programming
    IEEE Transactions on Automation Science and Engineering, 2015
    Co-Authors: Kun Deng, Sisi Li, Yan Lu, Jack Brouwer, Prashant G Mehta, Mengchu Zhou, Amit Chakraborty
    Abstract:

    This work considers the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the Central Plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

  • optimal scheduling of chiller Plant with thermal energy storage using mixed integer linear programming
    American Control Conference, 2013
    Co-Authors: Kun Deng, Yan Lu, Jack Brouwer, Amit Chakraborty, Prashant G Mehta
    Abstract:

    In this paper, we consider the optimal scheduling problem for a campus Central Plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.