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Model Predictive Control for Central Plant Optimization with Thermal Energy Storage

Linear Programming is used in order to determine how to distribute both hot and cold water loads across a central energy plant including heat pump chillers, conventional chillers, water heaters, and hot and cold water (thermal energy) storage. The objective of the optimization framework is to minimize cost in response to both real-time energy prices and demand charges. A planning tool that allows for the user to approximate a year’s load distribution, and thus cost, in a few minutes is demonstrated. The optimization framework can also be used in real-time plant operation as a model predictive control (MPC) problem. In simulation, the system has demonstrated more than 10% savings over other schedule based control trajectories even when the sub-plants are assumed to be running optimally in both cases (i.e., optimal chiller staging, etc.) For large plants this can mean savings of more than US $1 million per year.