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Generalized disjunctive programming-based, mixed integer linear MPC formulation for optimal operation of a district energy system for PV self-consumption and grid decarbonization: field implementation.

Number: 3223

Author(s) : KIM D., HONG T., PIETTE M. A.

Summary

Thermal energy storage (TES) for a cooling plant serving a campus or district of buildings can shift cooling energy from peak to non-peak hours so that it is able to reduce electrical operation costs in buildings and increase stability and operational efficiency in the grid. Traditionally, heuristic control approaches, such as the storage priority control, have been widely used in practice. Those simple control approaches have shown reasonable or even near-optimal performance in the past. However, the increasing penetration of renewables changes the situation: exposing the electrical grid to the growing supply demand challenge which encourages greater consumption at times when renewable energy is plentiful and lesser when dirtier supply is used. This paper presents a model predictive control (MPC) strategy, site demonstration and evaluation results for optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program using the Generalized Disjunctive Programming for better numerical and control properties. Results show that the MPC can effectively utilize PV generated electricity to drive chillers for charging the TES during the day and avoid sending excess electricity back to the grid. Compared with the storage priority control, the MPC reduces the excess PV power by around 25%, the greenhouse gas emission by 10%, and peak electricity demand by 10%.

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  • Original title: Generalized disjunctive programming-based, mixed integer linear MPC formulation for optimal operation of a district energy system for PV self-consumption and grid decarbonization: field implementation.
  • Record ID : 30030216
  • Languages: English
  • Source: 2022 Purdue Conferences. 7th International High Performance Buildings Conference at Purdue.
  • Publication date: 2022
  • Document available for consultation in the library of the IIR headquarters only.

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