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Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control.

Author(s) : HAO K.

Type of monograph: Master thesis

Summary

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. One approach to integrating thermal storage on site is to use ice in combination with the cooling system. The use of ice storage can enable a change in the time variation of electrical usage for cooling in response to variations in PV availability, utility prices, and cooling requirements.A number of studies can be found in the literature that address optimal operation of onsite PV systems with batteries or ice storage. However, although it is a natural and practical question, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery’s round-trip efficiency,on the other hand, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a significantly shorter life. This research presents a tool and provides a case study for comparing life-cycle economics of battery and ice storage systems for a commercial building that has chillers for cooling and an on-site photovoltaic system. A model predictive control algorithm was developed and implemented in simulation for the two systems in order to compare optimal costs. The effect of ice storage and battery sizing were studied in order to determine the best storage sizes from an economic perspective and to provide a fair comparison.

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Pages: 96 p.

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Details

  • Original title: Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control.
  • Record ID : 30032843
  • Languages: English
  • Subject: Technology
  • Publication: Purdue University
  • Publication date: 2020
  • DOI: http://dx.doi.org/https://doi.org/10.25394/PGS.12218159.v1

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