Optimized cold storage energy management: Miami and Los Angeles case study

Abstract

Smart management of cold thermal energy storages could help future sustainable energy systems drawing large shares of electricity from renewable sources to balance fluctuating generation. This paper introduces a model-based predictive control strategy for cold thermal energy storages. A novel ice storage model for simulating and optimizing partial charge and discharge storage operation is developed and validated. The optimization problem is solved using a Forward Dynamic Programming approach. A case study analysis for a very hot and humid location (Miami) and a rather temperate climate (Los Angeles) and for each four building types (apartment building, hospital, office, and school) reveals that total cost savings of up to 20% compared to conventional control strategies are possible.