Optimal Deployment of Thermal Energy Storage Under Diverse Economic and Climate Conditions

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This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50% and 100%) of total daily on-peak summer cooling loads.

Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5?15%) and peak electricity consumption (13?33%). The investigation also identifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60% and 100% in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances.

Citation Formats

TY - DATA AB - This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50% and 100%) of total daily on-peak summer cooling loads. Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5–15%) and peak electricity consumption (13–33%). The investigation also identifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60% and 100% in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances. AU - DeForest, Nicholas A2 - Mendes, Gonçalo A3 - Stadler, Michael A4 - Feng, Wei A5 - Lai, Judy A6 - Marnay, Chris DB - C-MIX - Community Microgrid Information Exchange DP - Open EI | National Laboratory of the Rockies DO - KW - Thermal energy systems KW - TENs KW - District energy KW - Battery energy storage KW - Solar KW - Photovoltaics KW - PV KW - Diesel generators KW - Other liquid-fuel generators KW - Financing KW - Business models KW - Case studies KW - Performance KW - Local energy resources (LER) LA - English DA - 2014/04/15 PY - 2014 PB - Lawrence Berkeley National laboratory T1 - Optimal Deployment of Thermal Energy Storage Under Diverse Economic and Climate Conditions UR - https://cmix.openei.org/submissions/18 ER -
Export Citation to RIS
DeForest, Nicholas, et al. Optimal Deployment of Thermal Energy Storage Under Diverse Economic and Climate Conditions. Lawrence Berkeley National laboratory, 15 April, 2014, C-MIX - Community Microgrid Information Exchange. https://cmix.openei.org/submissions/18.
DeForest, N., Mendes, G., Stadler, M., Feng, W., Lai, J., & Marnay, C. (2014). Optimal Deployment of Thermal Energy Storage Under Diverse Economic and Climate Conditions. [Data set]. C-MIX - Community Microgrid Information Exchange. Lawrence Berkeley National laboratory. https://cmix.openei.org/submissions/18
DeForest, Nicholas, Gonçalo Mendes, Michael Stadler, Wei Feng, Judy Lai, and Chris Marnay. Optimal Deployment of Thermal Energy Storage Under Diverse Economic and Climate Conditions. Lawrence Berkeley National laboratory, April, 15, 2014. Distributed by C-MIX - Community Microgrid Information Exchange. https://cmix.openei.org/submissions/18
@misc{CMIX_Dataset_18, title = {Optimal Deployment of Thermal Energy Storage Under Diverse Economic and Climate Conditions}, author = {DeForest, Nicholas and Mendes, Gonçalo and Stadler, Michael and Feng, Wei and Lai, Judy and Marnay, Chris}, abstractNote = { This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50\% and 100\%) of total daily on-peak summer cooling loads.

Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5?15\%) and peak electricity consumption (13?33\%). The investigation also identifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60\% and 100\% in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances.}, url = {https://cmix.openei.org/submissions/18}, year = {2014}, howpublished = {C-MIX - Community Microgrid Information Exchange, Lawrence Berkeley National laboratory, https://cmix.openei.org/submissions/18}, note = {Accessed: 2026-06-17} }

Details

Data from Apr 15, 2014

Last updated Mar 30, 2026

Submitted Jun 2, 2026

Organization

Lawrence Berkeley National laboratory

Contact

Gonçalo Cardoso

Authors

Nicholas DeForest

Lawrence Berkeley National laboratory

Gonçalo Mendes

Lawrence Berkeley National laboratory

Michael Stadler

Lawrence Berkeley National laboratory

Wei Feng

Lawrence Berkeley National laboratory

Judy Lai

Lawrence Berkeley National laboratory

Chris Marnay

Lawrence Berkeley National laboratory
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