Development of reduced order thermal dynamic models for building load flexibility of an electrically-heated high temperature thermal storage device

Date, Jennifer; Candanedo, José A.; Athienitis, Andreas; Lavigne, K.

doi:10.1080/23744731.2020.1735260

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…The offline approach identifies the values by minimizing the model error over a specific period, once or frequently; however, a global optimization such as the multi-start technique (De Coninck et al, 2016) is required due to various local optima (Drgona et al, 2020). The online approach can be formed for example based on Bayesian calibration techniques (Rouchier, Jimenez, & Castano, 2019;Shi & O'Brien, 2019) or re-initializing the optimization within the "model-reset" approach (Date et al, 2020). In contrast to black-box models that achieve higher accuracy if trained on more data, gray-box models' accuracy tends to decline if the training period is too long relative to the involved thermal mass.…”

Section: Gray-box Approachmentioning

confidence: 99%

“…Buildings' energy flexibility can be utilized to respond to the grid's requirements and lower the stress on it when needed. Athienitis et al (2020) states that energy flexibility may be enhanced by actions ahead of the peak periods, and is enabled through 1) building's thermal mass and modification of zone setpoints, 2) thermal storage in HVAC systems, 3) specific active mass systems like a radiant slab, 4) integrated operation of heat pumps, possible on-site renewable energy generators and thermal storage and 5) battery storage/electric vehicle and controllable appliance and lighting.…”

Section: Building Energy Flexibilitymentioning

confidence: 99%

“…However, a major challenge associated with the integration of intermittent solar electricity generation and the building is the mismatch between the period of maximum solar generation and the peak consumption and/or high energy price periods (Date et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control-oriented thermal network models for predictive load management in Canadian houses with on-Site solar electricity generation: application to a research house

Abtahi

Athienitis

Delcroix

2021

Journal of Building Performance Simulation

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This study presents a methodology to develop suitable control-oriented thermal RC network models for optimized HVAC load management in typical electrically heated single-family detached houses. Using recurring parameter identification and model-reset, the building dynamics are represented by an explicit discrete time-varying state-space formulation.Next, these models are applied in a predictive control framework in which the objective is to enhance energy efficiency and energy flexibility of the building by prioritizing the import from the most efficient energy source(s), storing energy in the building's thermal mass and/or a battery, and shifting the HVAC load to lower the stress on the local grid.Finally, the benefits of predictive control strategies for HVAC load management, both for the building owners and the local grid, are studied through a seasonal simulation where the performance of the building subject to a reference reactive controller and a predictive

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Section: Gray-box Approachmentioning

confidence: 99%

Section: Building Energy Flexibilitymentioning

confidence: 99%

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Control-oriented thermal network models for predictive load management in Canadian houses with on-Site solar electricity generation: application to a research house

Abtahi

Athienitis

Delcroix

2021

Journal of Building Performance Simulation

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Environmentally responsible steel-slag-based solid particles for highly efficient and durable solar selective absorptance and thermal storage

Yang,

Li,

et al. 2024

Journal of Cleaner Production

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A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building through Predictive Control of Passive and Active Storage

2021

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Optimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capacity. The presented paper outlines a methodology that utilizes the concept of Building Energy Flexibility Index (BEFI) and shows that implementing Model Predictive Control (MPC) with dedicated thermal storage can provide predictable energy flexibility to the grid during critical times. When the utility notifies the customer 12 h before a Demand Response (DR) event, a BEFI up to 65 kW (100% reduction) can be achieved. A dynamic rate structure as the objective function is shown to be successful in reducing the peak demand, while a greater reduction in energy consumption in a 24-hour period is seen with a rate structure with a demand charge. Contingency reserve participation was also studied and strategies included reducing the zone temperature setpoint by 2∘C for 3 h or using the stored thermal energy by discharging the device for 3 h. Favourable results were found for both options, where a BEFI of up to 47 kW (96%) is achieved. The proposed methodology for modeling and evaluation of control strategies is suitable for other similar convectively conditioned buildings equipped with active and passive storage.

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Development of reduced order thermal dynamic models for building load flexibility of an electrically-heated high temperature thermal storage device

Cited by 3 publications

References 32 publications

Control-oriented thermal network models for predictive load management in Canadian houses with on-Site solar electricity generation: application to a research house

Control-oriented thermal network models for predictive load management in Canadian houses with on-Site solar electricity generation: application to a research house

Environmentally responsible steel-slag-based solid particles for highly efficient and durable solar selective absorptance and thermal storage

A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building through Predictive Control of Passive and Active Storage

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