OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Blonsky, Michael; Maguire, Jeff; McKenna, Killian; Cutler, Dylan; Balamurugan, Sivasathya Pradha; Jin, Xin

doi:10.1016/j.apenergy.2021.116732

Cited by 26 publications

(15 citation statements)

References 22 publications

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“…OCHRE is a residential energy model that simulates energy consumption and occupant comfort with a reduced-order resistance capacitance network approach 7 (Blonsky, et al, 2021). OCHRE is designed to be used as an underlying model for energy control and optimization applications and includes controllable models for heating, ventilation, and air conditioning (HVAC) equipment, water heaters, electric vehicles, PV, and batteries.…”

Section: Ochre and Reopt Modelsmentioning

confidence: 99%

“…While OCHRE has previously been validated against detailed, physics-based models such as EnergyPlus (Blonsky, et al, 2021), the high-resolution data provided by sonnen presented a unique opportunity to validate the OCHRE-REopt modeling workflow against empirical data. In Appendix B, the OCHRE HVAC model was validated against whole home power data and compared with REopt HVAC dispatch results.…”

Section: Ochre and Reopt Modelsmentioning

confidence: 99%

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Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

O’Shaughnessy¹,

Cutler²,

Farthing³

et al. 2022

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The electric grid is rapidly evolving as small-scale, demand-side resources play increasingly important roles in grid operations and decarbonization. Maximizing the potential of demand-side resources involves incentivizing electricity customers to use those resources in ways that benefit the broader electric grid. These incentives depend largely on the electricity cost savings that customers can realize by adopting demand-side resources. Determining these potential cost savings is a complex task. Cost savings depend on numerous factors, including the characteristics of different technologies, the algorithms that control these devices, system performance, customer behavior, electricity rate structures, and climatic factors. Another challenge is that estimated cost savings are frequently based on modeled rather than observed system performance, particularly in the literature.In this study, we begin to fill the gap in empirical research of demand-side resources using data from a new-construction residential community equipped with rooftop solar and storage (S+S) in Arizona. We use these data to analyze the factors that determine customer electricity cost savings and emissions impacts of S+S in the real world. We then compare these data to modeled system performance to understand how models deviate from real-world outcomes. Based on these findings, we explore ways to improve such models and, conversely, use modeled results to suggest improvements to actual S+S deployment. The results of these analyses can be summarized in four key findings. Rate structures play a central role in the grid and customer value of demand-side resources.In the Arizona case study, the local utility enrolled all households in the community in an experimental rate designed for customers with demand-side resources. The data show that the distributed generation rate benefited the grid by reshaping customer grid demand profiles, especially by reducing demand during grid peak periods. At the same time, the challenge associated with reducing demand charges in the pilot rate plan eroded the customer cost savings from S+S adoption. The resulting erosion of customer value caused at least some community members to switch back to a time-of-use rate plan that was less beneficial to grid operations. In this case study and in other circumstances, there is a tension between designing rates that benefit the electric grid and providing incentives that induce customers to adopt demand-side resources.Certain customers can benefit more from demand-side resource adoption than others. Electricity cost savings varied significantly across households in our case study, even though the newly constructed, energy efficient homes were all equipped with similar S+S systems. Household-level factors that drive cost savings include total electricity demand, demand profiles (e.g., more use during on-peak hours), and differences in home square footage. Modeled battery dispatch and sizing reveals opportunities for additional cost savings.Modeled results show that current batte...

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Section: Ochre and Reopt Modelsmentioning

confidence: 99%

Section: Ochre and Reopt Modelsmentioning

confidence: 99%

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

O’Shaughnessy¹,

Cutler²,

Farthing³

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Inside each house agent, we used the Operational, Controllable, High-resolution Residential Energy (OCHRE) model [23] for modeling the buildings in this study. OCHRE is capable of implementing the control signals for HVAC, WH, PV, and battery, and it is designed to be easily integrated into the co-simulation platform.…”

Section: Co-simulation Platformmentioning

confidence: 99%

Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate

et al. 2022

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“…Another example that falls under the Thermal Comfort Priority Case 4 is a user who has a high income and is willing to spend more to be comfortable but also reduce their carbon footprint. NREL's Object-oriented Controllable High-resolution Residential Energy (COHRE) model was used to run the simulations in this study [16]. OCHRE is a residential building model that incorporates controllable devices such as HVAC equipment, water heaters, electric vehicles, photovoltaic systems, and batteries.…”

Section: Occupant Preference Cases and House Modelmentioning

confidence: 99%

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings

Pattawi

Munankarmi

Blonsky

et al. 2021

ASME 2021 15th International Conference on Energy Sustainability

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Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand response (DR) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs, but it is also important for HEMS to account for occupant preferences to ensure occupant satisfaction. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foresee™ HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact on energy consumption and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while also maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful in controlling the electrical grid and developing HEMS solutions.

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OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Cited by 26 publications

References 22 publications

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings

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