US-12619801-B2 - System and method for a post-modification building balance point temperature determination with the aid of a digital computer

US12619801B2US 12619801 B2US12619801 B2US 12619801B2US-12619801-B2

Abstract

A system and method for determining a balance point of a building that has undergone or is about to undergo modifications (such as shell improvements) are provided. A balance point of the building before the modifications can be determined using empirical data. Total thermal conductivity of the building before and after the modifications is determined and compared. Indoor temperature of the building is obtained. The balance point temperature after the modifications can be determined using a result of the comparison, the temperature inside the building, and the pre-modification balance point temperature. Knowing post-modification balance point temperature allows power grid operators to take into account fuel consumption by that building when planning for power production and distribution. Knowing the post-improvement balance point temperature also provides owners of the building information on which they can base the decision whether to implement the improvements.

Inventors

Thomas E. Hoff

Assignees

CLEAN POWER RESEARCH, L.L.C.

Dates

Publication Date: 20260505
Application Date: 20220822

Claims (20)

1 . A method for a post-modification building balance point temperature prediction with the aid of a digital computer, comprising the steps of: obtain by a computer, the computer comprising a memory storing program code and a processor coupled to the memory and configured to execute the code, a balance point temperature of a building; obtaining by the computer total thermal conductivity of the building; obtaining by the computer a temperature inside the building; obtaining by the computer one or more proposed changes to the building associated with a change to the total thermal conductivity and R-values of one or more materials associated with the changes; modeling by the computer the changed total thermal conductivity associated with an implementation of the building changes using the R-values; and determining by the computer a further balance point temperature of the building associated with the implementation of the one or more proposed changes in accordance with: T ˆ Balance ⁢ Point = T B ⁢ a ⁢ l ⁢ ance ⁢ Point - γ ⁢ T Indoor ( 1 - γ ) where {circumflex over (T)} Balance Point is the further balance point temperature, T Balance Point is the balance point temperature, γ is a fractional improvement in the total thermal conductivity associated with the implementation of the one or more proposed changes, T Indoor is the temperature inside the building, wherein the one or more proposed changes are implemented based on the further balance point temperature, wherein the building is interfaced to a power grid and operation of the power grid is performed based on the further balance point temperature.
2 . A method according to claim 1 , further comprising one of: obtaining a temperature proposed to be maintained inside the building following the implementation of the changes and setting the proposed temperature as the temperature inside the building; and obtaining a temperature inside the building prior to the implementation of the changes and setting the prior-to-implementation temperature as the temperature inside the building.
3 . A method according to claim 1 , further comprising receiving the proposed changes from a user device over an Internetwork.
4 . A method according to claim 3 , further comprising receiving data associated with the proposed changes necessary to model the changed thermal conductivity.
5 . A method according to claim 1 , further comprising performing an empirical test to obtain the building's total thermal conductivity.
6 . A method according to claim 1 , wherein the test is performed using an electric controllable interior heat source.
7 . A method according to claim 1 , further comprising determining γ by comparing the total thermal conductivity to the changed thermal conductivity.
8 . A method for a post-modification building balance point temperature determination with the aid of a digital computer, comprising the steps of: obtain by a computer, the computer comprising a memory storing program code and a processor coupled to the memory and configured to execute the code, a balance point temperature of a building; obtaining by the computer total thermal conductivity of the building prior to an implementation of one or more changes to the building that are associated with a change to the total thermal conductivity, comprising performing an empirical test using an electrical controllable heating source; obtaining by the computer a temperature inside the building following the implementation of the one or more changes to the building; modeling by the computer the changed total thermal conductivity associated with the implementation of the one or more changes; and determining by the computer a further balance point temperature of the building following the implementation of the one or more changes in accordance with: T ˆ Balance ⁢ Point = T B ⁢ a ⁢ l ⁢ ance ⁢ Point - γ ⁢ T Indoor ( 1 - γ ) where {circumflex over (T)} Balance Point is the further balance point temperature, T Balance Point is the balance point temperature, γ is a fractional improvement in the total thermal conductivity following the implementation of the one or more changes, and T indoor is the temperature inside the building, wherein the building is interfaced to a power grid and operation of the power grid is performed based on the further balance point temperature.
9 . A method according to claim 8 , further comprising using at least one of a standalone thermometer and a smart thermostat inside the building to obtain the temperature inside the building.
10 . A method according to claim 8 , further comprising determining γ by comparing the total thermal conductivity to the changed thermal conductivity.
11 . A method according to claim 8 , further receiving a listing of the changes from a user device over an Internetwork.
12 . A method according to claim 11 , further comprising receiving data associated with the changes necessary to model the changed thermal conductivity.
13 . A method according to claim 8 , further comprising remotely controlling the electrical controllable heat source.
14 . A system for a post-modification building balance point temperature determination with the aid of a digital computer, comprising: a computer comprising a memory storing program code and a processor coupled to the memory and configured to execute the code, the computer configured to: obtain a balance point temperature of a building; obtain total thermal conductivity of the building prior to an implementation of one or more changes to the building that are associated with a change to the total thermal conductivity, comprising performing an empirical test using an electrical controllable heating source; obtain a temperature inside the building following the implementation of the one or more changes to the building; model the changed total thermal conductivity associated with the implemented changes; and determine a further balance point temperature of the building following the implementation of the one or more changes in accordance with: T ˆ Balance ⁢ Point = T B ⁢ a ⁢ l ⁢ ance ⁢ Point - γ ⁢ T Indoor ( 1 - γ ) where T Balance Point is the further balance point temperature, {circumflex over (T)} Balance Point is the balance point temperature, γ is a fractional improvement in the total thermal conductivity following the implementation of the one or more changes, and T Indoor is the temperature inside the building, wherein the building is interfaced to a power grid and operation of the power grid is performed based on the further balance point temperature.
15 . A system according to claim 14 , the computer further configured to use at least one of a standalone thermometer and a smart thermostat inside the building to obtain the temperature inside the building.
16 . A system according to claim 14 , the computer further configured to determining γ by comparing the total thermal conductivity to the changed thermal conductivity.
17 . A system according to claim 14 , the computer further configured to receive a listing of the changes from a user device over an Internetwork.
18 . A system according to claim 14 , the computer further configured to receive data associated with the changes necessary to model the changed thermal conductivity.
19 . A system according to claim 14 , the computer further configured to remotely control the electrical controllable heat source.
20 . A system according to claim 14 , wherein the changes are to a shell of the building.

Description

CROSS REFERENCE TO RELATED APPLICATION This non-provisional patent application is a continuation of U.S. patent application Ser. No. 16/987,044, filed Aug. 6, 2020, pending, which is a continuation-in-part of U.S. Pat. No. 10,797,639, issued Oct. 6, 2020, the disclosures of which is incorporated by reference. FIELD This application relates in general to power generation fleet planning and operation and, in particular, to a system and method for a post-modification building balance point temperature determination with the aid of a digital computer. BACKGROUND A power grid is a geographically-distributed electricity generation, transmission, and distribution infrastructure that delivers electricity from power generation sources to regional and municipal power utilities and finally to end-consumers, including residential, commercial, and retail customers. Power generation and consumption balancing remains a crucial part of power grid and power utility planning and operations. As electricity is consumed almost immediately upon production, both power generation and power consumption must be continually balanced across the entire power grid. For instance, a power failure in one part of a power grid could cause electrical current to reroute from remaining power generation sources over transmission lines of insufficient capacity and in turn create the possibility of cascading power failures and widespread outages. As a result, the planners and operators of power grids and power utilities need to be able to accurately gauge both on-going and forecasted power generation from all sources, including photovoltaic fleets and individual photovoltaic systems, and on-going and forecasted consumption by all consumers. Estimating on-going and forecasted power generation requires examining the contribution made by each power generation system to a power grid. For instance, photovoltaic systems are widely used today for grid-connected distributed power generation, as well as for standalone off-grid power systems and residential and commercial sources of supplemental electricity. Power grid connection of photovoltaic power generation is a fairly recent development. Typically, when integrated into a power grid, photovoltaic systems are centrally operated by a supplier as a fleet, although the individual photovoltaic systems in the fleet may be deployed at different physical locations within a geographic region. Reliance on photovoltaic fleet power generation as part of a power grid implicates the need for these photovoltaic systems to exhibit predictable power generation behaviors, and accurate power production data is needed at all levels of a power grid, including power utilities, to which a fleet is connected. On-going and forecasted power production data is particularly crucial when a photovoltaic fleet makes a significant contribution to a power grid's overall power mix. For individual systems, power production forecasting first involves obtaining a prediction of solar irradiance, which can be derived from ground-based measurements, satellite imagery, numerical weather prediction models, or other sources. The predicted solar irradiance and each photovoltaic plant's system configuration is combined with a photovoltaic simulation model to generate a forecast of individual photovoltaic plant power output production. The individual photovoltaic plant forecasts can then be combined into a photovoltaic fleet forecast, such as described in commonly-assigned U.S. Pat. Nos. 8,165,811; 8,165,812; 8,165,813, all issued to Hoff on Apr. 24, 2012; U.S. Pat. Nos. 8,326,535; 8,326,536, issued to Hoff on Dec. 4, 2012; and U.S. Pat. No. 8,335,649, issued to Hoff on Dec. 18, 2012, the disclosures of which are incorporated by reference. As photovoltaic power generation relies on solar irradiance, photovoltaic fleets operating under cloudy conditions may exhibit variable and unpredictable performance, thereby complicating the need for predictable power generation behaviors. Conventionally, fleet variability is determined by collecting and feeding direct power measurements from individual photovoltaic systems or equivalent indirectly-derived power measurements into a centralized control computer or similar arrangement. To be of optimal usefulness, the direct power measurement data must be collected in near-real time at fine-grained time intervals to enable a high resolution time series of power output data to be created. However, the practicality of this form of optimal approach diminishes as the number of photovoltaic systems, variations in system configurations, and geographic dispersion of the photovoltaic fleet grow. Moreover, the costs and feasibility of providing remote power measurement data can make high speed data collection and analysis insurmountable due to the bandwidth needed to transmit data and the storage space needed to contain collected measurements; furthermore, the processing resources needed to scale quantitative power measur