Claims: A processor implemented method (200) comprising:
estimating a first set of demand response potentials (DRPs) for a pre-defined fraction of one or more buildings constituting a facility based on (i) a baseline energy consumption (E^*) associated with cost optimal operation of the facility with no DR and (ii) energy consumption (E_DR^*) of the facility with DR, the facility being associated with a given tariff and demand response (DR) incentive set by a utility (202);
generating one or more regression models for the pre-defined fraction of one or more buildings based on the estimated first set of DRPs, wherein parameters of the one or more regression models include area and the DR incentive associated thereof (204);
estimating a second set of DRPs for a remaining fraction of the one or more buildings constituting the facility based on the generated one or more regression models using the area and the DR incentive associated with the remaining one or more buildings (206); and
determining an optimal incentive for the facility based on the estimated first set and second set of DRPs using a heuristic method (208).

The processor implemented method of claim 1, wherein the pre-defined fraction of the one or more buildings is 1%.

The processor implemented method of claim 1, wherein the baseline energy consumption (E^*) and the energy consumption (E_DR^*) of the facility with DR are based on a cost criteria and a plurality of constraints.

The processor implemented method of claim 3, wherein the cost criteria and the plurality of constraints are based on (i) a dynamic thermal model of the facility to predict temperature evolution given ambient conditions, internal heat loads and building envelope parameters; (ii) a power model for an Heating Ventilation and Air Conditioning (HVAC) system having an Air Handling Unit (AHU) fan and chiller unit given the AHU fan mass flow rate and total cooling load respectively; and (iii) systemic inertia in the HVAC system.

The processor implemented method of claim 4, wherein the cost criteria for the baseline energy consumption (E^*) is associated with (i) total HVAC energy consumption due to AHU fan and chillers and is a function of AHU fan speed for a given time period and (ii) lighting power consumption; and wherein the cost criteria for the energy consumption (E_DR^*) of the facility with DR is associated with reduction in energy consumption from the baseline energy consumption based on the DR incentive from the utility and the total HVAC energy consumption and the lighting power consumption associated thereof.

The processor implemented method of claim 4, wherein the plurality of constraints are associated with maximum AHU flow rate, thermal comfort requirement, maximum available chiller capacity, temperature evolutions and luminous flux.

The processor implemented method of claim 6, wherein the temperature evolutions for given ambient conditions, internal heat loads and building envelope parameters are predicted by the dynamic thermal model.

The processor implemented method of claim 1, wherein the step of determining the optimal incentive for the facility comprises:
identifying clusters of buildings within the facility, the clusters being characterized by similar DRP curves based on the estimated first set and second set of DRPs, and wherein the similar DRP curves are further characterized by a dynamically generated threshold for Euclidean distance therebetween;
aggregating the DRP curves of individual buildings within each of the clusters to obtain an overall DRP for each of the identified clusters; and
determining the optimal incentive based on the overall DRP for each of the clusters by:
obtaining a demand (kWh) reduction target and the DR incentive set by the utility; and
iteratively performing:
identifying a cluster from the clusters of buildings associated with a maximum overall DRP; and
allocating a portion of the DR incentive to the identified cluster, the portion of the DR incentive being one of empirically defined equal portions, wherein the allocated portion constitutes the optimal incentive;
until the demand reduction target is reached.

The processor implemented method of claim 8, wherein the step of identifying clusters of buildings is based on k-means clustering method.

The processor implemented method of claim 8, wherein the DR incentive is allocated uniformly to the buildings within each of the clusters and is allocated non-uniformly across the clusters.

A system (100) comprising:
one or more data storage devices (102) operatively coupled to one or more hardware processors (104) and configured to store instructions configured for execution by the one or more hardware processors to:
estimate a first set of demand response potentials (DRPs) for a pre-defined fraction of one or more buildings constituting a facility based on (i) a baseline energy consumption (E^*) associated with cost optimal operation of the facility with no DR and (ii) energy consumption (E_DR^*) of the facility with DR, the facility being associated with a given tariff and demand response (DR) incentive set by a utility;
generate one or more regression models for the pre-defined fraction of one or more buildings based on the estimated first set of DRPs, wherein parameters of the one or more regression models include area and the DR incentive associated thereof;
estimate a second set of DRPs for a remaining fraction of the one or more buildings constituting the facility based on the generated one or more regression models using the area and the DR incentive associated with the remaining one or more buildings; and
determine an optimal incentive for the facility based on the estimated first set and second set of DRPs using a heuristic method.

The system of claim 11, wherein the baseline energy consumption (E^*) and the energy consumption (E_DR^*) of the facility with DR are based on a cost criteria and a plurality of constraints.

The system of claim 12, wherein the cost criteria and the plurality of constraints are based on (i) a dynamic thermal model of the facility to predict temperature evolution given ambient conditions, internal heat loads and building envelope parameters; (ii) a power model for an Heating Ventilation and Air Conditioning (HVAC) system having an Air Handling Unit (AHU) fan and chiller unit given the AHU fan mass flow rate and total cooling load respectively; and (iii) systemic inertia in the HVAC system.

The system of claim 13, wherein the cost criteria for the baseline energy consumption (E^*) is associated with (i) total HVAC energy consumption due to AHU fan and chillers and is a function of AHU fan speed for a given time period and (ii) lighting power consumption; and wherein the cost criteria for the energy consumption (E_DR^*) of the facility with DR is associated with reduction in energy consumption from the baseline energy consumption based on the DR incentive from the utility and the total HVAC energy consumption and the lighting power consumption associated thereof.

The system of claim 13, wherein the plurality of constraints are associated with maximum AHU flow rate, thermal comfort requirement, maximum available chiller capacity, temperature evolutions and luminous flux.

The system of claim 15, wherein the temperature evolutions for given ambient conditions, internal heat loads and building envelope parameters are predicted by the dynamic thermal model.

The system of claim 11, wherein the one or more hardware processors are further configured to determine the optimal incentive for the facility by:
identifying clusters of buildings within the pre-defined fraction of the one or more buildings, the clusters being characterized by similar DRP curves based on the estimated first set and second set of DRPs, and wherein the similar DRP curves are further characterized by a dynamically generated threshold for Euclidean distance therebetween;
aggregating the DRP curves of individual buildings within each of the clusters to obtain an overall DRP for each of the identified clusters; and
determining the optimal incentive based on the overall DRP for each of the clusters by:
obtaining a demand (kWh) reduction target and the DR incentive set by the utility; and
iteratively performing:
identifying a cluster from the clusters of buildings associated with a maximum overall DRP; and
allocating a portion of the DR incentive to the identified cluster, the DR portion of the incentive being one of empirically defined equal portions, wherein the allocated portion constitutes the optimal incentive;
until the demand reduction target is reached.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
METHODS AND SYSTEMS FOR SCALABLE ESTIMATION OF DEMAND RESPONSE

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application is a patent of addition of Indian Patent Application No. 201721020446, filed on June 12th, 2017, the entire content of which is hereby incorporated herein by way of reference.

TECHNICAL FIELD
The disclosure herein generally relates to energy consumption in facilities, and particularly to systems and methods for scalable estimation of demand response (DR).

BACKGROUND
Electrical utilities offer incentives to their customers to reduce their demand during periodic supply-demand mismatches. For a building facility manager, the ability to participate in Demand Response (DR) is determined by the building's constraints, while the willingness depends upon the incentive offered. While customers prefer a higher incentive to participate, utilities prefer to minimize the incentive while achieving a target reduction. Because the incentive affects the bottom line of the utility, identifying an optimal incentive reflecting this trade-off is important. When building population is large, it is practically difficult to collect fine grained data from each building to estimate demand response potential (DRP) of each building. Again, allotting non-uniform incentives to buildings is cost effective for the utility. However, calculation of non-uniform incentives is computationally intractable.

SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
In an aspect, there is provided a processor implemented method comprising: estimating a first set of demand response potentials (DRPs) for a pre-defined fraction of one or more buildings constituting a facility based on (i) a baseline energy consumption (E^*) associated with cost optimal operation of the facility with no DR and (ii) energy consumption (E_DR^*) of the facility with DR, the facility being associated with a given tariff and demand response (DR) incentive set by a utility; generating one or more regression models for the pre-defined fraction of one or more buildings based on the estimated first set of DRPs, wherein parameters of the one or more regression models include area and the DR incentive associated thereof; estimating a second set of DRPs for a remaining fraction of the one or more buildings constituting the facility based on the generated one or more regression models using the area and the DR incentive associated with the remaining one or more buildings; and determining an optimal incentive for the facility based on the estimated first set and second set of DRPs using a heuristic method.
In another aspect, there is provided a system comprising: one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors to: estimate a first set of demand response potentials (DRPs) for a pre-defined fraction of one or more buildings constituting a facility based on (i) a baseline energy consumption (E^*) associated with cost optimal operation of the facility with no DR and (ii) energy consumption (E_DR^*) of the facility with DR, the facility being associated with a given tariff and demand response (DR) incentive set by a utility; generate one or more regression models for the pre-defined fraction of one or more buildings based on the estimated first set of DRPs, wherein parameters of the one or more regression models include area and the DR incentive associated thereof; estimate a second set of DRPs for a remaining fraction of the one or more buildings constituting the facility based on the generated one or more regression models using the area and the DR incentive associated with the remaining one or more buildings; and determine an optimal incentive for the facility based on the estimated first set and second set of DRPs using a heuristic method.
In yet another aspect, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: estimate a first set of demand response potentials (DRPs) for a pre-defined fraction of one or more buildings constituting a facility based on (i) a baseline energy consumption (E^*) associated with cost optimal operation of the facility with no DR and (ii) energy consumption (E_DR^*) of the facility with DR, the facility being associated with a given tariff and demand response (DR) incentive set by a utility; generate one or more regression models for the pre-defined fraction of one or more buildings based on the estimated first set of DRPs, wherein parameters of the one or more regression models include area and the DR incentive associated thereof; estimate a second set of DRPs for a remaining fraction of the one or more buildings constituting the facility based on the generated one or more regression models using the area and the DR incentive associated with the remaining one or more buildings; and determine an optimal incentive for the facility based on the estimated first set and second set of DRPs using a heuristic method.
In an embodiment of the present disclosure, the pre-defined fraction of the one or more buildings is 1%.
In an embodiment of the present disclosure, the baseline energy consumption (E^*) and the energy consumption (E_DR^*) of the facility with DR are based on a cost criteria and a plurality of constraints.
In an embodiment of the present disclosure, the cost criteria and the plurality of constraints are based on (i) a dynamic thermal model of the facility to predict temperature evolution given ambient conditions, internal heat loads and building envelope parameters; (ii) a Heating Ventilation and Air Conditioning (HVAC) system power model for Air Handling Unit (AHU) fan and chiller unit given the fan mass flow rate and total cooling load respectively; and (iii) systemic inertia in the HVAC system.
In an embodiment of the present disclosure, the cost criteria for the baseline energy consumption (E^*) is associated with (i) total HVAC energy consumption due to AHU fan and chillers and is a function of AHU fan speed for a given time period and (ii) lighting power consumption; and wherein the cost criteria for the energy consumption (E_DR^*) of the facility with DR is associated with reduction in energy consumption from the baseline energy consumption based on the DR incentive from the utility and the total HVAC energy consumption and the lighting power consumption associated thereof.
In an embodiment of the present disclosure, the plurality of constraints are associated with maximum AHU flow rate, thermal comfort requirement, maximum available chiller capacity, temperature evolutions and luminous flux.
In an embodiment of the present disclosure, the temperature evolutions for given ambient conditions, internal heat loads and building envelope parameters are predicted by the dynamic thermal model.
In an embodiment of the present disclosure, the step of determining the optimal incentive for the facility comprises: identifying clusters of buildings within the pre-defined fraction of the one or more buildings, the clusters being characterized by similar DRP curves based on the estimated first set and second set of DRPs, and wherein the similar DRP curves are further characterized by a dynamically generated threshold for Euclidean distance therebetween; aggregating the DRP curves of individual buildings within each of the clusters to obtain an overall DRP for each of the identified clusters; and determining the optimal incentive based on the overall DRP for each of the clusters by: obtaining a demand (kWh) reduction target and the DR incentive set by the utility; and iteratively performing: identifying a cluster from the clusters of buildings associated with a maximum overall DRP; and allocating a portion of the DR incentive to the identified cluster, the portion of the DR incentive being one of empirically defined equal portions; until the demand reduction target is reached.
In an embodiment of the present disclosure, the step of identifying clusters of buildings is based on k-means clustering method.
In an embodiment of the present disclosure, the DR incentive is allocated uniformly to the buildings within each of the clusters and is allocated non-uniformly across the clusters.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the embodiments of the present disclosure, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
FIG.1 illustrates an exemplary block diagram of a system for scalable estimation of demand response, in accordance with an embodiment of the present disclosure.
FIG.2 is an exemplary flow diagram illustrating a computer implemented method for scalable estimation of demand response, in accordance with an embodiment of the present disclosure.
FIG.3 illustrates Demand Response Potential (DRP) of a building, in accordance with an embodiment of the present disclosure.
FIG.4 illustrates an exemplary representation of Heating Ventilation and Air Conditioning (HVAC) operation time line as known in the art.
FIG.5 illustrates an exemplary break-up of building types in a Demand Response (DR) population.
FIG.6 illustrates an exemplary Time-of-Use (TOU) tariff scheme as a baseline tariff used by the utility.
FIG.7A illustrates DRP of various building types under the TOU tariff scheme of FIG.6 in accordance with an embodiment of the present disclosure.
FIG.7B illustrates reduction in energy bill computed in accordance with an embodiment of the present disclosure.
FIG.8 illustrates error in estimating the DRP using regression models in accordance with an embodiment of the present disclosure.
FIG.9A illustrates DRP of a set of buildings when uniform DR incentive is paid to all the buildings in accordance with an embodiment of the present disclosure.
FIG.9B illustrates DRP of a set of buildings when non-uniform DR incentive is paid to all the buildings in accordance with an embodiment of the present disclosure.
FIG.10 illustrates DRP of buildings belonging to two clusters in accordance with an embodiment of the present disclosure.
FIG.11 illustrates Cumulative Distribution Function (CDF) of non-uniform DR incentives across buildings in accordance with an embodiment of the present disclosure.
FIG.12 illustrates performance of non-uniform DR incentives in a building population of two building types in accordance with an embodiment of the present disclosure.
FIG.13 illustrates breakup of DR across building types for various target DR reductions set by the utility in accordance with an embodiment of the present disclosure.
It should be appreciated by those skilled in the art that any block diagram herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

DETAILED DESCRIPTION
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
Voluntary curtailing of electricity demand by customers in response to a signal from the electric utility can help manage temporary supply-demand mismatches in an electricity grid. Electricity utilities offer monetary incentives to customers to reduce load during temporary demand-supply mismatch. This is referred to as demand response (DR). DR can help avoid the purchase of costly electricity or the use of costly or polluting generation sources thus improving the economic and environmental efficiency of the electric grid. Existing methods for deciding monetary DR incentive to commercial buildings for demand response events do not account for building constraints (e.g. indoor temperature that building needs to maintain for occupant comfort). There is also lack of literature on how an electricity utility can estimate demand response potential (DRP) of a building or an ensemble of buildings. DRP is the energy consumption reduced by a building during a demand response event with respect to the baseline consumption. It is a challenge for electricity utilities to plan DR incentives for a building or an ensemble of buildings; deciding on whether to allot equal or varying DR incentives for each building, building type or class. The Applicant has addressed these concerns in Indian patent application no. 201721020446, filed on June 12th, 2017. However, when building population is large, it is practically very difficult to collect fine grained data from each building such as construction material, schedule, and the like to estimate DRP for each building. Again, allotting non-uniform DR incentives to different buildings is more cost effective for the utility. However, for a large population of buildings, calculation of non-uniform DR incentives is computationally intractable. Systems and methods of the present disclosure provide a regression based model to estimate DRP of buildings which do not have fine grained data thereby providing a scalable solution. The Applicants’ Indian patent application no. 201721020446 focused on Heating, ventilation, and air conditioning (HVAC) alone, however, the present disclosure considers lighting operations along with HVAC for the demand response for a more accurate estimation of DRP. The present disclosure also provides a heuristic method for non-uniform DR incentive allocation that is computationally inexpensive.
While the utility loses revenue due to offering a DR incentive in addition to reducing the demand, it is still better than the cost of serving the (no-DR) higher peak demand. Thus, the utility can benefit from DR and from the utility's perspective, the DR incentive should be low enough to just meet the targeted demand reduction. On the other hand, a building can benefit from DR if the DR incentive is high enough to make it worthwhile to participate in DR. While buildings and the utility both have a case for DR, their objectives conflict. Because the DR incentive affects the bottom-line of the utility, identifying the optimum incentive which may ensure a target DR reduction for the utility is an important problem.
The expression “Demand Response Potential (DRP)” in the context of the given disclosure refers to energy consumption reduced by a facility during a demand response (DR) event.
The expression “facility” in the context of the given disclosure refers to one or more buildings and may be used interchangeably with the expression “population”.
The expression “incentive” or “DR incentive” in the context of the given disclosure refers to cost to be paid by a utility per unit of reduced energy consumption.
The DRP of a building is a function of a set of the physical and user-required constraints (ability) and the DR incentive offered (willingness). Once the DRPs of various buildings served by a utility are known as functions of the DR incentive offered, for a given utility-level target reduction, the utility can obtain the optimum incentive to offer. In accordance with the present disclosure, the utility can offer varying or non-uniform DR incentives to different building types to achieve the same target reduction with lesser total outflow due to DR incentives.
While there are many knobs to perform DR such as lighting, batteries, renewables, etc., the present disclosure focuses on HVAC system and lighting operations for the following reasons. Firstly, unlike newer technologies like batteries and integrated renewables that may or may not be available in a building, HVAC is a mandatory part of a modern building. Using HVAC and lighting for DR is an operational decision rather than a capital expenditure decision. So this knob would be first considered by almost all facility managers. Secondly, the HVAC systems account for 35% of energy consumed in a typical building while lighting accounts for around 20%. The building constraints that are considered in the present disclosure for estimating the DRP include those that pertain to the HVAC systems, namely thermal comfort requirements of the building occupants and lighting comfort constraints. The lighting constraints assume that during DR the lighting level can be reduced to the lowest comfortable value. While lighting can be controlled instantaneously, thermal comfort depends on the building’s thermal inertia and the HVAC’s system inertia. Due to this inertia, HVAC load can be shifted from DR periods to non-DR period by pre-cooling. While the energy consumed by pre-cooling may be higher than usual, the economic cost can be reduced due to the DR incentive. In accordance with the present disclosure, a DRP model is provided that factors the energy-cost trade-off in addition to accounting for time variations in ambient and internal heat loads. For a given DR incentive, the DRP model of the present disclosure outputs energy reduction during the DR period under rational operation. Further, the DRP model of the present disclosure is rich in terms of parameters to capture different types of buildings (e.g., schools, offices, warehouses, etc.).
In accordance with the present disclosure, lighting and HVAC loads are considered as control knobs available in a building to respond to a DR event. The DRP model of the present disclosure also takes into account the constraints that a building has on these loads. Specifically, for lighting, an intensity constraint is considered that allows reduction by 20% from normal operation. For thermal comfort, the standard American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort constraint is considered. Lighting may be reduced to a minimum comfortable value exactly during the DR period. For the HVAC system, in addition to scaling down during the DR, it can pre-cool during the non-DR period to save DR energy and hence cost. Thus, the DRP depends on building’s ability to shift some of the thermal load away from the DR time window subject to maintaining acceptable thermal comfort. The thermal comfort is a strong function of the indoor air temperature prevalent in the building. The indoor temperature in turn determines the cooling load, and hence the DRP through HVAC system’s energy consumption. Thus, to quantify DRP, the DRP model of the present disclosure considers both the building’s indoor air temperature evolution and HVAC system performance over time. To this end, the present disclosure uses: (1) a dynamic thermal model of the building to predict temperature evolution given ambient conditions, internal heat loads and building envelope parameters; and (2) HVAC system power models.
Thermal model of the building: As explained in the Applicants’ Indian patent application no. 201721020446, air temperature is considered to be an indicator for thermal comfort and a thermal model is used to predict indoor air temperature evolution. Thermal model for a facility having a single thermal zone and is served by one Air Handling Unit (AHU) and one chiller may be represented as follows –
Building indoor air temperature dynamics is largely driven by: (1) the ambient heat entering the space through the building envelop (side walls and roof); and (2) internal heat loads resulting from occupants and other sources such as computing and lighting. The present disclosure uses lumped capacitance models for tracking the evolution of the indoor air temperature and building envelope interior surface temperatures. Table 1 herein below defines notations used in the present disclosure.
Table 1: Notations
Symbol Meaning Symbol Meaning
Heat load model m ? Flow-rate of AHU (kg/s) qg Internal heat gain (W)
t Time (s) Ai Area of wall i(m2)
C Air thermal capacitance (J/K) Cp Specific heat capacity of air (J/kg/K)
T Indoor air temperature (ºC) C_(w_i ) Thermal capacitance of wall i (J/K)
Tsup Supply air temperature (ºC) Ta Ambient temperature (ºC)
Ui Overall heat transfer coefficient of wall i(W/m2-K) T_(w_i ) Temperature of wall i (ºC)
HVAC operation tstart HVAC operation start time(s) tend HVAC operation end time(s)
t_start^DR DR start time(s) t_end^DR DR end time(s)
t_start^c Occupancy start time(s)
m ?_min Minimum AHU flow rate m ?_max Rated AHU flow rate
Tmin Minimum acceptable comfort temperature Tmax Maximum acceptable comfort temperature
QL Cooling load (W) Qavail Available chiller capacity (W)
Energy and cost CT Total cost of HVAC operation ($) EHVAC Total HVAC energy consumption (kWh)
p(t) Time-of-day electricity tariff ($) v DR incentive ($)
EHVAC HVAC power consumption (kW) Elighting Lighting power consumption (kW)
E^* Energy consumed in DR period in baseline operation E_DR^* Energy consumed in DR period in DR program

Using the notations defined in Table 1, the thermal model may be expressed as follows:
?dT?^t/dt=1/C [(m ) ?C_P (T_sup-T^t )+ q_g^t+ ?_(i=1)^5¦?U_i A_i (T_(w_i)^t-T^t)?]
(1)
wherein (m ) ?C_P (T_sup-T^t ) represents cool air supply, q_g^t represent internal load and U_i A_i (T_(w_i)^t-T^t) represents surface convective load.

(?dT?_(w_i)^t)/dt=1/C_(w_i ) [U_i A_i (T^t-T_(w_i)^t )+ U_i A_i (T_a^t-T_(w_i)^t ]
(2)
wherein U_i A_i (T^t-T_(w_i)^t )+ U_i A_i (T_a^t-T_(w_i)^t) represents conductive and convective loads.
Heating Ventilation and Air Conditioning (HVAC) model: As explained in the Applicants’ Indian patent application no. 201721020446, a standard physics based model for AHU fan power is used: Pfan = km ?3, wherein k is a constant that depends on the rated fan static pressure rise, rated mass flow rate and overall efficiency of the fan motor. Again power consumed by a chiller is normally a function of the cooling load, leaving chilled water temperature, and condenser entering air/water temperature (for air/water cooled chillers). The cooling load, in turn, is a function of the mass flow rate of air across the AHU cooling coils and the supply air temperature. Given these parameters, the power consumed by the chiller can be obtained from manufacturer's system power models. Alternatively, in the absence of such information, the typical approaches to obtain power consumed by a chiller are based on thermodynamics models and regression based models. In accordance with the present disclosure, regression based chiller models are used for the various building types.
Data for model generation: As explained in the Applicants’ Indian patent application no. 201721020446, the utility may develop building and HVAC models for various buildings in its service area to estimate the buildings' DRP. When buildings enroll for DR programs with a utility, they provide the utility with details about their load structure. Further, energy professionals may visit the buildings to understand them better before enrolling them in DR programs. As a part of such surveys and site-visits, information pertaining to building envelope details, HVAC design parameter and performance curves, and historical logs of the parameter of interest may be collected to develop the models. The utility may also use standards such as ISO 52016 for reducing complexity of the building model for simulating approximate DR incentive strategy. It may be noted that collecting data pertaining to a building's models is a one-time activity and does not need to be repeated frequently.
Model generalization: As explained in the Applicants’ Indian patent application no. 201721020446, assumptions made in developing the thermal comfort and HVAC models can be relaxed in real-world scenarios. For instance, in a given geography, the complexity of a building with multiple thermal zones can be reduced to a single thermal zone. Again, for the thermal comfort model of a building with n AHUs, the n individual AHUs can be aggregated to be a single large AHU. The large AHU can then be considered to serve the single large zone (which is the entire space inside the building shell). Under one AHU, one zone configuration, the appropriate flow rate m ?(t) values that maintain the thermal comfort within the large zone is computed. After obtaining the flow rate, the flow is equally apportioned among the n AHUs. In the HVAC power models, a flow rate of m ?(t) /n is used for each of the AHUs and their individual power curves to obtain the overall AHU power. Reducing multiple chillers of similar type to a single chiller is relatively straight forward since the equation involves only the total cooling load, irrespective of the number of AHUs. A single chiller may be assumed by clubbing together the rated capacities of the individual chillers with a single similar performance curve (or single averaged performance curve).
Referring now to the drawings, and more particularly to FIGS. 1 through 13, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and method.
FIG.1 illustrates an exemplary block diagram of a system 100 for scalable estimation of demand response, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system 100 can be stored in the memory 102.
In an embodiment, the system 100 includes one or more data storage devices or memory 102 operatively coupled to the one or more processors 104 and is configured to store instructions configured for execution of steps of the method 200 by the one or more processors 104.
FIG.2 is an exemplary flow diagram illustrating a computer implemented method for scalable estimation of demand response, in accordance with an embodiment of the present disclosure. The steps of the method 200 will now be explained in detail with reference to the components of the system 100 of FIG.1. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
Cost optimal operation of a building with no DR is considered as a fair baseline; and energy reduction with respect to this optimal baseline is obtained during the DR period. This energy reduction is referred to as the DRP of a building. FIG.3 illustrates Demand Response Potential (DRP) of a building, in accordance with an embodiment of the present disclosure. Accordingly, in an embodiment of the present disclosure, the one or more processors 104 are configured to estimate, at step 202, a first set of demand response potentials (DRP) for a pre-defined fraction of one or more buildings constituting a facility based on (i) a baseline energy consumption (E^*) associated with cost optimal operation of the facility with no DR and (ii) energy consumption (E_DR^*) of the facility with DR, wherein the facility is associated with a given tariff and demand response (DR) incentive set by a utility. In an embodiment, the pre-defined fraction of the one or more buildings is 1%.
A building's rational operational behavior aims to minimize the total cost of energy consumption under a given tariff
and DR incentive, while maintaining an acceptable thermal comfort. FIG.4 illustrates an exemplary representation of Heating Ventilation and Air Conditioning (HVAC) operation time line as known in the art. FIG. 4 shows the DR event time window in a given day between t_start^DR and t_end^DR. Let E_DR^* and E^* be the energy consumed by a building b in the time window [t_start^DR,t_end^DR] when it is operating rationally with and without DR respectively. Then the DRP of the building b is defined as:
DRP(b) = E^*- E_DR^*
(3)
When expressed as a percentage, the DRP is defined as:
DRP(b) = ((E^*-E_DR^*)/E^* ) x 100
(4)
The present disclosure facilitates developing an optimization model that allows a utility to estimate the values of E_DR^* and E^* for a given building under a specific tariff and DR incentive.
Let C_T be the total monetary cost of energy consumed over the time horizon [t_start,t_end ]. The optimization problem that results in a rational building operation may be stated as follows:
¦(min@m ?,E_Lighting )C_T = ?_(t_start)^(t_end)¦?p(t)(E_HVAC (m ? ?(t))+E_Lighting (t)) dt
(5)
subject to the following constraints:
1. The AHU flow rate is bounded between the rated capacity and some min flow rate such as minimum ventilation requirement to maintain air quality.
m ?_min= m ?(t) = m ?_max, ?t
(6)
2. Thermal comfort requirement: indoor air temperature is bounded.
T_min=T^t=T_max,t?[t_start^c,t_end ]
(7)
3. At any point in time, the cooling load cannot exceed the maximum available chiller capacity.
Q_L^t=Q_avail^t, ?t
(8)
The available capacity is generally a function of leaving water temperature, condenser fluid temperature and rated capacity.
4. Luminous flux,
L(E_Lighting (t))=l ¯,?t
? (9)
where l ¯ is comfortable level of lighting intensity for the building type.
5. Indoor air temperature and building interior surface temperature evolutions as per Equations (1) and (2)
(10)
In Equation (5), EHVAC is the total HVAC energy consumption due to both AHU fan and chillers. It is a function of the AHU fan speed m ?(t); p(t) refers to the cost of per unit of electricity at time t. A linear model suffices for mapping electrical power to luminous flux. Equations (1) and (2) form a system of equation solved using the standard Runge-Kutta technique. The optimization problem (Equations 5-10) is solved using a standard non-linear constrained optimization technique. The control time-step is fixed at say, 10 mins, while the prediction horizon [t_start,t_end ] corresponds to the building's operational hours. The operational hours and comfort bounds for a building may be collected during the DR enrollment survey that many utilities have.
Let m ?^* (t) and E_Lighting^* be the solution for the optimization problem specified in Equation 5. Now E^* may be obtained as
E^*= ?_(t_start^DR)^(t_end^DR)¦?(E_(HVAC (m ?^* (t))) ?+E_Lighting^* (t)) dt
(11)
wherein equation (5) serves as the cost criteria and equations (6) through (10) serve as the plurality of constraints.
Energy consumption with DR (E_DR^*): In the presence of a DR signal, the building may be offered a DR incentive of $v for every kWh it reduces from the baseline consumption in the DR period. While lighting can be controlled instantaneously, the extent to which the HVAC loads can be shifted outside the DR interval through pre-cooling depends on the time variations in ambient, internal heat loads, and the thermal comfort constraints. The amount of energy consumed during pre-cooling (outside the DR window) may be higher than the baseline value. This higher energy cost incurred outside the DR window is offset by the DR incentive offered for the reduction achieved during the DR period. The DRP model of the present disclosure models the inherent trade-off to identity the sweet spot. The optimization problem that governs the economically rational behavior with a DR incentive may be stated as:
¦(min@m ?,E_Lighting )C_T = ?_(t_start)^(t_end)¦?p(t)(E_HVAC (m ? ?(t))+E_Lighting (t))dt – v (E^*-?_(t_start^DR)^(t_end^DR)¦E_(HVAC (m ?(t)) ) +E_Lighting (t))dt)
(12)
along with the constraints stated in Equations 6-10 with constraint (9) modified during the DR period as
al ¯=?(E?_Lighting (t))=l ¯,t?[t_start,t_end]
In accordance with an embodiment, a=0.8 as 20% decrease in lighting intensity during DR events does not affect visual comfort. To avoid new peaks in the non-DR period due to changes in HVAC operations (e.g. due to pre-cooling just before the DR event), an additional constraint has been imposed that caps building power consumption to the maximum power consumed in the baseline operation.
Let m ?_DR^* (t) and E_Lighting^* be the solution for the optimization problem specified in Equation 12. Now, E_DR^* may be obtained as
E_DR^*= ?_(t_start^DR)^(t_end^DR)¦?(E_HVAC (m ?_DR^* (t))?+E_Lighting (t))dt
(13)
wherein equation (12) serves as the cost criteria and equations (6) through (10) serve as the plurality of constraints.
From these expressions for E^*and E_DR^*, the DRP of a building can be calculated using Equation (3) or (4) for a given DR incentive v and price tariff p(t). Thus, in accordance with the present disclosure, the baseline energy consumption (E^*) and the energy consumption (E_DR^*) of the facility with DR are based on a cost criteria and a plurality of constraints, wherein the cost criteria and the plurality of constraints are based on (i) a dynamic thermal model of the facility to predict temperature evolution given the ambient conditions, internal heat loads and building envelope parameters; (ii) a Heating Ventilation and Air Conditioning (HVAC) model for Air Handling Unit (AHU) fan and chiller unit given the fan mass flow rate and total cooling load respectively; and (iii) systemic inertia in HVAC equipment. Furthermore, the cost criteria for the baseline energy consumption (E^*) is associated with (i) total HVAC energy consumption due to AHU fan and chillers and is a function of AHU fan speed for a given time period and (ii) lighting power consumption; and wherein the cost criteria for the energy consumption (E_DR^*) of the facility with DR is associated with reduction in energy consumption from the baseline energy consumption based on the DR incentive from the utility and the total HVAC energy consumption and the lighting power consumption associated thereof. Again, the plurality of constraints are associated with maximum AHU flow rate, thermal comfort requirement, maximum available chiller capacity, temperature evolutions and luminous flux, wherein the temperature evolutions for given ambient conditions, internal heat loads and building envelope parameters are predicted by a dynamic thermal model.
In accordance with the present disclosure, the DRP model is developed for each building assuming the EHVAC and ELighting models are available. It may be noted that developing the models involves collecting data about various building parameters. Although this is a one-time activity for each building and may be performed during DR enrollment, it would be beneficial for utilities to reduce the collection effort. In accordance with the present disclosure, a scalable model is provided, wherein based on data collected for a pre-defined fraction of the population, the DRP models may be estimated for the entire population. Specifically, for each building type, the utility may collect extensive data for a few select buildings of varying areas. The collected data for the select buildings constituting the pre-defined fraction serves as a training set for each building type. For these select buildings, the utility may develop a building system model and estimate the first set of DRPs in accordance with step 202 of the present disclosure and equations (3) or (4) for the given tariff and DR incentive.
In accordance with an embodiment of the present disclosure, the one or more processors 104 are configured to generate, at step 204, one or more regression models for the pre-defined fraction of the one or more buildings using the estimated first set of DRPs (i.e., DR reduction as a function of DR incentive) generated at step 202. For each building type, the parameters of the regression model are the area, and the DR incentive offered. In an embodiment, for certain building types, multiple regression models may be generated depending on the accuracy of the regression model across varying areas of the select buildings. In accordance with an embodiment of the present disclosure, the one or more processors 104 are configured to estimate, at step 206, a second set of DRPs for a remaining fraction of the one or more buildings constituting the facility based on the generation one or more regression models using the area and the DR incentive associated with the remaining one or more buildings. Generating such a regression model for the DRP addresses the problems of scale, lack of building data and effort involved in collecting exhaustive building data which otherwise is needed.
As explained in the Applicants’ Indian patent application no. 201721020446, given a target demand reduction in a DR interval, a utility needs to identify the optimal incentive to achieve reduction across a facility served by it. In accordance with an embodiment of the present disclosure, the one or more processors 104 are configured to, at step 208, determine an optimal incentive for the facility based on the estimated first set and second set of DRPs using a heuristic method. As explained earlier, the parameters required for calibrating the DRP model of each building can be obtained at the time of enrolling these buildings in the DR program. The Applicants’ Indian patent application no. 201721020446 provided methods for determining optimum incentive under two scenarios: (a) the same DR incentive is offered across the buildings constituting the facility and (b) varying DR incentives are offered across the buildings constituting the facility, wherein the buildings constituting the facility may be of different types as well.
Uniform DR incentive: For the scenario wherein uniform DR incentive is to be assigned to the buildings constituting the facility, the one or more processors 104 are configured to compute aggregate reduction for the facility based on the estimated DRP of the buildings; and select lowest DR incentive from the utility that achieves a target aggregate reduction for the utility.
Non-uniform DR incentive: For the scenario wherein non-uniform DR incentive is to be assigned to the buildings constituting the facility, the one or more processors 104 are configured to compute aggregated DR incentive from the utility as a weighted summation of the estimated DRP of the buildings; and adjust the DR incentives such that the aggregated DR incentive is minimized and the estimated DRP is at least the target aggregate reduction for the utility. Non-uniform DR incentive allocation is a global optimization problem that is solved using pattern search.
With an increase in building population size, solving the global optimization problem as provided in the Applicants’ Indian patent application no. 201721020446 may become computationally intractable. Intuitively, it is possible that different buildings in the population may have similar DRP curves and hence react similarly to the DR incentive offered by the utility. Such similar buildings are identified and grouped or clustered so that the entire population of |B| buildings gets partitioned into C clusters with C « |B|. In an embodiment, the step of identifying clusters of buildings is based on k-means clustering method. Similar DRP curves may be characterized by a dynamically generated threshold for Euclidean distance therebetween. Accordingly, the Euclidean distance between the DRP curves of a cluster are comparable. Once the clusters are obtained, the DRP curves of individual buildings within each of the clusters may be aggregated to obtain an overall DRP for each of the identified clusters. Aggregating the DRP curves of individual buildings in a cluster rather than considering the cluster mean as a representative DRP curve for cluster members removes any error in estimating a quantum of DR reduction possible from that cluster due to less-than-perfect clustering. However, it is possible that the incentive allocation within a cluster may become sub-optimal. Nevertheless, the clustering based approach achieves computational tractability. The number of clusters C, specified to the clustering method, is a parameter that balances computational tractability with sub-optimality in incentive design. The overall DRP of each of the clusters may be considered as the DRP curve of a hypothetical mega-building. Once the incentive to be offered for each mega-building is identified, the individual buildings that constitute a mega-building are offered the same incentive.
A method GET-INCENTIVE-NON-UNIFORM (RT, B, V) may then be executed on each of the clusters which are a relatively smaller set of buildings as outlined herein below.
GET-INCENTIVE-NON-UNIFORM (RT, B, V)
RT ? target reduction
V ? set of incentive values; B ? set of buildings
for each incentive v ? V
do for each building b ? B
Use Regression model to estimate DRP
do DRPb(v) ? Reduction in b for incentive v
? Global Optimization
Find varying incentives vb for each building b ? B
to ¦(min@v)(?_(b ? B)¦?v_b DRP_b (v_b)?)
such that (?_(b ? B)¦?v_b DRP_b (v_b)?)= TR
Initially, DRP reduction of each building constituting the facility is computed for various possible incentives in lines 1 through 3 and the optimization problem is solved. The objective function shown in line 4 is the revenue loss due to the incentives for all the buildings taken together. The decision variables(s) to be searched are the individual incentives v for all buildings. The constraint in line 5 requires that the total reduction in the DR energy consumption due to the choices of the incentives meets the target DR requirement.
For determining the optimal incentive based on the overall DRP for each of the clusters, firstly a demand (kWh) reduction target and the incentive set by the utility is obtained. Then a cluster associated with a maximum overall DRP from the clusters of buildings is identified and a portion of the incentive is allocated to the identified cluster, the portion of the incentive being one of empirically defined equal portions. The step of allocating a portion of the incentive is continued iteratively until the demand reduction target is reached. Thus, non-uniform allocation of incentive is achieved such that the incentive allocated is uniform within each of the clusters but may be non-uniform across the clusters. Non-uniform incentive allocation adds flexibility in deciding the incentives across buildings and hence may perform equal to or better than the uniform incentive scenario. It may also be noted from FIG.7A (described herein under) that for a given incentive of $v/kWh, different building types may reduce energy by different amounts. This also indicates that a non-uniform incentive scheme may be helpful for the utility to achieve a given target reduction across the population.
EVALUATION:
An ensemble of buildings available in the PLUTO dataset from Department of City Planning, New York City was used for evaluating the systems and methods of the present disclosure. The dataset has extensive information about the plots of land including geographical location, building type (e.g. school, hotel etc.), plot area, total building floor area, building age, and the like. The dataset has information about 860,000 plots present in New York City. Out of these plots, a subset of nearly 212,000 plots were selected based on data quality. Each of these plots (i.e., the buildings contained within) are considered as an individual customer who has enrolled in the DR program of a utility and constituting a DR population. FIG.5 illustrates an exemplary break-up of building types in the DR population under consideration. A time-of-use (TOU) tariff scheme from Pacific Gas and Electric as illustrated in FIG. 6 was used as the baseline tariff used by the utility. It may be noted that the baseline tariff by itself incentivizes people to move their demand away from the peak period. Any DR incentive is above and beyond this baseline TOU tariff. Accordingly, this baseline tariff plan is very conservative in terms of estimating the DRP.
While the PLUTO data set gives information about building sizes, types, and their relative mix, it does not contain any information about the HVAC and lighting systems of the buildings that are needed to estimate the first set of DRPs. For the purpose of evaluation, the lack of HVAC and lighting data was worked around by leveraging building energy reference models developed by the National Renewable Energy Laboratory (NREL). As per NREL database, sixteen building types represent approximately 70% of all commercial buildings in the United States. NREL has also developed energy models for the lighting and thermal loads that are expected for each of these building types across different sizes. DRP models estimated using the PLUTO data set and the NREL database serve as the ground truth for determining possible energy reduction during a DR event for a given incentive (DRP).
FIG.7A illustrates DRP of various building types under the TOU tariff scheme of FIG.6 while FIG.7B illustrates reduction in energy bill computed in accordance with an embodiment of the present disclosure. The X-axis represents the incentive offered by the utility in $/kWh. The Y-axis of FIG.7A represents the DRP expressed as a percentage, i.e., reduction during DR window normalized by the baseline energy during DR window and the Y-axis of FIG.7B represents relative reduction in bill in presence of a DR signal with respect to the bill in the baseline operation. These metric are average across buildings of that type. The DRP varies with building type. Some buildings like restaurants have very low DRP. High internal heat gain due to kitchens makes shifting the thermal load to other periods difficult. On the other hand, buildings like warehouse have relatively low internal heat gain (low process/plug load) leading to high DRP.
The DRP of all building types increases with the incentive initially. Beyond a value of the incentive, the DRP saturates. The marginal increase in energy reduction achieved due to a marginal increase in incentive is diminishing. This suggests that there exists a sweet spot for the utility in terms of the incentive to be offered to buildings to reduce the consumption during a DR period.
1% of the buildings from each building type in the DR population was then selected and the estimated DRPs of the selected buildings was used as training data for developing regression models which were then used to estimate the DRPs of a testing set comprising another subset of buildings in the DR population. The error introduced by the regression based DRP estimation is quantified by comparing against the ground truth DRP.
FIG.8 illustrates error (RMS value of relative error) in estimating the DRP (quantum of expected energy reduction during a DR event) using regression models in accordance with an embodiment of the present disclosure. It may be noted that the regression error is highest for warehouses and offices. This is possibly because of the higher diversity in areas of the underlying buildings. Nevertheless, it was noted that the relative errors are reasonable small to enable use of the regressions model to estimate the DRPs of buildings across a population.
FIG.9A illustrates DRP of a set of buildings when uniform incentive is paid to all the buildings in accordance with an embodiment of the present disclosure. The X-axis represents the incentive paid; and the primary (secondary) Y-axis represents the reduction (relative reduction) achieved. The maximum reduction of DR energy possible is about 3.85 MWh. For the price profile TOU, and a target reduction of 2.75 MWh (20% of the baseline demand) with respect to the baseline, utility needs to pay a uniform incentive of v = $0.18/kWh to all building types.
FIG.9B illustrates DRP of a set of buildings when non-uniform incentive is paid to all the buildings in accordance with an embodiment of the present disclosure. The X-axis represents the total incentive offered in dollars across all buildings because the incentive for each building varies. The Y-axis represents the total reduction achieved during the DR window. This DR energy reduction curve under non-uniform incentives has been generated by partitioning the buildings in the DR population into 86 clusters. FIG.10 illustrates DRP of buildings belonging to two clusters in accordance with an embodiment of the present disclosure.
FIG.9B also illustrates the DR energy reduction curve for the uniform incentive as a baseline for comparison. It may be noted that for high reduction targets, the non-uniform incentive scheme achieves an improvement (e.g. ~ $82,000 for reducing 3.85 MWh) over the uniform incentive scheme. In other words, for this target reduction, the utility saves nearly 19% of its revenue outflow. This is significant from the utility’s perspective. For this target reduction, FIG.11 illustrates Cumulative Distribution Function (CDF) of non-uniform DR incentives across buildings in accordance with an embodiment of the present disclosure. The DR incentives across the buildings for varying targeted reductions are quite different. However, the improvement for smaller targeted reductions is not very significant with the use of non-uniform DR incentives.
Apart from utilities, the method of determining optimal incentive in accordance with the present disclosure may also be used by other stakeholders in retail electricity markets such as demand aggregators and energy brokers. These stakeholders typically have fewer customers than a utility. Such aggregators offering target reductions as a service could benefit from non-uniform DR incentives more significantly than a utility. For example, FIG.12 illustrates performance of non-uniform DR incentives in a building population of two building types alone (schools and houses) in accordance with an embodiment of the present disclosure. In this case, a decrease of about 38% in the revenue outflow of an aggregator using non-uniform DR incentives is noted in comparison to uniform DR incentives.
The DRP of various building types may not be the best indicator of which buildings should be targeted during DR events. FIG.13 illustrates breakup of DR across building types for various target DR reductions set by the utility (under uniform DR incentives) in accordance with an embodiment of the present disclosure. For a given target reduction, in the population set of PLUTO, it is noted that houses contribute the maximum to the quantum of demand reduction achieved. Schools and mid-rise apartments are the second and third biggest contributors respectively. However, the DRP curves of different building types shown in FIG.7A illustrate that warehouses give the maximum reduction for a given DR incentive, followed by mid-rise apartments and offices. This shows that though a building type’s energy reduction during a DR interval might be high, the number of buildings of that type could be less. Therefore, such building types may not be of much help to an utility.
From FIG.13, it may also be noted that the contribution of various building types remains almost the same with varying target reductions. In other words, the relative usefulness of the building types remains the same for various target reductions. So, the utility can consistently target these building types (houses, schools and apartments) in a focused way during its DR campaigns.
In accordance with the present disclosure, systems and methods of the present disclosure facilitate estimating a DRP model of a building that accounts for its constraints and rational response to a DR incentive. A regression model is provided to make the DRP model scalable. From a building’s perspective, when using HVAC and lighting for DRP, it is noted that the buildings benefit, and therefore its DRP depends on offered DR incentive; internal heat gains; and baseline usage patterns. From a utility’s perspective, the mix of buildings in its customer base matters more than the DRP of individual buildings. Specifically, even buildings with low DRP can help the utility due to the number of buildings of such type. Also, in accordance with the present disclosure, non-uniform DR incentives offer a scope for better economics.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.