Abstract

Each year, more than 20% of electricity generated in the United States is consumed for meeting the thermal demands (e.g., space cooling, space heating, and water heating) in residential and commercial buildings. Integrating thermal energy storage (TES) with building’s HVAC systems has the potential to reshape the electric load profile of the building and mitigate the mismatch between the renewable generation and the demand of buildings. A novel ground source heat pump (GSHP) system integrated with underground thermal energy storage (UTES) has been proposed to level the electric demand of buildings while still satisfying their thermal demands. This study assessed the potential impacts of the proposed system with a bottom-up approach. The impacts on the electricity demand in various electricity markets were quantified. The results show that, within the capacity of the existing electric grids, the maximum penetration rate of the proposed system in different wholesale markets could range from 51% to 100%. Overall, about 46 million single-family detached houses can be retrofitted into the proposed system without increasing the annual peak demand of the corresponding markets. By implementing the proposed system at its maximum penetration rate, the grid-level summer peak demand can be reduced by 9.1% to 18.2%. Meanwhile, at the grid level, the annual electricity consumption would change by −12% to 2%. The nationwide total electricity consumption would be reduced by 9%.

1 Introduction

The trend toward electrification in many aspects of our society is resulting in a continuous increase in the electricity demand. The limited capacity of existing electric grids and the intermittent outputs from rapidly growing renewable generation (e.g., solar, wind) create challenges to balance supply and demand on the grid. When renewable sources generate more energy than that can be consumed, grid operators must curtail renewable generation, which reduces its economic and environmental benefits [1]. On the other hand, electric generators need to quickly ramp up their output when the contribution from renewable generation falls, leading to low efficiency and increased generation cost [2].

Energy storage could help to address the abovementioned challenges by enlarging the flexibility from either supply or demand side. Typical energy storage techniques from the power supply side (e.g., battery, pumped hydro, compressed air) have limited applications due to economic and/or technical barriers, such as the expensive cost of various batteries [3], the special geographical conditions required for constructing pumped hydro [4], and low efficiency of compressed air [5]. On the other hand, demand response is proven to be an efficient approach to reshaping load profile [6], and the energy storage from the demand side is more versatile to be deployed and controlled. About 74% of electricity consumption in the US is for buildings. Furthermore, 34% and 27% of the electricity consumed in residential and commercial buildings, respectively, is for heating, ventilation, and air-conditioning (HVAC) [7]. Thermal energy storage (TES) in residential and commercial buildings becomes a promising solution. By integrating distributed TES in many buildings with electricity grids, the electricity demand during peak hours can be shifted to off-peak hours, and the mismatch between the intermittent renewable generation and the fluctuating demand of buildings can be mitigated.

1.1 Review of Relative Studies.

Many studies have been conducted in recent years to investigate the potential of utilizing TES from building sectors to provide grid services. Buildings’ thermal inertia can be utilized as the passive TES, and it can be activated by alternating HVAC system operation. Wolisz et al. [8] developed a model predictive control (MPC) algorithm to control dynamic heating operation in a concrete structured residential room in West Germany. They concluded that the primary energy demand can be reduced by 3–7% while maintaining thermal comfort. Sperber et al. [9] developed gray-box reduced-order thermal response models for representative German residential buildings to estimate the demand response potential of heat pumps by storing heat in building thermal inertia for heating operation.

Besides passive TES, active TES (e.g., storage tanks) are widely proposed in HVAC systems for providing grid services. Previous studies show that both building thermal mass and water tanks can be used for thermal storage and providing grid services [10,11]. Water tanks are the most commonly used TES tanks for grid services [1219]. D’Ettorre et al. [17] numerically studied the impact of integrating a stratified water tank with an air source heat pump (ASHP; 4 kW heating capacity and supplemented with a gas boiler) for space heating in Italy. With a proposed MPC for cost minimization, the results showed that a water tank with 500 l volume would lead to an energy cost saving up to 8%, and a reduction of primary energy consumption by up to 13%, which is much higher than that in Ref. [8] who used passive TES. Whether installing TES tanks is economical for end-users depends on the local tariff and incentives. For example, Bechtel et al. [18] studied a similar system with D’Ettorre et al. [17] in Luxembourg. Their results showed that a water tank with a volume of 800 l was appropriate to conduct demand-side management (DSM), however, the life-cycle cost analysis indicated that a tank with a volume greater than 200 l was not economically competitive without any incentives. Overall, the energy density from the sensible heat of water is limited and it requires a large volume for the TES tank [20].

To enlarge the storage density and reduce the size, many studies applied phase change materials (PCMs) to the TES tank for DSM [2127]. Hirmiz et al. [26] integrated a hybrid PCM/water tank into a ground source heat pump (GSHP) to provide space heating in a detached single-family house (SFH) in Canada. A rule-based control was applied to shift electric load so that it can take advantages of the time-of-use tariff. They found that by filling 75% volume of the tank with PCM, the tank size was reduced by three folds while retaining the same thermal storage capacity. For cooling use, Tang and Wang [27] developed an MPC method to optimize the operation of active cold storage during fast demand response events. A virtual test platform was used to conduct a case study in a high-rise building in Hong Kong, and PCM cold storage with a 2-hour building cooling capacity (5300 kWh cooling) was equipped in the central air-conditioning system. The simulation results showed that the proposed MPC with the PCM storage can effectively optimize the HVAC system to achieve the required power reduction during peak hours while maintaining acceptable thermal comfort in the building.

1.2 Introduction of the Proposed System.

The review of previous studies indicates that TES tanks are applicable to be integrated into building thermal systems for grid services. Besides, adding PCM to TES tanks can enhance the energy storage density and thus reduce the required size of TES tanks. Most previous studies focused on air source heat pumps, but their coefficient of performance (COP) is low and a backup source is always needed for cold climate. A GSHP typically has better seasonal thermal performance, however, the initial cost is much higher than the air source heat pump. To solve these problems, a novel underground thermal energy storage (UTES) integrated GSHP system is proposed by the Oak Ridge National Laboratory [2], the schematics of the system are shown in Fig. 1. With a UTES integrated GSHP system, the overproduced renewable power (or electricity with low price) can be used to produce useful thermal energy to be stored in the UTES as shown in Fig. 1(a). Later, the stored thermal energy is directly utilized to meet buildings’ thermal demand during the grid’s peak hours as shown in Fig. 1(b). In this way, the system enables flexible electricity demand of buildings while meeting their thermal demands. A previous study indicates that the proposed system could significantly reduce the summer peak demand of a typical residential building in Baltimore, Maryland in the US [2].

Fig. 1
Fig. 1
Close modal

The UTES in this system is different from the conventional seasonal UTES in two aspects: first, the current UTES is used for intra-day load shifting application, thus it has lower storage capacity and higher charge/discharge frequency; second, the current UTES is a dual-function product, it also works as the ground heat exchanger for the GSHP. Figure 2 shows the concept of the novel UTES design. The UTES consists of two main parts: the inner tank works as a TES tank (stratified water tank), encapsulated PCM is applied to enlarge its energy storage density; the outer tank works as a shallow bore ground heat exchanger, a helical heat exchanger is installed in the outer tank that connects the UTES to the source side of the GSHP system. Since the UTES is installed underground, it does not occupy any floor or roof space. It is installed in a borehole much shallower (<6 m) than that used for installing a conventional vertical borehole ground heat exchanger so the installation cost can be reduced. With this compact design, the proposed UTES integrated GSHP system can effectively shift electric load with a high overall thermal efficiency and potentially lower initial cost than the conventional GSHP system.

Fig. 2
Fig. 2
Close modal

The proposed UTES integrated GSHP system can be more valuable in residential buildings than commercial buildings because thermal loads of residential buildings are mostly driven by weather conditions. Residential buildings’ thermal loads vary widely during the day and it provides opportunities for shifting the electric demand from peak hours to off-peak hours using the TES tank. Besides, in most parts of the US, residential buildings’ annual heating and cooling loads are closer to each other than in commercial buildings, which are usually cooling-dominated due to large internal heat gains. Therefore, a higher percentage of energy can be saved using the GSHP, which can provide both heating and cooling to the buildings, in residential buildings than in commercial buildings. Furthermore, the size of the ground heat exchanger per unit capacity of the GSHP can also be lower in residential buildings than in commercial buildings in the same climate zone (CZ). According to US Energy Information Administration (EIA) 2015 Residential Energy Consumption Survey (RECS) [29], about 63% of the 118 million housing units in the US are detached SFHs. SFHs usually have front and back yards where the UTES can be installed. Given the large amount and available land for installing UTES, SFHs are a clear target market for the proposed UTES integrated GSHP systems.

1.3 Scope of This Study.

Before detailed system simulation, lab tests, and optimal control development, an estimation of the potential market for the proposed system, and its impact on the grid resulting from a large-scale implementation should be investigated. Other than modeling the detailed dynamics of individual systems, a simplified model or methodology that captures the aggregated effects is desirable for evaluating the impacts of the proposed technology on a regional and or national scale. Several studies developed methodologies to investigate the grid-level impact of aggregated TES. Li et al. [30] proposed a risk-averse method for heterogeneous energy storage deployment in a residential multi-energy micro-grid. The case study results indicated that the method can effectively increase the profit from the grid through DSM. Yu and Pavlak [31] developed an uncertainty-aware transactive control framework for an aggregator to coordinate the TES of multiple buildings. This framework allows up to 3.7% cost savings for individual end-users and a slight positive profit for the aggregator. Stavrakas and Flamos [32] introduced a dynamic high-resolution DSM model that considered key features for both individual buildings and grid-level requirements. It is a modular model that can be very flexible and computationally effective. Apart from aggregated optimization and grid-level dynamic simulation, studies focusing on market potential assessment of building flexibility technologies on a large-scale are scarce. Huang et al. [33] analyzed the power flexibility for HVAC systems in commercial buildings in the United States. Flexibility for different representative commercial buildings was determined by prototype building models and the estimated number of buildings was applied to calculate the aggregated flexibility.

Because of diverse climate conditions and different electricity markets across the United States, assessment of large-scale retrofitting by region is crucial for understanding the technical and market potential of the novel GSHP + TES system. This study is a first-of-this-kind and it proposed a bottom-up approach to estimate the possible penetration rate of the proposed system according to the existing electric grid capacity in the US. It quantified the potential impacts of the proposed system to the electric grids on the regional and national scales resulting from maximum market penetration.

The rest of this paper is organized as follows. The methodology to assess the potential market penetration for the proposed system and its impact on the grid is described in Sec. 2. Section 3 presents the simulation results for individual buildings and the maximum penetration rate. It also analyzes the impact of the proposed system resulting from the maximum penetration. Finally, Sec. 4 summarizes the study and provides the conclusions.

2 Methodology

A bottom-up approach was applied to assess the potential impacts of the proposed UTES integrated GSHP system on the current electric grids in the US, as illustrated in Fig. 3. Three inputs are used for the assessment:

1. Annual demand profile of each investigated electricity market (Fig. 3 bottom left);

2. Change of end-use load profiles (EULPs) resulting from retrofitting conventional HVAC systems with the proposed system (Fig. 3 top right);

3. The maximum number of existing SFHs that can be retrofitted with the proposed system (Fig. 3 top left).

Fig. 3
Fig. 3
Close modal

The assessment started by evaluating the maximum number of possible retrofits in each electricity market in the US without considering the cost for implementing the system (Fig. 3 top left). This maximum number (market penetration potential) was determined by the current difference between the peak electric demands in summer and winter in each of the electricity markets and the increased winter electricity demand in each residential building resulting from retrofitting the existing conventional HVAC system to the proposed system. Then, the change in electric demand profile resulting from retrofitting the prototype building with the proposed system was scaled up by multiplying it with the maximum number of retrofits to estimate the aggregated changes in the residential sector of a given electricity market. Finally, the impacts to the grid, including peak demand and annual electricity consumption, were determined by comparing the current grid-level load profile with the estimated grid-level profile resulting from implementing the proposed system at its maximum potential (Fig. 3 bottom right).

2.1 Electricity Markets in the United States.

Currently, there are seven restructured competitive electricity wholesale markets in the U.S., each operated by one of the seven independent system operators (ISOs) or regional transmission organizations (RTOs). They are CAISO, PJM, MISO, SPP, ISO-NE, ERCOT, and NYISO. The service areas of the ISOs and RTOs are shown in Fig. 4. Except these, traditionally regulated wholesale electricity markets exist primarily in the Southeast, Southwest, and Northwest where vertically integrated utilities are responsible for the entire flow of electricity from power plants to consumers. For detailed information of these wholesale markets, refer to Ref. [34].

Fig. 4
Fig. 4
Close modal

Most grid operation data from utility companies are confidential, the available grid operation data to the authors of this study are in a large granularity—wholesale electricity markets. Except for Southeast, Southwest, and Northwest, hourly operation data for different electricity markets are available to the public and can be obtained from the official websites of these wholesale markets. The limitation of utilizing wholesale market data is that it does not consider the constraints in the transmission and distribution network. Therefore, the maximum penetration rate determined by the summer and winter peak loads in the wholesale market level may be overestimated.

Table 1 presents a collection of the operation data obtained from the seven electricity wholesale markets, including annual hourly demand and hourly demands on a typical day in each season (21st day of January, April, July, and October). From the annual hourly demand profiles, it can be observed that except CAISO, the average power demands in summer and winter are higher than those in the shoulder seasons, and the demand in summer is higher than that in winter.

Table 1

Demand profiles of seven electricity wholesale markets in 2019

CAISO
ERCOT
ISO-NE
MISO
NYISO
PJM
SPP
CAISO
ERCOT
ISO-NE
MISO
NYISO
PJM
SPP

The outcome of greater electric demands in summer and winter is consistent with the higher thermal demands for space cooling and space heating in buildings caused by the hot and cold weather. The fact that electric demand in summer is much higher than that in winter reflects that electricity is more widely used for space cooling than for space heating. According to EIA 2015 RECS [29], natural gas, oil, and other nonelectric fuels account for 61% of all the energy used for space heating in residential buildings.

The typical summer day hourly demand profiles show a significant difference between a valley in the early morning and a peak in the late afternoon in all markets. The ratio between the peak and valley ranges from 1.3 (MISO) to 1.6 (ISO-NE). Different from a single peak on a typical summer day, which occurs at around 18:00, there are usually two peaks on a typical winter day: one occurs at around 8:00 and the other occurs between 18:00 to 19:00. The ratio between the peak and valley on a typical winter day is usually less than 1.3. These features are similar to the thermal load profiles for residential buildings.

Table 2 lists the peak electric demands in summer (June, July, and August) and in winter (December, January, and February) in each wholesale market. As can be seen from Table 2, the summer peak is higher than the winter peak in all seven wholesale markets. It indicates that the existing grids can accept more electricity demands in winter. Using the proposed system to replace an existing natural gas furnace (GF) in a building for space heating would increase the winter electric demand of the building. Without enlarging the grid capacity, the increased overall winter peak due to the retrofit should not exceed the summer peak. Following this principle, the difference between the summer and the winter peaks of a grid determines the maximum market potential of the proposed system that the existing electric grid allows.

Table 2

Peak demands of the seven electricity wholesale markets

MarketSummer peak (MW)Winter peak (MW)Difference (MW)
CAISO44,12930,33413,795
MISO120,795100,90719,888
ISO-NE23,97320,7093264
NYISO30,39224,7285664
PJM151,570138,06913,501
SPP50,65640,37610,280
ERCOT74,66656,06618,600
MarketSummer peak (MW)Winter peak (MW)Difference (MW)
CAISO44,12930,33413,795
MISO120,795100,90719,888
ISO-NE23,97320,7093264
NYISO30,39224,7285664
PJM151,570138,06913,501
SPP50,65640,37610,280
ERCOT74,66656,06618,600

2.2 EULPs of a Typical Residential Building.

Changes of EULPs in a typical residential building resulting from retrofitting the existing HVAC systems with the proposed UTES integrated GSHP system are determined through a series of computer simulations using the US Department of Energy’s (DOE’s) prototype building model for SFHs [35]. By multiplying the total number of SHFs in a particular region, the aggregated EULPs for SFHs in the region can be roughly estimated.

2.2.1 Representative Locations.

Eighteen cities have been selected to represent the service areas of the seven electricity wholesale markets, as listed in Table 3. For wholesale markets covering several climate zones, multiple representative locations are selected to account for different climates within the service area of these markets.

Table 3

List of selected locations representing different CZs in service areas of the electricity wholesale markets

MarketCityStateASHRAE CZWeather typeCensus region
CAISOLos AngelesCA3BHot-dry/mixed-dryWest
MISOBaton RougeLA2AHot-humidSouth
JacksonMS3AHot-humidSouth
IndianapolisIN5ACold/very coldMidwest
DuluthMN7Cold/very coldMidwest
ISO-NEBostonMA5ACold/very coldNortheast
NYISONew YorkNY4ACold/very coldNortheast
NorthwestHelenaMT6BCold/very coldWest
Salt Lake CityUT5BCold/very coldWest
PJMBaltimoreMD4AMixed-humidSouth
SoutheastAtlantaGA3AMixed-humidSouth
TampaFL2AHot-humidSouth
SouthwestPhoenixAZ2BHot-dry/mixed-dryWest
AlbuquerqueNM4BHot-dry/mixed-dryWest
SPPOklahoma CityOK3AMixed-humidSouth
OmahaNE5ACold/very coldMidwest
ERCOTSan AntonioTX2AHot-humidSouth
MarketCityStateASHRAE CZWeather typeCensus region
CAISOLos AngelesCA3BHot-dry/mixed-dryWest
MISOBaton RougeLA2AHot-humidSouth
JacksonMS3AHot-humidSouth
IndianapolisIN5ACold/very coldMidwest
DuluthMN7Cold/very coldMidwest
ISO-NEBostonMA5ACold/very coldNortheast
NYISONew YorkNY4ACold/very coldNortheast
NorthwestHelenaMT6BCold/very coldWest
Salt Lake CityUT5BCold/very coldWest
PJMBaltimoreMD4AMixed-humidSouth
SoutheastAtlantaGA3AMixed-humidSouth
TampaFL2AHot-humidSouth
SouthwestPhoenixAZ2BHot-dry/mixed-dryWest
AlbuquerqueNM4BHot-dry/mixed-dryWest
SPPOklahoma CityOK3AMixed-humidSouth
OmahaNE5ACold/very coldMidwest
ERCOTSan AntonioTX2AHot-humidSouth

2.2.2 Prototype Residential Building and HVAC Systems.

The prototype SFH is chosen to represent the configuration of SFH in all regions. It has two floors above the ground and an attic. The entire house is divided into two thermal zones: the attic zone which is not air-conditioned, and the living zone (223 m2) which is air-conditioned.

To evaluate the impacts of the proposed UTES integrated GSHP system on the electricity use of the prototype building, the electricity consumption of the proposed system and three conventional HVAC systems for meeting the same thermal loads are calculated. The first conventional HVAC system uses a GF for space heating and an air conditioner (AC) for space cooling. This is the most typical HVAC system currently used for residential buildings in heating-dominated regions in the US. The rated efficiency of the AC unit, expressed with the COP for cooling, is 3.97. The heating efficiency of the GF is 0.8 and the electricity consumption for running the GF is negligible. The second system uses a conventional ASHP supplemented with an electric resistance heater. The rated cooling and heating COP of the ASHP are 3.97 and 3.63, respectively. The third system uses a GSHP for both space heating and space cooling. Because the ground temperature does not change widely as the ambient air temperature, the GSHP system has higher efficiencies than the ASHP. In this study, 5.5 cooling COP and 4.0 heating COP were used for the GSHP. Also, it is assumed that the GSHP system can meet the peak space heating demand in cold areas without using any supplemental heating.

The EULPs for the SFHs with the first two systems were retrieved by simulating the DOE prototype residential building models. Lacking a detailed prototype SFH model with the GSHP system, the electricity load profile for this system was calculated by assuming constant COP for both heating and cooling modes. Given the building thermal load profile (which was retrieved from the DOE prototype model), the electricity load profile for the GSHP system was roughly estimated.

Similar to the third system, the EULP estimation for the proposed UTES integrated GSHP system assumes constant COP for the GSHP. Besides, it is assumed that the UTES can store any specified amount of thermal energy provided by the GSHP when it is needed, and the stored thermal energy can be fully discharged without any thermal loss when it is needed. According to Sec. 2.1, the shape of the regional electric load profile resembles the profile of SFH. Therefore, the peak from the grid level can be mitigated if the load profile from the demand side is flattened. For the UTES integrated GSHP, an ideal modulating operation strategy is proposed to flatten the building power load profiles as much as possible. The principle of the strategy is shown in Fig. 5. The thermal energy-driven load which is the area between the dashed and the dotted curve is modulated to flat the total load of the building, resulting in the flat solid curve. Thus, the area between the solid and dotted curves equals the area between the dashed and dotted curves given the abovementioned assumptions. The value of the solid flat curve can be determined by solving Eq. (1)
$∑t=124max{0,[Etotalflat(t)−Enon−HVAC(t)]}=∑t=124EGSHP(t)$
(1)
where t is time in hour; Etotal flat(t) represents the constant electric demand of the proposed UTES + GSHP system on a particular day; EnonHVAC(t) represents all the electric demand of the building except for running the HVAC system; and EGSHP(t) represents the electric demand of a single GSHP (without integrated with UTES) for meeting all the space heating and cooling loads of the building.
Fig. 5
Fig. 5
Close modal

2.3 Census of Housing.

To identify the potential of the proposed system and to quantify its impacts on the existing electric grids, the grid side load and the building side load should be correlated. Therefore, the number of SFHs in each electricity wholesale market should be known. The number of SHFs in each state in 2000 was obtained from the United States Census Bureau [36]. Since detailed SHF census data in state granularity for recent years is not publicly available, an increasing rate of 5.7% for all states is applied to the 2000 data to estimate the total number of SFHs in 2015. This increasing rate is obtained by comparing the total number of SFHs in 2000 (69.9 million) and 2015 (73.9 million) [29]. Based on the geographical coverage of each electricity market as shown in Fig. 4, the number of SFHs in each electricity market was estimated and shown in Table 4. The reason 2015 census data was used in this study is that the latest IEA RECS data is for 2015.

Table 4

Number of single-family detached houses in each electricity market in 2015

MarketTotal number of single-family detached house
CAISO5,871,940
MISO14,241,131
ISO-NE3,603,944
NYISO3,383,998
PJM14,331,172
SPP4,309,747
ERCOT4,924,676
MarketTotal number of single-family detached house
CAISO5,871,940
MISO14,241,131
ISO-NE3,603,944
NYISO3,383,998
PJM14,331,172
SPP4,309,747
ERCOT4,924,676

The aggregated impacts of retrofitting existing SFHs with the proposed UTES integrated GSHP system in each electricity market can be estimated by multiplying the impacts resulting from retrofitting a prototype building with the maximum number of SFHs that can be retrofitted in each electricity market. For a market covering more than one representative location, the average impacts resulting from retrofitting the prototype building (e.g., change of electric demand, annual electricity consumption) at each location in the market are used to represent the impacts of retrofitting a typical SFH in the market.

It is assumed that all SFHs use an electric-driven air-conditioning system. For space heating, the energy sources are categorized into three categories, including electricity with heat pump, electricity with resistance, and natural gas or other fuels. The contributions of various energy sources used in 2015 can be obtained from IEA RECS 2015 [29], and they are summarized in Table 5. Lacking more detailed census data, it is assumed that all the electricity markets have the same distribution of energy sources as shown in Table 5.

Table 5

Energy source contribution for space heating in SFHs in 2015

Energy source for space heatingElectricity (heat pump)Electricity (resistance)Natural gas and other
Contribution12%16%72%
Energy source for space heatingElectricity (heat pump)Electricity (resistance)Natural gas and other
Contribution12%16%72%

When assessing the impacts resulting from retrofitting the existing HVAC with the proposed system, the following sequence is applied: SHFs using a natural gas furnace will be retrofitted at first, then the SFHs using an electric resistance heater, and finally the SFHs using a conventional ASHP.

3 Results and Discussion

3.1 EULP Simulation Results.

Table 6 presents a sample of simulation results of a typical SFH at Indianapolis, IN. It can be observed that the electric demand increases dramatically by 384% on a cold winter day when replacing the GF + AC system with the ASHP system. This is because the heating capacity of the conventional ASHP drops significantly when the ambient air temperature is low, and the electric resistance heater, which has a COP of 1, has to be turned on to supplement the ASHP. Besides, when the ambient temperature is below −17.8 °C the heat pump shuts down and only the electric resistance heater is used to meet the heating demand of the building. The conventional GSHP system, which has a higher COP than the ASHP system without using any supplemental heating, results in an increase (69%) in the winter peak electric demand compared with the GF + AC system, and it results in a small downward shift of the electric demand in summer due to its higher cooling efficiency than the AC and ASHP. By load leveling, the UTES integrated GSHP system reduces the summer peak demand by 43% compared with the AC and ASHP and it results in a moderate increase (36%) in the winter peak compared with GF. By replacing the GF + AC system with the UTES integrated GSHP, the annual peak demand of the building is reduced by 23% from 3766 kW to 2888 kW.

Table 6

Prototype building EULP simulation results for Indianapolis

SystemWinterSummer
GF + AC
ASHP
GSHP
UTES + GSHP
SystemWinterSummer
GF + AC
ASHP
GSHP
UTES + GSHP
The summer peak electric demands of the prototype buildings resulting from various HVAC systems in the 18 selected locations are listed in Table 7, along with the summer peak reduction rate resulting from replacing the commonly used GF + AC system with the proposed UTES integrated GSHP system. The summer peak reduction rate is calculated by using Eq. (2):
$Summerpeakreductionrate=Summerpeak(GF+AC)−Summerpeak(UTES+GSHP)Summerpeak(GF+AC)×100%$
(2)

The results show that by replacing the conventional HVAC system with the proposed system, the summer peak electric demand can be reduced by 27–50% depending on the weather—higher peak demand reduction at locations with warmer weather and thus larger cooling loads.

The winter peak electric demands under various conditions are summarized in Table 8, along with the winter peak increase rate resulting from replacing the commonly used GF + AC system with the proposed UTES integrated GSHP system. The winter peak increase rate is calculated with Eq. (3):
$Winterpeakincreaserate=Winterpeak(UTES+GSHP)−Winterpeak(GF+AC)Winterpeak(GF+AC)×100%$
(3)
Table 7

Summer peak electric demands of the prototype buildings at 18 locations resulting from various HVAC systems

MarketCitySummer peak (W)Summer peak reduction rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA255325532457187127%
MISOBaton Rouge, LA402840283476225544%
Jackson, MS358935893105201544%
Indianapolis, IN376637663295215543%
Duluth, MN320732072931198238%
ISO-NEBoston, MA365536553174203244%
NYISONew York, NY349234923104198343%
NorthwestHelena, MT340134012986201541%
Seattle, WA315931592876196038%
Salt Lake City, UT391339133285210646%
PJMBaltimore, MD378937893346214843%
SoutheastAtlanta, GA361136113158200744%
Tampa, FL387638763438219943%
SouthwestPhoenix, AZ498549853847262347%
Albuquerque, NM382838283226209945%
SPPOklahoma City, OK392839283315212446%
Omaha, NE399939993382209348%
ERCOTSan Antonio, TX427242723572214350%
MarketCitySummer peak (W)Summer peak reduction rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA255325532457187127%
MISOBaton Rouge, LA402840283476225544%
Jackson, MS358935893105201544%
Indianapolis, IN376637663295215543%
Duluth, MN320732072931198238%
ISO-NEBoston, MA365536553174203244%
NYISONew York, NY349234923104198343%
NorthwestHelena, MT340134012986201541%
Seattle, WA315931592876196038%
Salt Lake City, UT391339133285210646%
PJMBaltimore, MD378937893346214843%
SoutheastAtlanta, GA361136113158200744%
Tampa, FL387638763438219943%
SouthwestPhoenix, AZ498549853847262347%
Albuquerque, NM382838283226209945%
SPPOklahoma City, OK392839283315212446%
Omaha, NE399939993382209348%
ERCOTSan Antonio, TX427242723572214350%
Table 8

Winter peak electric demands of the prototype buildings at 18 locations resulting from various HVAC systems

MarketCityWinter peak (W)Winter peak increase rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA16902127192216900%
MISOBaton Rouge, LA182659053014213617%
Jackson, MS189657862811226319%
Indianapolis, IN202010,2573584288843%
Duluth, MN195011,0043718332470%
ISO-NEBoston, MA199910,5153765305753%
NYISONew York, NY199980183375292046%
NorthwestHelena, MT202597553463289243%
Seattle, WA19404906260920747%
Salt Lake City, UT210661903020234511%
PJMBaltimore, MD202974873174268232%
SoutheastAtlanta, GA186976713261240929%
Tampa, FL16613854261717475%
SouthwestPhoenix, AZ16313621234016310%
Albuquerque, NM18074391258118070%
SPPOklahoma City, OK204810,0083783326459%
Omaha, NE204910,7433727314053%
ERCOTSan Antonio, TX172257682991221128%
MarketCityWinter peak (W)Winter peak increase rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA16902127192216900%
MISOBaton Rouge, LA182659053014213617%
Jackson, MS189657862811226319%
Indianapolis, IN202010,2573584288843%
Duluth, MN195011,0043718332470%
ISO-NEBoston, MA199910,5153765305753%
NYISONew York, NY199980183375292046%
NorthwestHelena, MT202597553463289243%
Seattle, WA19404906260920747%
Salt Lake City, UT210661903020234511%
PJMBaltimore, MD202974873174268232%
SoutheastAtlanta, GA186976713261240929%
Tampa, FL16613854261717475%
SouthwestPhoenix, AZ16313621234016310%
Albuquerque, NM18074391258118070%
SPPOklahoma City, OK204810,0083783326459%
Omaha, NE204910,7433727314053%
ERCOTSan Antonio, TX172257682991221128%

The results show that by replacing the conventional GF with the proposed system, the winter peak electric demand will increase by up to 70%, varying by location. This increase is much smaller compared with that resulting from using the conventional ASHP system, especially at locations with cold climates. For locations with a hot climate, the winter peak increase is small, even negligible.

By comparing the summer and winter peaks, the annual peaks (the larger one of the summer/winter peaks) for various systems in different locations are determined. The annual peaks are listed in Table 9, along with the annual peak reduction rate resulting from replacing the commonly used GF + AC system with the proposed UTES integrated GSHP system. The annual peak reduction rate is calculated with Eq. (4):
$Annualpeakreductionrate=Annualpeak(GF+AC)−Annualpeak(UTES+GSHP)Annualpeak(GF+AC)×100%$
(4)
Table 9

Annual peak electric demands of the prototype buildings at 18 locations resulting from various HVAC systems

MarketCityAnnual peak (W)Annual peak reduction rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA255325532457187127%
MISOBaton Rouge, LA402859053476225544%
Jackson, MS358957863105226337%
Indianapolis, IN376610,2573584288823%
Duluth, MN320711,00437183324−4%
ISO-NEBoston, MA365510,5153765305716%
NYISONew York, NY349280183375292016%
NorthwestHelena, MT340197553463289215%
Seattle, WA315949062876207434%
Salt Lake City, UT391361903285234540%
PJMBaltimore, MD378974873346268229%
SoutheastAtlanta, GA361176713261240933%
Tampa, FL387638763438219943%
SouthwestPhoenix, AZ498549853847262347%
Albuquerque, NM382843913226209945%
SPPOklahoma City, OK392810,0083783326417%
Omaha, NE399910,7433727314021%
ERCOTSan Antonio, TX427257683572221148%
MarketCityAnnual peak (W)Annual peak reduction rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA255325532457187127%
MISOBaton Rouge, LA402859053476225544%
Jackson, MS358957863105226337%
Indianapolis, IN376610,2573584288823%
Duluth, MN320711,00437183324−4%
ISO-NEBoston, MA365510,5153765305716%
NYISONew York, NY349280183375292016%
NorthwestHelena, MT340197553463289215%
Seattle, WA315949062876207434%
Salt Lake City, UT391361903285234540%
PJMBaltimore, MD378974873346268229%
SoutheastAtlanta, GA361176713261240933%
Tampa, FL387638763438219943%
SouthwestPhoenix, AZ498549853847262347%
Albuquerque, NM382843913226209945%
SPPOklahoma City, OK392810,0083783326417%
Omaha, NE399910,7433727314021%
ERCOTSan Antonio, TX427257683572221148%

The above results show that except for Duluth, MN the annual peak for all other locations can be reduced by replacing the conventional HVAC system with the proposed system. The annual peak reduction rate ranges from 15% to 48%, which means the existing electricity supply system of the SFHs in these regions has enough capacity for running the proposed system. Cooling-dominated areas have a greater reduction rate than cold places.

The annual electricity consumption of the prototype building under various conditions is listed in Table 10, along with the annual electricity consumption reduction rate resulting from replacing the commonly used GF + AC system with the proposed UTES integrated GSHP system. The annual electricity consumption reduction rate is calculated by using Eq. (5):
$AnnualElec.Consump.ReductionRate=AnnualElec.Consump.(GF+AC)−AnnualElec.Consump.(UTES+GSHP)AnnualElec.Consump.(GF+AC)×100%$
(5)
Table 10

Annual electricity consumption of the prototype buildings at 18 locations resulting from various HVAC systems

MarketCityAnnual electricity consumption (kWh)Consumption reduction rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA13,92214,47712,54812,54810%
MISOBaton Rouge, LA16,53318,75015,57215,5726%
Jackson, MS15,97418,27014,96414,9646%
Indianapolis, IN16,34823,85517,05117,051−4%
Duluth, MN16,23728,84017,95117,951−11%
ISO-NEBoston, MA15,67521,87716,44016,440−5%
NYISONew York, NY15,80720,77816,15116,151−2%
NorthwestHelena, MT16,08224,01116,87516,875−5%
Seattle, WA14,72118,44614,71714,7170%
Salt Lake City, UT16,18420,99516,02216,0221%
PJMBaltimore, MD15,93120,38215,92015,9200%
SoutheastAtlanta, GA15,70218,31214,97914,9795%
Tampa, FL17,19217,85715,37215,37211%
SouthwestPhoenix, AZ18,86519,82916,40316,40313%
Albuquerque, NM15,98719,03515,18015,1805%
SPPOklahoma City, OK16,32220,51916,01716,0172%
Omaha, NE16,64624,15817,32217,322−4%
ERCOTSan Antonio, TX17,21819,08215,94815,9487%
MarketCityAnnual electricity consumption (kWh)Consumption reduction rate
GF + ACASHPGSHPUTES + GSHP
CAISOLos Angeles, CA13,92214,47712,54812,54810%
MISOBaton Rouge, LA16,53318,75015,57215,5726%
Jackson, MS15,97418,27014,96414,9646%
Indianapolis, IN16,34823,85517,05117,051−4%
Duluth, MN16,23728,84017,95117,951−11%
ISO-NEBoston, MA15,67521,87716,44016,440−5%
NYISONew York, NY15,80720,77816,15116,151−2%
NorthwestHelena, MT16,08224,01116,87516,875−5%
Seattle, WA14,72118,44614,71714,7170%
Salt Lake City, UT16,18420,99516,02216,0221%
PJMBaltimore, MD15,93120,38215,92015,9200%
SoutheastAtlanta, GA15,70218,31214,97914,9795%
Tampa, FL17,19217,85715,37215,37211%
SouthwestPhoenix, AZ18,86519,82916,40316,40313%
Albuquerque, NM15,98719,03515,18015,1805%
SPPOklahoma City, OK16,32220,51916,01716,0172%
Omaha, NE16,64624,15817,32217,322−4%
ERCOTSan Antonio, TX17,21819,08215,94815,9487%

The above results show that by replacing the commonly used GF + AC system with the proposed system, the annual electricity consumption of the building will increase in heating-dominated regions and decrease in cooling-dominated regions, and the variance is within 15%. While the proposed system reduces electricity consumption for space cooling due to its higher cooling efficiency than the conventional AC and ASHP, it consumes more electricity in winter than the GF + AC system for space heating. In heating-dominated regions, the increase in electricity consumption for space heating is more than the reduction of electricity consumption for space cooling, so that the annual electricity consumption increases. However, the corresponding natural gas consumption for the GF is eliminated. The proposed technology does not make it worse in terms of primary energy consumption footprint. On the other hand, in cooling-dominated regions, the increase in electricity consumption for space heating is less than the reduction of electricity consumption for space cooling, therefore the annual electricity consumption decreases.

3.2 Maximum Market Penetration.

To reduce the summer peak of the electric grids, the proposed system should be implemented as much as possible since the proposed system can reduce the summer peak demand of the prototype building at every investigated location. However, the possible winter peak increase may restrict its applicability. The large-scale installation of the proposed system may result in a winter peak greater than the summer peak, which is beyond the capacity of the existing electric power grid.

The maximum allowed number of SFHs that can be retrofitted with the proposed system without increasing the annual peak demand of an electricity market is determined by dividing the difference between the summer and winter peaks of the electricity market with the increase in the winter electric demand of the prototype building caused by implementing the proposed system. It is assumed that the increase in winter peak demand resulting from retrofitting a GF + AC system with the proposed system is identical to the maximum power of a GSHP (assuming the GSHP has a variable-speed compressor) for meeting the peak heating load of the building. The winter peak increase can be calculated by subtracting the GF + AC system-induced winter peak from the GSHP system-induced winter peak, which is listed in Table 8. The potential market penetration rate in each electricity market is calculated by using Eq. (6) and the results are listed in Table 11
$Potentialmarketpenetrationrate=Gridsidepeakdifference/SinglebuildingwinterpeakincreaseTotalnumberofexistingSFHs×100%$
(6)
Table 11

Potential penetration rate of the proposed system in each electricity wholesale market

MarketNumber of the existing single-family detached houseDifference between summer and winter peak of electric grid (MW)Winter peak difference between GSHP and GF + AC (W)Maximum allowed number of single-family houses for retrofitPotential market penetration rate
CAISO5,871,94013,79523259,461,2071013%
MISO14,241,13119,888135914,634,290103%
ISO-NE3,603,944326417661,848,24551%
NYISO3,383,998566413764,116,279122%
PJM14,331,17213,501114511,791,26682%
SPP4,309,74710,28017066,025,791140%
ERCOT4,924,67618,600126914,657,210298%
MarketNumber of the existing single-family detached houseDifference between summer and winter peak of electric grid (MW)Winter peak difference between GSHP and GF + AC (W)Maximum allowed number of single-family houses for retrofitPotential market penetration rate
CAISO5,871,94013,79523259,461,2071013%
MISO14,241,13119,888135914,634,290103%
ISO-NE3,603,944326417661,848,24551%
NYISO3,383,998566413764,116,279122%
PJM14,331,17213,501114511,791,26682%
SPP4,309,74710,28017066,025,791140%
ERCOT4,924,67618,600126914,657,210298%

It can be observed from Table 11 that except ISO-NE and PJM, other ISOs have more than enough capacity for retrofitting all current HVAC systems in SFHs with the proposed system. While for ISO-NE and PJM, the potential penetration rates are 51% and 82%, respectively. If all the seven markets combined, 46 million SFHs in total can be retrofitted to the proposed system without increasing the annual peak demand of the existing electric power grid.

3.3 Summer Peak Reduction.

The summer peak reduction rate in each of the seven electricity markets resulting from implementing the proposed system at its maximum market penetration rate was calculated based on the following assumptions:

1. Summer peaks of all the SFHs are identical to that of the prototype building using the GF + AC system, and

2. Summer peaks of all the SFHs occur at the same time (around 18:00).

The maximum summer peak demand reduction rate in each electricity market is calculated with Eq. (7) and the results are listed in Table 12.
$Summerpeakreductionrate=(Summerpeak(GF+AC)−Summerpeak(UTES+GSHP))×Maxretrofit#Summerpeakofelectricgrid×100%$
(7)
Table 12

Maximum summer peak demand reduction rate in each electricity market

MarketMaximum number of SFHs for retrofitGrid summer peak demand (MW)Prototype building summer peak using GF + AC (W)Prototype building summer peak using UTES + GSHP (W)Grid summer peak reduction rate
CAISO5,871,94044,129255318719.1%
MISO14,241,131120,7953648210218.2%
ISO-NE1,848,24523,9733655203212.5%
NYISO3,383,99830,3923492198316.8%
PJM11,791,266151,5703789214812.8%
SPP4,309,74750,6563964210915.8%
ERCOT4,924,67674,6664272214314.0%
MarketMaximum number of SFHs for retrofitGrid summer peak demand (MW)Prototype building summer peak using GF + AC (W)Prototype building summer peak using UTES + GSHP (W)Grid summer peak reduction rate
CAISO5,871,94044,129255318719.1%
MISO14,241,131120,7953648210218.2%
ISO-NE1,848,24523,9733655203212.5%
NYISO3,383,99830,3923492198316.8%
PJM11,791,266151,5703789214812.8%
SPP4,309,74750,6563964210915.8%
ERCOT4,924,67674,6664272214314.0%

The above results show that by retrofitting the existing HVAC systems with the proposed system at its maximum market penetration rate, the summer peak of the existing electric grid can be reduced by 9.1–18.2% in various electricity markets. Considering the many benefits of peak demand reduction discussed in Sec. 1, large-scale implementation of the proposed system would be greatly beneficial to both the grid operators and the ratepayers. It will defer the need for constructing new power plants while meeting the increasing electricity demand, such as charging more electric vehicles and powering more electric appliances in US homes.

3.4 Annual Electricity Consumption Reduction.

The contribution of various space heating systems used in SFHs when the proposed system is implemented at its maximum penetration rate in each electricity market is calculated based on the contribution of existing space heating systems (Table 5) and the retrofit priority (Sec. 2.3). The results are listed in Table 13.

Table 13

Contribution of space heating systems in SFHs when the proposed system is implemented at the maximum penetration rate in each market

MarketContribution
UTES + GSHPNonelectricityResistanceASHP
CAISO100%0%0%0%
MISO100%0%0%0%
ISO-NE51%21%16%12%
NYISO100%0%0%0%
PJM82%0%6%12%
SPP100%0%0%0%
ERCOT100%0%0%0%
MarketContribution
UTES + GSHPNonelectricityResistanceASHP
CAISO100%0%0%0%
MISO100%0%0%0%
ISO-NE51%21%16%12%
NYISO100%0%0%0%
PJM82%0%6%12%
SPP100%0%0%0%
ERCOT100%0%0%0%

The annual electricity consumption resulting from the maximum implementation of the proposed system in SFHs in each electricity market is calculated and listed in Table 14.

Table 14

Annual electricity consumption reduction potential resulting from the maximum implementation of the proposed system in SFHs in the U.S.

MarketAnnual electricity consumption (actual in 2019) (TWh)Annual electricity consumption resulting from the maximum implementation of the proposed system (TWh)Annual electricity consumption reduction rate
CAISO83.873.712%
MISO264.9233.312%
ISO-NE66.267.6−2%
NYISO61.154.711%
PJM256.9243.75%
SPP81.271.812%
ERCOT89.578.512%
Total903.6823.49%
MarketAnnual electricity consumption (actual in 2019) (TWh)Annual electricity consumption resulting from the maximum implementation of the proposed system (TWh)Annual electricity consumption reduction rate
CAISO83.873.712%
MISO264.9233.312%
ISO-NE66.267.6−2%
NYISO61.154.711%
PJM256.9243.75%
SPP81.271.812%
ERCOT89.578.512%
Total903.6823.49%

The above results show that by implementing the proposed system at its maximum penetration rate, except ISO-NE, the annual electricity consumption in each wholesale market would be reduced by 5–12%. The larger proportion the proposed system is implemented, the more electric energy would be saved. For all markets combined, annual electricity consumption would be reduced by 9%.

4 Conclusions

This study preliminarily investigated the maximum market penetration rate and the potential impacts of the proposed UTES integrated GSHP system in the United States. The UTES integrated GSHP system can facilitate peak demand reduction by shifting the electricity load that is driven by the building’s thermal demands and help level the electric grid load profile when applied on a large scale. The main conclusions drawn from this study include

1. The maximum penetration rate of the proposed system ranges from 51% to 100%, varying by electricity markets. Overall, about 46 million SFHs can be retrofitted into the proposed system without increasing the annual peak demand of the existing grids.

2. By implementing the proposed system at its maximum market penetration, the summer peak demand of the electric grid can be reduced by 9.1–18.2% at various electricity wholesale markets. This reduction allows the existing grid to meet other growing electric demands, such as electric vehicles, and more electric-driven appliances used in buildings. On the other hand, the annual electricity consumption would change from −2% to 12%, varying by electricity markets. The larger proportion the proposed system is implemented, the more electric energy would be saved. Nationwide the total electricity consumption would be reduced by 9%.

3. Large-scale implantation of the UTES integrated EDHP system can not only reduce the daily peak demand but also smooth the daily demand profile, which improves the efficiency of the whole electric system. Furthermore, large-scale implementation of the proposed technology has the potential to resolve or at least relieve the “duck curve” effect by shifting the cooling load to periods with high PV production or shifting the cooling load from daytime to nighttime when wind power production is abundant. The proposed technologies can electrify space heating with a much smaller increase in winter peak than that resulting from other electric-driven heating systems.

4. The simulations for the UTES + GSHP system are based on a few assumptions (e.g., constant heating/cooling COP of the GSHP, sufficient capacity and flexibility for storing and releasing thermal energy, etc.). Therefore, the simulation results can only provide a rough estimation of the potential of the proposed system. Further study with more detailed modeling of the UTES and the GSHP will be conducted in the future.

Acknowledgment

The authors would like to thank the guidance and inputs of Mrs. Arlene Anderson, the Lead Technology Manager of Low-Temperature Geothermal Program at the U.S. Department of Energy.

Conflict of Interest

There are no conflicts of interest.

Data Availability Statement

The datasets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.

Funding Data

This study is based upon work funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, under the Geothermal Technologies Office, Low-Temperature and Co-Produced Resources Program.

• t =

time, h

•
• E =

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