Energy and Buildings 68 (2014) 223–231

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Energy savings from direct-DC in U.S. residential buildings Vagelis Vossos ∗ , Karina Garbesi, Hongxia Shen Energy Analysis and Environmental Impacts Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, CA, USA

a r t i c l e

i n f o

Article history: Received 2 April 2013 Received in revised form 6 July 2013 Accepted 3 September 2013 Keywords: Direct current (DC) Photovoltaics (PV) Residential buildings Energy conservation

a b s t r a c t An increasing number of energy-efficient appliances operate on direct current (DC) internally, offering the potential to use DC directly from renewable energy systems, thereby avoiding the energy losses inherent in converting power to alternating current (AC) and back. This paper investigates that potential for netmetered residences with on-site photovoltaics (PV) by modeling the net power draw of a ‘direct-DC house’ compared to that of a typical net-metered house with AC distribution, assuming identical DCinternal loads. The model comparisons were run for 14 cities in the United States, using hourly, simulated PV-system output and residential loads. The model tested the effects of climate and battery storage. A sensitivity analysis was conducted to determine how future changes in the efficiencies of power system components might affect potential energy savings. Based on this work, we estimate that net-metered PV residences could save 5% of their total electricity load for houses without storage and 14% for houses with storage. Direct-DC energy savings are sensitive to power system and appliance conversion efficiencies but are not significantly influenced by climate. Published by Elsevier B.V.

1. Introduction A convergence of factors is driving recent interest in using direct current (DC) from photovoltaic (PV) systems in its DC form to power electricity loads in buildings, rather than converting it first to alternating current (AC), as is current practice. The new millennium has witnessed sustained and rapid growth in the adoption of rooftop PV systems, as concerns about climate change have intensified. PV is a DC power source. Batteries also act as a DC source and are the dominant energy storage technology used with PV systems. In addition to these two factors, an increasing fraction of the most efficient electric appliances operate internally on DC [1,2], making the direct use of DC (direct-DC) in a building more effective and compelling. Devices that operate internally on DC, referred to in this paper as ‘DC-internal’ appliances, include all consumer electronics—therefore, essentially all advanced communications technologies—fluorescent lighting with electronic ballasts, solidstate (such as light-emitting diode or LED) lighting, and brushless DC motors. Advanced brushless DC (permanent magnet) motors can save 5–15% of the energy used by traditional AC induction motors and up to 30–50% in variable-speed applications for pumping, ventilation, refrigeration, and space cooling [3]. DCmotor-driven heat pump technologies for water and space heating

∗ Corresponding author at: 1 Cyclotron Road, Mail Stop MS90-4000, Berkeley, CA 94720, USA. Tel.: +1 510 4952521. E-mail address: [email protected] (V. Vossos). 0378-7788/$ – see front matter. Published by Elsevier B.V. http://dx.doi.org/10.1016/j.enbuild.2013.09.009

can also displace conventional resistance heating with savings of 50% or more. Thus, ‘DC-internal’ technologies tend to be more efficient than their AC counterparts and are capable of servicing essentially all building loads [3]. These trends make a strong argument for investigating the potential benefits of directly coupling DC power sources with DC loads. The direct use of DC has been recommended as a key strategy for improved reliability and increased energy savings at the building level [4–6], and it is already being implemented in commercial buildings, particularly for lighting applications [7], while DC-compatible appliances are emerging on the market [8]. However, residential applications have received little attention and differ considerably from commercial applications. Most importantly, residential loads have poorer coincidence with PV system output than commercial loads and are less predictable. These issues would appear to make the residential sector a poorer candidate for direct-DC than the commercial sector. Acknowledging these barriers, this study assesses the relative energy savings of direct-DC power for residential buildings. The majority of studies that address DC power systems in the context of electricity savings have been analytical, rather than experimental, in nature. Savage et al. [9] estimated that electricity savings of 25% can be achieved in the U.S. residential sector by replacing appliance AC-to-DC converters with a more efficient centralized rectifier and using DC distribution to power DC-internal loads. Hammerstrom [10] compared the power conversions for various residential appliance categories under AC and DC power distribution and found that a residential building coupled with a DC power source will use 3% less electricity with DC distribution,

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Nomenclature AC BDCPM DC LED MPPT NEMS PV SAM VSD

alternating current brushless permanent magnet motor direct current light emitting diode maximum power point tracker national energy modeling system photovoltaic system advisor model variable speed drive

compared to AC distribution. Thomas et al. [11] reported that an LED lighting system supplied with DC power from PV can reduce its levelized annualized cost by 5% on average, as opposed to an LED lighting system supplied with AC power from PV. However, a 2002 U.K. study [12], found that a residential PV-powered DC distribution system with net-metering was 3% less efficient compared to the equivalent AC distribution system. Finally, Sannino et al. [13] compared the distribution losses of a DC power system in a commercial building with different supply voltages ranging from 48 VDC to 326 VDC , to an AC power system at 230 VAC . According to their analysis, the highest tested DC voltage (326 V) was the most suitable level, from an economic and technical standpoint. The reported savings (or losses) of DC distribution in these studies are largely dependent on the varying assumptions about power system efficiencies, load timing and type, distribution voltage, and whether net-metering was taken into consideration. This paper estimates the potential energy savings of direct-DC power systems in net-metered residences in the United States. Netmetered systems are considered explicitly here, both because they dominate on-site PV generation [14,15] and because savings could be overestimated if the DC-to-AC power conversions that occur when excess PV power is delivered to the grid are ignored. However, because of the increasing capacity of net-metered PV systems, the intermittence of the solar resource may become a barrier to their future penetration, because of too much power being released to the grid during solar peak periods [16]. Therefore, given that local battery storage, if handled properly, could buffer such fluctuations [17] and reduce the mismatch between PV generation and load [18], this paper also explores the impact of energy storage systems on direct-DC energy savings. Because of the large variability in insolation across the United States, the paper examines energy savings potential in 14 U.S. cities. This paper also includes a detailed load analysis to account for the changes in the nature of the load needed to facilitate direct-DC and to account for the timing of the load. The latter is essential because, in the absence of energy storage, only loads coincident with PV system output can benefit from direct-DC. Finally, we investigate the potential benefits of shifting cooling loads to earlier in the day (pre-cooling) to make these loads more synchronous to PV system output and, therefore, more able to benefit from direct-DC. 2. Direct-DC house modeling 2.1. Model inputs To address the research objectives, we developed a spreadsheet model of a hypothetical house with a net-metered rooftop PV system. To test the potential effect of large variations in insolation, we ran the model for an average house in 14 cities distributed across the contiguous United States. These cities, shown in Fig. 1, were chosen because they were the only cities for which consistent residential load data were available in the desired format, as described

below. The distribution of the 14 cities is analogous to the solar resource distribution in the United States. To obtain electricity load data and PV system output for the average house in each of the 14 cities, we used the System Advisor Model (SAM) [20]. The load data are provided in SAM as example average residential electricity loads and are climate-simulated for each hour of the year. These loads, therefore, already incorporate any building envelope effects. For the PV output data, we used SAM to generate hourly estimates of PV system output for one year for each of the 14 cities.1 It should be noted here that the use of simulated hourly load profiles and PV output data is likely to overestimate the instantaneous PV output that can be absorbed by the load [21] and the system storage dynamics, thus affecting the final energy savings estimates. 2.2. Model development 2.2.1. Distinguishing the cooling loads Because of the potential importance of load timing and type on energy savings and the large diurnal and seasonal variability of cooling loads, we separated cooling loads from non-cooling loads. To do so, we first converted each city’s load data to load data for the average day of each month and plotted the resulting average diurnal load curves. An example is shown for Sacramento in Fig. 2, which also includes the average PV output for June and January, represented by the dotted lines. Based on an examination of the load data, cooling loads are clearly distinguished from non-cooling loads. As seen in the graph, six monthly load curves have clearly distinguishable afternoon-to-evening cooling loads, while the non-cooling load curves of the remaining six months are almost matching. Accordingly, the cooling load was obtained by subtracting the non-cooling load from the total load. Note that for cities with a potentially significant heating load during the winter period (Seattle, Medford, Helena, Denver, Chicago, Lexington, New York), we calculated the baseline load from months with minimum heating or cooling activity (April and October) to avoid including winter electric heating load. We used this approach based on the methodology provided by Reichmuth [22]. 2.2.2. House configurations To quantify the potential energy savings of direct-DC, the model compares power conversion losses in a house with AC distribution, called the AC-house, and a house with DC distribution called the DChouse, as shown in Fig. 3. The DC-house power system configuration eliminates DC–AC–DC conversion losses to DC-internal appliances when adequate PV power is available, but incurs AC–DC losses via the bidirectional inverter when grid backup power, delivered as AC, is used. In the AC-house, which constitutes the base case, all power is distributed in AC form to appliances that accept AC power. In the DC-house, all power is distributed in DC-form to appliances that accept DC power, but these appliances are identical in every other way to their AC counterparts. That is, the AC appliances are assumed to be the DC-internal appliances with an AC–DC power converter on the input. As discussed earlier, cooling loads are separated from noncooling loads, while the latter are further broken up to high- (380 V) and low-voltage (24 V) loads. High voltage is used for high-power consumption devices and to distribute DC power throughout the house with fewer losses. Low voltage is used for low-power loads, like consumer electronics and lighting, to facilitate safer and easier

1 The inputs used in SAM to generate PV system outputs are 180◦ azimuth, 20◦ PV array tilt angle, and a 0.85 derate factor. Each house’s PV system DC rating was 1 kW, but the actual PV system capacity was later scaled to allow zero-net electricity consumption for the conventional AC-house, as discussed below.

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Fig. 1. PV solar resource map. Reproduced with permission from the author [19].

Table 1 Power system full-load conversion efficiencies. Power system component

Efficiency (%)

Component efficiency in literature

PV Inverter (AC House), includes MPPTa DC-house rectifier (meter → DC)b

95 93

DC-house inverter (DC → meter)b , c

97

Charge controller or MPPTd DC-house DC–DC converter: 380 V–24Vb Battery (one way)

98 95

[25]: 90%, [26]: 95% [27]: 90%, [26]: 95%, [28]: 90% Not available in the market [29], [30]: 97–99% [24]: 90%, [25]: 95%

90

Depends on technology and state of charge

a

Fig. 2. Average monthly diurnal load curves for Sacramento. From top to bottom, the larger peaks correspond to July, August, September, June, October, and May (descending). The superimposed base load curves correspond to November–April. This variance is attributed to the cooling load.

handling and flexibility. The chosen voltages for the DC-house reflect existing (24 VDC ) and pending (380 VDC ) EMerge Alliance standards for DC distribution [24]. It should be noted that, based on previous work [13], and the fact that high-power loads eventually make up for two-thirds of the total house load (versus one-third for low-power loads), we assume that AC-house versus DC-house distribution losses are comparable. For any given city, the PV arrays for the AC- and DC-houses are identically sized; that is, they are configured to have the same DC output. But, the capacity of the PV systems differs from city to city, because in each locale the systems are sized so that the AC house is net-zero in annual electricity usage—that is, the total electricity drawn from the grid equals the electricity delivered to the grid on an annual basis. 2.2.3. Power system conversion efficiencies Because DC power systems are only beginning to emerge on the market and are not yet produced for residential applications, all power system component efficiencies were based on similar devices used for other purposes and are representative of high-end products. Table 1 presents the power system conversion efficiencies assumed in the model and corresponding efficiencies found in recent literature. The listed efficiencies reflect the input of industry

Typical of today’s new PV-system string inverters. Represents best models that could be built today, according to industry experts interviewed. c Today’s PV-system inverter minus the MPPT, which has estimated losses of 2%. d Typical of today’s high-end charge controller efficiencies. b

experts at the 2011 Green Building Power Forum, including makers of the new generation of DC power supplies for data centers, and by Emerge Alliance members. 2.2.4. Switching to DC-internal loads To make a fair comparison of the performances of the AC- and DC-houses, their loads needed to be identical except for their power input characteristics. To obtain residential end-use consumption at as high a resolution as possible, we ran the Energy Information Administration’s 2010 release of the National Energy Modeling System (NEMS) using the Annual Energy Outlook reference case assumption. This resulted in an average annual U.S. residential electricity consumption for 2010 for 32 different appliances. We then determined whether these appliances could operate on DC power by considering the internal functions of the appliances. Table 2 summarizes the results of this investigation. With energy efficiency guiding the selection of the hypothetical suite of appliances for both houses, we decided to: replace all non-DC-compatible equipment with DC-internal models currently on the market; replace electric resistance heating applications with DC-driven heat pump technologies where applicable models exist (electric water heaters, electric dryers, electric furnaces); and replace all incandescent lights with electronic (fluorescent or LED).

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Fig. 3. AC- and DC-house power system configuration. Only components that generate, convert, and consume power are shown. The AC-house inverter (top) includes MPPT. The DC-house’s bidirectional inverter (bottom) does not include MPPT, because it is included separately [23].

This suite of appliances constitutes the efficient DC-compatible load assumed for both the AC- and DC-house load modeling. The house loads used for the modeling were generated as follows: The NEMS load data were separated into cooling and noncooling loads, and the energy usage for each constituent end-use was adjusted according to the DC-internal savings potential indicated in Table 2. The aggregate percentage savings for the cooling and non-cooling loads were then calculated and applied to cooling and non-cooling loads inferred from the SAM data for each of the 14 cities. The overall weighted average energy savings relative to standard residential loads were calculated to be about 33% (see Garbesi et al. [3] for details). This had the effect of both scaling and shifting the loads across load categories.

2.2.5. AC–DC appliance conversion efficiencies Because the appliances in both houses were DC-internal, each AC-house appliance was assumed to have an AC–DC converter appropriate to its power consumption. We based the converter efficiencies on external power supply data from the ENERGY STAR [31] and 80plus [32] databases, both of which include the most efficient products in the market. Fig. 4 shows the compiled efficiencies versus power supply power output from these two data sets. These efficiencies were applied to the NEMS appliance data, given typical wattages. Average conversion efficiencies were then determined by weighting the load fractions. The weighted average AC/DC appliance converter efficiencies for cooling and non-cooling loads were calculated to be 90% and 87%, respectively.

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Table 2 Residential appliances functions and equivalent DC-internal technologies. Function within appliance

Appliance type

Standard technology

DC-internal best technology

Energy savings compared to standard technologya

Lighting Heating

Incandescent, fluorescent, LED Heater

Incandescent Electric resistance

73% 50%

Cooling

Motor (compressor, pump, and motor-driven fan)

Induction motor, single-speed compressor, pump, and fan where applicable

Electronic Heat pump operated by BDCPMb BDCPM operating VSDc

Mechanical work Cooking Computing a b c

Motor Electric cook top Digital technology

Induction motor Electric resistance Digital technology is already DC

5–15% (motor only depending on size) 5–15% (depending on size) 12% 0

Energy savings assuming AC power source BDCPM: brushless DC permanent magnet motor VSD: variable-speed drive

2.3. Modeling scenarios 2.3.1. Overview of system configurations To compare the energy use of the AC- versus the DC-house and to test implications of storage and load shifting, we considered the system configurations presented in Table 3. Note that, for every system configuration, the AC-house is identical to the DC-house, except for the power system components and the form (AC or DC) in which power is delivered to the loads. Thus, both houses are assumed to have identical electricity storage systems in configurations where storage is considered (1b and 2b) and the same load-shifting mechanisms in configurations 2a and 2b. 2.3.2. Configurations with storage To test the effect of battery storage on the direct-DC energy savings, battery storage was included in the model runs for both the

Fig. 4. AC/DC power converter efficiencies of AC-house appliances.

Table 3 System configurations for the modeling scenarios. Without electricity storage

With electricity storage

1a. Average residential loada

1b. Average residential load with storage 2b. Shifted average residential load with storage

2a. Shifted average residential load a

BDCPM Induction cooker Same

30–50% (VSD)

Configuration 1a, average residential load (no energy storage) was presented graphically in Fig. 3.

AC- and the DC-houses.2 The charge controller, which is assumed to include maximum power point tracking (MPPT) to optimize PV power output, regulates current to and from the batteries. The storage system is charged only by excess PV power, but not by rectified grid power. The AC-house inverter is bidirectional, as is the norm for modern grid-interactive inverters with battery back-up. Fig. 5 shows system configuration 1b, Average residential load with storage, for both houses. Battery efficiency was assumed to be 90% one way [33] (81% roundtrip), as shown in Table 1. To identify a reasonable value for the maximum charging capacity of the battery (in kWh), we ran the model for one city (Sacramento) and performed a sensitivity analysis to determine how the amount of excess PV power sent to storage varied with battery capacity. The results of this analysis are presented in Fig. 6. For charging capacities of up to about 10 kWh, a linear relationship exists between the charging capacity and the percentage of excess PV sent to storage. For charging capacities greater than 10 kWh, the relationship becomes one of diminishing returns. Taking into account the results of this analysis, we assumed a battery capacity of 10 kWh for Sacramento. For every other city, the battery size was scaled to the PV system size, normalized to the optimal size for Sacramento. In this way, we used an integrated approach to design each house optimally for each city’s climate. The minimum battery charge was set to 20% of full capacity, a typical value for deep-cycle batteries. An additional objective in sizing the battery storage was to have the single battery configuration work reasonably well for all cities, so that the performance intercomparisons were affected by climate alone. Therefore, after sizing the battery, the model was run with the 10 kWh-storage capacity for all cities and the following outcomes were examined:

• The percentage of time the battery spends at minimum and maximum capacity: ideally not too large a fraction of the time should be spent in either state, but both states should be manifested. That is, a battery that is never maxed out is larger than needed. If the battery never drew down all the way, this would indicate that the battery capacity was not being effectively accessed. • The percentage of loads that are not coincident with PV output but are serviced by the battery: this should be reasonably high to indicate that the battery is being effective at servicing loads.

2 Because the model compares energy losses between the AC-house and the DChouse, only the storage system efficiency affects the modeling results and not the assumed storage technology.

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Fig. 5. House configurations with storage.

• The percentage of excess PV power that would have been sent to the grid in the absence of storage but is sent to storage instead. This should be reasonably high for storage to be effective at buffering the grid from large variations in power output. As shown in Table 4, on average the DC-houses’ batteries spend an estimated 73% of their time in the active state. On average, the AC-houses’ batteries spend an estimated 66% of their time in the active state. In addition, for the same size battery in the AC- and DC-houses, the DC houses’ batteries tend to spend more time at minimum capacity, favoring smaller, and therefore, lower cost batteries. The modeling also indicates that a relatively large fraction of excess PV is captured by batteries and, therefore, used to service the on-site load rather than being sent to the grid. Thus the battery sizing appears to be both adequate for all of the cities and effective at redirecting excess PV to the house load.

2.3.3. Configurations with load shifting To test the potential of load shifting to improve direct-DC savings, we modeled the impact of shifting the residential cooling load to start two hours earlier in the day throughout the cooling months (May–October). The cooling load was shifted because cooling dominates residential electricity use in general, it is possible to shift to earlier hours (as opposed to other residential loads such as lighting, cooking, refrigeration) and because the residential cooling load is skewed toward evening hours, as shown in Fig. 2. The intent here was to capture the potential of pre-cooling to improve direct-DC energy savings, not the usage of specially designed thermal storage systems. Therefore, load shifting was limited to two hours. The house configurations with load shifting do not require any additional power system components, apart from a home energy management system, which is assumed to have a negligent effect on house electricity consumption.

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Table 4 Storage system performance in the AC and DC houses. #

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Cities

Phoenix Tampa Houston Fort Worth Sacramento Atlanta Lexington Medford Los Angeles New York Denver Helena Chicago Seattle

Averages Standard deviation

Cooling load fraction (%)

66 56 48 43 32 28 19 15 15 13 11 10 10 3

Time battery is at minimum capacity (%)

Time battery is at maximum capacity (%)

Loads non-coincident with PV serviced by battery (%)

Excess PV sent to battery (%)

AC

DC

AC

DC

AC

DC

AC

DC

23 26 22 19 32 22 26 34 28 28 25 29 29 34

19 18 14 13 23 14 17 24 15 17 14 20 18 25

11 7 6 6 6 5 5 9 4 6 5 8 7 10

12 9 9 10 9 8 8 10 7 9 8 11 10 11

54 64 68 68 68 70 69 63 72 69 72 64 66 54

62 76 79 76 78 80 80 73 86 80 84 73 76 63

69 83 87 87 87 90 88 80 92 89 92 82 85 70

64 76 79 77 80 82 82 75 88 83 87 75 79 66

27 5

18 4

7 2

9 1

66 6

76 7

84 7

78 7

2.3.4. Model runs The model tracks the efficiency losses throughout the houses’ power conversion systems and in the AC appliances’ AC–DC power converters. The model calculates the impact of net-electricity at the electric meter for both houses over a one-year period for each system configuration. The reported energy savings are the directDC savings as a percentage of the total AC-house load for each city. The model was run for all house configurations and for all cities. In addition to these model runs, sensitivity analyses were conducted to test the effect of the power system converters operating under partial load conditions and possible future technology improvements.

with storage (1b). The results show only a weak trend between cooling load fraction and direct-DC savings. The average fraction of the load serviced directly by the PV system is significant, but virtually the same, for the AC- and DC-house, 37% and 38%, respectively. For system configurations that include load shifting (2a and 2b), the results show no significant impact on direct-DC savings, from the two-hour load shift, compared to their corresponding nonload shift configurations (1a and 1b). This is because the load shift increased the fraction of load serviced directly by the PV system only modestly and by about the same amount (by 5%) – to 42% and 43% – in both the AC- and the DC-houses, respectively.

3. Results

3.1. Sensitivity analyses

The energy savings reported in this section address only the direct-DC energy savings. The overall 33% appliance efficiency savings, which were obtained from switching existing appliances to efficient DC-internal appliances, are excluded. Table 5 shows the results for system configurations 1a and 1b (with and without storage, but no load-shifting). The cities are ranked by their cooling load fractions to reflect the effect of climate. The model predicts that the direct use of DC will save energy with respect to conventional AC distribution and that the savings for battery-integrated systems are about twice that of non-storage systems. Averaging over all cities, direct-DC saves an estimated 7% of total (AC-house) electricity use without storage (1a) and 13%

3.1.1. Technology improvements As discussed earlier, direct-DC savings depend inherently on the relative efficiencies of the power system components and the appliance converters. Although we use current high-end efficiencies for the modeling, it is likely that these technologies will improve in the future. Therefore, we ran the model for all cities testing efficiency improvement scenarios. These scenarios, and their resulting direct-DC energy savings for system configurations 1a and 1b, are presented in Table 6. As expected, if rectifier and DC/DC converter efficiencies improve, direct-DC energy savings increase. The opposite occurs if appliance AC–DC conversion efficiencies improve. Given that such improvements are likely to proceed together, the relative effects are likely to cancel each other out, and, therefore, the model estimates of energy savings will be relatively insensitive to future changes in the efficiencies of power system components and appliance power supplies.

Fig. 6. Excess PV to storage versus maximum battery charging capacity (consistent with Mulder et al. [34].

3.1.2. The effect of variable loads on energy savings Power converter efficiencies are lower under low-load conditions. Given that power converters are sized to meet maximum loads, the variability of residential loads should reduce the energy savings potential below what would be achieved at the rated full-load efficiencies. To model the magnitude of the impact that part-load conditions might have on direct-DC energy savings estimates, we assigned part-load efficiencies for five power system components, as shown in Table 7. Part-load efficiencies were considered for load levels below 20% of full load, because power system efficiencies drop sharply below that level [30,35].

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Table 5 Direct-DC savings and load serviced directly by PV (system configurations 1a and 1b, Table 3). Cities

Phoenix Tampa Houston Fort Worth Sacramento Atlanta Lexington Medford Los Angeles New York Denver Helena Chicago Seattle

Cooling load (%)

66 56 48 43 32 28 19 15 15 13 11 10 10 3

All cities (averages)

Fraction of load serviced directly by PV system (%)

Direct-DC savings as percent of total AC house load (%)

AC-house

DC-house

No storage

Storage

41 44 43 40 37 38 37 34 36 36 34 35 35 32

42 45 44 41 38 40 38 35 37 37 35 36 36 33

7.6 8.0 7.9 7.6 7.4 7.5 7.4 7.2 7.3 7.3 7.2 7.2 7.2 7.0

11.8 12.6 13.0 13.2 13.1 13.3 13.2 12.2 13.2 13.2 13.4 12.8 12.9 11.9

37

38

7.4

12.8

Table 6 Direct-DC savings for improved power system and appliance technologies. Scenario description

Efficiency improvements

Non-storage savings (%)

Storage savings (%)

Improved power system conversion efficiencies

House rectifier: 93% → 95% DC/DC converter: 95% → 97%

9.3

13.7

Improved appliance AC–DC conversion efficiencies

Cooling loads: 90% → 95% Non-cooling loads: 87 %→ 90%

4.0

9.3

Table 7 Power system components part-load efficiencies. Part-load efficiency (%)

Power system component

Full-load efficiency (%)

AC-house inverter, includes MPPT

95

DC-house rectifier (meter→DC)

93

84b

DC-house inverter (DC→meter)

97

92c

Charge controller or MPPT

98

94d

DC–DC converter: 380 V–24 V

95

87b

90a

a Based on the efficiency curve of the grid-connected string inverter SMA Sunny Boy 7000US [35]. b Based on 220 V AC–DC and 400 V DC–DC step-down power supply efficiency curve [Personal Communication, Tony Lai, Delta Corp.]. c Based on SMA Sunny Boy 7000US efficiency curve, excluding MPPT losses (2%). d Based on the efficiency curve of the MorningStar SunSaver charge controller with MPPT [30].

We incorporated the part-load efficiencies in the model (configurations 1a and 1b) for all cities. Fig. 7 shows the direct-DC energy savings for the average city. Partial-load effects reduce estimates of direct-DC energy savings from 7.4% to 5.0% for the non-storage case, but increase them from 12.8% to 13.5% for the storage case. The decrease in savings for the non-storage configuration (1a) is because of the low part-load efficiency of the DC-house rectifier. On the other hand, the increase in savings for the configuration with storage (1b) is because of the higher AC- versus DC-house losses incurred between the batteries and the loads, due to the presence of the inverter in the AC-house. Given the reduction in the already modest estimates of DC energy savings without storage, and the fact that actual loads are significantly more variable than the modeled average loads, savings in the field may be lower than estimated by the model. On the other hand, given the uncertainties in the input values in Table 7, based, as they are, on a snap-shot of an emerging market, these results might not persist in the long term. 4. Conclusions

Fig. 7. Effects of part-load conditions on direct-DC savings for the average city.

This paper finds that direct-DC could yield significant energy savings in U.S. houses with net-metered PV systems, if the entire load is constituted of DC-powered appliances, especially if those systems incorporate battery storage of sufficient capacity to significantly buffer the grid from PV system output fluctuations. Accounting for variable loads, for the average city direct-DC is estimated to save about 5% of total house electricity consumption for the non-storage case and about 14% for the storage case. Additional energy savings of approximately 33% would be obtained by completing the current transition toward the use of DC-internal technologies to supply residential electricity demands. While this transition is occurring even in the absence of direct-DC power systems, largely because of the efficiency advantages, this benefit demonstrates that the appliance modifications needed to accommodate direct-DC are consistent with overall energy efficiency goals and trends.

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The modest savings for the non-storage case are a reflection of the fact that residential loads, which peak in the late afternoon and evening, do not facilitate the direct use of PV power. The larger the mid-day and summertime loads, the greater the energy savings potential will be. On the other hand, on-site storage favors direct-DC, because excess power from the PV system can be sent to the batteries for storage directly in its DC form without DC–AC–DC conversion losses. Given that high PV penetration rates desired for decarbonization of the electricity supply also require on-site electricity storage, direct-DC could facilitate by significantly reducing the effective PV system load. In conclusion, while the modest energy savings for a house without electricity storage might not provide the impetus for transitioning away from an entrenched AC energy infrastructure, the larger savings for a house with storage might. More likely, if directDC takes off in the residential sector it will be as a spin-off of the commercial sector, for which products are already entering the market, mainly because commercial buildings tend to have higher day-time loads than residential buildings do, so their loads coincide better with DC output from PV. Starting with feasible DC infrastructure investments, one can then move an application at a time, implementing those that are most advantageous (e.g., lighting, sensors, projectors, fans). In the residential sector, one might want to start with high power hard wired systems like solar assisted HVAC. We also note that, for technologies that are already DC internal, there is no inherent reason why a DC power system should cost more than an AC system. In fact, DC appliances should be less costly compared to AC appliances because they eliminate the AC/DC converter. Acknowledgements The authors thank the following people for their significant contributions to this project: Robert Van Buskirk for initiating the Direct-DC Power Systems project and providing vision and encouragement along the way, Eric Fry and Tony Lai for input on power conversion technology, and Mary James for editing. References [1] K. George, DC Power Production, Delivery and Utilization, Electric Power Research Institute, 2006 (White Paper). [2] Galvin Electricity Initiative, The Galvin Path to Perfect Power – A Technical Assessment, Galvin Electricity Initiative, Palo Alto, CA, 2007. [3] K. Garbesi, V. Vossos, H. Shen, Catalog of DC Appliances and Power Systems, Lawrence Berkeley National Lab, Berkeley, CA, 2011. [4] M. McGranaghan, et al., Renewable Systems Interconnection Study: Advanced Grid Planning and Operations, Sandia National Laboratories, 2008. [5] Zero Energy Commercial Buildings Consortium (CBC), Barriers and Industry Recommendations for Commercial Buildings, Next Generation Technologies, 2011. [6] M. Sechilariu, B. Wang, F. Locment, Building-integrated microgrid: advanced local energy management for forthcoming smart power grid communication, Energy and Buildings 59 (2013) 236–243. [7] Nextek Power Systems, Direct-Coupling Demo. Available from: http://www.nextekpower.com/technology/direct-coupling-demo (cited 3 January 2013). [8] EMerge-Alliance, Registered Products. Available from: http://www. emergealliance.org/Products/RegisteredProducts.aspx (cited 10 October 2011).

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