Effect of Demand Response on Residential Energy. Efficiency with Direct Load Control and Dynamic. Price Control. Saurav MS Basnet, Haneen Aburub, Ward ...
Effect of Demand Response on Residential Energy Efficiency with Direct Load Control and Dynamic Price Control Saurav MS Basnet, Haneen Aburub, Ward Jewell, Fellow, IEEE Department of Electrical and Computer Engineering Wichita State University Kansas, USA Abstract— A demand response program designed to mitigate peak load during hot summer days has been designed. Pacific Northwest National Laboratory (PNNL) developed taxonomy distribution feeder has been used to evaluate the effectiveness of the demand response program. The simulation includes direct load control, dynamic price control, residential PV penetration and their comparisons. The results will help guide companies that are using demand response for peak reduction, and should help increase consumer participation in such programs. Keywords —Demand Response, Peak Demand, Direct Load Control, Dynamic Price Control, Artificial Neural Network, Cost Benefit Analysis and Renewable Portfolio Standard
I.
INTRODUCTION
Tradeoff between electricity demand and supply is always a challenging task for power companies, especially when demand is constantly increasing. Longer summer and winter days due to global climate change is not helping either. Moreover, recent clean energy initiatives like Clean Energy Act, California 50% Renewable Portfolio Standard (RPS) in 2030 makes the situation worst because they may not achieve the estimated goal without retiring some of their coal plants. Due to these circumstances power companies are in need of various options especially during peak demands and demand response (DR) is one of them. Demand response changes the normal electrical usage pattern. It can take the form of consumers reducing or shifting their electricity usages based on the time based rates, or other financial incentive or it can be direct load control program where power companies cycle the air conditioning (ac), water heater or other loads during peak demands. Residential consumption accounts for almost 40% of total electricity usage [1]. Therefore the effectiveness of DR highly depends on the efficiency of the residences, consumer willingness to participate, number of the occupants etc. Among these, residence efficiency has a greater impact. Residential energy efficiency includes a variety of features, including [2]
Heating efficiency (Annual Fuel Utilization Efficiency (AFUE), blower motor efficiency, heating Seasonal Performance Factor (HSPF)) and air conditioner (a/c) efficiency (Seasonal Energy Efficiency Ratio (SEER), Energy Efficiency Ratio (EER), blower motor efficiency) Wall, ceiling, floor and roof insulation. Glass coverage, orientation, and efficiency ( Solar Heat Gain Coefficient (SHGC), U-factor) Residential size and design Shading and exposure to wind Air infiltration
Less efficient residences are more likely to impact DR, because they are more sensitive to the heating and cooling schedule and thermostat setting than higher efficient residences, because the energy consumption of efficient residence is lower than less efficient residence. This paper illustrates the DR at the peak demand of moderately populated rural area in west-coast. This paper has been organized as follows. In Background section, the related work, standards, software model and weather data has been explained. In the case studies section different cases describes the direct load control (DLC), dynamic price control and residential PV penetration for year 2011 and 2030 respectively. Carbon emission reduction and cost benefit analysis (CBR) have been computed respectively in sections IV and V. Finally conclusion section lists out the concluding remarks. II.
BACKGROUND
A. Previous work In this section, previous literature related to the effect of residential ac demand response are discussed. The importance of residential ac demand response during the peak loads and ac sizing were illustrated in reference [3]. Air conditioning peak load depends on two factors: ac size and the way in which consumer operate it. Consumer constantly changing the
thermostat setting especially before the peak load more frequently experience the high demand than the constant thermostat setting. Reference [3] also points out the common practice of oversizing the residential ac. That is using rule of thumb instead of engineering calculation and contractors oversizing to reduce call backs due to overheating. It was found 5 out of 8 residences had oversized ac. According to the FERC (Federal Energy Regulatory Commission) 2009 National Assessment of Demand Response Potential, the peak demand without DR is estimated to increase 1.7 percent every year reaching approximately 950 GW by 2019. Among the various scenarios, the scenario including direct load control accounts almost 10 percentage reduction by 2019. However this study point out the limitation of data and importance of research in effects of energy efficiency programs [4]. Residential energy consumption survey (RECS) performed by EIA shows rapid increment in use of ac in US homes. 2009 survey showed 87% of US occupied houses had ac compare to 68% in 1993. The survey [5] showed the steady growth of house equipped with ac among all housing types in all census regions. It also showed housing types and age were the dominant drivers of ac saturation. Also apartments and lower incomes homes were found with less efficient cooling alternatives ASHRAE standard 55-2010 defines thermal comfort as “condition of mind that expresses satisfaction with thermal environment” [6]. The environmental condition required for comfort is different for everyone. There is large variation from person to person both physiologically and psychologically. Thus the acceptable thermal comfort temperature is very wide, 67°F-83°F (19°C-28°C). Six primary factors which define the thermal comfort are listed below. These parameters are addressed in section 5.4 of ASHARE standard. Metabolic rate Clothing insulation Air temperature Radiant temperature Air speed Humidity In papers [2, 7] 24 houses at hot summer day were modeled for demand response program. Houses were simulated with different ac size, various thermal integrity and 5 min, 10 min and 20 min ac shut off scheduled cycles at the peak demand. Since the indoor temperature increased with increasing length of shut off time, 5 min cycle showed better thermal comfort. Houses with higher thermal integrity had better results. Oversized ac showed comparatively higher demand reduction at the peak. The 2013 Annual Energy Outlook published by EIA [8] included three different cases that had economy-wide taxes starting in 2014 at $25, $15 and $10 per metric ton and rising
at 5% per year through 2040. It also stated the every 90% of economy-wide CO2 emission reduction induced by taxes come from electricity sector. Cost and benefit analysis of smart metering and direct load control report published by NERA (National Economic Research Associates) in 2008[9], described the importance of consumer participation and incentives for effective DLC program. It also highlighted the positive impacts at utility level. In the report “Assessment of Transmission and Distribution Losses in New York State” published by EPRI and SAIC in 2012 [10], net present value (NPV) and benefit to cost ratio (BCR) method were applied for PV cost benefit analysis. Inflation and discount rate used were respectively 2.2 % and 5.25%. In paper [11] cooling energy storage (CES) system was used to compensate summer peak demand, it also used NPV system to execute the cost benefit analysis of CES system. In paper [12] California Independent System Operator’s (CAISO’s) wholesale grid state indicator technique to enable the price responsive demand has been explained. Grid state can be determined by the amount of wholesale price and their difference across the location. Higher price indicates that more expensive generation being relied to serve the load and lower price indicates oversupply, which may need additional load to balance the system. This difference in price indicates the congested transmission system. Hence, CAISO developed the location grid state index to provide consistent indication of grid state across the region. This grid state index was designed by computing composite price which combined the location marginal price (LMP) for each location, the highest ancillary service price for incremental capacity and the flexible ramping price for region over twelve month period data from April,012011 to March,31-2012. CAISO proposed a series of 11 grid state index values that separates extreme low and high values from commonly-occurring price levels. Grid state index values and corresponding lower and upper limit condition are shown in reference [12], where the off-peak and the on-peak average price is the average composite price between 7 PM to 7AM and 7AM-7PM respectively. Report [13] “Investing a Higher Renewable Portfolio Standard (RPS) in California”, described four scenarios to meet California’s 50% RPS. The roof top solar scenario has been used in this paper. B. Software Model 1) GridLab-D: GridLAB–D is an open source simulation and analysis tool developed under the funding of DOE (United States Department of Energy) in association with industry and academia [14]. This enables provides users valuable information to design and operate distribution system. It couples the power flow modules with the demand modules, market modules, distribution automation modules, generator module etc. The PNNL developed taxonomy of 24 prototypical feeder models in the GridLAB-D simulations environment that
contain the fundamental characteristics of non-urban core, radial distribution feeders from the various regions of the U.S [15]. Detail residential and market module characteristics and parameters are explained in [16,17]. Table I shows default R values and air exchange rates to represent type of house ranging from little insulated to good insulated. TABLE I.
R-value, walls (°Fft²-hr/Btu) R-value, ceilings R-value, floors R-value, doors R-value, windows Air exchanges per hour
RESIDENTIAL THERMAL INTEGRITY Little (old home, insulated)
Normal (old home, upgraded)
Good (very well insulated)
19
30
30
11 4 3 1.2
11 19 3 1.7
19 22 5 2.1
1.5
1
0.5
In this paper Feeder-2 (R1-12.7-2) of region 1 which represents a moderately populated suburban and lightly populated rural area of West Coast of US, has been considered. This region is composed mainly of single family residences with small amounts of light commercial, feeder specification are shown in reference [15]. 2) NeuralWare: NeuralWare is a developing and deploying empirical modeling solutions based on neural networks [18]. It computes forecasting, classification, or pattern recognition. Aetificial Neural Network (ANN) has been widely used in electrical power sector mainly for short term forecasting. The ANN computes sytem with learning the input variables which is also called traning. In this paper input variables are load and market price.Hourly September 27, 28 and 29 load and market price data for year 2009-2011 [19], were uesed to train the network to forecast the 09/28/2030 hourly market pricce. The details od ANN structure that has been used in this paper is summarized below Type of Network: Feed Forward Back Propagation Number of inputs: 2 Number of outputs: 1 Hidden Layer structure: 3-3-3 Epoch: 216 Activation Function: Norm-Cum-Delta Sigmoid RSME: 0.2935 Table II shows the forecasted hourly grid state index of 09/28/2030. The estimated load was calculated based on 09/28/2011 load data and 2.5 percent annual growth rate [20]. The average real time on peak price and off peak price were calculated based on forecasted price, which were respectively 46.5$/MWh and 42$/MWh. Grid state index was determined based on [14].
TABLE II. FORECASTED GRID STATE INDEX FOR 09/28/2030 Time 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
Estimated load for 2030 18557.13 17642.70 17044.81 16816.20 17087.97 18080.73 19943.16 21220.48 22075.76 23145.26 24003.73 25006.09 25942.90 27122.70 27977.98 28876.42 29463.12 29181.76 28254.54 28479.95 27407.26 25528.84 23028.56 20643.37
Forecasted price ($/MWh) 34.18624 34.36943 34.71777 35.25855 36.06104 37.24219 38.88467 40.22616 41.34842 42.58546 43.71223 44.91544 46.08374 47.38033 48.50204 49.64413 50.61818 51.13005 51.29942 52.07581 52.16661 51.83368 51.17361 50.57304
Grid State 2 2 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 5 4 4
C. TMY2 Data Typical Metrological year data (TMY2) has been used in the research. TMY2 data sets are produce by National Renewable Energy Data Laboratory’s (NREL’s) Analytic Studies Division. TMY2 data’s are based on the National Solar Radiation Data Base (NSRDB). , In this paper TMY2 data of San Francisco CA has been used to match the feeder region temperature pattern [21]. According to the historical weather data of San Francisco the hottest month of the year 2011was September with average daily high temperature 73°F and the hottest day was September 28. Therefor 09/28/11 weather data has been used to simulate the feeder. III.
CASE STUDIES AND RESULTS
The study has been divided into two different cases. Case 1 represents 2011 and Case 2 represents 2030 feeder load distribution condition, Table III shows detail load distribution. Three types of demand response technique implemented in this paper are described below. 1) DLC: Air condition load was controlled between 2pm to 4pm. The 5 min shutoff cycle was spread evenly through 2 hour period. 2) Dynamic price control: CAISO’s wholesale grid state indicator technique to enable the price responsive demand. In case 1; the average on peak and off peak energy market prices in the Pacific Gas and Electric (PG&E) area was used to develop the grid state. At the end of the third quarter of 2011, the average real time on peak price was 38$/MWh, and the average real time off peak price was 28$/MWh [22]. Table II forecasted grid state indicator was used in case 2 where the
average real time on peak price is 46.5$/MWh and the average real time off peak price is 42$/MWh. Grid state between the hours were assumed to be same as previous hour, for example 12 to 1 PM was assumed as grid state 3. 3) PV: 7 percentage of residential PV was penetrated in case 1 [23] and to reflect the 50 percent RPS, 50 percent residential PV was penetrated in case2 [13]. 4kW roof top residential PV were used in this paper. FEEDER 2 LOAD DISTRIBUTION
Residential Units Single family home Apartment Mobile home CommerciaUnits Stripmall Bigbox
14 12
602 154 59 16 26
A. Case 1 The effects of DLC, dynamic price control and PV during 09/28/2011 have been studied. As shown in figure 1, DLC and dynamic price control were implemented to the residential and commercial units with electrical cooling system or ac system. The peak demand reduction in DLC was 20kW, which accounted for 1.3 percent demand reduction and dynamic price control reduced 56kW at the peak, which accounted for 4 percent demand reduction. During dynamic price control peak load shifted towards the lower grid state value and the new peak shoed higher demand than actual peak. These rebound or peak shifting effects can be reduced by scheduling storages units like battery [22]. The low demand reduction was due to the less number of units participated in DR because out of 570 units only 235 were equipped with ac. Even the PV had very little effect; the peak demand reduction was only 81kW, which accounted for 5.5 percent reduction. 1.6
Demand, MW
1.4 1.2 1 0.8 0.6 0.2
Actual Demand DLC Dynamic Price Control PV
0 1 6 111621263136414651566166717681869196 Time, min
Fig 1. Demand profile with or with DR Figure 2, shows typical indoor and outdoor temperature of residential and commercial houses during DLC event. Stripmall responded to DLC better than residential units, indoor temperature spiked to 78°F. Temperature during DLC event at single family home, apartment and mobile home were respectively, 73.2°F, 72.5°F and 71.5°F. During dynamic price control event effect could be seen throughout the day.
Single Family Home
87
Case 2
404 99 41
97 90 83 76 69 62 55 97 90 83 76 69 62 55
82 77 72 67 Apartment
62 87 82 77 72 67 62
97 90 83 76 69 62 55
Mobile Home
87
97 90 83 76 69 62 55
82 77 72 67
Strip-Mall
62 1
16 31 46 61 76 Time, min Actual DLC Dynamic Price Control Out Door
Outdoor Temperature, °F
Case 1
0.4
87 82 77 72 67 62
In door Temperature, °F
TABLE III.
Highest indoor temperature of 82.3°F was seen in the apartment at 4:30 PM, which is an acceptable thermal comfort according to ASHARE standard.
91
Fig2. Indoor and outdoor temperature with or without DR. B. Case 2 To meet the 2.5 percent annual growth rate almost 1MW load was added in the feeder 2, in which 15 percent was commercial unit and 85 percent was residential unit. Assuming the ac growth rate most of the new residential units were equipped with ac system [20]. The effects of DLC, dynamic price control and PV during 09/28/2030 have been studied. As shown in figure 3, DLC and dynamic price control were implemented among the residents and commercial units with electrical cooling units or air conditioner units. The peak demand reduction in DLC was 108kW, which accounted for 4.5 percent demand reduction and dynamic price control reduced 376kW at the peak, which accounted for 15.3 percent demand reduction. As in case 1 during dynamic price control peak load shifted towards the lower grid state value. The demand reduction in this case was better than case 1 mainly due the increment in ac equipped units percentage, out of 857 units 451 units were equipped with ac. PV had significant effect on the peak demand reduction. Almost 792kW was reduced, which accounted for 32.3 percent reduction. Since same typical houses were considered, the indoor temperatures during DR events were relatively close and
minimum and maximum temperatures were same. Indoor and outdoor temperature profile with or without DR is not included in this pap
N
2.4
NPV t 1
(1)
N
1.2 0.8
Actual DLC Dynamic Price Control PV
0.4 0
1 8 15 22 29 36 43 50 57 64 71 78 85 92 Time, min
Fig 3. Demand profile with or with DR. IV.
CARBON EMISSION REDUCTION ANALYSIS
Carbon emission reduction was calculated using energy saved during DR event and emission factor (6.89551×10-4 metric tons CO2 / kWh) [24]. Figures 4, shows carbon emission saved during DLC was minimal, 1.8 metric ton in 2011 and 3.2 metric ton in 2030. Dynamic price control was moderate, 227.3 metric ton in 2011 and 385 metric ton in 2030. PV showed the 3.6 metric ton in 2011 and 496 metric ton in 2030. The impressive PV result is mainly due to 50 percentage of PV penetration and the simulation was done for hottest day. reduced, metric-tons
Benefitt Costt (1 i )t
1.6
600 Dynamic Price Control
500
PV
400 300 200 100 0
DLC 9/28/2011
9/28/2030
Fig 4. Carbon emission reduction in year 2011 and 2030 V.
ECONOMIC ANALYSIS
It is always beneficial to execute economic analysis to find out effectiveness and profitability of the model. Here, cost benefit ratio (CBA) has been used to compute investment and benefits associated with reduction of demand and energy requirements. The investment does not always appear as immediate profit. However, in the long run the benefit and cost generated by the investment maybe more beneficial. This section estimates residential PV CBA. Some of the residential PV benefits in utility perspective are listed below [10]. Improve system efficiency and reliability Reduce need for expensive investment in additional generation capacity Improve capacity utilization on system Reduce environmental impacts Reduce spikes in wholesale spot price
Benefitt (1 i )t BCR t 1N Costt t t 1 (1 i )
(2)
Where, i is discount rate, t is time of cash flow, and N is total period. Parameters used to calculate NPV and BCR are listed below. Inflation and discount rate were 2.2% and 5.25% respectively [10]. California residential renewable tax credit and rebate was $11,778 [25]. 4kW PV and O&M cost/year were $26,000 and $84 respectively [25, 26]. Annul energy produced by single residential PV unit and life span were 5601kWh and 25 years respectively [25, 27]. Carbon price per metric ton were $25, $15 and $10 respectively. [8] Total T&D loss was 7%. Average power plant operation, maintenance and fuel cost based on energy consumption in California [28, 29]. In equation 1 and 2 benefit accounted for total energy cost, operation, maintenance and fuel cost, T&D loss cost and carbon emission cost saved using residential PV and cost accounted for PV installation cost and O&M cost. Carbon price per metric ton was converted into price per kWh using emission factor. Then the calculated carbon emission price was multiplied with the annual PV energy production to find the carbon emission price saved. Equivalent utility operation, maintenance and fuel cost to produce corresponding annual PV energy was calculated to find out operation, maintenance and fuel cost saved. T&D losses cost saved due to annual PV energy production was calculated.
Net Present Value, 103$
Demand, kW
2
CO2emission
Residential PV CBA requires the comparison of PV cost and benefits. Net present (NPV) and benefit cost ratio (BCR) method were used.
15 10 5 0 $25/Mton $15/Mton $10/Mton $0/Mton
Fig 5. Net present value with $25, $15, $10 and $0 per Mton carbon price
Figure 5, shows NPV of $9913, $8941, $8127 and $7684 for a single family residence with 4kW PV system and Table IV, show BCR greater than 1 for all carbon prices. Since NPV was greater than zero and BCR was greater than 1, 4kW residential PV system was good for implementation. TABLE IV.
BCR FOR DIFFERENT CARBON PRICE
carbon price/Mton ($)
NPV Benefit
NPV Cost
BCR
25 15 10 0
35771.26 34879.56 34433.70 33542.00
25857.88 25857.88 25857.88 25857.88
1.38 1.35 1.33 1.30
VI.
CONCLUSION
A demand reduction program was studied to mitigate peak demand. DLC, dynamic load control and residential PV were implemented in the DR program for 2011 and 2030 cases. Since the percentage of air conditioning equipped houses and residential PV penetration were lower in 2011, DR had less impact on demand reduction. Air conditioning percentage and residential PV were increased in the 2030 case, which showed relatively higher impact in DR events. Carbon emission reduction analysis directly depended on DR effect on demand. Carbon reduction was higher when demand reduction was high. A cost benefit analysis was performed on residential PV. It showed $7684 net present value with even $0 carbon price per metric ton and BCR greater than 1. Therefore with the proper incentives for DR programs, mainly residential PV would help solve the peak demand issues. Moreover, these DR programs are essential to achieving clean energy initiatives like Clean Energy Act, California and 50% Renewable Portfolio Standard (RPS) in 2030. REFERENCES [1]
[2]
[3]
[4]
[5]
[6] [7] [8]
US Energy Information Administration (EIA), electricity power monthly, released September, 25, 2014, [online] Available: http://www.eia.gov/totalenergy/data/monthly/archive/00351409.pdf W. Jewell, “The Effects of Demand Response and Residential Energy Efficiency on Consumer Comfort”, 46th Annual Frontiers of Power Conference, Oklahoma State University, Aug. 2013 T.A. Reddy, D.E. Claridge, “Effect of air-conditioner over sizing and control on electric-peak loads in a residence” Energy, Volume 18, Issue 11, November 1993 Pages 1139-1152 A National Assessment of Demand Response Potential. Federal Energy Regulatory Commission, June 2009. [Online] Available www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf Residential Energy Consumption Survey (RECS) ,”Air conditioning in 100 million US home” [Online] Available: http://www.eia.gov/ consumption/residential/reports/2009/air-conditioning.cfm ANSI/ASHRAE Standard 55-2010, “Thermal Environmental Conditions for Human Occupancy”. ASHRAE, 2010. W. Jewell, “Effects of Residential Energy Efficiency on Electric Demand Response Program”, Wichita State University Annual Energy Outlook 2013 Supplement (July, 2013). “Further Sensitivity Analysis of Hypothetical Policies to Limit Energy-Related
Carbon Dioxide Emissions” [Online]. Available: http://www.eia.gov /forecasts/aeo/supplement/co2/pdf/aeo2013_supplement.pdf [9] NERA Report for the Ministerial Council on Energy Smart Meter Working Group (Feb, 2008). “Cost Benefit Analysis of Smart Metering” [Online]. Available: http://sedc-coalition.eu/wpcontent/uploads/2011/05/NERA-08-02-29-Cost-Benefit-AnalysisReport-for-Australia.pdf[10] [10] EPRI and SAIC final report, (Nov, 2012). “Assessment of Transmission and Distribution Losses in New York”.PID071178 (NYSERDA 15464), [Online].Available: http://www.nyserda.ny.gov/ Publications/Research-and-Development- Technical-Reports/ElectricPower-Delivery-Reports.aspx [11] C.S Chen, J.N. Sheen, “Cost Benefit Analysis of a Cooling Energy Storage System’, National Sun Yat-Sen University, Kaohsiung, Taiwan, IEEE Transactions Power System, Vol.8, No4, Nov 1993 [12] California Independent System Operator (June. 2012). “Wholesale Grid State Indicator to Enable Price Responsive Demand”, [Online]. Available:http://www.caiso.com/Documents/WhitePaperProposal WholesaleGridStateIndicator-EnablePriceResponsiveDemand.pdf. [13] Energy and Environmental Economics, Inc “Investigating a Higher Renewables Portfolio Standard in California” ”. [Online]. Available: https://ethree.com/documents/E3_Final_RPS_Report_2014_01_06_wit h_appendices.pdf [14] GridLAB-D, “The Next-Generation Simulation Software,” [Online] Available: http://www.gridlabd.org [15] Gridlab-D, “Feeder Taxonomy” [Online] Available: sourceforge.net/ apps/ mediawiki/gridlabd/index.php?title=Feeder_Taxonomy, May 2013. [16] Gridlab-D, “Residential module user’s guide” [Online] Available: sourceforge.net/apps/mediawiki/gridlabd/index.php?title=Residential_ Module_Guide, May 2013. [17] Gridlab-D, “Market module user’s guide” [Online] Available: http://gridlabd.me.uvic.ca/wiki/index.php/Market_User_Guide [18] Artificial Neural Network Software, “Neural Ware “[Online], Available: http://www.neuralware.com/ [19] “CAISO Hourly load and price 2009-2011” [Online] Available: https://www.pge.com/regulation/RateDesignWindow2009/Pleadings/D RA/2011/RateDesignWindow2009_Plea_DRA_20111031_221195Atch 02_221197.pdf [20] Berkeley Lab “Energy Growth Rate In CA” [Online], Available: http://www2.lbl.gov/Science-Articles/Archive/energy-myths2.html [21] National Solar Radiation Data Base 1961- 1990, [Online] Available: Typical Meteorological Year 2, National Renewable Energy Lab, rredc.nrel.gov/solar/old_data/nsrdb/1961-1990/tmy2/, accessed June 2013. [22] H. Aburub and W. T. Jewell, “Assessment of the Use of CAISO Wholesale Grid State Indicator in the Distribution System,” North American Power Symposium, Manhattan, KS, September 2013. [23] Z.Hu “Optimal Generation Expansion Planning with Integration of Variable Renewables and Bulk Storage Systems” [Online] Available: http://soar.wichita.edu/bitstream/handle/10057/6724/d13008_Hu.pdf?se quence=1 [24] “EPA Clean Energy Calculation and Reference”, [Online]. Available: http://www.epa.gov/cleanenergy/energyresources/refs.html [25] “California energy tax credit, rebates, grants and incentives” [Online], Available: http://www.dasolar.com/energytaxcredit-rebatesgrants/california [26] Distributed Generation Renewable Energy Estimate of Costs (updated August 2013). “Costs for Electric Generating Technologies” [Online]. Available: http://www.nrel.gov/analysis /tech_lcoe_re_cost_est.html[27] “NREL PV Watts Calculator”, [Online]. Available: http://pvwatts.nrel.gov/ pvwatts.php [28] “Residential Energy Consumption Survey (Recs)”, [Online]. Available: http://www.eia.gov/consumption/residential/index.cfm [29] “EPA Clean Energy Calculation and Reference”, [Online]. Available: http://www.epa.gov/cleanenergy/energyresources/refs.html
