Power Resilience Enhancement of a Residential Electricity User Using Photovoltaics and a Battery Energy Storage System under Uncertainty Conditions
"> Figure 1
<p>Heat map highlighting locations where power outages occurred across the globe [<a href="#B15-energies-13-04193" class="html-bibr">15</a>].</p> "> Figure 2
<p>The number of power outage events in each state of the USA [<a href="#B17-energies-13-04193" class="html-bibr">17</a>].</p> "> Figure 3
<p>Four component resilience framework.</p> "> Figure 4
<p>Schematic view of the proposed three power supply system configurations for residential electricity users [Note: EG—Electric grid; BES—Battery energy storage; NGPG—Natural gas power generator; PV—Photovoltaics.]</p> "> Figure 5
<p>Schematic view of EG + BES based power supply system configuration [Note: EG—Electric grid; BES—Battery energy storage; DC—Direct current; AC—Alternating current.]</p> "> Figure 6
<p>Schematic view of EG + NGPG + BES based power supply system configuration [Note: EG—Electric grid; BES—Battery energy storage; NGPG—Natural gas power generator; DC—Direct current; AC—Alternating current].</p> "> Figure 7
<p>Schematic view of EG + PV + BES based power supply system configuration [Note: EG—Electric grid; BES—Battery energy storage; PV—Photovoltaics; DC—Direct current; AC—Alternating current; MPPT—Maximum power point tracking].</p> "> Figure 8
<p>Electricity load profile of a multi stair residential building located in New York, USA [Note: REU—Residential electricity users; USA—United States of America).</p> "> Figure 9
<p>Trends in natural gas price variation.</p> "> Figure 10
<p>Flowchart for simulation modeling of power supply system configurations. [Notes: EG-Electric grid; BES-Battery energy storage; NGPG—Natural gas power generator; PV—Photovoltaics.].</p> "> Figure 11
<p>Electricity purchased from grid highlighting on the power outage duration for the year.</p> "> Figure 12
<p>The increasing trends of unmet electric load profile vs. electric grid restoration time: (<b>a</b>) outage-1 (intentional attack); (<b>b</b>). outage-2 (system operational error); (<b>c</b>). outage-3 (fuel supply issue).</p> "> Figure 13
<p>Time series graph showing the operation of the natural gas power generator.</p> "> Figure 14
<p>Power compensated by the natural gas power generator.</p> "> Figure 15
<p>Share of power production in EG + NGPG + BES based power supply system configuration [Note: EG-Electric grid; NGPG-Natural gas power generator; BES-Battery energy storage].</p> "> Figure 16
<p>Solar radiation potential available in the location.</p> "> Figure 17
<p>Share of power production EG + PV + BES based power supply system configuration [Note: EG-Electric grid; PV-Photovoltaics; BES-Battery energy storage].</p> ">
Abstract
:1. Introduction
- A four-component resilience framework with techno-economic and environmental indicators to understand the resilience of residential electricity user (REU) power supply system (PSS).
- A battery energy storage (BES) as a preparedness measure that is not considered in most of the literature is considered here while modeling the proposed PSS configurations.
- The proposed three different REUs are modeled considering power outage duration as well as the electric load conditions of the New York-based residential multi-story building as a case study.
- Evaluation of unmet and compensated electric loads for resilience comparison between the three PSS configurations of an REU.
2. Resilience Framework
- As a preparedness measure, battery energy storage is used.
- For understanding this component, an indicator, i.e., an increase in unmet electric load, is considered.
- Here compensated load by the PSS configuration during the event of a power outage is considered as an indicator.
- The adaptation step demands the renovation and modernization of the energy infrastructure.
3. Power Supply System (PSS) Modelling for Residential Electricity Users (REUs)
3.1. PSS Configurations for REUs
3.1.1. REU with EG + BES based PSS Configuration
3.1.2. REU with EG + NGPG + BES based PSS Configuration
3.1.3. REU with EG + PV + BES based PSS Configuration
3.2. REUs PSS Equipment Modelling
3.2.1. Electrical Loads and Electric Grid (EG)
3.2.2. Natural Gas Power Generator (NGPG)
3.2.3. Photovoltaics (PV)
3.2.4. Power Converter
3.2.5. Battery Energy Storage (BES)
4. Data Inputs and Simulation of Proposed PSS Configurations
4.1. Data Inputs
4.1.1. Electrical Load Profile
4.1.2. Electric Grid Power Outage and Tariff Data
4.1.3. Power Supply Systems Equipment Cost
4.1.4. Data Inputs on Natural Gas Fuel and Emission Factors
4.2. Simulation of PSS Configurations
5. Results and Discussion
5.1. REU with EG + BES Based PSS Configuration
5.2. REU with EG + NGPG + BES Based PSS Configuration
5.3. REU with EG + PV + BES based PSS Configuration
5.3.1. Weather Data for PV Modelling
5.3.2. Power Performance of EG + PV + BES based PSS Configuration
5.4. Comparison of PSS Configurations
5.5. Discussion on Ensuring Resilience and Future Research Directions
- ∙
- Investigation of the investments made in energy infrastructure and their impacts on improving resilience can be considered as one of the research directions, as in our study. We observed the variations in economic indicators based on the proposed energy infrastructure.
- ∙
- Resilience framework incorporating islanding and other grid disturbance detection approaches could be considered as one possible future research direction.
- ∙
- Tools that support grid restoration can be beneficial in optimal scheduling of on-site power generation facilities at REUs side, and in fact, they could influence the robustness component of the resilience cycle.
- ∙
- Modernization and re-design of PSS infrastructure by using advanced technologies like Blockchain [42,43,44], Internet of Things [45], Energy Internet [46], Blockchain-based Internet of Things (B-IoT) [47], and Artificial Intelligence (AI) techniques such as Machine Learning (ML), and Deep Learning (DL) [48] could favor in tracking the power outage events and thereby allows us to have a data-driven solution. Hence, power sector digital transformation from the context of resilience can be one of the possible future research directions.
- ∙
- Location and ecosystem specific studies can be further modeled to evaluate the feasibilities of PSS configurations in the context of the proposed resilience framework.
6. Conclusions
- The configuration with the BES alone as a support option may not be feasible for longer power outage scenarios.
- The configuration with fossil fuel-based PSS configuration (i.e., NGPG) would definitely be a solution; however, it is not feasible from the perspective of the least cost of energy and lowest life-cycle emissions.
- A PV plus BES-based PSS configuration would be much more feasible under the prosumer only category, as it allows energy trade between the REUs and EG.
- The EG + PV + BES based PSS configuration is observed to enhance the overall resilience, thereby help in improving the energy accessibility to REUs.
Author Contributions
Funding
Conflicts of Interest
References
- Cifor, A.; Denholm, P.; Ela, E.; Hodge, B.M.; Reed, A. The policy and institutional challenges of grid integration of renewable energy in the western United States. Utilities Policy 2015, 33, 34–41. [Google Scholar] [CrossRef] [Green Version]
- Eid, C.; Codani, P.; Perez, Y.; Reneses, J.; Hakvoort, R. Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design. Renew. Sustain. Energy Rev. 2016, 64, 237–247. [Google Scholar] [CrossRef]
- Hertwich, E.G.; Gibon, T.; Bouman, E.A.; Arvesen, A.; Suh, S.; Heath, G.A.; Bergesen, J.D.; Ramirez, A.; Vega, M.I.; Shi, L. Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc. Natl. Acad. Sci. USA 2015, 112, 6277–6282. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bayati, N.; Aghaee, F.; Sadeghi, S.H. The Adaptive and Robust Power System Protection Schemes in the Presence of DGs. Int. J. Renew. Energy Res. 2019, 9, 732–740. [Google Scholar]
- Field, C.B.; Barros, V.R.; Stocker, T.F.; Dahe, Q.; Dokken, D.J.; Ebi, K.L.; Mastrandrea, M.D.; Mach, K.J.; Plattner, G.; Allen, S.K.; et al. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation; Cambridge University Press: New York, NY, USA, 2012. [Google Scholar]
- Chopra, S.S.; Khanna, V. Toward a Network Perspective for Understanding Resilience and Sustainability in Industrial Symbiotic Networks. 2012 IEEE International Symposium on Sustainable Systems and Technology (ISSST), Boston, MA, USA, 16–18 May 2012; pp. 1–6. [Google Scholar]
- Chopra, S.S.; Khanna, V. Understanding resilience in industrial symbiosis networks: Insights from network analysis. J. Environ. Manag. 2014, 141, 86–94. [Google Scholar] [CrossRef] [PubMed]
- Chopra, S.S.; Khanna, V. Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience. Phys. A Stat. Mech. Appl. 2015, 436, 865–877. [Google Scholar] [CrossRef]
- Chopra, S.S.; Dillon, T.; Bilec, M.; Khanna, V. A network-based framework for assessing infrastructure resilience: A case study of the London metro system. J. R. Soc. Interface 2016, 13, 20160113. [Google Scholar] [CrossRef] [Green Version]
- Roege, P.E.; Collier, Z.A.; Mancillas, J.; McDonagh, J.A.; Linkov, I. Metrics for energy resilience. Energy Policy 2014, 72, 249–256. [Google Scholar] [CrossRef]
- Panteli, M.; Mancarella, P. Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies. Electr. Power Syst. Res. 2015, 127, 259–270. [Google Scholar] [CrossRef]
- Power Systems Engineering Center (PSERC). Engineering Resilient Cyber Physical Systems; Power Systems Engineering Center (PSERC): Tempe, AZ, USA, 2011. [Google Scholar]
- Linkov, I.; Bridges, T.S.; Creutzig, F.; Decker, J.; Foxlent, C.; Kroger, W.; Lambert, J.H.; Levermann, A.; Montreuil, B.; Nathwani, J.; et al. Changing the resilience paradigm. Nat. Clim. Chang. 2014, 4, 407–409. [Google Scholar] [CrossRef]
- Linkov, I.; Trump, B.D.; Keisler, J. Risk and resilience must be independently managed. Nature 2018, 555, 30. [Google Scholar] [CrossRef] [PubMed]
- Global Outage Tracker Powered by Machine Learning: DataCapable Adds Global Power Outage Maps to Esri’s ArcGIS Marketplace. Available online: https://gisuser.com/2017/06/global-outage-tracker-powered-by-machine-learning-datacapable-adds-global-power-outage-maps-to-esris-arcgis-marketplace/ (accessed on 29 June 2020).
- Mukherjee, S.; Nateghi, R.; Hastak, M. Data on major power outage events in the continental US. Data Brief 2018, 19, 2079. [Google Scholar] [CrossRef]
- USA Blackout Annual Report 2014. Eaton USA’s Blackout Tracker. Available online: http://pqlit.eaton.com/ll_download_bylitcode.asp?doc_id=33333 (accessed on 15 June 2020).
- Najafi, J.; Peiravi, A.; Anvari-Moghaddam, A.; Guerrero, J.M. Resilience improvement planning of power-water distribution systems with multiple microgrids against hurricanes using clean strategies. J. Clean. Prod. 2019, 223, 109–126. [Google Scholar] [CrossRef]
- Rosales-Asensio, E.; de Simón-Martín, M.; Borge-Diez, D.; Blanes-Peiró, J.J.; Colmenar-Santos, A. Microgrids with energy storage systems as a means to increase power resilience: An application to office buildings. Energy 2019, 172, 1005–1015. [Google Scholar] [CrossRef]
- Marqusee, J.; Don, D.J., II. Reliability of emergency and standby diesel generators: Impact on energy resiliency solutions. Appl. Energy 2020, 268, 114918. [Google Scholar] [CrossRef]
- Anderson, K.; Laws, N.D.; Marr, S.; Lisell, L.; Jimenez, T.; Case, T.; Li, X.; Lohmann, D.; Cutler, D. Quantifying and Monetizing Renewable Energy Resiliency. Sustainability 2018, 10, 933. [Google Scholar] [CrossRef] [Green Version]
- Balasubramaniam, K.; Saraf, P.; Hadidi, R.; Makram, E. Energy management system for enhanced resiliency of microgrids during islanded operation. Electr. Power Syst. Res. 2016, 137, 133–141. [Google Scholar] [CrossRef]
- Ouyang, M.; Dueñas-Osorio, L. Multi-dimensional hurricane resilience assessment of electric power systems. Struct. Saf. 2014, 48, 15–24. [Google Scholar] [CrossRef]
- Faraji, J.; Babaei, M.; Bayati, N.; Hejazi, M.A. A comparative study between traditional backup generator systems and renewable energy based microgrids for power resilience enhancement of a local clinic. Electronics 2019, 8, 1485. [Google Scholar] [CrossRef] [Green Version]
- Li, J.; Niu, D.; Wu, M.; Wang, Y.; Li, F.; Dong, H. Research on Battery Energy Storage as Backup Power in the Operation Optimization of a Regional Integrated Energy System. Energies 2018, 11, 2990. [Google Scholar] [CrossRef] [Green Version]
- Kumar, N.M.; Subathra, M.S.P.; Moses, J.E. On-Grid Solar Photovoltaic System: Components, Design Considerations, and Case Study. In Proceedings of the 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, India, 7–9 February 2018; pp. 616–619. [Google Scholar]
- Open EI Database for Energy Load Profiles in USA. Available online: https://openei.org/wiki/Data (accessed on 12 June 2020).
- HOMER Energy. Homer Pro Version 3.7 User Manual; HOMER Energy: Boulder, CO, USA, 2016; Available online: https://www.homerenergy.com/pdf/HOMERHelpManual.pdf (accessed on 12 June 2020).
- Kumar, N.M.; Chopra, S.S.; Chand, A.A.; Elavarasan, R.M.; Shafiullah, G.M. Hybrid renewable energy microgrid for a residential community: A techno-economic and environmental perspective in the context of the SDG7. Sustainability 2020, 12, 3944. [Google Scholar] [CrossRef]
- Kumar, N.M.; Gupta, R.P.; Mathew, M.; Jayakumar, A.; Singh, N.K. Performance, energy loss, and degradation prediction of roof-integrated crystalline solar PV system installed in Northern India. Case Stud. Therm. Eng. 2019, 13, 100409. [Google Scholar] [CrossRef]
- Karmaker, A.K.; Hossain, M.A.; Manoj Kumar, N.; Jagadeesan, V.; Jayakumar, A.; Ray, B. Analysis of Using Biogas Resources for Electric Vehicle Charging in Bangladesh: A Techno-Economic-Environmental Perspective. Sustainability 2020, 12, 2579. [Google Scholar] [CrossRef] [Green Version]
- Krishan, O.; Suhag, S. Techno-economic analysis of a hybrid renewable energy system for an energy poor rural community. J. Energy Storage 2019, 23, 305–319. [Google Scholar] [CrossRef]
- Adefarati, T.; Bansal, R.C. Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources. Appl. Energy 2019, 236, 1089–1114. [Google Scholar] [CrossRef]
- New York State Energy Profile. Available online: https://www.eia.gov/state/print.php?sid=NY (accessed on 12 June 2020).
- Electric Power Monthly. Available online: https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a (accessed on 12 June 2020).
- How Much do Solar Panels Cost in the U.S. in 2020? Available online: https://news.energysage.com/how-much-does-the-average-solar-panel-installation-cost-in-the-u-s/ (accessed on 12 June 2020).
- Bloomberg New Energy Finance, Battery Pack Prices Fall as Market Ramps up with Market Average at $156/kWh in 2019. Available online: https://about.bnef.com/blog/battery-pack-prices-fall-as-market-ramps-up-with-market-average-at-156-kwh-in-2019/?sf113554299=1 (accessed on 12 June 2020).
- Monthly Average Price of Natural Gas—Residential, U.S. Department of Energy, Energy Information Administration “Natural Gas Navigator” Historic Price Series. Available online: http://eia.doe.gov/dnav/ng/hist/n3035ny3m.htm (accessed on 12 June 2020).
- US Grid Emissions Factors. HOMER Pro Version 3.14; HOMER Energy: Boulder, CO, USA, 2020; Available online: https://www.homerenergy.com/products/pro/docs/latest/us_grid_emissions_factors.html (accessed on 12 June 2020).
- Fthenakis, V.M.; Kim, H.C.; Alsema, E. Emissions from photovoltaic life cycles. Environ. Sci. Technol. 2008, 42, 2168–2174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, W.; Sang, J.; Chen, L.; Tian, J.; Zhang, H.; Palma, G.O. Life cycle assessment of lead-acid batteries used in electric bicycles in China. J. Clean. Prod. 2015, 108, 1149–1156. [Google Scholar] [CrossRef]
- Kumar, N.M. Blockchain: Enabling wide range of services in distributed energy system. BeniSuef Univ. J. Basic Appl. Sci. 2018, 7, 701–704. [Google Scholar] [CrossRef]
- Ahl, A.; Yarime, M.; Tanaka, K.; Tanaka, K.; Sagawa, D. Review of blockchain-based distributed energy: Implications for institutional development. Renew. Sustain. Energy Rev. 2019, 107, 200–211. [Google Scholar] [CrossRef]
- Ahl, A.; Yarime, M.; Goto, M.; Chopra, S.S.; Kumar, N.M.; Tanaka, K.; Sagawa, D. Exploring blockchain for the energy transition: Opportunities and challenges based on a case study in Japan. Renew. Sustain. Energy Rev. 2020, 117, 109488. [Google Scholar] [CrossRef]
- Kumar, N.M.; Dash, A.; Singh, N.K. Internet of Things (IoT): An Opportunity for Energy-Food-Water Nexus. In Proceedings of the 2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC), Greater Noida, Uttar Pradesh, India, 13–14 April 2018; pp. 68–72. [Google Scholar]
- Kabalci, Y.; Kabalci, E.; Padmanaban, S.; Holm-Nielsen, J.B.; Blaabjerg, F. Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments. Electronics 2019, 8, 972. [Google Scholar] [CrossRef] [Green Version]
- Kumar, N.M.; Mallick, P.K. Blockchain technology for security issues and challenges in IoT. Procedia Comput. Sci. 2018, 132, 1815–1823. [Google Scholar] [CrossRef]
- Xu, Y.; Ahokangas, P.; Louis, J.-N.; Pongrácz, E. Electricity Market Empowered by Artificial Intelligence: A Platform Approach. Energies 2019, 12, 4128. [Google Scholar] [CrossRef] [Green Version]
Start Time | Restoration Time | Outage Duration | Reason | Notation |
---|---|---|---|---|
7:49 AM on 7 May | 9:02 AM on 7 May | 1.21 h | Intentional attack | Outage-1 |
9:29 PM on 26 May | 12:40 AM on 27 May | 3.18 h | System disruption | Outage-2 |
7:30 AM on 31 May | 7:27 AM on 13 June | 311.95 h | Fuel supply issue | Outage-3 |
Cost Parameters | Photovoltaics | Power Converter | Battery | Natural Gas Power Generator |
---|---|---|---|---|
Capital cost | 3100 $/kW | 137.5 $/kW | 156 $/kWh | 500 $/kW |
Replacement cost | 3100 $/kW | 137.5 $/kW | 156 $/kWh | 500 $/kW |
Operation & maintenance cost | 310 $/y | 13.7 $/y | 15.6 $/y | 0.03 $/op. h |
Parameter | Values with Units |
---|---|
Photovoltaics (PV) | |
Peak power | 1 kW |
Temperature coefficient | −0.3%/°C |
Nominal operating temperature | 47 °C |
Efficiency at the standard test condition | 21% |
Lifetime | 25 y |
Battery Energy Storage (BES) | |
Type | Lithium-ion |
Nominal capacity | 16.7 kWh |
Nominal voltage | 12 V |
The initial state of charge | 100% |
Minimum state of charge | 20% |
Self-discharge rate (including the safety circuit) | 5%/day |
Lifetime | 15 y |
Natural Gas Power Generator (NGPG) | |
Input fuel | Natural gas |
Capacity | 65 kW |
Efficiency | 95% |
Lifetime | 15,000 h |
Power Converter (PC) | |
Capacity | 60 kW |
Efficiency | 95% |
Lifetime | 15 y |
Parameters | Value with Units |
---|---|
Lower heating value | 45 MJ/kg |
Density | 0.79 kg/m3 |
Carbon content | 67% |
Sulfur content | 0% |
Parameters | Value with Units |
---|---|
Initial capital cost | 0 (the user does not invest) |
Operation cost | 37,458.05 $/y |
Cost of energy | 0.1742 $/kWh |
Total load demand | 222,834 kWh/y |
Load consumption | 215,029 kWh/y |
Purchase value of electricity from grid | 37,458.05 $/y |
Grid sales | 0 (there is no provision) |
Unmet electric load | 7805 kWh/y |
CO2 emissions | 61,713,323.00 g/y |
SO2 emissions | 77,410.44 g/y |
NOx emissions | 43,005.80 g/y |
Parameters | Value with Units |
---|---|
Initial capital cost | $2,028,188.00 |
Operation cost | 37,458.05 $/y |
Cost of energy | 0.1742 $/kWh |
Net present cost | $4,045,528.28 |
Total load demand | 222,834 kWh/y |
Load consumption | 215,029 kWh/y |
Purchase value of electricity from grid | 37,458.05 $/y |
Grid sales | 0 (there is no provision) |
Unmet electric load | 0 kWh/y |
CO2 emissions | 64,351,413.00 g/y |
SO2 emissions | 94,815.59 g/y |
NOx emissions | 43,005.80 g/y |
Parameters | Value with Units |
---|---|
Initial capital cost | $38,741.09 |
Operation cost | 39,857.95 $/y |
Cost of energy | 0.1903 $/kWh |
Net present cost | $634,431.83 |
Total load demand | 222,834 kWh/y |
Load consumption from grid | 215,029 kWh/y |
Purchase value of electricity from grid | 38,817.68 $/y |
Grid sales | 0 (there is provision but used as standby option) |
Unmet electric load | 0 kWh/y |
Load supplied by a natural gas generator | 7805 kWh/y |
Fuel cost | 1157.41 $/y |
CO2 emissions | 65,741,000.00 g/y |
SO2 emissions | 76,400.00 g/y |
NOx emissions | 46,000.00 g/y |
Parameters | Value with Units |
---|---|
Initial capital cost | $83,078.76 |
Operation cost | 47,236.07 $/y |
Cost of energy | 0.05560 $/kWh |
Net present cost | $485,892.00 |
Total load demand | 222,834 kWh/y |
Load consumption from grid | 26,515.00 kWh/y |
Purchase value of electricity from the grid | 4618.91 $/y |
Energy fed to grid sales | 471,079 kWh/y |
Grid sales value | 349,540.62 $/y |
Unmet electric load | 0 kWh/y |
CO2 emissions | 18,407,350.00 g/y |
SO2 emissions | 84,146.62 g/y |
NOx emissions | 44,566.80 g/y |
Parameters | EG + BES | EG + NGPG + BES | EG + PV + BES |
---|---|---|---|
Initial capital cost | $2,028,188.00 | $38,741.09 | $83,078.76 |
Operation cost | 37,458.05 $/y | 39,857.95 $/y | 47,236.07 $/y |
Cost of energy | 0.1742 $/kWh | 0.1903 $/kWh | 0.05560 $/kWh |
Net present cost | $4,045,528.28 | $634,431.83 | $485,892.90 |
Unmet electric load | 0 kWh/y | 0 kWh/y | 0 kWh/y |
CO2 emissions | 64,351,413.00 g/y | 65,741,000.00 g/y | 44,316,890.00 g/y |
SO2 emissions | 94,815.59 g/y | 76,400.00 g/y | 263,161.24 g/y |
NOx emissions | 43,005.80 g/y | 46,000.00 g/y | 138,859.60 g/y |
Parameters | EG + PV + BES | |
---|---|---|
With Sales to EG | Without Sales to EG | |
Initial capital cost | $83,078.76 | $83,078.76 |
Operation cost | 47,236.07 $/y | 47,236.07 $/y |
Cost of energy | 0.05560 $/kWh | 0.05560 $/kWh |
Net present cost | $485,892.90 | $485,892.90 |
Unmet electric load | 0 kWh/y | 0 kWh/y |
CO2 emissions | 44,316,890.00 g/y | 18,407,350.00 g/y |
SO2 emissions | 263,161.24 g/y | 84,146.62 g/y |
NOx emissions | 138,859.60 g/y | 44,566.80 g/y |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Manoj Kumar, N.; Ghosh, A.; Chopra, S.S. Power Resilience Enhancement of a Residential Electricity User Using Photovoltaics and a Battery Energy Storage System under Uncertainty Conditions. Energies 2020, 13, 4193. https://doi.org/10.3390/en13164193
Manoj Kumar N, Ghosh A, Chopra SS. Power Resilience Enhancement of a Residential Electricity User Using Photovoltaics and a Battery Energy Storage System under Uncertainty Conditions. Energies. 2020; 13(16):4193. https://doi.org/10.3390/en13164193
Chicago/Turabian StyleManoj Kumar, Nallapaneni, Aritra Ghosh, and Shauhrat S. Chopra. 2020. "Power Resilience Enhancement of a Residential Electricity User Using Photovoltaics and a Battery Energy Storage System under Uncertainty Conditions" Energies 13, no. 16: 4193. https://doi.org/10.3390/en13164193