Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review
Abstract
:1. Introduction
2. The Potential of Solar Energy in Australia
3. Grid-Connected Rooftop Solar PV Smart Homes Integrated with Electric Vehicles
3.1. Rooftop Solar PV System
3.2. Energy Storage System (ESS)
Sustainable EV Battery Solutions
3.3. Home-Based Electric Vehicle Charging Infrastructure
3.3.1. X2V or V2X Technologies (X = Home, Grid)
- Home-to-Vehicle (H2V) Technology
- 2.
- Vehicle-to-Home (V2H) Technology
- 3.
- Grid-to-Vehicle (G2V) Technology
- 4.
- Vehicle-to-Grid (V2G) Technology
3.3.2. EV Battery Charging and Discharging Challenges
3.4. Smart Metering
3.5. Home Energy Management System (HEMS)
4. Selected Literature on Solar PV Smart Homes Integrated with EVs
5. Discussion and Near to Future Advancements and Future Roadmap
- Increasing the Adoption of Solar PV Systems: solar PV systems will continue to gain popularity in Australia; the Australian government supports solar energy adoption [44] through subsidies [45] and incentive schemes [39]. Falling capital costs of solar PV panels, coupled with rising electricity prices, and government initiatives have led to shorter payback periods for solar PV systems [48]. Australia is experiencing a significant growth in the solar PV market and aims to have solar energy contribute 30% of its energy supply by 2050 [51,52], as discussed above in detail. This will result in more solar PV installations in residential properties, enabling homeowners to generate clean and green energy for powering their home appliances and selling the extra energy to the utility.
- Integration with the Energy Storage System: to maximize the utilization of solar energy, homeowners will increasingly incorporate ESSs, such as batteries, into their smart homes. This will allow them to store excess solar energy generated during the day for use during the night or periods of low sunlight. Moreover, with an ESS in place, households have the option to sell any excess energy back to the utility. According to the “State of the Energy Market 2022” report [168], households equipped with solar panels and batteries have the opportunity to sell surplus energy to their retailer or neighbors, as well as participate in demand response programs. Home battery systems can contribute significantly to addressing the grid demand peaks, contingent upon the advancements in technology that result in lower installation costs.
- EV Integration: to reduce energy consumption levels, certain EVs can serve as energy storage for both households and the electricity grid. The advancements in bidirectional charging technology, which enables EVs to both receive and discharge energy, will expand the number of EV models that can provide electricity to power homes (V2H) and the grid (V2G). Furthermore, EVs have the potential to store surplus power generated by solar PVs and other renewable energy systems, offering assistance in electricity grid management [79]. According to the “Future Fuels and Vehicles Strategy 2021” report [94], the Australian government introduced pioneering concepts and incentives for EVs, fueling, and charging, which drive the development of innovative home-based EV charging solutions.
- Home Energy Management System: an HEMS is a technological platform that enables the monitoring and control of at least one residential customer’s assets. These assets commonly integrated within an HEMS include solar PVs, batteries, EV chargers, as well as household appliances [169]. The Australian government encourages the implementation of HEMSs throughout the country according to the “Smarter Homes for Distributed Energy 2022” report. This study aims to develop HEMSs that can adapt to dynamic operating conditions and unleash the full potential of distributed energy resources. By providing real-time data on energy generation, usage, and storage, the HEMS empowers homeowners to make informed decisions, optimize energy management, and decrease grid dependency.
- Neighborhood/Community Storage and Shared Electricity: local energy communities are emerging as a collaborative approach for both consumers and prosumers to collectively invest in distributed RESs, community storage systems, and share electricity within their community. These communities, composed of various generation and storage units, hold significant potential as flexible assets that can be effectively utilized by the distribution system operator [170], where residents can share and exchange the excess electricity generated from RESs. The Australian government introduced “Community Batteries Funding Round 1” to implement community batteries nationwide, aiming for cost reductions, emission mitigation, grid relief, and increased distributed solar installations [69].
- Energy on Wheels/Mobile Energy Storage System: the system primarily consists of ESSs and vehicles, which play a vital role in meeting the emergency energy requirements following significant power outages/maintenance [171]. Mobile energy storage systems have garnered considerable attention in the research due to their inherent mobility and flexibility, offering distinct advantages over static resources. In recent years, these systems have been deployed within the existing energy systems to enhance their resilience [172]. Transportation carriers within the system are equipped to transport and distribute the stored energy to the necessary locations. These carriers serve as mobile power sources, delivering the stored energy to primary loads that require an uninterrupted operation.
- Virtual Power Plant: virtual power plant technology offers a powerful solution for aggregating distributed energy resources and user-side assets to actively engage in energy market activities. By integrating various energy sources, a virtual power plant can overcome the challenges associated with relying solely on a single resource and effectively cater to users’ diverse energy requirements while ensuring system stability and security [173]. A virtual power plant is a collection of resources that are managed through software and communication technology to provide services typically offered by a traditional power plant. In Australia, grid-connected virtual power plants specifically aim to coordinate rooftop solar panels, battery storage, and controllable load devices [174].
- Smart Grid Technology: the increasing adoption of solar PV smart home integration with EVs poses challenges for the power grid, even with the installation of solar-powered charging stations. To address these challenges, the existing grid stations should incorporate smart grid technology. A smart grid is capable of managing bidirectional power flow, allowing EV owners to return electricity back to the grid. This bidirectional power flow enhances the efficiency and cost-effectiveness of EV charging, while facilitating the integration of RESs, like solar power [88]. A smart grid is sought after in Australia to address the rising electricity costs, aging infrastructure, and transition from coal-fired power. Investments in the smart grid enhance utility operations, ensure grid reliability, and prioritize robust and secure electricity supply for economic growth and technological innovation [175].
- Grid Integration and Demand Response: with a significant number of solar PV smart homes and EVs in the system, there is a need for grid integration and demand response mechanisms. These technologies enable the seamless communication between homes, EVs, and the electricity grid, allowing for an optimized energy flow, load management, and participation in grid-balancing programs. In October 2021, Australia introduced a wholesale demand response mechanism to enable consumers, either individually or through aggregators, to actively participate in reducing their electricity loads during peak periods. By voluntarily reducing their energy consumption, consumers have the opportunity to receive rewards. This demand response initiative not only benefits consumers, but also contributes to the stability of the power system during high-demand periods [168].
- SolarEV City Concept: cities considerably contribute (60–70%) to the pollution that causes climate change due to how much energy they use. With urban populations growing, it is vital to find affordable methods for enhancing cities’ cleanliness and livability standards [176]. Using rooftop solar PVs and EVs proves to be highly efficient in reducing carbon emissions from urban energy systems in a cost-effective manner [177]. Kobashi et al. introduced a “SolarEV City” concept, which combined roof-top solar PVs and EVs to provide cheap and clean electricity to urban residents [176]. The Australian government actively encourages rooftop solar PV systems and EV adoption through incentives, while also prioritizing technologies, like HEMSs, community battery storage, shared electricity, and virtual power plants, all collaborating to create a SolarEV city. With its ample sunlight, extensive land area, and increasing focus on renewable energy and electric mobility, Australia is well-suited for such a development.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
- Irfan, M.; Abas, N.; Saleem, M.S. Net Zero Energy Buildings (NZEB): A Case Study of Net Zero Energy Home in Pakistan. In Proceedings of the 2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET), Islamabad, Pakistan, 10–12 September 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar]
- Sinha, A.; Sengupta, T.; Alvarado, R. Interplay between Technological Innovation and Environmental Quality: Formulating the SDG Policies for next 11 Economies. J. Clean. Prod. 2020, 242, 118549. [Google Scholar] [CrossRef]
- Wang, X.; Wang, C.; Xu, T.; Guo, L.; Li, P.; Yu, L.; Meng, H. Optimal Voltage Regulation for Distribution Networks with Multi-Microgrids. Appl. Energy 2018, 210, 1027–1036. [Google Scholar] [CrossRef]
- Alshammari, N.; Asumadu, J. Optimum Unit Sizing of Hybrid Renewable Energy System Utilizing Harmony Search, Jaya and Particle Swarm Optimization Algorithms. Sustain. Cities Soc. 2020, 60, 102255. [Google Scholar] [CrossRef]
- Ashraf, S.; Saleem, S.; Chohan, A.H.; Aslam, Z.; Raza, A. Challenging Strategic Trends in Green Supply Chain Management. J. Res. Eng. Appl. Sci. 2020, 5, 71–74. [Google Scholar] [CrossRef]
- Greenhouse Gases Continued to Increase Rapidly in 2022. Available online: https://www.noaa.gov/news-release/greenhouse-gases-continued-to-increase-rapidly-in-2022 (accessed on 25 August 2023).
- Orbiting Carbon Observatory (Science Writers’ Guide). Available online: https://eospso.gsfc.nasa.gov/missions/orbiting-carbon-observatory (accessed on 24 August 2023).
- IEA. CO2 Emissions from Fuel Combustion: Highlights—2020 Edition; International Energy Agency: Paris, France, 2020. Available online: https://www.soe.epa.nsw.gov.au/all-themes/climate-and-air/greenhouse-gas-emissions (accessed on 24 August 2023).
- Ritchie, H.; Roser, M.; Rosado, P. CO2 and Greenhouse Gas Emissions. 2020. Available online: https://ourworldindata.org/co2-and-greenhouse-gas-emissions (accessed on 24 August 2023).
- Wu, W.; Skye, H.M. Residential Net-Zero Energy Buildings: Review and Perspective. Renew. Sustain. Energy Rev. 2021, 142, 110859. [Google Scholar] [CrossRef] [PubMed]
- Nejat, P.; Jomehzadeh, F.; Taheri, M.M.; Gohari, M.; Majid, M.Z.A. A Global Review of Energy Consumption, CO2 Emissions and Policy in the Residential Sector (with an Overview of the Top Ten CO2 Emitting Countries). Renew. Sustain. Energy Rev. 2015, 43, 843–862. [Google Scholar] [CrossRef]
- Li, L.; Wang, Z.; Gao, F.; Wang, S.; Deng, J. A Family of Compensation Topologies for Capacitive Power Transfer Converters for Wireless Electric Vehicle Charger. Appl. Energy 2020, 260, 114156. [Google Scholar] [CrossRef]
- Distribution of Carbon Dioxide Emissions Produced by the Transportation Sector Worldwide in 2021, by Subsector. Available online: https://www.statista.com/statistics/1185535/transport-carbon-dioxide-emissions-breakdown/ (accessed on 18 December 2022).
- Department of Climate Change, Energy, the Environment and Water. Australian Energy Statistics. 2022. Available online: https://www.energy.gov.au/sites/default/files/Australian%20Energy%20Statistics%202022%20Energy%20Update%20Report.pdf (accessed on 12 November 2022).
- Yousaf, I.; Nekhili, R.; Umar, M. Extreme Connectedness between Renewable Energy Tokens and Fossil Fuel Markets. Energy Econ. 2022, 114, 106305. [Google Scholar] [CrossRef]
- Klinlampu, C.; Chimprang, N.; Sirisrisakulchai, J. The Sufficient Level of Growth in Renewable Energy Generation for Coal Demand Reduction. Energy Rep. 2023, 9, 843–849. [Google Scholar] [CrossRef]
- Chittur Ramaswamy, P.; Chardonnet, C.; Rapoport, S.; Czajkowski, C.; Rodríguez Sanchez, R.; Gomez Arriola, I.; Bulto, G.O. Impact of Electric Vehicles on Distribution Network Operation: Real World Case Studies. In Proceedings of the CIRED Workshop 2016, Helsinki, Finland, 14–15 June 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–4. [Google Scholar]
- Sheng, M.S.; Sreenivasan, A.V.; Sharp, B.; Du, B. Well-to-Wheel Analysis of Greenhouse Gas Emissions and Energy Consumption for Electric Vehicles: A Comparative Study in Oceania. Energy Policy 2021, 158, 112552. [Google Scholar] [CrossRef]
- Ben Arab, M.; Rekik, M.; Krichen, L. Suitable Various-Goal Energy Management System for Smart Home Based on Photovoltaic Generator and Electric Vehicles. J. Build. Eng. 2022, 52, 104430. [Google Scholar] [CrossRef]
- Javadi, M.S.; Firuzi, K.; Rezanejad, M.; Lotfi, M.; Gough, M.; Catalao, J.P.S. Optimal Sizing and Siting of Electrical Energy Storage Devices for Smart Grids Considering Time-of-Use Programs. In Proceedings of the IECON 2019—45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal, 14–17 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 4157–4162. [Google Scholar]
- Schetinger, A.M.; Dias, D.H.N.; Borba, B.S.M.C.; Pimentel da Silva, G.D. Techno-economic Feasibility Study on Electric Vehicle and Renewable Energy Integration: A Case Study. Energy Storage 2020, 2, e197. [Google Scholar] [CrossRef]
- Tran, M.; Banister, D.; Bishop, J.D.K.; McCulloch, M.D. Realizing the Electric-Vehicle Revolution. Nat. Clim. Change 2012, 2, 328–333. [Google Scholar] [CrossRef]
- Fachrizal, R.; Shepero, M.; Åberg, M.; Munkhammar, J. Optimal PV-EV Sizing at Solar Powered Workplace Charging Stations with Smart Charging Schemes Considering Self-Consumption and Self-Sufficiency Balance. Appl. Energy 2022, 307, 118139. [Google Scholar] [CrossRef]
- Olatunde, O.; Hassan, M.Y.; Abdullah, M.P.; Rahman, H.A. Hybrid Photovoltaic/Small-Hydropower Microgrid in Smart Distribution Network with Grid Isolated Electric Vehicle Charging System. J. Energy Storage 2020, 31, 101673. [Google Scholar] [CrossRef]
- Balakrishnan, R.; Geetha, V. Review on Home Energy Management System. Mater. Today Proc. 2021, 47, 144–150. [Google Scholar] [CrossRef]
- Ali, A.O.; Elmarghany, M.R.; Abdelsalam, M.M.; Sabry, M.N.; Hamed, A.M. Closed-Loop Home Energy Management System with Renewable Energy Sources in a Smart Grid: A Comprehensive Review. J. Energy Storage 2022, 50, 104609. [Google Scholar] [CrossRef]
- Rhodes, C.J. Solar Energy: Principles and Possibilities. Sci. Prog. 2010, 93, 37–112. [Google Scholar] [CrossRef] [PubMed]
- Mousazadeh, H.; Keyhani, A.; Javadi, A.; Mobli, H.; Abrinia, K.; Sharifi, A. A Review of Principle and Sun-Tracking Methods for Maximizing Solar Systems Output. Renew. Sustain. Energy Rev. 2009, 13, 1800–1818. [Google Scholar] [CrossRef]
- Abas, N.; Rauf, S.; Saleem, M.S.; Irfan, M.; Hameed, S.A. Techno-Economic Feasibility Analysis of 100 MW Solar Photovoltaic Power Plant in Pakistan. Technol. Econ. Smart Grids Sustain. Energy 2022, 7, 16. [Google Scholar] [CrossRef]
- Chiradeja, P. Benefit of Distributed Generation: A Line Loss Reduction Analysis. In Proceedings of the 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, Dalian, China, 15–18 August 2005; IEEE: Piscataway, NJ, USA, 2005; pp. 1–5. [Google Scholar]
- Davy, R.J.; Troccoli, A. Interannual Variability of Solar Energy Generation in Australia. Sol. Energy 2012, 86, 3554–3560. [Google Scholar] [CrossRef]
- Australian Energy Resource Assessment. Available online: https://www.ga.gov.au/scientific-topics/energy/resources/other-renewable-energy-resources/solar-energy (accessed on 22 June 2023).
- Maisch, M. Australia Could Install 179 GW of Rooftop Solar. PV Magazine Australia. Available online: https://www.pv-magazine-australia.com/2019/06/13/australia-could-install-179-gw-of-rooftop-solar/ (accessed on 26 May 2022).
- Poddar, S.; Evans, J.P.; Kay, M.; Prasad, A.; Bremner, S. Estimation of Future Changes in Photovoltaic Potential in Australia Due to Climate Change. Environ. Res. Lett. 2021, 16, 114034. [Google Scholar] [CrossRef]
- Irfan, M.; Abas, N.; Saleem, M.S. Thermal Performance Analysis of Net Zero Energy Home for Sub Zero Temperature Areas. Case Stud. Therm. Eng. 2018, 12, 789–796. [Google Scholar] [CrossRef]
- Baulch, B.; Duong Do, T.; Le, T.-H. Constraints to the Uptake of Solar Home Systems in Ho Chi Minh City and Some Proposals for Improvement. Renew. Energy 2018, 118, 245–256. [Google Scholar] [CrossRef]
- Halder, P.K. Potential and Economic Feasibility of Solar Home Systems Implementation in Bangladesh. Renew. Sustain. Energy Rev. 2016, 65, 568–576. [Google Scholar] [CrossRef]
- Igualada, L.; Corchero, C.; Cruz-Zambrano, M.; Heredia, F.-J. Optimal Energy Management for a Residential Microgrid Including a Vehicle-to-Grid System. IEEE Trans. Smart Grid 2014, 5, 2163–2172. [Google Scholar] [CrossRef]
- Flannery, T.; Sahajwalla, V. The Critical Decade: Australia’s Future—Solar Energy; Climate Commission Secretariat, Department of Industry, Innovation, Climate Change, Science and Tertiary Education: Canberra, Australia, 2013; ISBN 978-1-921916-50-2. [Google Scholar]
- Tavakoli, A.; Saha, S.; Arif, M.T.; Haque, M.E.; Mendis, N.; Oo, A.M.T. Impacts of Grid Integration of Solar PV and Electric Vehicle on Grid Stability, Power Quality and Energy Economics: A Review. IET Energy Syst. Integr. 2020, 2, 243–260. [Google Scholar] [CrossRef]
- El Hammoumi, A.; Chtita, S.; Motahhir, S.; El Ghzizal, A. Solar PV Energy: From Material to Use, and the Most Commonly Used Techniques to Maximize the Power Output of PV Systems: A Focus on Solar Trackers and Floating Solar Panels. Energy Rep. 2022, 8, 11992–12010. [Google Scholar] [CrossRef]
- IPCC. IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation; Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschuss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schlömer, S., et al., Eds.; Prepared by Working Group III of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2011; 1075p. [Google Scholar]
- Choudhary, P.; Srivastava, R.K. Sustainability Perspectives- a Review for Solar Photovoltaic Trends and Growth Opportunities. J. Clean. Prod. 2019, 227, 589–612. [Google Scholar] [CrossRef]
- Chapman, A.J.; McLellan, B.; Tezuka, T. Residential Solar PV Policy: An Analysis of Impacts, Successes and Failures in the Australian Case. Renew. Energy 2016, 86, 1265–1279. [Google Scholar] [CrossRef]
- Imteaz, M.A.; Ahsan, A. Solar Panels: Real Efficiencies, Potential Productions and Payback Periods for Major Australian Cities. Sustain. Energy Technol. Assess. 2018, 25, 119–125. [Google Scholar] [CrossRef]
- Budget Review 2009–10; Parliament of Australia, Australia, 29 May 2009; ISSN 1834-9854. Available online: https://parlinfo.aph.gov.au/parlInfo/search/display/display.w3p;query=Id:%22library/prspub/IDQT6%22 (accessed on 5 July 2022).
- Small-Scale Renewable Energy Scheme. Available online: https://www.dcceew.gov.au/energy/renewable/target-scheme#toc_1 (accessed on 23 March 2023).
- Li, H.X.; Horan, P.; Luther, M.B.; Ahmed, T.M.F. Informed Decision Making of Battery Storage for Solar-PV Homes Using Smart Meter Data. Energy Build. 2019, 198, 491–502. [Google Scholar] [CrossRef]
- Nicholls, A.; Sharma, R.; Saha, T.K. Financial and Environmental Analysis of Rooftop Photovoltaic Installations with Battery Storage in Australia. Appl. Energy 2015, 159, 252–264. [Google Scholar] [CrossRef]
- Bandyopadhyay, A.; Leibowicz, B.D.; Webber, M.E. Solar Panels and Smart Thermostats: The Power Duo of the Residential Sector? Appl. Energy 2021, 290, 116747. [Google Scholar] [CrossRef]
- Bahadori, A.; Nwaoha, C. A Review on Solar Energy Utilisation in Australia. Renew. Sustain. Energy Rev. 2013, 18, 1–5. [Google Scholar] [CrossRef]
- Australia’s Long-Term Emissions Reduction Plan. Available online: https://www.dcceew.gov.au/climate-change/publications/australias-long-term-emissions-reduction-plan#:~:text=Australia%E2%80%99s%20whole-of-economy%20Long,to%20serve%20our%20traditional%20markets (accessed on 19 May 2023).
- Bibri, S.E.; Krogstie, J. Smart Sustainable Cities of the Future: An Extensive Interdisciplinary Literature Review. Sustain. Cities Soc. 2017, 31, 183–212. [Google Scholar] [CrossRef]
- Worku, M.Y. Recent Advances in Energy Storage Systems for Renewable Source Grid Integration: A Comprehensive Review. Sustainability 2022, 14, 5985. [Google Scholar] [CrossRef]
- Li, X.; Wang, S. A Review on Energy Management, Operation Control and Application Methods for Grid Battery Energy Storage Systems. CSEE J. Power Energy Syst. 2019, 7, 1026–1040. [Google Scholar] [CrossRef]
- Das, C.K.; Bass, O.; Kothapalli, G.; Mahmoud, T.S.; Habibi, D. Overview of Energy Storage Systems in Distribution Networks: Placement, Sizing, Operation, and Power Quality. Renew. Sustain. Energy Rev. 2018, 91, 1205–1230. [Google Scholar] [CrossRef]
- Albadi, M.H.; El-Saadany, E.F. Demand Response in Electricity Markets: An Overview. In Proceedings of the 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 24–28 June 2007; IEEE: Piscataway, NJ, USA, 2007; pp. 1–5. [Google Scholar]
- Tant, J.; Geth, F.; Six, D.; Tant, P.; Driesen, J. Multiobjective Battery Storage to Improve PV Integration in Residential Distribution Grids. IEEE Trans. Sustain. Energy 2013, 4, 182–191. [Google Scholar] [CrossRef]
- Muñoz, M.J.; Castillo Martínez, E. Prussian Blue Based Batteries; SpringerBriefs in Applied Sciences and Technology; Springer International Publishing: Cham, Switzerland, 2018; ISBN 978-3-319-91487-9. [Google Scholar]
- Esmaeel Nezhad, A.; Rahimnejad, A.; Gadsden, S.A. Home Energy Management System for Smart Buildings with Inverter-Based Air Conditioning System. Int. J. Electr. Power Energy Syst. 2021, 133, 107230. [Google Scholar] [CrossRef]
- Pourjafar, S.; Shayeghi, H.; Hashemzadeh, S.M.; Sedaghati, F.; Maalandish, M. A Non-isolated High Step-up DC–DC Converter Using Magnetic Coupling and Voltage Multiplier Circuit. IET Power Electron. 2021, 14, 1637–1655. [Google Scholar] [CrossRef]
- Song, Z.; Guan, X.; Cheng, M. Multi-Objective Optimization Strategy for Home Energy Management System Including PV and Battery Energy Storage. Energy Rep. 2022, 8, 5396–5411. [Google Scholar] [CrossRef]
- LI, X.; YAO, L.; HUI, D. Optimal Control and Management of a Large-Scale Battery Energy Storage System to Mitigate Fluctuation and Intermittence of Renewable Generations. J. Mod. Power Syst. Clean Energy 2016, 4, 593–603. [Google Scholar] [CrossRef]
- Jeong, H.-W.; Cha, J.; Oh, U.; Choi, J. Reliability Assessment of BESS Integrated with PCS by Using Operation Data. IFAC-PapersOnLine 2016, 49, 235–237. [Google Scholar] [CrossRef]
- Liu, H.; Li, T. Energy Management Strategy Development for Fuel Cell Hybrid Loaders. IOP Conf. Ser. Earth Environ. Sci. 2018, 189, 052015. [Google Scholar] [CrossRef]
- Minchala-Avila, L.I.; Armijos, J.; Pesántez, D.; Zhang, Y. Design and Implementation of a Smart Meter with Demand Response Capabilities. Energy Procedia 2016, 103, 195–200. [Google Scholar] [CrossRef]
- Yong, J.Y.; Ramachandaramurthy, V.K.; Tan, K.M.; Mithulananthan, N. A Review on the State-of-the-Art Technologies of Electric Vehicle, Its Impacts and Prospects. Renew. Sustain. Energy Rev. 2015, 49, 365–385. [Google Scholar] [CrossRef]
- Du, Y.; Zhou, X.; Bai, S.; Lukic, S.; Huang, A. Review of Non-Isolated Bi-Directional DC-DC Converters for Plug-in Hybrid Electric Vehicle Charge Station Application at Municipal Parking Decks. In Proceedings of the 2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Palm Springs, CA, USA, 21–25 February 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 1145–1151. [Google Scholar]
- Funding Announcement—Community Batteries Funding Round 1 Expressions of Interest. April 2023. Available online: https://arena.gov.au/funding/community-batteries-round-1/#step-1-read-the-program-guidelines (accessed on 21 May 2023).
- Da Silva Lima, L.; Wu, J.; Cadena, E.; Groombridge, A.S.; Dewulf, J. Towards Environmentally Sustainable Battery Anode Materials: Life Cycle Assessment of Mixed Niobium Oxide (XNOTM) and Lithium-titanium-Oxide (LTO). Sustain. Mater. Technol. 2023, 37, e00654. [Google Scholar] [CrossRef]
- Lithium-Sulfur Batteries for Large-Scale Energy Storage. Available online: https://arena.gov.au/projects/lithium-sulfur-batteries-for-large-scale-energy-storage/ (accessed on 29 March 2023).
- Kamath, D.; Moore, S.; Arsenault, R.; Anctil, A. A System Dynamics Model for End-of-Life Management of Electric Vehicle Batteries in the US: Comparing the Cost, Carbon, and Material Requirements of Remanufacturing and Recycling. Resour. Conserv. Recycl. 2023, 196, 107061. [Google Scholar] [CrossRef]
- Ahmadi, L.; Yip, A.; Fowler, M.; Young, S.B.; Fraser, R.A. Environmental Feasibility of Re-Use of Electric Vehicle Batteries. Sustain. Energy Technol. Assess. 2014, 6, 64–74. [Google Scholar] [CrossRef]
- Scheller, C.; Kishita, Y.; Blömeke, S.; Thies, C.; Schmidt, K.; Mennenga, M.; Herrmann, C.; Spengler, T.S. Designing Robust Transformation toward a Sustainable Circular Battery Production. Procedia CIRP 2023, 116, 408–413. [Google Scholar] [CrossRef]
- Boyden, A.; Soo, V.K.; Doolan, M. The Environmental Impacts of Recycling Portable Lithium-Ion Batteries. Procedia CIRP 2016, 48, 188–193. [Google Scholar] [CrossRef]
- Zhao, C.; Andersen, P.B.; Træholt, C.; Hashemi, S. Grid-Connected Battery Energy Storage System: A Review on Application and Integration. Renew. Sustain. Energy Rev. 2023, 182, 113400. [Google Scholar] [CrossRef]
- Mohamed, N.; Aymen, F.; Alqarni, M.; Turky, R.A.; Alamri, B.; Ali, Z.M.; Abdel Aleem, S.H.E. A New Wireless Charging System for Electric Vehicles Using Two Receiver Coils. Ain Shams Eng. J. 2022, 13, 101569. [Google Scholar] [CrossRef]
- Liu, J. Electric Vehicle Charging Infrastructure Assignment and Power Grid Impacts Assessment in Beijing. Energy Policy 2012, 51, 544–557. [Google Scholar] [CrossRef]
- National Electric Vehicle Strategy. April 2023. Available online: https://www.dcceew.gov.au/energy/transport/national-electric-vehicle-strategy (accessed on 21 May 2023).
- Kutkut, N.H.; Klontz, K.W. Design Considerations for Power Converters Supplying the SAE J-1773 Electric Vehicle Inductive Coupler. In Proceedings of the APEC 97—Applied Power Electronics Conference, Atlanta, GA, USA, 27 February 1997; IEEE: Piscataway, NJ, USA, 1997; Volume 2, pp. 841–847. [Google Scholar]
- Metais, M.O.; Jouini, O.; Perez, Y.; Berrada, J.; Suomalainen, E. Too Much or Not Enough? Planning Electric Vehicle Charging Infrastructure: A Review of Modeling Options. Renew. Sustain. Energy Rev. 2022, 153, 111719. [Google Scholar] [CrossRef]
- International Energy Agency (IEA). Nordic EV Outlook 2018: Insights from Leaders in Electric Mobility; IEA: Paris, France, 2018; Available online: https://www.oecd.org/finland/nordic-ev-outlook-2018-9789264293229-en.htm (accessed on 22 August 2022).
- Burkhardt, J.; Gillingham, K.; Kopalle, P. Experimental Evidence on the Effect of Information and Pricing on Residential Electricity Consumption; National Bureau of Economic Research: Cambridge, MA, USA, 2019. [Google Scholar]
- Lü, X.; Wu, Y.; Lian, J.; Zhang, Y.; Chen, C.; Wang, P.; Meng, L. Energy Management of Hybrid Electric Vehicles: A Review of Energy Optimization of Fuel Cell Hybrid Power System Based on Genetic Algorithm. Energy Convers. Manag. 2020, 205, 112474. [Google Scholar] [CrossRef]
- Wang, H.; Hasanzadeh, A.; Khaligh, A. Transportation Electrification: Conductive Charging of Electrified Vehicles. IEEE Electrif. Mag. 2013, 1, 46–58. [Google Scholar] [CrossRef]
- Miller, J.M.; White, C.P.; Onar, O.C.; Ryan, P.M. Grid Side Regulation of Wireless Power Charging of Plug-in Electric Vehicles. In Proceedings of the 2012 IEEE Energy Conversion Congress and Exposition (ECCE), Raleigh, NC, USA, 15–20 September 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 261–268. [Google Scholar]
- Zheng, Y.; Dong, Z.Y.; Xu, Y.; Meng, K.; Zhao, J.H.; Qiu, J. Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning. IEEE Trans. Power Syst. 2014, 29, 221–229. [Google Scholar] [CrossRef]
- Gupta, R.S.; Tyagi, A.; Anand, S. Optimal Allocation of Electric Vehicles Charging Infrastructure, Policies and Future Trends. J. Energy Storage 2021, 43, 103291. [Google Scholar] [CrossRef]
- Rajendran, G.; Vaithilingam, C.A.; Naidu, K.; Oruganti, K.S.P. Energy-Efficient Converters for Electric Vehicle Charging Stations. SN Appl. Sci. 2020, 2, 583. [Google Scholar] [CrossRef]
- Sujitha, N.; Krithiga, S. RES Based EV Battery Charging System: A Review. Renew. Sustain. Energy Rev. 2017, 75, 978–988. [Google Scholar] [CrossRef]
- Alsharif, A.; Tan, C.W.; Ayop, R.; Dobi, A.; Lau, K.Y. A Comprehensive Review of Energy Management Strategy in Vehicle-to-Grid Technology Integrated with Renewable Energy Sources. Sustain. Energy Technol. Assess. 2021, 47, 101439. [Google Scholar] [CrossRef]
- Australian Electric Vehicle Industry Recap. Available online: https://electricvehiclecouncil.com.au/2022-australian-electric-vehicle-industry-recap/ (accessed on 14 March 2023).
- State of Electric Vehicles. August 2021. Available online: https://electricvehiclecouncil.com.au/wp-content/uploads/2021/08/EVC-State-of-EVs-2021-sm.pdf (accessed on 19 February 2023).
- Future Fuels and Vehicles Strategy, Powering Choice. 9 November 2021. Available online: https://apo.org.au/node/315003 (accessed on 10 October 2022).
- Shemami, M.S.; Alam, M.S.; Asghar, M.S.J. Reliable Residential Backup Power Control System Through Home to Plug-In Electric Vehicle (H2V). Technol. Econ. Smart Grids Sustain. Energy 2018, 3, 8. [Google Scholar] [CrossRef]
- Qudrat-Ullah, H. A Review and Analysis of Renewable Energy Policies and CO2 Emissions of Pakistan. Energy 2022, 238, 121849. [Google Scholar] [CrossRef]
- Johnston, S.; Bras, B. Analysis of Financial and Carbon Savings of Grid-Tied Home Energy Systems in Conjunction with Photo-Voltaic Solar Generation and Electric Vehicle Use. Procedia CIRP 2022, 105, 73–79. [Google Scholar] [CrossRef]
- Elkholy, M.H.; Metwally, H.; Farahat, M.A.; Nasser, M.; Senjyu, T.; Lotfy, M.E. Dynamic Centralized Control and Intelligent Load Management System of a Remote Residential Building with V2H Technology. J. Energy Storage 2022, 52, 104839. [Google Scholar] [CrossRef]
- Poorani, S. Hybrid Power Generation by Using Solar and Wind Energy Hybrid Power Generation Applicable To Future Electric Vehicle. Int. J. Emerg. Trends Sci. Technol. 2017, 4, 11. [Google Scholar] [CrossRef]
- Liu, D.; Zeng, X.; Liu, G. Control Method for EV Charging and Discharging in V2G/V2H Scenario Based on the Synchronvter Technology and H∞ Repetitive Control. J. Eng. 2019, 2019, 1350–1355. [Google Scholar] [CrossRef]
- Abul’Wafa, A.; El’Garably, A.F.; Mohamed, W. EV-to-Home Concept Including Home Energy Management. Int. J. Eng. Inf. Syst. 2017, 1, 20–28. [Google Scholar]
- Vadi, S.; Bayindir, R.; Colak, A.M.; Hossain, E. A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies. Energies 2019, 12, 3748. [Google Scholar] [CrossRef]
- Amirioun, M.H.; Kazemi, A. A New Model Based on Optimal Scheduling of Combined Energy Exchange Modes for Aggregation of Electric Vehicles in a Residential Complex. Energy 2014, 69, 186–198. [Google Scholar] [CrossRef]
- Liu, C.; Chau, K.T.; Wu, D.; Gao, S. Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies. Proc. IEEE 2013, 101, 2409–2427. [Google Scholar] [CrossRef]
- Solanke, T.U.; Ramachandaramurthy, V.K.; Yong, J.Y.; Pasupuleti, J.; Kasinathan, P.; Rajagopalan, A. A Review of Strategic Charging–Discharging Control of Grid-Connected Electric Vehicles. J. Energy Storage 2020, 28, 101193. [Google Scholar] [CrossRef]
- Khoucha, F.; Benbouzid, M.; Amirat, Y.; Kheloui, A. Integrated Energy Management of a Plug-in Electric Vehicle in Residential Distribution Systems with Renewables. In Proceedings of the 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), Buzios, Brazil, 3–5 June 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 717–722. [Google Scholar]
- García-Villalobos, J.; Zamora, I.; San Martín, J.I.; Asensio, F.J.; Aperribay, V. Plug-in Electric Vehicles in Electric Distribution Networks: A Review of Smart Charging Approaches. Renew. Sustain. Energy Rev. 2014, 38, 717–731. [Google Scholar] [CrossRef]
- Jain, P.; Das, A.; Jain, T. Aggregated Electric Vehicle Resource Modelling for Regulation Services Commitment in Power Grid. Sustain. Cities Soc. 2019, 45, 439–450. [Google Scholar] [CrossRef]
- Jain, P.; Jain, T. Development of V2G and G2V Power Profiles and Their Implications on Grid Under Varying Equilibrium of Aggregated Electric Vehicles. Int. J. Emerg. Electr. Power Syst. 2016, 17, 101–115. [Google Scholar] [CrossRef]
- Liu, Z.; Wang, D.; Jia, H.; Djilali, N.; Zhang, W. Aggregation and Bidirectional Charging Power Control of Plug-in Hybrid Electric Vehicles: Generation System Adequacy Analysis. IEEE Trans. Sustain. Energy 2015, 6, 325–335. [Google Scholar] [CrossRef]
- Kempton, W.; Tomić, J. Vehicle-to-Grid Power Fundamentals: Calculating Capacity and Net Revenue. J. Power Sources 2005, 144, 268–279. [Google Scholar] [CrossRef]
- Salvatti, G.; Carati, E.; Cardoso, R.; da Costa, J.; Stein, C. Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids. Energies 2020, 13, 1191. [Google Scholar] [CrossRef]
- Kempton, W.; Tomić, J. Vehicle-to-Grid Power Implementation: From Stabilizing the Grid to Supporting Large-Scale Renewable Energy. J. Power Sources 2005, 144, 280–294. [Google Scholar] [CrossRef]
- Richardson, D.B. Encouraging Vehicle-to-Grid (V2G) Participation through Premium Tariff Rates. J. Power Sources 2013, 243, 219–224. [Google Scholar] [CrossRef]
- Tan, K.M.; Ramachandaramurthy, V.K.; Yong, J.Y. Integration of Electric Vehicles in Smart Grid: A Review on Vehicle to Grid Technologies and Optimization Techniques. Renew. Sustain. Energy Rev. 2016, 53, 720–732. [Google Scholar] [CrossRef]
- Ehsani, M.; Falahi, M.; Lotfifard, S. Vehicle to Grid Services: Potential and Applications. Energies 2012, 5, 4076–4090. [Google Scholar] [CrossRef]
- Sangswang, A.; Konghirun, M. Optimal Strategies in Home Energy Management System Integrating Solar Power, Energy Storage, and Vehicle-to-Grid for Grid Support and Energy Efficiency. IEEE Trans. Ind. Appl. 2020, 56, 5716–5728. [Google Scholar] [CrossRef]
- Aghajan-Eshkevari, S.; Azad, S.; Nazari-Heris, M.; Ameli, M.T.; Asadi, S. Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies. Sustainability 2022, 14, 2137. [Google Scholar] [CrossRef]
- Ray, S.; Kasturi, K.; Patnaik, S.; Nayak, M.R. Review of Electric Vehicles Integration Impacts in Distribution Networks: Placement, Charging/Discharging Strategies, Objectives and Optimisation Models. J. Energy Storage 2023, 72, 108672. [Google Scholar] [CrossRef]
- Noori, M.; Zhao, Y.; Onat, N.C.; Gardner, S.; Tatari, O. Light-Duty Electric Vehicles to Improve the Integrity of the Electricity Grid through Vehicle-to-Grid Technology: Analysis of Regional Net Revenue and Emissions Savings. Appl. Energy 2016, 168, 146–158. [Google Scholar] [CrossRef]
- Kostopoulos, E.D.; Spyropoulos, G.C.; Kaldellis, J.K. Real-World Study for the Optimal Charging of Electric Vehicles. Energy Rep. 2020, 6, 418–426. [Google Scholar] [CrossRef]
- Ahmadian, A.; Sedghi, M.; Mohammadi-ivatloo, B.; Elkamel, A.; Aliakbar Golkar, M.; Fowler, M. Cost-Benefit Analysis of V2G Implementation in Distribution Networks Considering PEVs Battery Degradation. IEEE Trans. Sustain. Energy 2018, 9, 961–970. [Google Scholar] [CrossRef]
- Ida, T.; Murakami, K.; Tanaka, M. A Stated Preference Analysis of Smart Meters, Photovoltaic Generation, and Electric Vehicles in Japan: Implications for Penetration and GHG Reduction. Energy Res. Soc. Sci. 2014, 2, 75–89. [Google Scholar] [CrossRef]
- Dede, A.; Della Giustina, D.; Rinaldi, S.; Ferrari, P.; Flammini, A.; Vezzoli, A. Smart Meters as Part of a Sensor Network for Monitoring the Low Voltage Grid. In Proceedings of the 2015 IEEE Sensors Applications Symposium (SAS), Zadar, Croatia, 13–15 April 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1–6. [Google Scholar]
- Kabalci, Y. A Survey on Smart Metering and Smart Grid Communication. Renew. Sustain. Energy Rev. 2016, 57, 302–318. [Google Scholar] [CrossRef]
- Edwards, R.E.; New, J.; Parker, L.E. Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study. Energy Build. 2012, 49, 591–603. [Google Scholar] [CrossRef]
- Javed, F.; Arshad, N.; Wallin, F.; Vassileva, I.; Dahlquist, E. Forecasting for Demand Response in Smart Grids: An Analysis on Use of Anthropologic and Structural Data and Short Term Multiple Loads Forecasting. Appl. Energy 2012, 96, 150–160. [Google Scholar] [CrossRef]
- Goncalves Da Silva, P.; Ilic, D.; Karnouskos, S. The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading. IEEE Trans. Smart Grid 2014, 5, 402–410. [Google Scholar] [CrossRef]
- Chicco, G.; Napoli, R.; Piglione, F.; Postolache, P.; Scutariu, M.; Toader, C. Load Pattern-Based Classification of Electricity Customers. IEEE Trans. Power Syst. 2004, 19, 1232–1239. [Google Scholar] [CrossRef]
- McLoughlin, F.; Duffy, A.; Conlon, M. A Clustering Approach to Domestic Electricity Load Profile Characterisation Using Smart Metering Data. Appl. Energy 2015, 141, 190–199. [Google Scholar] [CrossRef]
- Barbato, A.; Capone, A.; Rodolfi, M.; Tagliaferri, D. Forecasting the Usage of Household Appliances through Power Meter Sensors for Demand Management in the Smart Grid. In Proceedings of the 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium, 17–20 October 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 404–409. [Google Scholar]
- Tostado-Véliz, M.; Arévalo, P.; Kamel, S.; Zawbaa, H.M.; Jurado, F. Home Energy Management System Considering Effective Demand Response Strategies and Uncertainties. Energy Rep. 2022, 8, 5256–5271. [Google Scholar] [CrossRef]
- Parise, G.; Martirano, L.; Kermani, M.; Kermani, M. Designing a Power Control Strategy in a Microgrid Using PID/Fuzzy Controller Based on Battery Energy Storage. In Proceedings of the 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, Italy, 6–9 June 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–5. [Google Scholar]
- Beraldi, P.; Violi, A.; Carrozzino, G. The Optimal Management of the Prosumer’s Resources via Stochastic Programming. Energy Rep. 2020, 6, 274–280. [Google Scholar] [CrossRef]
- Wu, H.; Pratt, A.; Munankarmi, P.; Lunacek, M.; Balamurugan, S.P.; Liu, X.; Spitsen, P. Impact of Model Predictive Control-Enabled Home Energy Management on Large-Scale Distribution Systems with Photovoltaics. Adv. Appl. Energy 2022, 6, 100094. [Google Scholar] [CrossRef]
- Shakeri, M.; Pasupuleti, J.; Amin, N.; Rokonuzzaman, M.; Low, F.W.; Yaw, C.T.; Asim, N.; Samsudin, N.A.; Tiong, S.K.; Hen, C.K.; et al. An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid. Energies 2020, 13, 3299. [Google Scholar] [CrossRef]
- Pranee, P.; Jirasuwankul, N. Experimental Study and Modeling of Automatic Home Energy Management System Using AI. In Proceedings of the 2021 International Conference on Power, Energy and Innovations (ICPEI), Nakhon Ratchasima, Thailand, 20–22 October 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 103–106. [Google Scholar]
- Ouramdane, O.; Elbouchikhi, E.; Amirat, Y.; Sedgh Gooya, E. Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends. Energies 2021, 14, 4166. [Google Scholar] [CrossRef]
- Liemthong, R.; Srithapon, C.; Chatthaworn, R. Home Energy Management Strategy for Electricity Energy Cost Minimization Considering ToU Tariff. In Proceedings of the 2021 International Conference on Power, Energy and Innovations (ICPEI), Nakhon Ratchasima, Thailand, 20–22 October 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 107–110. [Google Scholar]
- Hou, X.; Wang, J.; Huang, T.; Wang, T.; Wang, P. Smart Home Energy Management Optimization Method Considering Energy Storage and Electric Vehicle. IEEE Access 2019, 7, 144010–144020. [Google Scholar] [CrossRef]
- Dinh, H.T.; Lee, K.; Kim, D. Supervised-Learning-Based Hour-Ahead Demand Response for a Behavior-Based Home Energy Management System Approximating MILP Optimization. Appl. Energy 2022, 321, 119382. [Google Scholar] [CrossRef]
- Huang, Y.; Wang, H.; Khajepour, A.; He, H.; Ji, J. Model Predictive Control Power Management Strategies for HEVs: A Review. J. Power Sources 2017, 341, 91–106. [Google Scholar] [CrossRef]
- Yang, J.; Liu, J.; Fang, Z.; Liu, W. Electricity Scheduling Strategy for Home Energy Management System with Renewable Energy and Battery Storage: A Case Study. IET Renew. Power Gener. 2018, 12, 639–648. [Google Scholar] [CrossRef]
- Paterakis, N.G.; Erdinc, O.; Bakirtzis, A.G.; Catalao, J.P.S. Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies. IEEE Trans. Ind. Inform. 2015, 11, 1509–1519. [Google Scholar] [CrossRef]
- Hale, E.; Bird, L.; Padmanabhan, R.; Volpi, C. Potential Roles for Demand Response in High-Growth Electric Systems with Increasing Shares of Renewable Generation; Rep. NREL/TP-6A20-70630; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2018; pp. 1–3. [Google Scholar]
- Tsui, K.M.; Chan, S.C. Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing. IEEE Trans. Smart Grid 2012, 3, 1812–1821. [Google Scholar] [CrossRef]
- Da Silva, I.R.S.; Rabêlo, R.d.A.L.; Rodrigues, J.J.P.C.; Solic, P.; Carvalho, A. A Preference-Based Demand Response Mechanism for Energy Management in a Microgrid. J. Clean. Prod. 2020, 255, 120034. [Google Scholar] [CrossRef]
- Zupančič, J.; Filipič, B.; Gams, M. Genetic-Programming-Based Multi-Objective Optimization of Strategies for Home Energy-Management Systems. Energy 2020, 203, 117769. [Google Scholar] [CrossRef]
- Thorvaldsen, K.E.; Korpås, M.; Farahmand, H. Long-Term Value of Flexibility from Flexible Assets in Building Operation. Int. J. Electr. Power Energy Syst. 2022, 138, 107811. [Google Scholar] [CrossRef]
- Aguilar-Dominguez, D.; Ejeh, J.; Brown, S.F.; Dunbar, A.D.F. Exploring the Possibility to Provide Black Start Services by Using Vehicle-to-Grid. Energy Rep. 2022, 8, 74–82. [Google Scholar] [CrossRef]
- Yan, Q.; Zhang, B.; Kezunovic, M. Optimized Operational Cost Reduction for an EV Charging Station Integrated With Battery Energy Storage and PV Generation. IEEE Trans. Smart Grid 2019, 10, 2096–2106. [Google Scholar] [CrossRef]
- Fathabadi, H. Novel Stand-Alone, Completely Autonomous and Renewable Energy Based Charging Station for Charging Plug-in Hybrid Electric Vehicles (PHEVs). Appl. Energy 2020, 260, 114194. [Google Scholar] [CrossRef]
- Aguilar-Dominguez, D.; Dunbar, A.; Brown, S. The Electricity Demand of an EV Providing Power via Vehicle-to-Home and Its Potential Impact on the Grid with Different Electricity Price Tariffs. Energy Rep. 2020, 6, 132–141. [Google Scholar] [CrossRef]
- Yi, Y.; Verbic, G.; Chapman, A.C. Optimal Energy Management Strategy for Smart Home with Electric Vehicle. In Proceedings of the 2021 IEEE Madrid PowerTech, Madrid, Spain, 28 June–2 July 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 1–6. [Google Scholar]
- Boström, T.; Babar, B.; Hansen, J.B.; Good, C. The Pure PV-EV Energy System—A Conceptual Study of a Nationwide Energy System Based Solely on Photovoltaics and Electric Vehicles. Smart Energy 2021, 1, 100001. [Google Scholar] [CrossRef]
- Ben Slama, S. Design and Implementation of Home Energy Management System Using Vehicle to Home (H2V) Approach. J. Clean. Prod. 2021, 312, 127792. [Google Scholar] [CrossRef]
- Mehrjerdi, H. Resilience-Robustness Improvement by Adaptable Operating Pattern for Electric Vehicles in Complementary Solar-Vehicle Management. J. Energy Storage 2021, 37, 102454. [Google Scholar] [CrossRef]
- Gong, H.; Ionel, D.M. Combined Use of EV Batteries and PV Systems for Improving Building Resilience to Blackouts. In Proceedings of the 2021 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 21–25 June 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 584–587. [Google Scholar]
- Minhas, D.M.; Meiers, J.; Frey, G. A Rule-Based Expert System for Home Power Management Incorporating Real-Life Data Sets. In Proceedings of the 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE), Doha, Qatar, 20–22 March 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1–6. [Google Scholar]
- Gholinejad, H.R.; Adabi, J.; Marzband, M. Hierarchical Energy Management System for Home-Energy-Hubs Considering Plug-in Electric Vehicles. IEEE Trans. Ind. Appl. 2022, 58, 5582–5592. [Google Scholar] [CrossRef]
- Chakir, A.; Abid, M.; Tabaa, M.; Hachimi, H. Demand-Side Management Strategy in a Smart Home Using Electric Vehicle and Hybrid Renewable Energy System. Energy Rep. 2022, 8, 383–393. [Google Scholar] [CrossRef]
- Alzahrani, A.; Sajjad, K.; Hafeez, G.; Murawwat, S.; Khan, S.; Khan, F.A. Real-Time Energy Optimization and Scheduling of Buildings Integrated with Renewable Microgrid. Appl. Energy 2023, 335, 120640. [Google Scholar] [CrossRef]
- Beheshtikhoo, A.; Pourgholi, M.; Khazaee, I. Design of Type-2 Fuzzy Logic Controller in a Smart Home Energy Management System with a Combination of Renewable Energy and an Electric Vehicle. J. Build. Eng. 2023, 68, 106097. [Google Scholar] [CrossRef]
- Irfan, M. SWOT Analysis of Energy Policy 2013 of Pakistan. Eur. J. Eng. Sci. Technol. 2019, 2, 71–94. [Google Scholar] [CrossRef]
- Shah, S.A.A.; Solangi, Y.A. A sustainable Solution for Electricity Crisis in Pakistan: Opportunities, Barriers, and Policy Implications for 100% Renewable Energy. Environ. Sci. Pollut. Res. 2019, 26, 29687–29703. [Google Scholar] [CrossRef]
- Gielen, D.; Boshell, F.; Saygin, D.; Bazilian, M.D.; Wagner, N.; Gorini, R. The Role of Renewable Energy in the Global Energy Transformation. Energy Strategy Rev. 2019, 24, 38–50. [Google Scholar] [CrossRef]
- Future Energy Action Plan 2020–2025, Future Transport 2056. Available online: https://future.transport.nsw.gov.au/documents/future-energy-action-plan (accessed on 28 May 2022).
- State of the Energy Market 2022. Available online: https://www.aer.gov.au/publications/state-of-the-energy-market-reports/state-of-the-energy-market-2022 (accessed on 27 June 2023).
- Smarter Homes for Distributed Energy. February 2022. Available online: https://arena.gov.au/knowledge-bank/smarter-homes-for-distributed-energy/ (accessed on 27 June 2023).
- Berg, K.; Rana, R.; Farahmand, H. Quantifying the Benefits of Shared Battery in a DSO-Energy Community Cooperation. Appl. Energy 2023, 343, 121105. [Google Scholar] [CrossRef]
- Niu, M.-B.; Wang, H.-C.; Li, J.; Liu, H.; Yin, R. Coordinated Energy Dispatch of Highway Microgrids with Mobile Storage System Based on DMPC Optimization. Electr. Power Syst. Res. 2023, 217, 109119. [Google Scholar] [CrossRef]
- Wang, Y.; Rousis, A.O.; Strbac, G. Resilience-Driven Optimal Sizing and Pre-Positioning of Mobile Energy Storage Systems in Decentralized Networked Microgrids. Appl. Energy 2022, 305, 117921. [Google Scholar] [CrossRef]
- Feng, Y.; Jia, H.; Wang, X.; Ning, B.; Liu, Z.; Liu, D. Review of Operations for Multi-Energy Coupled Virtual Power Plants Participating in Electricity Market. Energy Rep. 2023, 9, 992–999. [Google Scholar] [CrossRef]
- Virtual Power Plant Demonstrations Consumer Insights Report. 10 September 2021. Available online: https://arena.gov.au/knowledge-bank/virtual-power-plant-demonstrations-consumer-insights-report/ (accessed on 27 June 2023).
- Australia Country Commercial Guide. Available online: https://www.privacyshield.gov/ps/article?id=Australia-smart-grid#:~:text=and%20trade%20data.-,Overview,close%20coal-fired%20power%20stations (accessed on 27 June 2023).
- Kobashi, T.; Jittrapirom, P.; Yoshida, T.; Hirano, Y.; Yamagata, Y. SolarEV City Concept: Building the next Urban Power and Mobility Systems. Environ. Res. Lett. 2021, 16, 024042. [Google Scholar] [CrossRef]
- Chang, S.; Cho, J.; Heo, J.; Kang, J.; Kobashi, T. Energy Infrastructure Transitions with PV and EV Combined Systems Using Techno-Economic Analyses for Decarbonization in Cities. Appl. Energy 2022, 319, 119254. [Google Scholar] [CrossRef]
S. No. | Zone | PV Potential (GW) | Annual Energy Output (GWh) |
---|---|---|---|
1 | Water | 0.0 | 0 |
2 | Transport/Infrastructure | 0.6 | 774 |
3 | Recreational/Open space | 1.7 | 2346 |
4 | Conservation/National park | 2.1 | 2884 |
5 | Unknown | 2.2 | 3052 |
6 | Community use | 3.9 | 5371 |
7 | Mixed use | 4.0 | 5584 |
8 | Special use | 6.7 | 9357 |
9 | Commercial/Business | 9.3 | 12,601 |
10 | Industrial/Utilities | 19.0 | 26,464 |
11 | Rural/Primary production | 33.9 | 46,680 |
12 | Residential | 96.0 | 130,153 |
The main objectives of these types of houses are: |
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S. No. | Advantages |
---|---|
PV power generation systems offer numerous benefits that make them highly desirable. | |
1 | Free source of energy. |
2 | Clean, green, and environmentally friendly energy source. |
3 | Noiseless energy generation, an ideal solution for residential and urban areas. |
4 | During electricity generation, there are no harmful GHG emissions *. |
5 | The generation can be performed closer to the consumer. |
6 | The operational and maintenance costs are low, approximately negligible compared to other renewable energy systems. |
Years | Total Number of EVs Sold in Australia | % of Total Vehicles Sold in Australia |
---|---|---|
2015 | 1771 | 0.15% |
2016 | 1369 | 0.12% |
2017 | 2287 | 0.19% |
2018 | 2216 | 0.3% |
2019 | 6718 | 0.65% |
2020 | 6900 | 0.78% |
2021 | 8688 | 1.57% |
2022 | 26,356 | 3.8% |
S. No. | Modules | Description |
---|---|---|
1 | Monitoring | Monitoring capabilities, which involve tracking real-time data from smart appliances and displaying information related to the users’ preferences, current energy utilization, and energy cost. |
2 | Logging | Logging functionality involves an authentication process that verifies and grants the user access to sensitive data, allowing them to read or modify the information securely. |
3 | Control | Control allows the direct control of both the end-user’s control system and facilities, empowering users to manage and manipulate various smart devices. This direct control is attained through portable personal computers or smart phones, enabling the convenient monitoring and assessment of the user’s consumption habits even from remote locations. |
4 | Management | The management component focuses on optimizing energy efficiency by effectively controlling renewable distributed energy resources, home energy storage systems, and smart appliances. This optimization is achieved through a demand response analysis, which involves the real-time adjustments of electricity prices. |
5 | Alarm | The alarm actively monitors and promptly alerts users in the event of any detected irregularities or anomalies. |
Ref. | Year | Methods | Key Findings |
---|---|---|---|
[151] | 2019 | Four-Stage Optimization and Control Algorithm | An advanced four-stage intelligent technique developed to optimize energy management schedules for day-ahead and hour-ahead planning, provide timely EV price updates, and enable real-time control. The main objective of the control algorithm is to reduce the operational costs of the integrated smart charging station, while simultaneously enhancing customer satisfaction. |
[48] | 2019 | Smart Meter Data/Intelligent Algorithm | An innovative and generic decision-making framework, incorporating an intelligent algorithm, utilized to develop an economic model and calculate electricity quantities for enabling EV charging through solar energy instead of grid power. The adoption of this approach resulted in a significant reduction in electricity costs (38%) for homes with solar PVs. |
[152] | 2020 | Variable Step-Size MPPT | To facilitate the charging of plug-in hybrid EVs, the authors developed a self-contained charging station that incorporated a fuel cell system with RESs. With a high level of efficiency (99.6%) and a short payback period (16 months), the charging station also reduced the burden on the grid. |
[153] | 2020 | Pyomo Framework and Gurobi Optimization Solver | The system model incorporates solar PV generation, two EVs equipped with V2H technology, an ESS, and two batteries. The proposed strategy yielded significant cost savings, reducing electricity tariffs by 30% when utilizing V2H technology, 50% when the battery was added, and up to 85% when utilizing a specific EV under the time-of-use tariff without the use of a battery. |
[154] | 2021 | Markov Decision Process and Gradient Algorithm | The model considers solar PV generation, household demand, and the unpredictability of EV mobility with an objective of electricity cost minimization and network load alleviation during peak periods. The simulation results demonstrate that utilizing EVs for home energy management is an efficient and cost-effective technique. |
[155] | 2021 | MATLAB | This research work presents a conceptual model for a pure PV–EV nationwide energy system. The surplus power generated by the solar PV system is stored in EV batteries, which can be used to meet the energy demand when solar energy is unavailable. This theoretical study assumes that the widespread adoption of EVs can be achieved, as EVs remain stationary 95% of the time, making them an ideal source of energy storage. Ultimately, the model showcases how solar PV systems have the potential to provide energy to the entire country. |
[156] | 2021 | MATLAB/Simulink | By utilizing the optimal automation, controlling, and scheduling of appliances effectively, the approach is capable of countering unexpected fluctuations. The surplus power generated by the solar PV system is fed back into the grid and used to charge an EV. The stored energy in the EV battery can then be used to power the smart home during high-load periods, delivering a reliable 2.8–3 kW of power. The simulation results demonstrate the home-centralized PV with an HEMS’s ability to deliver high-quality power, even during undesirable fluctuations, showcasing its reliability. |
[157] | 2021 | Mixed-Integer Linear Programming.GAMS Software (GmbH, US and Germany) /CPLEX Solver | The use of an optimized strategy in a solar PV system and EV tied to a grid improves energy resilience and robustness. During the daytime, the solar PV system generates energy, while during the nighttime, the EV supplies power to the building. The load profiles consist of a base load of 16 kW, a critical load of 2.4 kW, and solar energy varying from 1–20 kW. In the absence of solar and grid energy, the system can cover 51.4% of critical loads. The proposed model is highly efficient, reducing critical loads by 10% and energy loss by approximately 15%. |
[158] | 2021 | Energy Plus | Buildings equipped with solar panels can store energy to power appliances during blackouts, while an EV battery can expand the energy capacity of the ESS. The SOC of the battery is set to 20%, which is the typical SOC discharging limit. Different load percentages are considered in various scenarios, and the study results show that reducing the energy demand can enhance a building’s resilience during blackouts. |
[159] | 2022 | Rule-Based Intelligent EMS/Priority-Based Decision Algorithm | This article proposes an intelligent energy management system integrated with a small-scale home area power network that utilizes a cost-effective power schedule. The study analyzes residential power usage, EV driving behavior, and battery charging/discharging patterns using real-world data collected over the course of a year. The simulation results demonstrate that the combination of PV arrays, EV storage, and the grid serves as a source of clean and affordable energy. |
[160] | 2022 | Extended Bellman–Ford–Moore Algorithm | This study implements a two-level laboratory hierarchical EMS to optimize the profiles of PV and battery storage as DC sources for day-ahead and real-time decisions. This smart charging tactic is designed for charging EVs in a home–energy–hub system, using data on solar irradiation, electricity tariffs, and power consumption. The proposed strategy provides a solution for the smart charging of EVs in domestic applications, with rapid real-time decision making for users to maximize profits. The simulation results show that the power grid and home–energy–hub system have a positive impact on the behavior of EVs. |
[19] | 2022 | Particle Swarm Optimization Algorithm | This research proposes an EMS optimization with two hierarchical layers to achieve various goals in four different scenarios. The study aims to reduce electricity bills and provide free charging for plug-in electric vehicles in a smart home connected to a PV generator by managing the charging/discharging of PEVs to and from the smart home. The simulation results indicate that the proposed approach provides smooth energy profiles for the home, achieves almost free PEV charging, and reduces electricity bills by 26%, 15.57%, 31.68%, and 25% in autumn, winter, summer, and spring, respectively. |
[132] | 2022 | Novel Demand Response Strategies | This study compares three novel strategies with the existing demand response mechanisms in an HEMS for managing uncertainties. The first scenario involves a BSS supplying energy when a PEV is not in a V2H capacity, while the second scenario involves a bidirectional charging system allowing the PEV to provide energy in a unique V2H capacity, without requiring battery storage deployment. The objective is to evaluate the electricity bills through a mathematical formulation. The study results indicate that demand response strategies are more effective than other programs, with the peak demand reduced by approximately 2 kW when the time-of-use tariff is applied. |
[161] | 2022 | MATLAB Toolboxes/TRNSYS | The authors proposed an EMS for a house that included controllable loads, EV batteries, and grid-connected hybrid renewable energies. The system uses hourly based simulations of meteorological data to make decisions about connection architecture through switch control states and variation conditions for H2V, V2H, and G2V scenarios. The study concludes that, during the daytime, the H2V scenario is performed, and in the insufficiency of renewable energies, the EV cooperates with the home when the grid-to-home scenario is in progress. |
[162] | 2023 | MATLAB Lyapunov Optimization Technique | This study focuses on the analysis of a smart home that incorporates inflexible loads, such as lighting, a computer, and a TV, as well as flexible EV loads, HVAC systems, water heaters, and RESs. The home operates in a grid-connected mode, allowing for energy trading, both purchasing and selling energy. The objective is to optimize the overall cost, minimize thermal discomfort, and efficiently manage the charging and discharging of batteries and EVs. The findings highlight the effectiveness and superiority of the proposed algorithm in achieving optimized energy consumption and management in a real-time scenario. |
[163] | 2023 | MATLAB Type-2 Fuzzy logic Controller | This paper presents an EMS for a smart home that utilizes RESs along with EVs and an ESS. The EMS, based on a type-2 fuzzy logic controller and smart switches, connects various controllable and non-controllable household appliances to meet the electricity demand. To determine the optimal configuration of the controller, the number and distribution of membership functions are chosen based on real input data collected over a year in Tehran, Iran. The results show that the smart home system can reduce its reliance on grid electricity by 49.186 kWh on a daily basis, leading to a weekly reduction of approximately 343.95 kWh from the grid. Furthermore, the implementation of the proposed strategy leads to a 71.5% reduction in electricity costs and a 64.6% decrease in the peak-to-average ratio. |
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Irfan, M.; Deilami, S.; Huang, S.; Veettil, B.P. Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review. Energies 2023, 16, 7248. https://doi.org/10.3390/en16217248
Irfan M, Deilami S, Huang S, Veettil BP. Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review. Energies. 2023; 16(21):7248. https://doi.org/10.3390/en16217248
Chicago/Turabian StyleIrfan, Muhammad, Sara Deilami, Shujuan Huang, and Binesh Puthen Veettil. 2023. "Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review" Energies 16, no. 21: 7248. https://doi.org/10.3390/en16217248
APA StyleIrfan, M., Deilami, S., Huang, S., & Veettil, B. P. (2023). Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review. Energies, 16(21), 7248. https://doi.org/10.3390/en16217248