Internal Solar Energy Credits Exchange in a Microgrid, Case Study of a Townhouse Complex
Keywords:
Microgrids, Solar Photovoltaics, Renewable Energy, Distributed Generation, Microgrid Value Streams, Energy Tariffs, Residential Cooperatives, Energy Credits Exchange, P2P Energy Trade, Energy Pricing
Abstract
This paper addresses the critical challenge of optimising solar energy utilisation in residential microgrids through an internal energy credits exchange system. The research aims to develop and validate a comprehensive energy management approach that maximises economic benefits for both solar and non-solar users within solar-powered residential microgrids; the energy management approach must be easy to implement with minimal interventions and additional costs. The study presents results from a three-year field implementation in an Australian townhouse complex, comprising two microgrids with 19 and 24 units respectively, and seven solar installations. The proposed system introduces several novel elements: (1) an internal energy credit exchange mechanism based on real consumption patterns, (2) a balanced tariff model set at 65 % of the base internal tariff, determined through collective decision-making, and (3) a flexible credit accumulation system with overpayment refund capabilities. Field data collected at quarterly intervals demonstrates that in this architecture over up to 35 % of total energy consumed by non-solar users is produced by their solar neighbours. If solar energy export is accounted for with the mathematical model presented in this paper, it allows for either radical reduction of energy costs, or continuous credit accumulation for solar users depending on their overall energy consumption. It also provided consistent 5–10 % cost reduction (community solar discounts) to non-solar users. The developed and implemented approach offers simple methodology to account for solar energy export into small residential microgrids offering significant advantages to solar users over traditional direct grid connection. Significant economic benefits are achieved to both solar and non-solar users vie fairer feed-in tariffs for solar users and community solar discounts for non-solar users. The methodology and findings can be applied to similar residential microgrids globally, especially in contexts where embedded network architectures are prevalent. Mathematical model presented in this work and validated via real-life field testing can be applied to automated energy metering data flow with short sampling time of several minutes allowing for real-time pricing implementation. It is readily applicable to the use of intelligent energy management systems and automatic and user-induced flexible demand response.
References
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25. Burke W.J., Auslander D.M. Residential Electricity Auction with Uniform Pricing and Cost Constraints. – 41st North American Power Symposium, 2009, DOI: 10.1109/NAPS.2009.5484058.
26. Alferidi A. et al. AI-Powered Microgrid Networks: Multi-Agent Deep Reinforcement Learning for Optimized Energy Trading in Interconnected Systems. – Arabian Journal for Science and Engineering, 2024, DOI: 10.1007/s13369-024-09754-4
#
1. Jirdehi M.A. et al. Different Aspects of Microgrid Management: A Comprehensive Review. – Journal of Energy Storage, 2020, vol. 30, DOI: 10.1016/j.est.2020.101457.
2. Agupugo C.P. et al. Advancements in Technology for Renewable Energy Microgrids. – International Journal of Novel Research in Engineering and Science, 2024, vol. 11, pp. 122–137, DOI: 10.5281/zenodo.13292182.
3. Embedded Networks Factsheet [Electron. resource], URL: https://ewosa.com.au/assets/volumes/general-downloads/fact-sheets/Embedded-networks.pdf (Accessed on 13.02.2025).
4. Katiraei F. et al. Microgrids Management. Control and Operation Aspects of Microgrids. – IEEE Power & Energy Magazine, 2008, pp. 55–65, DOI: 10.1109/MPE.2008.918702.
5. Marzal S. et al. An Embedded Internet of Energy Communication Platform for the Future Smart Microgrids Management. – IEEE Internet of Things Journal, 2019, vol. 6, No 4, pp. 7241–7251, DOI: 10.1109/JIOT.2019.2915389.
6. Gallo D. et al. Low Cost Smart Power Metering. – IEEE International Instrumentation and Measurement Technology Conference, 2013, pp. 763–767, DOI: 10.1109/I2MTC.2013.6555518.
7. Abate F. et al. A Low Cost Smart Power Meter for IoT. – Measurement, 2019, vol. 136, pp. 59–66, DOI: 10.1016/j.measurement.2018.12.069.
8. Palacios-Garcia E.J. et al. Smart Metering System for Microgrids. – IECON 2015 – 41st Annual Conference of the IEEE Industrial Electronics Society, 2015, pp. 3289–3294, DOI: 10.1109/IECON.2015.7392607.
9. Stadler M. et al. Value Streams in Microgrids: A Literature Review. – Applied Energy, 2016, vol. 162, pp. 980–989, DOI: 10.1016/j.apenergy.2015.10.081.
10. Gerbinet S., Belboom S., Léonard A. Life Cycle Analysis (LCA) of Photovoltaic Panels: A Review. – Renewable and Sustainable Energy Reviews, 2014, vol. 38, pp. 747–753, DOI: 10.1016/j.rser.2014.07.043.
11. PVWatts® Calculator by National Renewable Energy Laboratory [Electron. resource], URL: https://pvwatts.nrel.gov/ (Accessed on 08.01.2025).
12. The RETScreen® Clean Energy Management Software [Electron. resource], URL: https://natural-resources.canada.ca/maps-tools-and-publications/tools/modelling-tools/retscreen/7465 (Accessed on 08.01.2025).
13. McManus M.C. Environmental Consequences of the Use of Batteries in Low Carbon Systems: The Impact of Battery Production. – Applied Energy, 2012, vol. 93, pp. 288–295, DOI: 10.1016/j.apenergy.2011.12.062.
14. Jenkins D.P., Fletcher J., Kane D. Mode for Evaluating Impact of Battery Storage on Microgeneration in Dwellings. – Energy Conversion and Management, 2008, vol. 49, pp. 2413–2424, DOI: 10.1016/j.enconman.2008.01.011.
15. Desrochers K. et al. Real-world, Full-scale Validation of Power Balancing Services from Packetized Virtual Batteries. – IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019, DOI: 10.1109/ISGT.2019.8791628.
16. Morgan M.G., Talukdar S.N. Electric Power Load Management: Some Technical, Economic, Regulatory and Social Issues. – Proceedings of the IEEE, 1979, vol. 67, No. 2, pp. 241–312, DOI: 10.1109/PROC.1979.11234.
17. Schweppe F.C. et al. Homeostatic Utility Control. – IEEE Transactions on Power Apparatus and Systems, 1980, No. 3, pp. 1151–1163, DOI: 10.1109/TPAS.1980.319745.
18. Binyamin S.S., Ben Slama S.A., Zafar B. Artificial Intelligence-Powered Energy Community Management for Developing Renewable Energy Systems in Smart Homes. – Energy Strategy Reviews, 2024, vol. 51, DOI: 10.1016/j.esr.2023.101288.
19. Ukoba K. et al. Optimizing Renewable Energy Systems Through Artificial Intelligence: Review and Future Prospects. – Energy & Environment, 2024, vol. 35(7), pp. 3833–3879, DOI: 10.1177/0958305X24125629.
20. Long C. et al. Peer-To-Peer Energy Trading in a Community Microgrid. – IEEE Power Energy Society General Meeting, 2017, DOI: 10.1109/PESGM.2017.8274546.
21. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – International Workshop on Flexibility and Resiliency Problems of Electric Power Systems. EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
22. Shahzad S. et al. Possibilities, Challenges, and Future Opportunities of Microgrids: A Review. – Sustainability, 2023, vol. 15, DOI: 10.3390/su15086366.
23. Celli G. et al. Optimal Participation of a Microgrid to the Energy Market with an Intelligent EMS. –International Power Engineering Conference, 2005, pp. 663–668, DOI: 10.1109/IPEC.2005.206991.
24. Fridgen G. et al. One Rate Does Not Fit All: An Empirical Analysis of Electricity Tariffs for Residential Microgrids. – Applied Energy, 2018, vol. 210, pp. 800–814, DOI: 10.1016/j.apenergy.2017.08.138.
25. Burke W.J., Auslander D.M. Residential Electricity Auction with Uniform Pricing and Cost Constraints. – 41st North American Power Symposium, 2009, DOI: 10.1109/NAPS.2009.5484058.
26. Alferidi A. et al. AI-Powered Microgrid Networks: Multi-Agent Deep Reinforcement Learning for Optimized Energy Trading in Interconnected Systems. – Arabian Journal for Science and Engineering, 2024, DOI: 10.1007/s13369-024-09754-4
2. Agupugo C.P. et al. Advancements in Technology for Renewable Energy Microgrids. – International Journal of Novel Research in Engineering and Science, 2024, vol. 11, pp. 122–137, DOI: 10.5281/zenodo.13292182.
3. Embedded Networks Factsheet [Электрон. ресурс], URL: https://ewosa.com.au/assets/volumes/general-downloads/fact-sheets/Embedded-networks.pdf (дата обращения 13.02.2025).
4. Katiraei F. et al. Microgrids Management. Control and Operation Aspects of Microgrids. – IEEE Power & Energy Magazine, 2008, pp. 55–65, DOI: 10.1109/MPE.2008.918702.
5. Marzal S. et al. An Embedded Internet of Energy Communication Platform for the Future Smart Microgrids Management. – IEEE Internet of Things Journal, 2019, vol. 6, No 4, pp. 7241–7251, DOI: 10.1109/JIOT.2019.2915389.
6. Gallo D. et al. Low Cost Smart Power Metering. – IEEE International Instrumentation and Measurement Technology Conference, 2013, pp. 763–767, DOI: 10.1109/I2MTC.2013.6555518.
7. Abate F. et al. A Low Cost Smart Power Meter for IoT. – Measurement, 2019, vol. 136, pp. 59–66, DOI: 10.1016/j.measurement.2018.12.069.
8. Palacios-Garcia E.J. et al. Smart Metering System for Microgrids. – IECON 2015 – 41st Annual Conference of the IEEE Industrial Electronics Society, 2015, pp. 3289–3294, DOI: 10.1109/IECON.2015.7392607.
9. Stadler M. et al. Value Streams in Microgrids: A Literature Review. – Applied Energy, 2016, vol. 162, pp. 980–989, DOI: 10.1016/j.apenergy.2015.10.081.
10. Gerbinet S., Belboom S., Léonard A. Life Cycle Analysis (LCA) of Photovoltaic Panels: A Review. – Renewable and Sustainable Energy Reviews, 2014, vol. 38, pp. 747–753, DOI: 10.1016/j.rser.2014.07.043.
11. PVWatts® Calculator by National Renewable Energy Laboratory [Электрон. ресурс], URL: https://pvwatts.nrel.gov/ (дата обращения 08.01.2025).
12. The RETScreen® Clean Energy Management Software [Электрон. ресурс], URL: https://natural-resources.canada.ca/maps-tools-and-publications/tools/modelling-tools/retscreen/7465 (дата обращения 08.01.2025).
13. McManus M.C. Environmental Consequences of the Use of Batteries in Low Carbon Systems: The Impact of Battery Produc-tion. – Applied Energy, 2012, vol. 93, pp. 288–295, DOI: 10.1016/j.apenergy.2011.12.062.
14. Jenkins D.P., Fletcher J., Kane D. Mode for Evaluating Impact of Battery Storage on Microgeneration in Dwellings. – Energy Conversion and Management, 2008, vol. 49, pp. 2413–2424, DOI: 10.1016/j.enconman.2008.01.011.
15. Desrochers K. et al. Real-world, Full-scale Validation of Power Balancing Services from Packetized Virtual Batteries. – IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019, DOI: 10.1109/ISGT.2019.8791628.
16. Morgan M.G., Talukdar S.N. Electric Power Load Management: Some Technical, Economic, Regulatory and Social Issues. – Proceedings of the IEEE, 1979, vol. 67, No. 2, pp. 241–312, DOI: 10.1109/PROC.1979.11234.
17. Schweppe F.C. et al. Homeostatic Utility Control. – IEEE Trans-actions on Power Apparatus and Systems, 1980, No. 3, pp. 1151–1163, DOI: 10.1109/TPAS.1980.319745.
18. Binyamin S.S., Ben Slama S.A., Zafar B. Artificial Intelligence-Powered Energy Community Management for Developing Renewable Energy Systems in Smart Homes. – Energy Strategy Reviews, 2024, vol. 51, DOI: 10.1016/j.esr.2023.101288.
19. Ukoba K. et al. Optimizing Renewable Energy Systems Through Artificial Intelligence: Review and Future Prospects. – Energy & Environment, 2024, vol. 35(7), pp. 3833–3879, DOI: 10.1177/0958305X24125629.
20. Long C. et al. Peer-To-Peer Energy Trading in a Community Microgrid. – IEEE Power Energy Society General Meeting, 2017, DOI: 10.1109/PESGM.2017.8274546.
21. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – International Workshop on Flexibility and Resiliency Problems of Electric Power Systems. EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
22. Shahzad S. et al. Possibilities, Challenges, and Future Opportunities of Microgrids: A Review. – Sustainability, 2023, vol. 15, DOI: 10.3390/su15086366.
23. Celli G. et al. Optimal Participation of a Microgrid to the Energy Market with an Intelligent EMS. –International Power Engineering Conference, 2005, pp. 663–668, DOI: 10.1109/IPEC.2005.206991.
24. Fridgen G. et al. One Rate Does Not Fit All: An Empirical Analysis of Electricity Tariffs for Residential Microgrids. – Ap-plied Energy, 2018, vol. 210, pp. 800–814, DOI: 10.1016/j.apenergy.2017.08.138.
25. Burke W.J., Auslander D.M. Residential Electricity Auction with Uniform Pricing and Cost Constraints. – 41st North American Power Symposium, 2009, DOI: 10.1109/NAPS.2009.5484058.
26. Alferidi A. et al. AI-Powered Microgrid Networks: Multi-Agent Deep Reinforcement Learning for Optimized Energy Trading in Interconnected Systems. – Arabian Journal for Science and Engineering, 2024, DOI: 10.1007/s13369-024-09754-4
#
1. Jirdehi M.A. et al. Different Aspects of Microgrid Management: A Comprehensive Review. – Journal of Energy Storage, 2020, vol. 30, DOI: 10.1016/j.est.2020.101457.
2. Agupugo C.P. et al. Advancements in Technology for Renewable Energy Microgrids. – International Journal of Novel Research in Engineering and Science, 2024, vol. 11, pp. 122–137, DOI: 10.5281/zenodo.13292182.
3. Embedded Networks Factsheet [Electron. resource], URL: https://ewosa.com.au/assets/volumes/general-downloads/fact-sheets/Embedded-networks.pdf (Accessed on 13.02.2025).
4. Katiraei F. et al. Microgrids Management. Control and Operation Aspects of Microgrids. – IEEE Power & Energy Magazine, 2008, pp. 55–65, DOI: 10.1109/MPE.2008.918702.
5. Marzal S. et al. An Embedded Internet of Energy Communication Platform for the Future Smart Microgrids Management. – IEEE Internet of Things Journal, 2019, vol. 6, No 4, pp. 7241–7251, DOI: 10.1109/JIOT.2019.2915389.
6. Gallo D. et al. Low Cost Smart Power Metering. – IEEE International Instrumentation and Measurement Technology Conference, 2013, pp. 763–767, DOI: 10.1109/I2MTC.2013.6555518.
7. Abate F. et al. A Low Cost Smart Power Meter for IoT. – Measurement, 2019, vol. 136, pp. 59–66, DOI: 10.1016/j.measurement.2018.12.069.
8. Palacios-Garcia E.J. et al. Smart Metering System for Microgrids. – IECON 2015 – 41st Annual Conference of the IEEE Industrial Electronics Society, 2015, pp. 3289–3294, DOI: 10.1109/IECON.2015.7392607.
9. Stadler M. et al. Value Streams in Microgrids: A Literature Review. – Applied Energy, 2016, vol. 162, pp. 980–989, DOI: 10.1016/j.apenergy.2015.10.081.
10. Gerbinet S., Belboom S., Léonard A. Life Cycle Analysis (LCA) of Photovoltaic Panels: A Review. – Renewable and Sustainable Energy Reviews, 2014, vol. 38, pp. 747–753, DOI: 10.1016/j.rser.2014.07.043.
11. PVWatts® Calculator by National Renewable Energy Laboratory [Electron. resource], URL: https://pvwatts.nrel.gov/ (Accessed on 08.01.2025).
12. The RETScreen® Clean Energy Management Software [Electron. resource], URL: https://natural-resources.canada.ca/maps-tools-and-publications/tools/modelling-tools/retscreen/7465 (Accessed on 08.01.2025).
13. McManus M.C. Environmental Consequences of the Use of Batteries in Low Carbon Systems: The Impact of Battery Production. – Applied Energy, 2012, vol. 93, pp. 288–295, DOI: 10.1016/j.apenergy.2011.12.062.
14. Jenkins D.P., Fletcher J., Kane D. Mode for Evaluating Impact of Battery Storage on Microgeneration in Dwellings. – Energy Conversion and Management, 2008, vol. 49, pp. 2413–2424, DOI: 10.1016/j.enconman.2008.01.011.
15. Desrochers K. et al. Real-world, Full-scale Validation of Power Balancing Services from Packetized Virtual Batteries. – IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019, DOI: 10.1109/ISGT.2019.8791628.
16. Morgan M.G., Talukdar S.N. Electric Power Load Management: Some Technical, Economic, Regulatory and Social Issues. – Proceedings of the IEEE, 1979, vol. 67, No. 2, pp. 241–312, DOI: 10.1109/PROC.1979.11234.
17. Schweppe F.C. et al. Homeostatic Utility Control. – IEEE Transactions on Power Apparatus and Systems, 1980, No. 3, pp. 1151–1163, DOI: 10.1109/TPAS.1980.319745.
18. Binyamin S.S., Ben Slama S.A., Zafar B. Artificial Intelligence-Powered Energy Community Management for Developing Renewable Energy Systems in Smart Homes. – Energy Strategy Reviews, 2024, vol. 51, DOI: 10.1016/j.esr.2023.101288.
19. Ukoba K. et al. Optimizing Renewable Energy Systems Through Artificial Intelligence: Review and Future Prospects. – Energy & Environment, 2024, vol. 35(7), pp. 3833–3879, DOI: 10.1177/0958305X24125629.
20. Long C. et al. Peer-To-Peer Energy Trading in a Community Microgrid. – IEEE Power Energy Society General Meeting, 2017, DOI: 10.1109/PESGM.2017.8274546.
21. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – International Workshop on Flexibility and Resiliency Problems of Electric Power Systems. EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
22. Shahzad S. et al. Possibilities, Challenges, and Future Opportunities of Microgrids: A Review. – Sustainability, 2023, vol. 15, DOI: 10.3390/su15086366.
23. Celli G. et al. Optimal Participation of a Microgrid to the Energy Market with an Intelligent EMS. –International Power Engineering Conference, 2005, pp. 663–668, DOI: 10.1109/IPEC.2005.206991.
24. Fridgen G. et al. One Rate Does Not Fit All: An Empirical Analysis of Electricity Tariffs for Residential Microgrids. – Applied Energy, 2018, vol. 210, pp. 800–814, DOI: 10.1016/j.apenergy.2017.08.138.
25. Burke W.J., Auslander D.M. Residential Electricity Auction with Uniform Pricing and Cost Constraints. – 41st North American Power Symposium, 2009, DOI: 10.1109/NAPS.2009.5484058.
26. Alferidi A. et al. AI-Powered Microgrid Networks: Multi-Agent Deep Reinforcement Learning for Optimized Energy Trading in Interconnected Systems. – Arabian Journal for Science and Engineering, 2024, DOI: 10.1007/s13369-024-09754-4
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2025-01-30
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