Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid
Abstract
:1. Introduction
- Section 2 presents the concept of HC and reviews various methods, tools (including their advantages and limitations), and AI techniques used to quantify the HC of DERs.
- Section 3 discusses the factors influencing DER hosting capacity.
- Section 4 examines various techniques for enhancing DER hosting capacity.
- Section 5 explores the role of DOEs in integrating distributed energy resources into low- and medium-voltage distribution networks within an Australian context. It highlights the importance of DOEs, their use cases, a general framework, related Australian projects, implementation strategies, the calculation of operating envelopes, their role in the energy market, and the challenges associated with their deployment.
2. Hosting Capacity
2.1. Methods for Quantification of DER Hosting Capacity
2.1.1. Deterministic
2.1.2. Stochastic
2.1.3. Time Series
2.1.4. Streamlined
2.1.5. Optimization-Based Method
2.1.6. Other Approaches
- i.
- Iterative method.An iterative method is a mathematical procedure used to generate a sequence of improving approximate solutions for a class of problems. This method utilizes software packages for distribution network analysis to estimate HC by assessing individual DER locations incrementally until limits are exceeded. Commercial software such as Cyme and Synergy also employ this approach. The advantages of this method include multi-feeder analysis and the utilization of accessible tools [11]. Time-based HC analysis necessitates load and DER forecasts.
- ii.
- Hybrid Drive method.DRIVE is an abbreviation for distribution resource integration and value estimation. The Electric Power Research Institute (EPRI) recently developed this method to address the primary drawback of previous methods, which was the computational burden, and to provide accurate estimates of hosting capacity. This method can be described as a combination of features from stochastic, streamlined, and iterative methods.
2.1.7. Comparison and Discussion:
2.2. AI-Based Hosting Capacity Assessment Techniques
2.3. Power Flow Analysis Tools
3. Main Factors Affecting the DERs Hosting Capacities
- 1.
- Voltage level;
- 2.
- Thermal overloading (ampacity);
- 3.
- Unbalance (phase);
- 4.
- Power quality issues (harmonics and flickering);
- 5.
- Protection.
4. Different Techniques Used for HC Enhancement of DERs
5. The Role of Dynamic Operating Envelopes in the Integration of DERs in an LV/MV Distribution Network in Australian Context
- i.
- Enhanced solar PV/BESS export.
- ii.
- Improved market efficiency: OEs may result in increased embedded energy in the market, potentially leading to reduced wholesale energy prices for all customers.
- iii.
- Enhanced interoperability: This can facilitate efficient balancing of generation and demand, potentially reducing the need for costly infrastructure investments. Participation in real-time energy markets can be advantageous for all customer categories.
- iv.
- Improved network efficiency.
5.1. Dynamic Operating Envelopes
5.2. Australian Projects Related to Dynamic Operating Envelopes
5.3. Implementation of DOE
- i.
- Active customers utilizing the DOE facility (prosumers).
- ii.
- Fixed customers operating within fixed limits (may have DERs).
5.4. Calculation of OEs
- i.
- Iterative approach.
- ii.
- Optimization-based approach.
5.5. Prosumer Participation and Market Integration of DERs
5.6. Challenges in the Implementation of OEs for DERs Grid Integration
5.6.1. Network Visibility
5.6.2. Factor of Uncertainties
5.6.3. Calculation of OEs in Terms of Computational and Scalability
5.6.4. Capacity Allocation to Consumers
5.6.5. Cybersecurity
6. Discussion
- Innovative approaches: Novel software, advanced modelling techniques, and sophisticated algorithms are essential to address emerging challenges and enhance DOE functionality.
- Real-time data: Accurate and reliable real-time data from sensors and monitoring devices is crucial, though it may require significant investment in infrastructure and data management systems.
- Alignment with infrastructure: DOE implementation must align with existing infrastructure, investment plans, and local DER roadmaps to ensure compatibility and scalability.
- Collaboration: Successful adoption of DOEs requires close coordination among utilities, regulators, technology providers, and researchers.
- Regulatory frameworks: Supportive policies and creative regulatory solutions are necessary to integrate DOEs into modern distribution grids effectively.
- Public engagement: Gaining public acceptance is vital, requiring clear communication of benefits and proactive efforts to address stakeholder concerns. Policies that incentivize participation in energy markets can further enhance public engagement.
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- IEA. Renewables. Available online: https://www.iea.org/energy-system/renewables (accessed on 23 May 2025).
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Mitigation of Climate Change; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar] [CrossRef]
- IEA. Global EV Outlook 2025. Available online: https://www.iea.org/reports/global-ev-outlook-2025 (accessed on 23 May 2025).
- World Economic Forum. Why Renewable Energy Is the Future—And a Solution to Reducing Global Carbon Emissions. Available online: https://www.weforum.org/stories/2020/02/renewable-energy-future-carbon-emissions/ (accessed on 16 April 2025).
- Statista. Projected Electricity Generation in Australia by Type. Available online: https://www.statista.com/chart/20732/projected-electricity-generation-australia-by-type/ (accessed on 16 April 2025).
- Haque, M.M.; Wolfs, P. A Review of High PV Penetrations in LV Distribution Networks: Present Status, Impacts and Mitigation Measures. Renew. Sustain. Energy Rev. 2016, 62, 1195–1208. [Google Scholar] [CrossRef]
- Fatima, S.; Püvi, V.; Lehtonen, M. Review on the PV Hosting Capacity in Distribution Networks. Energies 2020, 13, 4756. [Google Scholar] [CrossRef]
- Villalobos, J.G. Optimized Charging Control Method for Plug-in Electric Vehicles in LV Distribution Networks. Ph.D. Thesis, University of the Basque Country, Biscay, Spain, 2016. [Google Scholar]
- Hassan, Q.; Sameen, A.Z.; Salman, H.M.; Jaszczur, M. Large-scale green hydrogen production using alkaline water electrolysis based on seasonal solar radiation. Energy Harvest. Syst. 2024, 11, 20230011. [Google Scholar] [CrossRef]
- Ismael, S.M.; Aleem, S.H.A.; Abdelaziz, A.Y.; Zobaa, A.F. State-of-the-Art of Hosting Capacity in Modern Power Systems with Distributed Generation. Renew. Energy 2019, 130, 1002–1020. [Google Scholar] [CrossRef]
- Rajabi, A.; Elphick, S.; David, J.; Pors, A.; Robinson, D. Innovative Approaches for Assessing and Enhancing the Hosting Capacity of PV-Rich Distribution Networks: An Australian Perspective. Renew. Sustain. Energy Rev. 2022, 161, 112365. [Google Scholar] [CrossRef]
- Adefarati, T.; Bansal, R.C. Integration of Renewable Distributed Generators into the Distribution System: A Review. IET Renew. Power Gener. 2016, 10, 873–884. [Google Scholar] [CrossRef]
- Ebad, M.; Grady, W.M. An Approach for Assessing High-Penetration PV Impact on Distribution Feeders. Electr. Power Syst. Res. 2016, 133, 347–354. [Google Scholar] [CrossRef]
- Gaunt, C.T.; Herman, R.; Namanya, E.; Chihota, J. Voltage Modelling of LV Feeders with Dispersed Generation: Probabilistic Analytical Approach Using Beta PDF. Electr. Power Syst. Res. 2017, 143, 25–31. [Google Scholar] [CrossRef]
- Karimi, M.; Mokhlis, H.; Naidu, K.; Uddin, S.; Bakar, A.H.A. Photovoltaic Penetration Issues and Impacts in Distribution Network—A Review. Renew. Sustain. Energy Rev. 2016, 53, 594–605. [Google Scholar] [CrossRef]
- Mohammadi, P.; Mehraeen, S. Challenges of PV Integration in Low-Voltage Secondary Networks. IEEE Trans. Power Deliv. 2017, 32, 525–535. [Google Scholar] [CrossRef]
- Wang, Y.; Silva, V.; Lopez-Botet-Zulueta, M. Impact of High Penetration of Variable Renewable Generation on Frequency Dynamics in the Continental Europe Interconnected System. IET Renew. Power Gener. 2016, 10, 10–16. [Google Scholar] [CrossRef]
- Razavi, S.E.; Rahimi, E.; Javadi, M.S.; Nezhad, A.E.; Lotfi, M.; Shafie-khah, M.; Catalão, J.P.S. Impact of Distributed Generation on Protection and Voltage Regulation of Distribution Systems: A Review. Renew. Sustain. Energy Rev. 2019, 105, 157–167. [Google Scholar] [CrossRef]
- Australian Energy Regulator. Final DER Integration Expenditure Guidance Note; Australian Energy Regulator: Canberra, ACT, Australia, 2022. Available online: https://www.aer.gov.au/system/files/Final%20DER%20integration%20expenditure%20guidance%20note%20-%20June%202022.pdf (accessed on 24 May 2025).
- Bollen, M.H.J.; Hassan, F. Integration of Distributed Generation in the Power System; IEEE Press Series on Power Engineering; John Wiley & Sons Inc.: Hoboken, NJ, USA, 2011. [Google Scholar]
- Jenicek, D.; Inam, W.; Ilic, M. Locational Dependence of Maximum Installable PV Capacity in LV Networks While Maintaining Voltage Limits. In Proceedings of the 2011 North American Power Symposium, Boston, MA, USA, 15 September 2011; pp. 1–5. [Google Scholar] [CrossRef]
- Ding, F.; Mather, B.; Gotseff, P. Technologies to Increase PV Hosting Capacity in Distribution Feeders. In Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17–21 July 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Mulenga, E.; Bollen, M.H.J.; Etherden, N. A Review of Hosting Capacity Quantification Methods for Photovoltaics in Low-Voltage Distribution Grids. Int. J. Electr. Power Energy Syst. 2020, 115, 105445. [Google Scholar] [CrossRef]
- Abideen, M.Z.U.; Ellabban, O.; Al-Fagih, L. A Review of the Tools and Methods for Distribution Networks’ Hosting Capacity Calculation. Energies 2020, 13, 2758. [Google Scholar] [CrossRef]
- Kharrazi, A.; Sreeram, V.; Mishra, Y. Assessment Techniques of the Impact of Grid-Tied Rooftop Photovoltaic Generation on the Power Quality of Low Voltage Distribution Network—A Review. Renew. Sustain. Energy Rev. 2020, 120, 109643. [Google Scholar] [CrossRef]
- Quintero-Molina, V.; Romero-L, M.; Pavas, A. Assessment of the Hosting Capacity in Distribution Networks with Different DG Location. In Proceedings of the 2017 IEEE Manchester PowerTech, Manchester, UK, 18–22 June 2017. [Google Scholar]
- Estorque, L.K.L.; Pedrasa, M.A.A. Utility-Scale DG Planning Using Location-Specific Hosting Capacity Analysis. In Proceedings of the 2016 IEEE Innovative Smart Grid Technologies—Asia (ISGT-Asia), Melbourne, Australia, 28 November–1 December 2016; pp. 984–989. [Google Scholar] [CrossRef]
- Shayani, R.A.; Oliveira, M.A.G.d. Photovoltaic Generation Penetration Limits in Radial Distribution Systems. IEEE Trans. Power Syst. 2011, 26, 1625–1631. [Google Scholar] [CrossRef]
- Balamurugan, K.; Srinivasan, D.; Reindl, T. Impact of Distributed Generation on Power Distribution Systems. Energy Procedia 2012, 25, 93–100. [Google Scholar] [CrossRef]
- Carollo, R.; Chaudhary, S.K.; Pillai, J.R. Hosting Capacity of Solar Photovoltaics in Distribution Grids under Different Pricing Schemes. In Proceedings of the 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Brisbane, Australia, 15–18 November 2015; pp. 1–5. [Google Scholar] [CrossRef]
- Baut, J.L.; Smith, A.B.; Jones, C.D. Probabilistic Evaluation of the Hosting Capacity in Distribution Networks. In Proceedings of the 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Ljubljana, Slovenia, 9–12 October 2016; pp. 1–6. [Google Scholar]
- Duwadi, K.; Ingalalli, A.; Hansen, T.M. Monte Carlo Analysis of High Penetration Residential Solar Voltage Impacts Using High Performance Computing. In Proceedings of the 2019 IEEE International Conference on Electro Information Technology (EIT), Brookings, SD, USA, 20–22 May 2019; pp. 1–6. [Google Scholar] [CrossRef]
- Schwanz, D.; Busatto, T.; Bollen, M.H.J.; Larsson, A. A Stochastic Study of Harmonic Voltage Distortion Considering Single-Phase Photovoltaic Inverters. In Proceedings of the 2018 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljubljana, Slovenia, 13–16 May 2018; pp. 1–6. [Google Scholar]
- Conti, S.; Raiti, S. Probabilistic Load Flow for Distribution Networks with Photovoltaic Generators Part 1: Theoretical Concepts and Models. In Proceedings of the 2007 International Conference on Clean Electrical Power, Capri, Italy, 21–23 May 2007; pp. 132–136. [Google Scholar] [CrossRef]
- Chihota, M.J.; Bekker, B.; Gaunt, T. A Stochastic Analytic-Probabilistic Approach to Distributed Generation Hosting Capacity Evaluation of Active Feeders. Int. J. Electr. Power Energy Syst. 2022, 136, 107598. [Google Scholar] [CrossRef]
- Saint, B. Update on IEEE P1547.7-Draft Guide to Conducting Distribution Impact Studies for Distributed Resource Interconnection. In Proceedings of the 2012 IEEE PES T&D Conference, Orlando, FL, USA, 7–10 May 2012; pp. 1–2. [Google Scholar] [CrossRef]
- Chathurangi, D.; Jayatunga, U.; Rathnayake, M.; Wickramasinghe, A.; Agalgaonkar, A.P.; Perera, S. Potential Power Quality Impacts on LV Distribution Networks with High Penetration Levels of Solar PV. In Proceedings of the 2018 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljubljana, Slovenia, 13–16 May 2018; pp. 1–6. [Google Scholar]
- Do, M.T.; Bruyere, A.; Francois, B. Sensitivity Analysis of the CIGRE Distribution Network Benchmark According to the Large-Scale Connection of Renewable Energy Generators. In Proceedings of the 2017 IEEE Manchester PowerTech, Manchester, UK, 18–22 June 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Bartecka, M.; Barchi, G.; Paska, J. Time-Series PV Hosting Capacity Assessment with Storage Deployment. Energies 2020, 13, 2524. [Google Scholar] [CrossRef]
- Athari, M.H.; Wang, Z.; Eylas, S. Time-Series Analysis of Photovoltaic Distributed Generation Impacts on a Local Distributed Network. In Proceedings of the 2017 IEEE Manchester PowerTech, Manchester, UK, 18–22 June 2017; pp. 1–6. [Google Scholar]
- Tricarico, G.; Gonzalez-Longatt, F.; Marasciuolo, F.; Ishchenko, O.; Dicorato, M.; Forte, G. A Time-Series Hosting Capacity Assessment of the Maximum Distributed Energy Resource Production. In Proceedings of the 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Madrid, Spain, 6–9 June 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Rogers, L. Hosting Capacity Methods, Applications, Opportunities and Challenges; EPRI: Palo Alto, CA, USA, 2019. [Google Scholar]
- Rylander, M.; Smith, J.; Sunderman, W. Streamlined Method for Determining Distribution System Hosting Capacity. IEEE Trans. Ind. Appl. 2016, 52, 105–111. [Google Scholar] [CrossRef]
- Rylander, M.; Smith, J.; Sunderman, W. Streamlined Method for Determining Distribution System Hosting Capacity. In Proceedings of the 2015 IEEE Rural Electric Power Conference, Asheville, NC, USA, 19–21 April 2015; pp. 3–9. [Google Scholar] [CrossRef]
- Umoh, V.; Davidson, I.; Adebiyi, A.; Ekpe, U. Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review. Energies 2023, 16, 3609. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, X.; Tang, L. Practical Power Distance Test Tool Based on OPF to Assess Feeder DG Hosting Capacity. In Proceedings of the 2017 IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, Canada, 22–25 October 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Xu, X.; Gunda, J.; Dowling, R.; Djokic, S.Z. A Two-stage Approach for Renewable Hosting Capacity Assessment. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, 29 September–2 October 2019; pp. 1–5. [Google Scholar]
- Lakshmi, S.; Ganguly, S. Simultaneous Optimisation of Photovoltaic Hosting Capacity and Energy Loss of Radial Distribution Networks with Open Unified Power Quality Conditioner Allocation. Iet Renew. Power Gener. 2018, 12, 1382–1389. [Google Scholar] [CrossRef]
- Sadeghian, H.; Wang, Z. A Novel Impact-Assessment Framework for Distributed PV Installations in Low-Voltage Secondary Networks. Renew. Energy 2020, 147, 2179–2194. [Google Scholar] [CrossRef]
- Sakar, S.; Balci, M.E.; Aleem, S.H.E.A.; Zobaa, A.F. Increasing PV Hosting Capacity in Distorted Distribution Systems Using Passive Harmonic Filtering. Electr. Power Syst. Res. 2017, 148, 74–86. [Google Scholar] [CrossRef]
- Alturki, M.; Khodaei, A. Optimal Loading Capacity in Distribution Grids. In Proceedings of the 2017 North American Power Symposium (NAPS), Fargo, ND, USA, 17–19 September 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Alturki, M.; Khodaei, A. Marginal Hosting Capacity Calculation for Electric Vehicle Integration in Active Distribution Networks. In Proceedings of the 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, USA, 16–19 April 2018; pp. 1–9. [Google Scholar] [CrossRef]
- Xu, X.; Li, Q.; Sheng, M.; Gao, H. An Optimization-Based Approach for the Distribution Network Electric Vehicle Hosting Capacity Assessment. In Proceedings of the 2022 China Automation Congress (CAC), Zhengzhou, China, 25–27 November 2022; pp. 3795–3800. [Google Scholar] [CrossRef]
- Bassi, V.; Ochoa, L.; Alpcan, T.; Leckie, C. Final Report: Model-Free Operating Envelopes. In Technical Report; The University of Melbourne: Parkville, Australia, 2023. [Google Scholar]
- Wu, J.; Yuan, J.; Weng, Y.; Ayyanar, R. Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids. IEEE Trans. Smart Grid 2023, 14, 354–364. [Google Scholar] [CrossRef]
- Tomin, N.; Voropai, N.; Kurbatsky, V.; Rehtanz, C. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning. Energies 2021, 14, 8270. [Google Scholar] [CrossRef]
- Breker, S.; Rentmeister, J.; Sick, B.; Braun, M. Hosting Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Machine Learning Techniques. Appl. Soft Comput. 2018, 70, 195–207. [Google Scholar] [CrossRef]
- Ma, Y.; Azuatalam, D.; Power, T.; Chapman, A.C.; Verbič, G. A Novel Probabilistic Framework to Study the Impact of Photovoltaic-Battery Systems on Low-Voltage Distribution Networks. Appl. Energy 2019, 254, 113669. [Google Scholar] [CrossRef]
- Shahriar, S.; Al-Ali, A.R.; Osman, A.H.; Dhou, S.; Nijim, M. Prediction of EV Charging Behavior Using Machine Learning. IEEE Access 2021, 9, 111576–111586. [Google Scholar] [CrossRef]
- Islam, M.T.; Hossain, M.J.; Habib, M.A. Machine Learning-Based Hosting Capacity Analysis and Forecasting in Low-Voltage Networks. In Proceedings of the 2023 IEEE International Future Energy Electronics Conference (IFEEC), Sydney, Australia, 20–23 November 2023; pp. 461–464. [Google Scholar] [CrossRef]
- Chihota, M.J.; Lewis, W.; Bekker, B. Using Machine Learning to Advance Computational Efficiency in Stochastic Hosting Capacity Evaluations. In Proceedings of the IEEE EUROCON 2023—20th International Conference on Smart Technologies, Zagreb, Croatia, 6–8 July 2023; pp. 245–250. [Google Scholar] [CrossRef]
- Qammar, N.; Arshad, A.; Miller, R.; Mahmoud, K.; Lehtonen, M. Machine Learning Based Hosting Capacity Determination Methodology for Low Voltage Distribution Networks. IET Gener. Transm. Distrib. 2024, 18, 911–920. [Google Scholar] [CrossRef]
- Bam, L.; Jewell, W. Review: Power System Analysis Software Tools. In Proceedings of the IEEE Power Engineering Society General Meeting, San Francisco, CA, USA, 16 June 2005; pp. 139–144. [Google Scholar] [CrossRef]
- Siemens. PSS®SINCAL—Power System Planning Software. Available online: https://www.siemens.com/global/en/products/energy/grid-software/planning/pss-software/pss-sincal.html (accessed on 24 May 2025).
- Manitoba Hydro International Ltd. PSCAD—Power System Studies. Available online: https://www.pscad.com/engineering-services/power-system-studies (accessed on 24 May 2025).
- DIgSILENT. PowerFactory Applications. Available online: https://www.digsilent.de/en/powerfactory.html (accessed on 24 May 2025).
- NEPLAN. NEPLAN | Target Grid Planning. Available online: https://www.neplan.ch/description/target-grid-planning/ (accessed on 24 May 2025).
- Synergi Electric. Power Distribution System and Electrical Simulation Software—Synergi Electric. Available online: https://www.dnv.com/services/power-distribution-system-and-electrical-simulation-software-synergi-electric-5005 (accessed on 24 May 2025).
- CYME. CYME Power Engineering Software. Available online: https://www.cyme.com/software/ (accessed on 24 May 2025).
- Thurner, L.; Scheidler, A.; Schäfer, F.; Jan-Hendrik, M.; Dollichon, J.; Meier, F. Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems. IEEE Trans. Power Syst. 2018, 33, 6510–6521. [Google Scholar] [CrossRef]
- Electric Power Research Institute (EPRI). OpenDSS-Simulation Tool. Available online: https://www.epri.com/pages/sa/opendss?lang=en (accessed on 24 May 2025).
- PowerModelsDistribution Software Tool. PowerModelsDistribution. Available online: https://lanl-ansi.github.io/PowerModelsDistribution.jl/stable/reference/internal.html#PowerModelsDistribution._dss2eng_load (accessed on 24 May 2025).
- Arshad, A.; Lindner, M.; Lehtonen, M. An Analysis of Photo-Voltaic Hosting Capacity in Finnish Low Voltage Distribution Networks. Energies 2017, 10, 1702. [Google Scholar] [CrossRef]
- Ding, F.; Horowitz, K.A.W.; Mather, B.A.; Palmintier, B. Sequential Mitigation Solutions to Enable Distributed PV Grid Integration. In Proceedings of the 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018; pp. 1–5. [Google Scholar]
- Navarro, B.B.; Navarro, M.M. A Comprehensive Solar PV Hosting Capacity in MV and LV Radial Distribution Networks. In Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Warsaw, Poland, 26–29 September 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Navarro, A.; Ochoa, L.F.; Randles, D. Monte Carlo-Based Assessment of PV Impacts on Real UK Low Voltage Networks. In Proceedings of the 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5. [Google Scholar]
- Hu, J.; Marinelli, M.; Coppo, M.; Zecchino, A.; Bindner, H.W. Coordinated Voltage Control of a Decoupled Three-Phase On-Load Tap Changer Transformer and Photovoltaic Inverters for Managing Unbalanced Networks. Electr. Power Syst. Res. 2016, 131, 264–274. [Google Scholar] [CrossRef]
- Jothibasu, S.; Santoso, S.; Dubey, A. Determining PV Hosting Capacity Without Incurring Grid Integration Cost. In Proceedings of the 2016 North American Power Symposium (NAPS), Denver, CO, USA, 18–20 September 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Kitworawut, P.; Azuatalam, D.; Collin, A.J. An Investigation into the Technical Impacts of Microgeneration on UK-Type LV Distribution Networks. In Proceedings of the 2016 Australasian Universities Power Engineering Conference (AUPEC), Melbourne, Australia, 25–28 September 2016; pp. 1–5. [Google Scholar]
- Martin, W.; Stauffer, Y.; Ballif, C.; Hutter, A.; Alet, P.-J. Automated Quantification of PV Hosting Capacity in Distribution Networks Under User-Defined Control and Optimisation Procedures. In Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Ljubljana, Slovenia, 21–25 October 2018; pp. 1–6. [Google Scholar]
- EPRI. Impact Factors, Methods, and Considerations for Calculating and Applying Hosting Capacity; EPRI Report 3002011009; EPRI: Palo Alto, CA, USA, 2018. [Google Scholar]
- EPRI. Impact Factors and Recommendations on How to Incorporate Them When Calculating Hosting Capacity; EPRI: Palo Alto, CA, USA, 2018. [Google Scholar]
- Tang, N.C.; Chang, G.W. A stochastic approach for determining PV hosting capacity of a distribution feeder considering voltage quality constraints. In Proceedings of the 2018 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljubljana, Slovenia, 13–16 May 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Lusis, P.; Andrew, L.L.H.; Chakraborty, S.; Liebman, A.; Tack, G. Reducing the Unfairness of Coordinated Inverter Dispatch in PV-Rich Distribution Networks. In Proceedings of the 2019 IEEE Milan PowerTech, Milan, Italy, 23–27 June 2019; pp. 1–6. [Google Scholar] [CrossRef]
- Niederhuemer, W.; Schwalbe, R. Increasing PV hosting capacity in LV grids with a probabilistic planning approach. In Proceedings of the 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), Vienna, Austria, 8–11 September 2015; pp. 537–540. [Google Scholar] [CrossRef]
- Asano, M.; Wong, F.; Ueda, R.; Moghe, R.; Rahimi, K.; Chun, H.; Tholomier, D. On the Interplay between SVCs and Smart Inverters for Managing Voltage on Distribution Networks. In Proceedings of the 2019 IEEE Power and Energy Society General Meeting (PESGM), Atlanta, GA, USA, 4–8 August 2019; pp. 1–5. [Google Scholar] [CrossRef]
- Nova, D.S.; Vergara, P.P.; Silva, L.C.P.; Almeida, M.D. Increasing the PV hosting capacity with OLTC technology and PV VAr absorption in a MV/LV rural Brazilian distribution system. In Proceedings of the 2016 17th International Conference on Harmonics and Quality of Power (ICHQP), Belo Horizonte, Brazil, 16–19 October 2016. [Google Scholar]
- Dong, Y.; Xie, X.; Shi, W.; Zhou, B.; Jiang, Q. Demand-Response-Based Distributed Preventive Control to Improve Short-Term Voltage Stability. IEEE Trans. Smart Grid 2018, 9, 4785–4795. [Google Scholar] [CrossRef]
- Medina, J.; Muller, N.; Roytelman, I. Demand Response and Distribution Grid Operations: Opportunities and Challenges. IEEE Trans. Smart Grid 2010, 1, 193–198. [Google Scholar] [CrossRef]
- Fu, Y.Y.; Chiang, H.D. Toward optimal multi-period network reconfiguration for increasing the hosting capacity of distribution networks. In Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 16–20 July 2017; pp. 1–5. [Google Scholar] [CrossRef]
- Heinrich, C.; Fortenbacher, P.; Fuchs, A.N.; Andersson, G. PV-integration strategies for low voltage networks. In Proceedings of the 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium, 4–8 April 2016; pp. 1–6. [Google Scholar]
- Jamal, T.; Carter, C.; Schmidt, T.; Shafiullah, G.M.; Calais, M.; Urmee, T. An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system. Appl. Energy 2019, 254, 113718. [Google Scholar] [CrossRef]
- Oliveira, T.E.C.d.; Carvalho, P.M.S.; Ribeiro, P.F.; Bonatto, B.D. PV Hosting Capacity Dependence on Harmonic Voltage Distortion in Low-Voltage Grids: Model Validation with Experimental Data. Energies 2018, 11, 465. [Google Scholar] [CrossRef]
- Su, X.; Masoum, M.A.S.; Wolfs, P.J. Optimal PV Inverter Reactive Power Control and Real Power Curtailment to Improve Performance of Unbalanced Four-Wire LV Distribution Networks. IEEE Trans. Sustain. Energy 2014, 5, 967–977. [Google Scholar] [CrossRef]
- Ali, S.; Haque, M.M.; Wolfs, P.J. A review of topological ordering based voltage rise mitigation methods for LV distribution networks with high levels of photovoltaic penetration. Renew. Sustain. Energy Rev. 2019, 103, 163–176. [Google Scholar] [CrossRef]
- Jiang, F.; Chen, L.; Tu, C.; Guo, Q.; Zhu, R.; Liserre, M. Operations and Coordination of Dual-Functional DVR and Recloser in a Power Distribution System. IEEE Access 2019, 7, 140908–140921. [Google Scholar] [CrossRef]
- Rezaeian-Marjani, S.; Galvani, S.; Talavat, V.; Farhadi-Kangarlu, M. Optimal allocation of D-STATCOM in distribution networks including correlated renewable energy sources. Int. J. Electr. Power Energy Syst. 2020, 122, 106178. [Google Scholar] [CrossRef]
- Sayed, M.A.; Takeshita, T. All nodes voltage regulation and line loss minimization in loop distribution systems using UPFC. IEEE Trans. Power Electron. 2010, 26, 1694–1703. [Google Scholar] [CrossRef]
- Rudion, K.; Orths, A.; Styczynski, Z.A.; Strunz, K. Design of Benchmark of Medium Voltage Distribution Network for Investigation of DG Integration. In Proceedings of the 2006 IEEE Power Engineering Society General Meeting, Montreal, QC, Canada, 18–22 June 2006; pp. 1–6. [Google Scholar] [CrossRef]
- The role of Decentralised Control for Managing Network Constraints for DER on Regional, Rural, and Remote Networks Dynamic Limits; Australian Renewable Energy Agency: Sydney, Australia, 2020.
- The Role of Dynamic Operating Envelopes in Co-Ordinating and Optimising DER. Available online: https://arena.gov.au/knowledge-bank/the-role-of-dynamic-operating-envelopes-in-co-ordinating-and-optimising-der/ (accessed on 24 May 2025).
- Abbas, A.S.; El-Ela, A.A.A.; El-Sehiemy, R.A.; Fetyan, K.K. Assessment and Enhancement of Uncertain Renewable Energy Hosting Capacity With/out Voltage Control Devices in Distribution Grids. IEEE Syst. J. 2023, 17, 1986–1994. [Google Scholar] [CrossRef]
- Chen, D.; Xu, L.; Zhang, W. Active Distribution Power System with Multi-Terminal DC Links. IET Renew. Power Gener. 2017, 11, 27–34. [Google Scholar] [CrossRef]
- Liu, N.; Chen, Q.; Lu, X.; Liu, J.; Zhang, J. A Charging Strategy for PV-Based Battery Switch Stations Considering Service Availability and Self-Consumption of PV Energy. IEEE Trans. Ind. Electron. 2015, 62, 4878–4889. [Google Scholar] [CrossRef]
- Dahal, S.; Mithulananthan, N.; Saha, T. Impact of composite loads on dynamic loadability of emerging distribution systems. In Proceedings of the AUPEC 2011, Brisbane, Australia, 25–28 September 2011; pp. 1–6. [Google Scholar]
- Procopiou, A.; Ochoa, L.F. Voltage Control in PV-Rich LV Networks without Remote Monitoring. IEEE Trans. Power Syst. 2017, 32, 1224–1236. [Google Scholar] [CrossRef]
- Long, C.; Ochoa, L.F. Voltage Control of PV-Rich LV Networks: OLTC-Fitted Transformer and Capacitor Banks. IEEE Trans. Power Syst. 2016, 31, 4016–4025. [Google Scholar] [CrossRef]
- Dubey, A. Impacts of Voltage Control Methods on Distribution Circuit’s Photovoltaic (PV) Integration Limits. Inventions 2017, 2, 28. [Google Scholar] [CrossRef]
- Rahman, M.M.; Shafiullah, A.A.; Pezeshki, H.; Hettiwatte, S.N. Improvement of Voltage Magnitude and Unbalance in LV Network by Implementing Residential Demand Response. In Proceedings of the 2017 IEEE Power and Energy Society General Meeting, Chicago, IL, USA, 16–20 July 2017; pp. 1–5. [Google Scholar] [CrossRef]
- Li, X.; Borsche, T.S.; Andersson, G. PV Integration in Low-Voltage Feeders with Demand Response. In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, The Netherlands, 29 June–2 July 2015; pp. 1–6. [Google Scholar] [CrossRef]
- Xiong, Q.; Liu, F.; Lv, L.; Liu, Y.; Li, Y.; Zhu, C. Urban Power Grids Dynamic Control Model with Photovoltaic and Electric Vehicles. In Proceedings of the 2018 China International Conference on Electricity Distribution (CICED), Tianjin, China, 17–19 September 2018; pp. 2462–2466. [Google Scholar] [CrossRef]
- Demirok, E.; Sera, D.; Teodorescu, R.; Rodriguez, P.; Borup, U. Clustered PV Inverters in LV Networks: An Overview of Impacts and Comparison of Voltage Control Strategies. In Proceedings of the 2009 IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, Canada, 22–23 October 2009; pp. 1–6. [Google Scholar] [CrossRef]
- Padullaparti, H.V.; Jothibasu, S.; Santoso, S.; Todeschini, G. Increasing Feeder PV Hosting Capacity by Regulating Secondary Circuit Voltages. In Proceedings of the 2018 IEEE Power and Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Falabretti, D.; Merlo, M.; Delfanti, M. Network Reconfiguration and Storage Systems for the Hosting Capacity Improvement. In Proceedings of the 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, BC, Canada, 21–24 October 201; pp. 1–4. [CrossRef]
- Xu, X.; Li, J.; Xu, Z.; Zhao, J.; Lai, C.S. Enhancing Photovoltaic Hosting Capacity—A Stochastic Approach to Optimal Planning of Static Var Compensator Devices in Distribution Networks. Appl. Energy 2019, 238, 952–962. [Google Scholar] [CrossRef]
- Son, Y.J.; Lim, S.H.; Yoon, S.G.; Khargonekar, P.P. Residential Demand Response-Based Load-Shifting Scheme to Increase Hosting Capacity in Distribution System. IEEE Access 2022, 10, 18544–18556. [Google Scholar] [CrossRef]
- Clean Energy Regulator. Quarterly Carbon Market Report. 2020. Available online: https://cer.gov.au/news-and-media/media/2021/march/quarterly-carbon-market-report-december-quarter-2020 (accessed on 1 January 2020).
- ARENA. On the Calculation and Use of Dynamic Operating Envelopes; Australian Renewable Energy Agency: Sydney, Australia, 2020. Available online: https://arena.gov.au/assets/2020/09/on-the-calculation-and-use-of-dynamic-operating-envelopes.pdf (accessed on 1 January 2020).
- DOE Working Group. Dynamic Operating Envelopes Working Group Outcomes Report; Australian Renewable Energy Agency: Sydney, Australia, 2022. [Google Scholar]
- AEMO. 2023 Electricity Statement of Opportunities; AEMO: Docklands, Australia, 2023. [Google Scholar]
- Bridge, J. Export Limits for Embedded Generators up to 200 kVA Connected at Low Voltage; AusNet Services: Melbourne, VIC, Australia, 2017. [Google Scholar]
- Petrou, K.; Procopiou, A.T.; Gutierrez-Lagos, L.; Liu, M.Z.; Ochoa, L.F.; Langstaff, T.; Theunissen, J.M. Ensuring Distribution Network Integrity Using Dynamic Operating Limits for Prosumers. IEEE Trans. Smart Grid 2021, 12, 3877–3888. [Google Scholar] [CrossRef]
- Zabihinia Gerdroodbari, Y.; Khorasany, M.; Razzaghi, R. Dynamic PQ Operating Envelopes for Prosumers in Distribution Networks. Appl. Energy 2022, 325, 119757. [Google Scholar] [CrossRef]
- O’Neil, R.L.; Braslavsky, L.; Brinsmead, T.; McDonald, C.; Ward, A.; Williams, B. Advanced VPP Grid Integration Project—Analysis of the VPP Dynamic Network Constraint Management; CSIRO: Canberra, Australia, 2020. [Google Scholar]
- SA Power Networks. South Australia Power Networks Flexible Exports for Solar PV Trial; SA Power Networks: Keswick, Australia, 2022. [Google Scholar]
- AEMO. Project EDGE (Energy Demand and Generation Exchange); AEMO: Melbourne, Australia, 2020. [Google Scholar]
- Western Power. Western Australia Distributed Energy Resources Orchestration Pilot (Project Symphony); Western Power: Perth, Australia, 2021. [Google Scholar]
- Ausgrid. Project Edith. Available online: https://www.ausgrid.com.au/About-Us/Future-Grid/Project-Edith#:~:text=Project%20Overview.%20Project%20Edith,%20named (accessed on 1 January 2024).
- ARENA. Project Converge ACT Distributed Energy Resources Demonstration Pilot. Available online: https://arena.gov.au/projects/project-converge-act-distributed-energy-resources-demonstration-pilot/#:~:text=Project%20Converge%20will:%20design%20and (accessed on 1 January 2024).
- ARENA. Redback Technologies Project SHIELD: Project Journey Report. Available online: https://arena.gov.au/knowledge-bank/redback-technologies-project-shield-project-journey-report/#:~:text=The%20Project%20SHIELD%20initiative,%20launched (accessed on 1 January 2024).
- CutlerMerz. Review of Dynamic Operating Envelope Adoption by DNSPs. Available online: https://www.cutlermerz.com/projects/review-of-dynamic-operating-envelope-adoption-by-dnsps (accessed on 1 January 2024).
- Olivier, F.; Aristidou, P.; Ernst, D.; Cutsem, T.V. Active Management of Low-Voltage Networks for Mitigating Over-voltages Due to Photovoltaic Units. IEEE Trans. Smart Grid 2016, 7, 926–936. [Google Scholar] [CrossRef]
- Zhang, B.; Lam, A.Y.S.; Domínguez-García, A.D.; Tse, D. An Optimal and Distributed Method for Voltage Regulation in Power Distribution Systems. IEEE Trans. Power Syst. 2015, 30, 1714–1726. [Google Scholar] [CrossRef]
- AEMO. Project EDGE (Energy Demand and Generation Exchange)—Lessons Learnt 1; AEMO: Melbourne, Australia, 2021. [Google Scholar]
- Petrou, K.; Liu, M.; Procopiou, A.; Ochoa, L.; Theunissen, J.; Harding, J. Managing Residential Prosumers Using Operating Envelopes: An Australian Case Study. In Proceedings of the CIRED Workshop, Berlin, Germany, 22–23 September 2020. [Google Scholar]
- Bennett, C.J.; Stewart, R.A.; Lu, J.W. Development of a Three-Phase Battery Energy Storage Scheduling and Operation System for Low Voltage Distribution Networks. Appl. Energy 2015, 146, 122–134. [Google Scholar] [CrossRef]
- Iria, J.; Scott, P.; Attarha, A.; Gordon, D.; Franklin, E. MV-LV Network-Secure Bidding Optimisation of an Aggregator of Prosumers in Real-Time Energy and Reserve Markets. Energy 2022, 242, 122962. [Google Scholar] [CrossRef]
- Givisiez, A.G.; Ochoa, L.; Liu, M.; Bassi, V. Assessing the Pros and Cons of Different Operating Envelopes Implementations Across Australia. In Proceedings of the 27th International Conference on Electricity Distribution (CIRED 2023), Rome, Italy, 12–15 June 2023. [Google Scholar]
- Iria, J.; Soares, F.; Matos, M. Optimal Bidding Strategy for an Aggregator of Prosumers in Energy and Secondary Reserve Markets. Appl. Energy 2019, 238, 1361–1372. [Google Scholar] [CrossRef]
- Tushar, W.; Saha, T.K.; Yuen, C.; Azim, M.I.; Morstyn, T.; Poor, H.V.; Niyato, D.; Bean, R. A Coalition Formation Game Framework for Peer-to-Peer Energy Trading. Appl. Energy 2020, 261, 114436. [Google Scholar] [CrossRef]
- Khorasany, M.; Razzaghi, R.; Shokri Gazafroudi, A. Two-Stage Mechanism Design for Energy Trading of Strategic Agents in Energy Communities. Appl. Energy 2021, 295, 117036. [Google Scholar] [CrossRef]
- Azim, M.I.; Lankeshwara, G.; Tushar, W.; Sharma, R.; Alam, M.R.; Saha, T.K.; Khorasany, M.; Razzaghi, R. Dynamic Operating Envelope-Enabled P2P Trading to Maximize Financial Returns of Prosumers. IEEE Trans. Smart Grid 2024, 15, 1978–1990. [Google Scholar] [CrossRef]
- AEMO. Project EDGE. Available online: https://aemo.com.au/en/initiatives/major-programs/nem-distributed-energy-resources-der-program/der-demonstrations/project-edge (accessed on 1 January 2024).
- Milford, T.; Krause, O. Managing DER in Distribution Networks Using State Estimation & Dynamic Operating Envelopes. In Proceedings of the 2021 IEEE PES Innovative Smart Grid Technologies—Asia (ISGT Asia), Brisbane, Australia, 5–8 December 2021; pp. 1–5. [Google Scholar] [CrossRef]
- AEMO. Western Australia Distributed Energy Resources Orchestration Pilot (Project Symphony). Available online: https://aemo.com.au/initiatives/major-programs/wa-der-program/project-symphony (accessed on 1 January 2024).
- Operating Envelopes and Technical Challenges in Its Calculation. Available online: https://smartgrid.ieee.org/bulletins/august-2022/technical-challenges-in-the-calculation-and-implementation-of-operating-envelopes-for-der-grid-integration-in-australia#:~:text=Operating%20Envelopes%20and%20Technical%20Challenges%20in%20its%20Calculation (accessed on 5 March 2025).
- Ullah, F.U.M.; Ullah, A.; Haq, I.U.; Rho, S.; Baik, S.W. Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks. IEEE Access 2020, 8, 123369–123380. [Google Scholar] [CrossRef]
- Sajjad, M.; Khan, A.Z.; Sajjad, M.; Khan, A.Z.; Ullah, A.; Hussain, T.; Ullah, W.; Lee, M.Y.; Baik, S.W. A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting. IEEE Access 2020, 8, 143759–143768. [Google Scholar] [CrossRef]
- Popovic, Z.N.; Knezevic, S.D. Dynamic Reconfiguration of Distribution Networks Considering Hosting Capacity: A Risk-Based Approach. IEEE Trans. Power Syst. 2023, 38, 3440–3450. [Google Scholar] [CrossRef]
- Saint-Pierre, A.; Mancarella, P. Active Distribution System Management: A Dual-Horizon Scheduling Framework for DSO/TSO Interface Under Uncertainty. IEEE Trans. Smart Grid 2017, 8, 2186–2197. [Google Scholar] [CrossRef]
- Wang, Y.; Zhang, N.; Chen, Q.; Kirschen, D.S.; Li, P.; Xia, Q. Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV. IEEE Trans. Power Syst. 2018, 33, 3255–3264. [Google Scholar] [CrossRef]
- Shaker, H.; Zareipour, H.; Wood, D. A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites. IEEE Trans. Smart Grid 2016, 7, 2466–2476. [Google Scholar] [CrossRef]
- Lin, J.; Ma, J.; Zhu, J. A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation Disaggregation. IEEE Trans. Smart Grid 2022, 13, 268–279. [Google Scholar] [CrossRef]
- Lin, S.; Zhu, H. Enhancing the Spatio-Temporal Observability of Grid-Edge Resources in Distribution Grids. IEEE Trans. Smart Grid 2021, 12, 5434–5443. [Google Scholar] [CrossRef]
- Kabir, F.; Yu, N.; Yao, W.; Yang, R.; Zhang, Y.Y.C. Joint Estimation of Behind-the-Meter Solar Generation in a Community. IEEE Trans. Sustain. Energy 2021, 12, 682–694. [Google Scholar] [CrossRef]
- Liu, M.Z.; Ochoa, L.F.; Wong, P.K.C.; Theunissen, J. Using OPF-Based Operating Envelopes to Facilitate Residential DER Services. IEEE Trans. Smart Grid 2022, 13, 4494–4504. [Google Scholar] [CrossRef]
- ARENA. Dynamic Operating Envelope Working Group Outcomes Report. Available online: https://arena.gov.au/assets/2022/03/dynamic-operating-envelope-working-group-outcomes-report.pdf (accessed on 1 January 2024).
- AEMO. Project EDGE Fairness in Dynamic Operating Envelope Objective Functions; AEMO: Melbourne, Australia, 2023. [Google Scholar]
- Rose, S.; Borchert, O.; Mitchell, S.; Connelly, S. Zero Trust Architecture; Special Publication (NIST SP); National Institute of Standards and Technology: Gaithersburg, MD, USA, 2020. [Google Scholar]
- McCarthy, J.; Faatz, D.; Division, E.; Urlaub, N.; Wiltberger, J.; Yimer, T. Securing Distributed Energy Resources: An Example of Industrial Internet of Things Cybersecurity; Special Publication (NIST SP); National Institute of Standards and Technology: Gaithersburg, MD, USA, 2022. [Google Scholar]
Ref. | DER Type | Performance Indices | Study Objective |
---|---|---|---|
[26] | DG | –OV/UV–TOL | Assessing the impact of DG location on hosting capacity. |
[27] | DG | –OV–TOL | Analysing the hosting capacity of utility-level DG planning based on location. |
[28] | Solar PV | –OV–TOL | Examining the impacts of conductor ampacity and voltage fluctuations on photovoltaic (PV) hosting capacity. |
[29] | Solar PV | –PL–OV/UV–UP–FC | Investigating the influence of solar PV generation on the actual and reactive power losses, voltage distribution, phase asymmetry, and fault capacity of the distribution network. |
[30] | Solar PV/EV | –OV/UV–TOL–UP | Analysis of the impact of EV charging on solar PV hosting capacity under various electricity tariffs in LV distribution networks. |
Ref. | DER Type | Performance Indices | Study Objective |
---|---|---|---|
[31] | DERs | –OV–TOL | Probabilistic hosting capacity evaluation for smart grid solution scalability. |
[32] | Solar PV | –OV–UP | Voltage implications analysis of high residential solar penetration in LV feeder. |
[33] | Solar PV | –HD | Hosting capacity evaluation considering harmonic distortion from PV inverters. |
[34] | Solar PV | –OV–TOL | Hosting capacity assessment across 1264 LV distribution networks. |
[35] | DG | –OV–UP–TOL | Hosting capacity assessment using analytical-probabilistic methodology. |
Ref. | DER Type | Performance Indices | Study Objective |
---|---|---|---|
[37] | Solar PV | –OV–RPF–PL–PF–UP | Effects of increased solar PV penetration on operational constraints in LV Distribution Network in Sri Lanka. |
[38] | RES | –OV/UV–TOL | Probability of constraint occurrences upon exceeding RES hosting capacity threshold. |
[39] | Solar PV and BESS | –OV/UV–UP–TOL | Influence of PV systems on voltage quality using EN 50160 standard and BESS impact on PVHC. |
[40] | Solar PV | –OV–RPF | Effects of PV integration into LV distribution networks. |
[41] | DERs | –OV/UV–TOL | Investigating load characteristics and hosting capacity in MV radial distribution network. |
Ref. | DER Type | Performance Indices | Study Objective |
---|---|---|---|
[43] | Solar PV | –OV/UV–TOL Ampacity (Line and T/F)–PD | Assessment of PV hosting capacity through a stochastic method of a feeder. |
[44] | DER | –OV/UV–TOL Ampacity (Line and T/F)–PD | Assessment of the positive and negative effects of distributed energy resources on distribution networks through a new streamlined method. |
Ref. | DER Type | Performance Indices | Study Objective |
---|---|---|---|
[49] | Solar PV | –OV –RPF | A framework is proposed to assess the impact of two types of DPV installations on a real distribution network, with a multi-objective optimization formulated to determine optimal sizing and placement for minimizing reverse power flow and voltage violations while maximizing energy conservation and voltage stability. |
[50] | Solar PV | –OV/UV –TOL Ampacity (Line) –HD | PV hosting capacity is improved and evaluated by implementing passive harmonic filters in a distorted distribution system, with optimization considering capacitive reactance, inductive reactance, damping resistance, and PV unit capacity. |
[51] | DG | –OV/UV –TOL Ampacity (Line) | In this study, a proficient linearized model was introduced to ascertain the optimal loading capacity of radial distribution networks. |
[52] | EV | –OV/UV –TOL Ampacity (Line) | The aim of the investigation was to evaluate the incremental hosting capacity values for distribution networks integrating Electric Vehicles (EVs) employing an optimization-centered hosting capacity model. |
[53] | EV | –OV/UV –TOL Ampacity (Line) | An optimization-based approach for electric vehicle hosting capacity (EVHC) is developed in two stages, and its effectiveness is assessed by comparing it with conventional methods using the IEEE-123 Node test feeder. |
Method | Data Requirement | Complexity | Calculation Time | Scenarios Tested | Results |
---|---|---|---|---|---|
Deterministic | Small | Simple | Small | Few | Exact (worst-case) |
Stochastic | Moderate | Complex | Large | Many | Statistically Accurate |
Time Series | Large | Moderate | Large | Few | Accurate |
Streamlined | Large | Moderate | Moderate | Several | Approximate |
Optimization-Based | Moderate | Complex | Large | Several | Exact (within constraints) |
Iterative Method | Large | Complex | Large | Several | Accurate |
Hybrid (DRIVE) | Moderate | Moderate | Moderate | Several | Accurate |
Ref. | Network | Study | Techniques Used | Limitation |
---|---|---|---|---|
[55] | IEEE 34-, 123-bus | PV HC assessment in real-time | Deep learning-based ST-LSTM method | High computational cost, substantial resources for training and implementation |
[56] | IEEE 34-bus | HC enhancement of converter-interfaced generators | Multi-agent reinforcement learning (MARL) algorithm | Exponential growth in state-action space complexity, scalability challenges |
[57] | 300 real rural and suburban LV grids | PV HC of LV Distributed Generators | Support Vector Machines (SVM) | High computational complexity and memory requirements for large datasets |
[58] | Two 4-wire 3-phase unbalanced LV test networks (ENWL) | Rooftop PV with BESS at homes | Battery scheduling in Monte Carlo analysis with Policy Function Approximation (PFA) | Approximation errors, learning instability, high data requirements, difficulties in high-dimensional spaces, local optima convergence, lack of interpretability |
[59] | ACN dataset (2019) | EV charging behavior | ML algorithms: random forest, SVM, XGBoost, deep neural networks, ensemble learning | Requires validation during uncertain circumstances |
[60] | IEEE 13-bus network | HC analysis of DERs | Multiple linear regression (MLR), multivariate linear regression (MVLR), SVM | Limited constraints: over/under-voltage violations, conductor-rated current, equipment-rated power |
[61] | Three LV distribution feeders | HC assessment of DERs | ML-driven stochastic HC (SHC-ML) method based on linear regression | Only considers PV among DERs, uses voltage performance as constraint |
[62] | 503 simulated realistic LV distribution feeders in Finland | HC assessment in LV Distribution networks | ML models: decision tree, random forest, linear regression, k-nearest neighbors, logistic regression, SVM | Network topology not considered |
Ref. | Software | Methods | Key Parameters | Features | Strengths | Limitations |
---|---|---|---|---|---|---|
[64] | PSS/Sincal | Time series/steady-state and transient | Voltage, short circuit, thermal loading, protection, reverse power flow | Comprehensive suite for system planning, including load flow, short circuit, transient stability, and protection system coordination. Handles balanced and unbalanced networks. | Comprehensive, user-friendly | Expensive, complex |
[65] | PSCAD | Time-domain analysis | Voltage, active power, reactive power, phase angle | Detailed modelling of dynamic behaviours, transient stability, and electromagnetic transients. Supports custom models and multi-rate simulation. | Detailed modelling, Multi-rate simulation | Not primarily for hosting capacity, Complex for general use |
[66] | DIgSILENT PowerFactory | Stochastic (binomial search method) | Voltage, power quality, thermal, protection | Versatile for various power system studies including steady-state, dynamic, probabilistic assessments, and renewable energy integration. Extensive modelling capabilities. | Advanced features, Good for renewable energy modelling | Expensive, Complex |
[67] | NEPLAN | Stochastic (Monte Carlo simulation) | Voltage, thermal, harmonic distortion, protection, voltage fluctuation | Extensive features for analysis, planning, and optimization. Includes transmission, distribution, and generation models, customizable scripting, and multi-user functionality. | Flexible data import/export, customizable | Complex interface |
[68] | Synergy Electric | Iterative time-series approach with stochastic characteristics | Over voltage, thermal, reverse power flow | Detailed modelling of real-world distribution systems, including PV, storage, transformer management, and power quality assessment. | Comprehensive spatial environment modelling, PV modelling, Weather simulation | Requires advanced data integration, High complexity |
[69] | CYME | Streamlined (iterative hourly constant source) | Voltage, power quality, thermal, protection, reliability/safety | User-friendly interface for complex power system analyses. Suitable for steady-state and transient simulations. | Extensive modelling, Customization | Requires expertise, Complex interface |
[70] | PandaPower | Time-series analysis | Voltage, overloading, power loss | Open-source, user-friendly, ideal for smaller systems and educational use. | Open-source, Easy to use | Limited scalability for larger systems |
[71] | OpenDSS | Quasi-static time series | Voltage, Voltage unbalance, transformer overloading, harmonics, power loss | Detailed component modelling for distribution systems. | Open-source, Detailed modelling | Limited control strategies for DERs |
[72] | PowerModels Distribution | Steady-state analysis | Voltage, Overloading, Fault currents | Good visualization and user interface for steady-state analysis of radial distribution systems. | User-friendly, Good for steady-state analysis | Limited analysis capabilities for complex systems |
Ref. No. | HC Enhancement Method | Effect on HC | Reference (HC) |
---|---|---|---|
[80] | Smart inverter Volt-VAR control | Increased up to 19.7% | Customer PV |
[106] | OLTC (1-min control cycle) | Increased from 40% to 100% | Customer PV |
[107] | OLTC (setting of ±8%) | Increased from 30% to 50% | Customer PV |
[73] | OLTC (balanced feed-in for rural and urban cases) | Increased by 17.5% and 43.5% for 0% and 5% MV change, respectively | Peak load |
[77] | OLTC and reactive power support | Increased from 40% to 70% | Customer PV |
[83] | Tap setting of T/Fs and capacitor settings | Increased from 38% to 64.4% | Peak load |
[108] | LTC and smart inverters (PF 0.995 and 0.98 lag) | 158% increase in PV HC | Peak load |
[86] | OLTC, smart inverter functions and SVCs | Increased from 77% to 154% | Peak load |
[109] | RPC and APC (urban distribution system) | Increased from 35.65% to 66.7% | T/F rating |
[83] | Smart inverter (Volt-VAR control) | Increased from 116.4% to 213.2% | Peak load |
[110] | Demand response | Increased from 28.57% to 52.78% | Energy consumption |
[111] | Network reconfiguration (NR) of HVDN | 30–78% increase in PV HC | – |
[112] | APC (single-phase load) | 59.72% of total generation | Energy consumption |
[113] | Static compensator | Increased from 15% to 100% | Peak load |
[114] | NR (load modelling as P and Q buses and 0.9 PF lag) | 0–20% increase in HC | – |
[115] | SVC | HC increase of 0.05 p.u up to 9 installations (37-bus) and 2.459 p.u in IEEE 123-node systems | Over-voltage and under voltage |
[116] | Demand response | HC increase of 33.6% using modified IEEE 15-bus system | Over-voltage |
[102] | OLTC, SVC, and PF (DERs) | Increase of 77.8% and 74.5% in HC levels with 33-bus and 118-bus systems, respectively | Voltage and line current |
Ref No. | Name of the Project | Main Objectives |
---|---|---|
[100] | Distributed Energy Resources Feasibility Study (12 December 2018–30 August 2021) |
|
[118] | Evolve Project: On the calculation and use of dynamic operating envelopes (4 February 2019–31 March 2023) |
|
[124] | Advanced VPP Grid Integration (15 January 2019–12 June 2021) |
|
[125] | Flexible Exports for Solar PV (1 July 2020–24 September 2023) |
|
[126] | Project Edge (3 August 2020–13 August 2023) |
|
[127] | Project Symphony (2 July 2021–10 February 2024) |
|
[128] | Project Edith (Late 2021–June 2023) |
|
[129] | Project Converge ACT DERs (24 August 2021–15 January 2024) |
|
[130] | Project SHIELD (January 2020–November 2023) |
|
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Brohi, N.A.; Thirunavukkarasu, G.; Seyedmahmoudian, M.; Ahmed, K.; Stojcevski, A.; Mekhilef, S. Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid. Energies 2025, 18, 2922. https://doi.org/10.3390/en18112922
Brohi NA, Thirunavukkarasu G, Seyedmahmoudian M, Ahmed K, Stojcevski A, Mekhilef S. Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid. Energies. 2025; 18(11):2922. https://doi.org/10.3390/en18112922
Chicago/Turabian StyleBrohi, Naveed Ali, Gokul Thirunavukkarasu, Mehdi Seyedmahmoudian, Kafeel Ahmed, Alex Stojcevski, and Saad Mekhilef. 2025. "Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid" Energies 18, no. 11: 2922. https://doi.org/10.3390/en18112922
APA StyleBrohi, N. A., Thirunavukkarasu, G., Seyedmahmoudian, M., Ahmed, K., Stojcevski, A., & Mekhilef, S. (2025). Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid. Energies, 18(11), 2922. https://doi.org/10.3390/en18112922