Operation Method of PV–Battery Hybrid Systems for Peak Shaving and Estimation of PV Generation
<p>Configuration of a PV–Battery Hybrid System.</p> "> Figure 2
<p>Altitude angle of the sun at solar noon.</p> "> Figure 3
<p>The Sun’s position can be described by its altitude <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> and azimuth <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∅</mo> </mrow> <mrow> <mi mathvariant="normal">S</mi> </mrow> </msub> </mrow> </semantics></math> angle.</p> "> Figure 4
<p>Illustration of the PV panel angles.</p> "> Figure 5
<p>Solar radiation striking a collector <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>C</mi> </mrow> </msub> </mrow> </semantics></math> is a combination of direct beam <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi mathvariant="italic">BC</mi> </mrow> </msub> </mrow> </semantics></math> diffuse <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi mathvariant="italic">DC</mi> </mrow> </msub> </mrow> </semantics></math>, and reflected <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi mathvariant="italic">RC</mi> </mrow> </msub> </mrow> </semantics></math> radiation.</p> "> Figure 6
<p>PV module equivalent electrical circuit.</p> "> Figure 7
<p>Comparison of measured PV generation and calculated PV max generation.</p> "> Figure 8
<p>50 kW building integrated PV.</p> "> Figure 9
<p>Load peak shaving by systems.</p> "> Figure 10
<p>Concept of operation algorithm.</p> "> Figure 11
<p>Flowchart of the operation method.</p> "> Figure 12
<p>Residential load pattern.</p> "> Figure 13
<p>12 March 2008 system output schedule.</p> "> Figure 14
<p>13 March 2008 system output schedule.</p> "> Figure 15
<p>Comparison of initial load and algorithm results on 12 March 2008.</p> "> Figure 16
<p>Comparison of initial load and algorithm results on 13 March 2008.</p> ">
Abstract
:1. Introduction
- (1)
- The PV prediction model was designed based on mathematical modeling and cumulative data analysis. Historical data is classified as PV output data according to the weather and expressed as a generation rate, and other factors are not considered. The generation rate can predict the maximum output of the PV through a simple calculation.
- (2)
- Battery charge/discharge settings are determined based on predicted weather information and load patterns. In addition, the output error of load and PV can be compensated for by considering an operating margin. It has been validated as providing improved performance through simulation.
- (3)
- The method proposed in this paper utilized data from an actual PV–BESS system. The data of the installed PV was utilized, which is suitable for validating the simulation.
2. PV–Battery Hybrid Systems
3. Estimation of PV Generation
4. Operation Method
5. Simulation Studies
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Carbone, R. Grid-connected photovoltaic systems with energy storage. In Proceedings of the IEEE International Conference on Clean Electrical Power, Coimbatore, India, 19–20 January 2009; pp. 760–767. [Google Scholar]
- European Commission. Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing Directives 2001/77/EC and 2003/30/EC; Official Journal of the European Union L 140/16; Publications Office of the European Union: Luxembourg, 2009. [Google Scholar]
- Al Badwawi, R.; Abusara, M.; Mallick, T. A Review of Hybrid Solar PV and Wind Energy System. Smart Sci. 2015, 3, 127–138. [Google Scholar] [CrossRef]
- European Commission. Directive 2009/29/EC of the European Parliament and of the Council of 23 April 2009 Amending Directive 2003/87/EC so as to Improve and Extend the Greenhouse Gas Emission Allowance Trading Scheme of the Community; Official Journal of the European Union L 140/63; Publications Office of the European Union: Luxembourg, 2009. [Google Scholar]
- Capros, P.; De Vita, A.; Tasios, N.; Papadopoulos, D.; Siskos, P.; Apostolaki, E.; Zampara, M.; Paroussos, L.; Fragiadakis, K.; Kouvartakis, N.; et al. EU Energy, Transport and GHG Emissions. Trends to 2050. Reference Scenario 2013; Publications Office of the European Union: Luxembourg, 2014. [Google Scholar]
- Hong, J.; Zhang, H.; Xu, X. Thermal fault prognosis of lithium-ion batteries in real-world electric vehicles using self-attention mechanism networks. Appl. Therm. Eng. 2023, 226, 120304. [Google Scholar] [CrossRef]
- Hu, X.; Yuan, H.; Zou, C.; Li, Z.; Zhang, L. Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus. IEEE Trans. Veh. Technol. 2018, 67, 10319–10329. [Google Scholar] [CrossRef]
- Weniger, J.; Tjaden, T.; Quaschning, V. Sizing of Residential PV Battery Systems. Energy Procedia 2014, 46, 78–87. [Google Scholar] [CrossRef] [Green Version]
- Brusco, G.; Burgio, A.; Menniti, D.; Pinnarelli, A.; Sorrentino, N. The economic viability of a feed-in tariff scheme that solely rewards self-consumption to promote the use of integrated photovoltaic battery systems. Appl. Energy 2016, 183, 1075–1085. [Google Scholar] [CrossRef]
- Castillo-Cagigal, M.; Caamaño-Martín, E.; Matallanas, E.; Masa-Bote, D.; Gutiérrez, A.; Monasterio-Huelin, F.; Jiménez-Leube, J. PV self-consumption optimization with storage and Active DSM for the residential sector. Sol. Energy 2011, 85, 2338–2348. [Google Scholar] [CrossRef] [Green Version]
- Spiers, D. Chapter IIB-2—Batteries in PV Systems. In Practical Handbook of Photovoltaics, 2nd ed.; McEvoy, A., Markvart, T., Castañer, L.B.T.-P.H., Eds.; Academic Press: Boston, MA, USA, 2012; pp. 721–776. ISBN 978-0-12-385934-1. [Google Scholar]
- Muenzel, V.; Mareels, I.; de Hoog, J.; Vishwanath, A.; Kalyanaraman, S.; Gort, A. PV generation and demand mismatch: Evaluating the potential of residential storage. In Proceedings of the IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 17–20 February 2015; pp. 1–5. [Google Scholar]
- Teng, F.; Pudjianto, D.; Strbac, G.; Brandon, N.; Thomson, A.; Miles, J. Potential value of energy storage in the UK electricity system. Proc. Inst. Civ. Eng.-Energy 2015, 168, 107–117. [Google Scholar] [CrossRef]
- Barros, J.; Leite, H. Feed-in tariffs for wind energy in Portugal: Current status and prospective future. In Proceedings of the 11th International Conference on Electrical Power Quality and Utilisation, Lisbon, Portugal, 17–19 October 2011; pp. 1–5. [Google Scholar] [CrossRef] [Green Version]
- Khalilpour, R.; Vassallo, A. Planning and operation scheduling of PV-battery systems: A novel methodology. Renew. Sustain. Energy Rev. 2016, 53, 194–208. [Google Scholar] [CrossRef]
- Menictas, C.; Skyllas-Kazacos, M.; Lim, T.M. Advances in Batteries for Medium and Large-Scale Energy Storage; Elsevier: Amsterdam, The Netherlands, 2015; ISBN 978-1-78242-013-2. [Google Scholar]
- Vassallo, A. Applications of batteries for grid-scale energy storage. In Advances in Batteries for Medium and Large-Scale Energy Storage; Woodhead Publishing: Sawston, UK, 2015; pp. 587–607. [Google Scholar] [CrossRef]
- Pyrgou, A.; Kylili, A.; Fokaides, P.A. The future of the Feed-in Tariff (FiT) scheme in Europe: The case of photovoltaics. Energy Policy 2016, 95, 94–102. [Google Scholar] [CrossRef]
- Muqeet, H.A.; Munir, H.M.; Javed, H.; Shahzad, M.; Jamil, M.; Guerrero, J.M. An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges. Energies 2021, 14, 6525. [Google Scholar] [CrossRef]
- Bin, L.; Shahzad, M.; Javed, H.; Muqeet, H.A.; Akhter, M.N.; Liaqat, R.; Hussain, M.M. Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis. Sensors 2022, 22, 6150. [Google Scholar] [CrossRef]
- Fakham, H.; Lu, D.; Francois, B. Power Control Design of a Battery Charger in a Hybrid Active PV Generator for Load-Following Applications. IEEE Trans. Ind. Electron. 2010, 58, 85–94. [Google Scholar] [CrossRef]
- Rana, M.M.; Atef, M.; Sarkar, M.R.; Uddin, M.; Shafiullah, G.M. A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend. Energies 2022, 15, 2278. [Google Scholar] [CrossRef]
- Martins, R.; Hesse, H.C.; Jungbauer, J.; Vorbuchner, T.; Musilek, P. Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications. Energies 2018, 11, 2048. [Google Scholar] [CrossRef] [Green Version]
- He, S.; Gao, H.; Liu, J.; Zhang, X.; Chen, Z. Distribution system planning considering peak shaving of energy station. Appl. Energy 2022, 312, 118692. [Google Scholar] [CrossRef]
- Hong, Z.; Wei, Z.; Li, J.; Han, X. A novel capacity demand analysis method of energy storage system for peak shaving based on data-driven. J. Energy Storage 2021, 39, 102617. [Google Scholar] [CrossRef]
- Salles, R.S.; Souza AZ, D.; Ribeiro, P.F. Energy Storage for Peak Saving in a Microgrid in the Context of Brazilian Time-of-Use Rate. Proceedings 2020, 58, 16. [Google Scholar]
- Deng, W.; Liu, H.; Xu, J.; Zhao, H.; Song, Y. An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network. IEEE Trans. Instrum. Meas. 2020, 69, 7319–7327. [Google Scholar] [CrossRef]
- Hwang, J.S.; Fitri, I.R.; Kim, J.-S.; Song, H. Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast. Energies 2020, 13, 5633. [Google Scholar] [CrossRef]
- Go, S.-I.; Choi, J.-H. Design and Dynamic Modelling of PV-Battery Hybrid Systems for Custom Electromagnetic Transient Simulation. Electronics 2020, 9, 1651. [Google Scholar] [CrossRef]
- Kremer, P. Photovoltaic hybrid systems enhance reliability of power supply. In Proceedings of the 17th European Photovoltaic Solar Energy Conference, Munich, Germany, 22–26 October 2001. [Google Scholar]
- Zhang, C.; Zhao, D.; Wang, J.; Chen, G. A modified MPPT method with variable perturbation step for photovoltaic system. In Proceedings of the 2009 IEEE 6th International Power Electronics and Motion Control Conference, Wuhan, China, 17–20 May 2009; pp. 2096–2099. [Google Scholar] [CrossRef]
- Hasan, K.N.; Haque, M.E.; Negnevitsky, M.; Muttaqi, K.M. Control of energy storage interface with a bidirectional converter for photovoltaic systems. In Proceedings of the IEEE Power Engineering Conference, Australasian Universities, Sydney, Australia, 14–17 December 2008; pp. 1–6. [Google Scholar]
- Pena, R.; Clare, J.; Asher, G. Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation. IEE Proc.-Electr. Power Appl. 1996, 143, 231–241. [Google Scholar] [CrossRef] [Green Version]
- Masters, G.M. Renewable and Efficient Electric Power Systems; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2013. [Google Scholar]
- Radovan, A.; Šunde, V.; Kučak, D.; Ban, Ž. Solar Irradiance Forecast Based on Cloud Movement Prediction. Energies 2021, 14, 3775. [Google Scholar] [CrossRef]
- Son, Y.; Yoon, Y.; Cho, J.; Choi, S. Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting. Sustainability 2022, 14, 4427. [Google Scholar] [CrossRef]
Categories | Values |
---|---|
Latitude | 35.18° |
Local Longitude | 126.9° |
Local Time Meridian | 135° |
Azimuth Angle | 20° |
PV Module Tilt Angle | 90° |
Classification | Range |
---|---|
Clear | 0–2 |
Partly cloudy | 2–5 |
Mostly cloudy | 5–8 |
Cloudy | 8–10 |
Weather | Generation Rate | Weather | Generation Rate |
---|---|---|---|
clear | 0.84 | mostly cloudy, fog | 0.52 |
clear, fog | 0.73 | mostly cloudy, rain | 0.46 |
partly cloudy | 0.73 | cloudy | 0.39 |
partly cloudy, fog | 0.62 | cloudy, rain | 0.24 |
mostly cloudy | 0.56 | cloudy, fog, rain | 0.24 |
Categories | Setting Values |
---|---|
Average Load | 7.69 MW |
Battery Capacity | 12 MWh |
PV Capacity | 2.5 MWp |
Inverter Capacity | 2.5 MW |
Initial SoC (margin) | 0.2 |
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. |
© 2023 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
Jo, K.-Y.; Go, S.-I. Operation Method of PV–Battery Hybrid Systems for Peak Shaving and Estimation of PV Generation. Electronics 2023, 12, 1608. https://doi.org/10.3390/electronics12071608
Jo K-Y, Go S-I. Operation Method of PV–Battery Hybrid Systems for Peak Shaving and Estimation of PV Generation. Electronics. 2023; 12(7):1608. https://doi.org/10.3390/electronics12071608
Chicago/Turabian StyleJo, Kun-Yik, and Seok-Il Go. 2023. "Operation Method of PV–Battery Hybrid Systems for Peak Shaving and Estimation of PV Generation" Electronics 12, no. 7: 1608. https://doi.org/10.3390/electronics12071608