A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service
Abstract
:1. Introduction
2. The Current PV Tariff and Assessment Mechanism and Economic Benefits of PV-BESS Power Plants in China
2.1. The Current PV Tariff Mechanism
2.2. The Current PV Assessment Mechanism
2.3. The Economic Benefits of PV-BESS Power Plants
3. Analysis of Economic Cost and Income of PV-BESS Power Plants Based on the Three-Part Electricity Price
3.1. Analysis of Economic Cost of PV-BESS Power Plants
3.1.1. Construction Costs
3.1.2. Operating Costs
3.2. The Design Principle and Implication of the Three-Part Electricity Price
3.3. Analysis of Economic Income of PV-BESS Power Plants Based on the Three-Part Electricity Price
4. The Capacity Price and Benchmark Electricity Price Based on the Discounted Cash Flow Method
5. The Graded Electricity Price Based on the AHP-CRITIC Method and Improved TOPSIS
6. The Ancillary Service Price of the PV-BESS Power Plant
6.1. Analysis of the Ancillary Service of the BESS
6.2. Calculation of Electricity Stored in the BESS for Ancillary Services in the PV-BESS Power Plant
- (1)
- The objective of the optimization: the average absolute deviation of the actual power generation and short-term forecast value of the 96 points is less than the target value. That is:
- (2)
- Constraint conditions:
- The power balance constraint of the whole system
- The power constraint of the BESS
- The SOC constraint of the BESS
7. Case Studies
7.1. Calculation of the Capacity Price and the Benchmark Electricity Price
7.2. Sampling Data of Power Quality Indexes and Power Quality Evaluation
7.3. The Comparison of the IRR of the PV-BESS Power Plant with the Three-Part Electricity Price and Current Stake Electrovalence
- (1)
- With the assessment of the power grid, the IRR with the current benchmark electricity price is gradually decreased with the increase of the capacity of the energy storage, which is due to the increase in the investment in energy storage systems and reflects the lack of the current benchmark electricity price.
- (2)
- With the three-part electricity price proposed in this paper, the IRR of the PV plant is gradually increased and then gradually decreased after reaching the peak value with the increase of the capacity of the energy storage systems.
- (3)
- When the 50 MW PV plant is equipped with the BESS of 15 MW/18 MWh, and the power quality grade is level 5/excellent, the capacity price is 724.7478 yuan/(kWh·a), the graded electricity price is 0.9619 yuan/kWh, and the ancillary service price is 1.2619 yuan/kWh. The investment would receive the maximum IRR of 16.91%. However, under the current PV stake electrovalence, the IRR is 7.11%, which is lower than the industry standard 8%.
- (4)
- If the 50 MW PV power plant is not equipped with the BESS, the IRR is 14.06% with the current PV stake electrovalence 0.9 yuan/kWh. While with the three-part electricity price proposed in this paper, the IRR of the PV-BESS power plant equipped with BESS of 15 MW/18 MWh is 16.91%. The result indicates that the three-part electricity price can effectively promote the development of the PV-BESS power plants.
- (5)
- When the proportion of BESS is high, which means that the investment costs of BESS is great, the three-part price proposed in this paper can effectively shorten the payback period of investment of the PV-BESS power plant. For example, when the PV plant is equipped with the BESS of 15 MW/18 MWh, as the demonstration PV-BESS power plant in Qinghai, the investment would receive the maximum IPP under the three-part price of 6.9 years, while the IPP is 13.9 years under the current PV stake electrovalence.
- (6)
- When the BESS is 15 MW/18 MWh, both the IRR and IPP are optimal. So under the three-part price proposed in this paper, the economic efficiency of the PV-BESS power plant is optimal when the power plant is equipped with BESS of 15 MW/18 MWh.
8. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
PV | Photovoltaic |
BESS | Battery energy storage system |
AHP | Analytic hierarchy process |
CRITIC | Criteria Importance Though Intercriteria Correlation |
TOPSIS | Technique for Order Preference by Similarity to Ideal Solution |
IRR | Internal rate of return |
SOC | State of Charge |
References
- Wei, Y.-M.; Wu, G.; Liang, Q.-M.; Hua, M. China Energy Report (2012): Energy Security Research; Science Press: Beijing, China, 2012. [Google Scholar]
- IEA Statistics. CO2 Emissions from Fuel Combustion-Highlights 2015; IEA: Paris, France, 2015; Available online: http://sa.indiaenvironmentportal.org.in/files/file/CO2EmissionsFromFuelCombustionHighlights2015.pdf (accessed on 18 August 2017).
- Intelligence Consulting Group. Forecast Report of Market Trends and Development Prospects of Photovoltaic Power Generation of China in 2017–2022. R454964. 2016. Available online: http://www.chyxx.com/research/201610/454964.html (accessed on 18 August 2017).
- Gao, Y.; Zhu, J.; Cheng, H.; Xue, F.; Xie, Q.; Li, P. Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories. Energies 2016, 9, 523. [Google Scholar] [CrossRef]
- Omran, W.A.; Kazerani, M.; Salama, M.M.A. Investigation of methods for reduction of power fluctuations generated from large grid-connected photovoltaic systems. IEEE Trans. Energy Convers. 2011, 26, 318–327. [Google Scholar] [CrossRef]
- Hoff, T.E.; Perez, R.; Margolis, R.M. Maximizing the value of customer-sited PV systems using storage and controls. Sol. Energy 2007, 81, 940–945. [Google Scholar] [CrossRef]
- Gao, Y.; Cheng, H.; Zhu, J.; Liang, H.; Li, P. The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather. Sustainability 2016, 8, 71. [Google Scholar] [CrossRef]
- Woyte, A.; Belmans, R.; Nijs, J. Fluctuations in instantaneous clearness index: Analysis and statistics. Sol. Energy 2007, 81, 195–206. [Google Scholar] [CrossRef]
- Bignucolo, F.; Cerretti, A.; Coppo, M.; Savio, A.; Turri, R. Effects of energy storage systems grid code requirements on interface protection performances in low voltage networks. Energies 2017, 10, 387. [Google Scholar] [CrossRef]
- Atawi, I.E.; Kassem, A.M. Optimal Control Based on Maximum Power Point Tracking (MPPT) of an Autonomous Hybrid Photovoltaic/Storage System in Micro Grid Applications. Energies 2017, 10, 643. [Google Scholar] [CrossRef]
- Paatero, J.V.; Lund, P.D. Effect of energy storage on variations in wind power. Wind Energy 2010, 8, 421–441. [Google Scholar] [CrossRef]
- Jung, S.; Kim, D. Pareto-Efficient Capacity Planning for Residential Photovoltaic Generation and Energy Storage with Demand-Side Load Management. Energies 2017, 10, 426. [Google Scholar] [CrossRef]
- Alam, M.J.E.; Muttaqi, K.M.; Sutanto, D.A. Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing. IEEE Trans. Energy Convers. 2014, 29, 507–518. [Google Scholar]
- Li, X.; Hui, D.; Lai, X. Battery Energy Storage Station (BESS)-Based Smoothing Control of Photovoltaic (PV) and Wind Power Generation Fluctuations. IEEE Trans. Sustain. Energy 2013, 4, 464–473. [Google Scholar] [CrossRef]
- The Largest Photovoltaic-Battery Energy Storage System Based Power Station Put into Operation: The New Era of “Photovoltaic + Energy Storage” Is Coming. Available online: http://shupeidian.bjx.com.cn/html/20160802/757779.shtml (accessed on 18 August 2017).
- Talavera, D.L.; Pérez-Higueras, P.; Ruíz-Arias, J.A. Levelised cost of electricity in high concentrated photovoltaic grid connected systems: Spatial analysis of Spain. Appl. Energy 2015, 151, 49–59. [Google Scholar] [CrossRef]
- Singh, P.P.; Singh, S. Realistic generation cost of solar photovoltaic electricity. Renew. Energy 2010, 35, 563–569. [Google Scholar] [CrossRef]
- Jülch, V. Comparison of electricity storage options using levelized cost of storage (LCOS) method. Appl. Energy 2016, 183, 1594–1606. [Google Scholar] [CrossRef]
- Berrada, A.; Loudiyi, K.; Zorkani, I. Profitability, risk, and financial modeling of energy storage in residential and large scale applications. Energy 2017, 119, 94–109. [Google Scholar] [CrossRef]
- Zakeri, B.; Syri, S. Electrical energy storage systems: A comparative life cycle cost analysis. Renew. Sustain. Energy Rev. 2015, 42, 569–596. [Google Scholar] [CrossRef]
- Guo, S.; Zhao, J.; Yan, J.; Jin, G.; Wang, X. Economic Assessment of Mobilized Thermal Energy Storage for Distributed Users: A Case Study in China. Energy Procedia 2016, 88, 656–661. [Google Scholar] [CrossRef]
- Lai, C.S.; McCulloch, M.D. Levelized cost of electricity for solar photovoltaic and electrical energy storage. Appl. Energy 2017, 190, 191–203. [Google Scholar] [CrossRef]
- Lesser, J.A.; Su, X. Design of an economically efficient feed-in tariff structure for renewable energy development. Energy Policy 2008, 36, 981–990. [Google Scholar] [CrossRef]
- Zhang, S.; Sun, Y. Design of Two-Part Grid Purchase Price Mechanism based on Energy Conservation Generation Dispatching. Power Syst. Technol. 2013, 37, 1304–1310. [Google Scholar]
- Tang, H.; Peng, J. Research on synthetic and quantificated appraisal index of power quality based on fuzzy theory. Power Syst. Technol. 2003, 12, 19. [Google Scholar]
- Ramanathan, R. A note on the use of the analytic hierarchy process for environmental impact assessment. J. Environ. Manag. 2001, 63, 27–35. [Google Scholar] [CrossRef] [PubMed]
- Gaing, Z.L. Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans. Power Deliv. 2004, 19, 1560–1568. [Google Scholar] [CrossRef]
- Barton, J.P.; Infield, D.G. Energy storage and its use with intermittent renewable energy. IEEE Trans. Energy Convers. 2004, 19, 441–448. [Google Scholar] [CrossRef]
- Khooban, M.H.; Niknam, T.; Blaabjerg, F.; Dragičević, T. A new load frequency control strategy for micro-grids with considering electrical vehicles. Electr. Power Syst. Res. 2017, 143, 585–598. [Google Scholar] [CrossRef]
- Lin, C.-E.; Shiao, Y.-S.; Huang, C.-L.; Sung, P.S. A real and reactive power control approach for battery energy storage system. IEEE Trans. Power Syst. 1992, 7, 1132–1140. [Google Scholar] [CrossRef]
- Hou, R.; Song, H.; Nguyen, T.T.; Qu, Y.; Kim, H. Robustness Improvement of Superconducting Magnetic Energy Storage System in Microgrids Using an Energy Shaping Passivity-Based Control Strategy. Energies 2017, 10, 671. [Google Scholar] [CrossRef]
- Fares, R.L.; Meyers, J.P.; Webber, M.E. A dynamic model-based estimate of the value of a vanadium redox flow battery for frequency regulation in Texas. Appl. Energy 2014, 113, 189–198. [Google Scholar] [CrossRef]
- Eyer, J.; Corey, G. Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide; Sandia National Laboratories: Washington, DC, USA, 2010; Volume 20, p. 5. [Google Scholar]
- Chen, H.; Cong, T.-N.; Yang, W.; Tan, C.; Li, Y.; Ding, Y. Progress in electrical energy storage system: A critical review. Prog. Nat. Sci. 2009, 19, 291–312. [Google Scholar] [CrossRef]
- Barros, J.J.C.; Coira, M.L.; de la Cruz López, M.P.; del Caño Gochi, A. Probabilistic life-cycle cost analysis for renewable and non-renewable power plants. Energy 2016, 112, 774–787. [Google Scholar] [CrossRef]
- Li, W.; Luo, D.; Yuan, J. A new approach for the comprehensive grading of petroleum reserves in China: Two natural gas examples. Energy 2017, 118, 914–926. [Google Scholar] [CrossRef]
- Berwal, A.K.; Kumar, S.; Kumari, N.; Kumar, V.; Haleem, A. Design and analysis of rooftop grid tied 50kW capacity Solar Photovoltaic (SPV) power plant. Renew. Sustain. Energy Rev. 2017, 77, 1288–1299. [Google Scholar] [CrossRef]
- Saaty, R.W. The analytic hierarchy process—What it is and how it is used. Appl. Math. Model. 1987, 9, 161–176. [Google Scholar] [CrossRef]
- Yagmur, L. Multi-criteria evaluation and priority analysis for localization equipment in a thermal power plant using the AHP (analytic hierarchy process). Energy 2016, 94, 476–482. [Google Scholar] [CrossRef]
- Calabrese, A.; Costa, R.; Menichini, T. Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry. Expert Syst. Appl. 2013, 40, 3747–3755. [Google Scholar] [CrossRef]
- Diakoulaki, D.; Mavrotas, G.; Papayannakis, L. Determining objective weights in multiple criteria problems: The critic method. Comput. Oper. Res. 1995, 22, 763–770. [Google Scholar] [CrossRef]
- Behzadian, M.; Otaghsara, S.K.; Yazdani, M.; Ignatius, J. A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 2012, 39, 13051–13069. [Google Scholar] [CrossRef]
- Torlak, G.; Sevkli, M.; Sanal, M.; Zaim, S. Analyzing business competition by using fuzzy TOPSIS method: An example of Turkish domestic airline industry. Expert Syst. Appl. 2011, 38, 3396–3406. [Google Scholar] [CrossRef]
- Kubler, S.; Robert, J.; Derigent, W.; Voisin, A.; le Traon, Y. A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert Syst. Appl. 2016, 65, 398–422. [Google Scholar]
- Goyal, T.; Kaushal, S. An Intelligent Scheduling Scheme for Real-Time Traffic management using Cooperative Game Theory and AHP-TOPSIS methods for Next Generation Telecommunication Networks. Expert Syst. Appl. 2017, 86, 125–134. [Google Scholar] [CrossRef]
- The Circular on the Promotion of Electric Energy Storage to Participate in the “Three North” Regional Power Ancillary Services Compensation (Market) Mechanism (State Regulation [2016] No. 164). Available online: http://zfxxgk.nea.gov.cn/auto92/201606/t20160617_2267.htm (accessed on 18 August 2017).
- Cho, J.; Kleit, A.N. Energy storage systems in energy and ancillary markets: A backwards induction approach. Appl. Energy 2015, 147, 176–183. [Google Scholar] [CrossRef]
- Nguyen, T.T.; Yoo, H.J.; Kim, H.M. Analyzing the Impacts of System Parameters on MPC-Based Frequency Control for a Stand-Alone Microgrid. Energies 2017, 10, 417. [Google Scholar] [CrossRef]
- Makarov, Y.V.; Ma, J.; Lu, S.; Nguyen, T.B. Assessing the Value of Regulation Resources Based on Their Time Response Characteristics; Pacific Northwest National Laboratory: Richland, WA, USA, 2008. [Google Scholar]
- Ruqi, L.; Haoyi, S. A synthetic power quality evaluation model based on extension cloud theory. Autom. Electr. Power Syst. 2012, 36, 66–70. [Google Scholar]
Resource Zone | Annual Total Radiation (MJ/m2) | Some Provinces | PV Stake Electrovalence (Yuan/kWh) | ||
---|---|---|---|---|---|
2015 | 2016 | 2017 | |||
Class I | 6700–8370 | Ningxia, Qinghai | 0.9 | 0.8 | 0.55 |
Class II | 5400–6700 | Beijing, Tianjin | 0.95 | 0.88 | 0.65 |
Class III | 4200–5400 | Shanghai, Zhejiang | 1 | 0.98 | 0.42 |
Power Quality Grade | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
μ(%) | 0 | 10 | 20 | 30 | 40 |
Power Deviation (%) | More than 25 | 20–25 | 15–20 | 10–15 | 5–10 | 0–5 |
---|---|---|---|---|---|---|
ν (yuan/×104 kWh) | −100 | −50 | 0 | 50 | 70 | 80 |
Unit Type | Gradeability (%/min) | Short-Term Gradeability Demand of the Grid (MW/min) | Total Power Demand of the Unit (MW) | Total Power Demand of the BESS (MW) | Substitution Effect of the BESS |
---|---|---|---|---|---|
Hydroelectric unit | 30 | 10 | 33.33 | 20 | 1.67 |
Gas generator unit | 20 | 10 | 50.00 | 20 | 2.50 |
Coal-fired unit | 2 | 10 | 500.00 | 20 | 25.00 |
Season | (a) | (b) | (c) | (d) | (e) | |||||
---|---|---|---|---|---|---|---|---|---|---|
P | ∆SOC | P | ∆SOC | P | ∆SOC | P | ∆SOC | P | ∆SOC | |
Spring | 26.6 | 41 | 23.3 | 35 | 15.0 | 36 | 15.0 | 31 | 20.0 | 30 |
Summer | 28.4 | 43 | 22.2 | 36 | 19.7 | 35 | 13.5 | 32 | 16.0 | 28 |
Autumn | 28.4 | 39 | 23.8 | 35 | 18.1 | 33 | 15.9 | 31 | 13.6 | 27 |
Winter | 43.9 | 35 | 19.5 | 34 | 14.6 | 32 | 12.2 | 34 | 9.7 | 29 |
Period | Item | Amount (×104 yuan) |
---|---|---|
Construction | Investment of the PV system | 33,878 |
Investment of the energy storage system | 4.97 yuan/Wh | |
Other expenses | 3235 | |
Operation | Annual operating maintenance cost (2%) | 995 |
Annual repayment of the loan principal | 2322.83 | |
Annual repayment of the loan interest | 34,842.5 × (16 − n)/16 (n = 1, 2, …, 15) |
Proportion of BESS (%) | The BESS Unit | Capacity Price (yuan/kWh·a) | Capacity Charge (×104 yuan/a) | Benchmark Electricity Price (yuan/kWh) | Benchmark Electricity Charge (×104 yuan/a) | Equivalent Single Price (yuan/kWh) | |
---|---|---|---|---|---|---|---|
Power (MW) | Capacity (MWh) | ||||||
0 | 0 | 0 | 0 | 0 | 0.7145 | 5638.5 | 0.7145 |
10 | 5 | 6 | 746.2644 | 447.7 | 0.7019 | 5539.0 | 0.7587 |
20 | 10 | 12 | 734.7575 | 881.7 | 0.6911 | 5453.8 | 0.8028 |
30 | 15 | 18 | 724.7478 | 1304.5 | 0.6871 | 5422.2 | 0.8470 |
40 | 20 | 24 | 715.9609 | 1718.3 | 0.6734 | 5314.1 | 0.8912 |
50 | 25 | 30 | 708.1857 | 2124.6 | 0.6661 | 5256.5 | 0.9353 |
Index Sample | Voltage Deviation/% | Frequency Deviation/Hz | Harmonic/% | Voltage Fluctuation/% | Voltage Unbalance/% |
---|---|---|---|---|---|
1 | 3.212 | 0.0922 | 1.72 | 1.33 | 0.83 |
2 | 6.68 | 0.1562 | 4.28 | 1.53 | 1.36 |
3 | 4.35 | 0.118 | 2.67 | 1.95 | 1.35 |
4 | 5.33 | 0.1787 | 3.36 | 1.37 | 1.74 |
5 | 4.22 | 0.1892 | 4.57 | 1.58 | 1.83 |
Sample | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Grade | Level 3/medium | Level 4/qualified | Level 4/qualified | Level 4/qualified | Level 4/qualified |
Proportion of BESS (%) | RRE (×104 yuan/a) | RC (×104 yuan/a) | RA (×104 yuan/a) | Power Quality Grade | (yuan/kWh) | RE (×104 yuan/a) | Three-Part Electricity Price | Stake Electrovalence (%) | ||
---|---|---|---|---|---|---|---|---|---|---|
IRR (%) | IPP (year) | IRR (%) | IPP (year) | |||||||
0 | −78.9 | - | - | 1 | 0.715 | 5638.5 | 7.61 | 14.7 | 14.06 | 8.5 |
2 | 0.786 | 6202.7 | 10.35 | 11.8 | ||||||
10 | −39.4 | 447.8 | 27.3 | 1 | 0.702 | 5539.0 | 10.30 | 12.5 | 11.71 | 10.1 |
2 | 0.772 | 6093.0 | 12.84 | 11.6 | ||||||
20 | 0 | 881.7 | 54.2 | 2 | 0.829 | 6544.4 | 12.59 | 11.5 | 9.70 | 11.9 |
3 | 0.898 | 7089.7 | 14.97 | 8.9 | ||||||
30 | 39.4 | 1304.5 | 80.9 | 3 | 0.893 | 7048.7 | 14.63 | 9.1 | 7.11 | 13.9 |
4 | 0.962 | 7590.8 | 16.91 | 6.9 | ||||||
40 | 55.2 | 1718.3 | 106.4 | 4 | 0.875 | 6908.2 | 14.15 | 8.4 | 6.45 | 15.4 |
5 | 0.943 | 7440.1 | 16.22 | 7.3 | ||||||
50 | 63.1 | 2124.6 | 132.0 | 4 | 0.866 | 6833.2 | 13.75 | 9.5 | 5.11 | 16.4 |
5 | 0.933 | 7358.8 | 15.66 | 7.9 |
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Gao, Y.; Xue, F.; Yang, W.; Sun, Y.; Sun, Y.; Liang, H.; Li, P. A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service. Energies 2017, 10, 1257. https://doi.org/10.3390/en10091257
Gao Y, Xue F, Yang W, Sun Y, Sun Y, Liang H, Li P. A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service. Energies. 2017; 10(9):1257. https://doi.org/10.3390/en10091257
Chicago/Turabian StyleGao, Yajing, Fushen Xue, Wenhai Yang, Yanping Sun, Yongjian Sun, Haifeng Liang, and Peng Li. 2017. "A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service" Energies 10, no. 9: 1257. https://doi.org/10.3390/en10091257