Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions
Abstract
:1. Introduction
2. Energy Storage Capacity Configuration Architecture with Embedded Dual Battery Packs
2.1. Two-Layer Battery Capacity Configuration Model Architecture
2.2. Dual Battery Packs Control Strategy
3. The Two-Layer Model of Energy Storage Configuration Considering Dual Operating Conditions
3.1. Outer Model
3.1.1. Objective Function
- (a)
- Penalty reduction cost
- (b)
- Benefit of additional grid-connected power
- (c)
- Frequency regulation service revenue
- (d)
- Energy storage power purchase cost
- (e)
- Energy storage frequency regulation charging and discharging loss cost
- (f)
- Energy storage equivalent annual value cost
3.1.2. Constraints
- (a)
- Energy storage capacity constraint
- (b)
- Energy storage power constraints
- (c)
- Charge state constraint
- (d)
- Wind power grid power fluctuation constraints
3.2. Inner Model
3.2.1. Objective Function
- (a)
- Wind power load-shedding standby cost
- (b)
- Wind power frequency regulation cost
- (c)
- Frequency regulation deficiency cost
3.2.2. Constraints
- (a)
- Energy storage frequency regulation power constraints
- (b)
- Wind farm load-shedding power constraints
- (c)
- Wind turbine frequency regulation power constraint
- (d)
- Frequency regulation capacity constraints
3.3. Solution Methods and Procedures
3.3.1. Algorithm Principle
3.3.2. Solving Steps
4. Example Analysis
4.1. Configuration Results Analysis
4.2. SOC Comparative Analysis
4.3. Application Effect Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Xu, Y.; Hu, Z. Source-Grid-Load Cross-Area Coordinated Optimization Model Based on IGDT and Wind-Photovoltaic-Photothermal System. Sustainability 2024, 16, 2056. [Google Scholar] [CrossRef]
- Chakir, A.; Tabaa, M. Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings. Sustainability 2024, 16, 2218. [Google Scholar] [CrossRef]
- Lei, M.; Meng, K.; Feng, H.; Bai, J.; Jiang, H.; Zhang, Z. Flywheel energy storage controlled by model predictive control to achieve smooth short-term high-frequency wind power. J. Energy Storage 2023, 63, 2218. [Google Scholar] [CrossRef]
- Gu, F.; Chen, H. Modelling and control of vanadium redox flow battery for smoothing wind power fluctuation. IET Renew. Power Gener. 2021, 15, 3552–3563. [Google Scholar] [CrossRef]
- Luo, X.; Zhu, M.; Wang, X.; Guan, X. Detection and isolation of false data injection attack via adaptive Kalman filter bank. J. Control Decis. 2024, 11, 60–72. [Google Scholar] [CrossRef]
- Shang, Q.; Li, F.; Wang, S.; Li, Y.; Yin, C. Primary Frequency Modulation Strategy for Wind-storage Combined System Based on Multivariable Fuzzy Logic Control. Power Syst. Technol. 2023, 47, 2344–2352. [Google Scholar]
- Zhang, X.; Qin, S.; Zhang, Y.; Hao, S.; Wu, Q.; Zhang, J. Wind Turbine and Storage Joint Frequency Modulation Control Strategy Considering Energy Storage State of Charge. High Volt. Eng. 2023, 49, 4120–4130. [Google Scholar]
- Jiang, H.; Cai, J.; Xiao, R. A wind-storage coordinated control strategy for improving system frequency response characteristics. Electr. Power Autom. Equip. 2021, 41, 44–51. [Google Scholar]
- Li, J.; Xin, D.; Liu, C.; Hou, X.; Li, D. Research on the Frequency Regulation Characteristics and Control Strategy of Wind Power Generation with Energy Storage Synergy. Batteries 2023, 9, 117. [Google Scholar] [CrossRef]
- Maluenda, M.; Córdova, S.; Lorca, A.; Pincetic, M. Optimal operation scheduling of a PV-BESS-Electrolyzer system for hydrogen production and frequency regulation. Appl. Energy 2023, 344, 121243. [Google Scholar] [CrossRef]
- Sun, Y.; Pei, W.; Jia, D. Application of integrated energy storage system in wind power fluctuation mitigation. J. Energy Storage 2020, 32, 101835. [Google Scholar] [CrossRef]
- Sewnet, A.; Khan, B.; Gidey, I.; Mahela, O.P.; ElShahat, A.; Abdelaziz, A.Y. Mitigating generation schedule deviation of wind farm using battery energy storage system. Energies 2022, 15, 1768. [Google Scholar] [CrossRef]
- Yi, T.; Ye, H.; Li, Q. Energy storage capacity optimization of wind-energy storage hybrid power plant based on dynamic control strategy. J. Energy Storage 2022, 55, 105372. [Google Scholar] [CrossRef]
- Ankar, S.J.; Pinkymol, K.P. Optimal Sizing and Energy Management of Electric Vehicle Hybrid Energy Storage Systems with Multi-Objective Optimization Criterion. IEEE Trans. Veh. Technol. 2024, 1–16. [Google Scholar] [CrossRef]
- Härtel, F.; Bocklisch, T. Minimizing Energy Cost in PV Battery Storage Systems Using Reinforcement Learning. IEEE Access 2023, 11, 39855–39865. [Google Scholar] [CrossRef]
- Floris, A.; Damiano, A.; Serpi, A. A Combined Design Procedure of High-Speed/High-Power PMSMs for an Adiabatic Compressed Air Energy Storage System. IEEE Trans. Ind. Appl. 2024, 60, 256–268. [Google Scholar] [CrossRef]
- Rostamnezhad, Z.; Mary, N.; Dessaint, L.; Monfet, D. Electricity Consumption Optimization Using Thermal and Battery Energy Storage Systems in Buildings. IEEE Trans. Smart Grid 2023, 14, 251–265. [Google Scholar] [CrossRef]
- Yurter, G.; Nadar, E.; Kocaman, A.S. The impact of pumped hydro energy storage configurations on investment planning of hybrid systems with renewables. Renew. Energy 2024, 222, 119906. [Google Scholar] [CrossRef]
- Jain, N.K.; Nangia, U.; Jain, J. Economic load dispatch using adaptive social acceleration constant based particle swarm optimization. J. Inst. Eng. 2018, 99, 431–439. [Google Scholar] [CrossRef]
- Basu, M. Modified particle swarm optimization for nonconvex economic dispatch problems. Int. J. Electr. Power Energy Syst. 2015, 69, 304–312. [Google Scholar] [CrossRef]
- Supajaidee, N.; Chutsagulprom, N.; Moonchai, S. An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation. Algorithms 2024, 17, 57. [Google Scholar] [CrossRef]
- Xiong, Q.; Liu, M.; Li, Y.; Zheng, C.; Deng, S. Short-Term Load Forecasting Based on VMD and Deep TCN-Based Hybrid Model with Self-Attention Mechanism. Appl. Sci. 2023, 13, 12479. [Google Scholar] [CrossRef]
- GB/T 40594-2021; Technical Guide for Power Grid and Source Coordination. State Administration for Market Regulation, Standardization Administration of the People’s Republic of China: Beijing, China, 2021.
- Li, C.; Qin, L. Sizing optimization for hybrid energy storage system independently participating in regulation market using improved particle swarm optimization. Acta Energiae Solaris Sin. 2023, 44, 426–434. [Google Scholar]
- Zhang, X.; Chen, C.; Zhang, Y.; Hao, S.; Zhang, J. Energy storage capacity optimization of wind farm considering battery running state. Autom. Electr. Power Syst. 2022, 46, 199–207. [Google Scholar]
Parameters | Value |
---|---|
DODr | 1 |
cei | CNY 1085/kWh |
cpi | CNY 3224/kW |
cem | CNY 50/kWh |
cpm | CNY 100/kW |
b | 0.05 |
ηc | 0.95 |
ηd | 0.95 |
ab | 0.1 |
cqd | CNY 500/MWh |
cqf | CNY 250/MWh |
abuy | CNY 0.60/kWh |
asell | CNY 0.52/kWh |
cw | CNY 0.52/kWh |
ape | CNY 0.13/kWh |
Rt | 0.03 |
dmax | 20% |
ktp | CNY 1.0/kWh |
β1 | 0.85 |
Type | Number | Percentage (%) |
---|---|---|
1 | 61 | 16.7 |
2 | 185 | 50.7 |
3 | 119 | 32.6 |
Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | |
---|---|---|---|---|
Er/MWh | 40.6 × 2 | 108.6 | 18.7 × 2 | 20.9 × 2 |
Pr/MW | 32.9 | 22.9 | 15.3 | 16.7 |
Y/ year | 16.0 | 12.5 | 14.8 | 13.2 |
fou/CNY 104 | 5075.4 | 3933.7 | 3316.4 | 942.4 |
fin/CNY 104 | 119.4 | 282.9 | 441.8 | 2420.3 |
De/MWh | 551.4 | 2101.6 | 1799.1 | 20,075.0 |
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. |
© 2024 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
Duanmu, C.; Shi, L.; Jian, D.; Ding, R.; Li, Y.; Wu, F. Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions. Sustainability 2024, 16, 3615. https://doi.org/10.3390/su16093615
Duanmu C, Shi L, Jian D, Ding R, Li Y, Wu F. Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions. Sustainability. 2024; 16(9):3615. https://doi.org/10.3390/su16093615
Chicago/Turabian StyleDuanmu, Chenrui, Linjun Shi, Deping Jian, Renshan Ding, Yang Li, and Feng Wu. 2024. "Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions" Sustainability 16, no. 9: 3615. https://doi.org/10.3390/su16093615