Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method
Abstract
:1. Introduction
2. Problem Description
2.1. System Description
2.2. Mathematical Model for Reducing Wind Power Fluctuation
3. Proposed Approach with Markov Prediction and PSO Method
3.1. Markov Prediction Model
3.2. Coordinated Control Strategy Using the PSO Method
4. Numerical Simulation Results and Discussion
4.1. Numerical Simulation Parameters
4.2. Case 1
4.3. Case 2
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Tang, X.; Sun, Y.; Zhou, G.; Miao, F. Coordinated control of multi-type energy storage for wind power fluctuation suppression. Energies 2017, 10, 1212. [Google Scholar] [CrossRef]
- Hvelplund, F. Renewable energy and the need for local energy markets. Energy 2006, 31, 2293–2302. [Google Scholar] [CrossRef] [Green Version]
- Kook, K.S.; Mckenzie, K.J.; Liu, Y.; Atcitty, S. A study on applications of energy storage for the wind power operation in power systems. In Proceedings of the 2006 IEEE Power Engineering Society General Meeting, Montreal, QC, Canada, 18–22 June 2006. [Google Scholar]
- Bouffard, F.; Galiana, F.D. Stochastic security for operations planning with significant wind power generation. IEEE Trans. Power Syst. 2008, 23, 306–316. [Google Scholar] [CrossRef]
- Li, W.; Joós, G.; Bélanger, J. Real-time simulation of a wind turbine generator coupled with a battery supercapacitor energy storage system. IEEE Trans. Ind. Electron. 2010, 57, 1137–1145. [Google Scholar] [CrossRef]
- Martinez-Cesena, E.A.; Mutale, J. Impact of wind speed uncertainty and variability on the planning and design of wind power projects in a smart grid environment. In Proceedings of the 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, Manchester, UK, 5–7 December 2011; pp. 1–8. [Google Scholar]
- 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]
- Lee, H.; Shin, B.Y.; Han, S.; Jung, S.; Park, B.; Jang, G. Compensation for the power fluctuation of the large scale wind farm using hybrid energy storage applications. IEEE Trans. Appl. Supercond. 2012, 22, 5701904. [Google Scholar]
- Díaz-González, F.; Bianchi, F.D.; Sumper, A.; Gomis-Bellmunt, O. Control of a flywheel energy storage system for power smoothing in wind power plants. IEEE Trans Energy Convers. 2014, 29, 204–214. [Google Scholar] [CrossRef]
- Jiang, Q.; Gong, Y.; Wang, H. A battery energy storage system dual-layer control strategy for mitigating wind farm fluctuations. IEEE Trans. Power Syst. 2013, 28, 3263–3273. [Google Scholar] [CrossRef]
- Shi, J.; Lee, W.J.; Liu, X. Generation scheduling optimization of wind-energy storage system based on wind power output fluctuation features. IEEE Trans. Ind. Appl. 2017, 54, 10–17. [Google Scholar] [CrossRef]
- Jiang, Q.; Hong, H. Wavelet-based capacity configuration and coordinated control of hybrid energy storage system for smoothing out wind power fluctuations. IEEE Trans. Power Syst. 2013, 28, 1363–1372. [Google Scholar] [CrossRef]
- Liu, J.; Zhang, L. Strategy design of hybrid energy storage system for smoothing wind power fluctuations. Energies 2016, 9, 991. [Google Scholar] [CrossRef]
- Ding, M.; Wu, J. A novel control strategy of hybrid energy storage system for wind power smoothing. Electr. Power Compon. Syst. 2017, 45, 1–10. [Google Scholar] [CrossRef]
- Lamsal, D.; Sreeram, V.; Mishra, Y.; Kumar, D. Kalman filter approach for dispatching and attenuating the power fluctuation of wind and photovoltaic power generating systems. IET Gener. Transm. Dis. 2018, 12, 1501–1508. [Google Scholar] [CrossRef]
- Dang, J.; Seuss, J.; Suneja, L.; Harley, R.G. SOC feedback control for wind and ESS hybrid power system frequency regulation. IEEE J. Emerg. Selected Top. Power Electron. 2014, 2, 79–86. [Google Scholar] [CrossRef]
- Yun, J.Y.; Yu, G.; Kook, K.S.; Rho, D.H. SOC-based control strategy of battery energy storage system for power system frequency regulation. Trans. Korean Inst. Electr. Eng. 2014, 63, 7424–7425. [Google Scholar]
- Trung, T.T.; Ahn, S.J.; Choi, J.H.; Go, S.I.; Nam, S.R. Real-time wavelet-based coordinated control of hybrid energy storage systems for denoising and flattening wind power output. Energies 2014, 7, 6620–6644. [Google Scholar] [CrossRef]
- Foley, A.M.; Leahy, P.G.; Marvuglia, A.; McKeogh, E.J. Current methods and advances in forecasting of wind power generation. Renew. Energy 2012, 37, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Sahin, A.D.; Sen, Z. First-order Markov chain approach to wind speed modelling. J. Wind Eng. Ind. Aerod. 2001, 89, 263–269. [Google Scholar] [CrossRef]
- Bizrah, A.; Almuhaini, M. Modeling wind speed using probability distribution function, Markov and ARMA models. In Proceedings of the 2015 IEEE Power & Energy Society General Meeting, Denver, CO, USA, 26–30 July 2015; pp. 1–5. [Google Scholar]
- Xie, K.; Liao, Q.; Tai, H.M.; Hu, B. Non-homogeneous Markov wind speed time series model considering daily and seasonal variation characteristics. IEEE Trans. Sustain. Energy 2017, 8, 1281–1290. [Google Scholar] [CrossRef]
- Carpinone, A.; Giorgio, M.; Langella, R.; Testa, A. Markov chain modeling for very-short-term wind power forecasting. Electr. Power Syst. Res. 2015, 122, 152–158. [Google Scholar] [CrossRef]
- Nunnari, S.; Fortuna, L.; Gallo, A. Wind Time Series Modeling for Power Turbine Forecasting. Int. J. Electr. Energy 2014, 2, 112–118. [Google Scholar] [CrossRef]
- Sun, J.; Yun, Z.; Liang, J.; Feng, Y.; Zhang, T. Wind power forecasting based on a Markov chain model of variation. In Proceedings of the 2015 IEEE International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, 15–17 August 2015; pp. 778–782. [Google Scholar]
- Wu, Q.; Peng, C. A least squares support vector machine optimized by cloud-based evolutionary algorithm for wind power generation prediction. Energies 2016, 9, 585. [Google Scholar] [CrossRef]
- Yang, X.; Fu, G.; Zhang, Y.; Kang, N.; Gao, F. A naive Bayesian wind power interval prediction approach based on rough set attribute reduction and weight optimization. Energies 2017, 10, 1903. [Google Scholar] [CrossRef]
- Ro, H.T.V.G.; Aquino, R.; Ferreira, A.A. Enhancing short-term wind power forecasting through multiresolution analysis and echo state networks. Energies 2018, 11, 824. [Google Scholar]
- Cheng, L.; Zang, H.; Ding, T.; Sun, R.; Wang, M.; Wei, Z.; Sun, G. Ensemble recurrent neural network based probabilistic wind speed forecasting approach. Energies 2018, 11, 1958. [Google Scholar] [CrossRef]
- López, E.; Valle, C.; Allende, H.; Gil, E.; Madsen, H. Wind power forecasting based on echo state networks and long short-term memory. Energies 2018, 11, 526. [Google Scholar] [CrossRef]
- Huo, Y.; Jiang, P.; Zhu, Y.; Feng, S.; Wu, X. Optimal real-time scheduling of wind integrated power system presented with storage and wind forecast uncertainties. Energies 2015, 8, 1080–1100. [Google Scholar] [CrossRef]
- Han, L.; Zhang, R.; Wang, X.; Dong, Y. Multi-time scale rolling economic dispatch for wind/storage power system based on forecast error feature extraction. Energies 2018, 11, 2124. [Google Scholar] [CrossRef]
- Gui, Y.; Kim, C.; Chung, C.C. Economic dispatch for wind farm using model predictive control method. In Proceedings of the 2013 13th International Conference on Control, Automation and Systems, Gwangju, South Korea, 20–23 October 2013; pp. 1836–1839. [Google Scholar]
- Yang, D.; Wen, J.; Chan, K.W.; Cai, G. Dispatching of wind/battery energy storage hybrid systems using inner point method-based model predictive control. Energies 2016, 9, 629. [Google Scholar] [CrossRef]
- Tribioli, L.; Cozzolino, R.; Evangelisti, L.; Bella, G. Energy Management of an Off-Grid Hybrid Power Plant with Multiple Energy Storage Systems. Energies 2016, 9, 661. [Google Scholar] [CrossRef]
- Zhang, Y.; Meng, F.; Wang, R.; Zhu, W.; Zeng, X.J. A stochastic MPC based approach to integrated energy management in microgrids. Sustain. Cities Soc. 2018, 41, 349–362. [Google Scholar] [CrossRef]
- Lin, W.M.; Tu, C.S.; Tsai, M.T. Energy management strategy for microgrids by using enhanced bee colony optimization. Energies 2016, 9, 5. [Google Scholar] [CrossRef]
- Zhang, Y.; Le, J.; Liao, X.; Zheng, F.; Liu, K.; An, X. Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO. Renew. Energy 2018, 128, 91–107. [Google Scholar] [CrossRef]
- Khaled, U.; Eltamaly, A.M.; Beroual, A. Optimal power flow using particle swarm optimization of renewable hybrid distributed generation. Energies 2017, 10, 1013. [Google Scholar] [CrossRef]
- Gambuzza, L.V.; Buscarino, A.; Fortuna, L.; Porfiri, M.; Frasca, M. Analysis of Dynamical Robustness to Noise in Power Grids. IEEE J. Emerg. Sel. Top. Circ. Syst. 2017, 7, 413–421. [Google Scholar] [CrossRef]
- Kennedy, J. Particle Swarm Optimization. In Encyclopedia of Machine Learning; Sammut, C., Webb, G.I., Eds.; Springer: Boston, MA, USA, 2011. [Google Scholar]
PSO algorithm |
1: Define PSO parameters, such as w, c1, c2, N, and intermax. The fitness function is f(•). |
2: Install iter = 1 and initialize Xi(iter) and vi(iter), where X ∈ nR, i ∈ 1, 2, …, N. |
3: Calculate Ji, best = f(Xi), Xi, best = Xi(iter), [Jbest, min, p] = min{ Ji, best }, and Xbest = Xp(iter). |
4: While inter < intermax, inter = inter + 1 and i = 0. |
5: While i < N, i = i + 1. Update Xi(iter) according to the following formula: vi(iter) = w × vi(iter − 1) + k1 × rand × (Xi, best − Xi(iter − 1)) + k2 × rand × (Xbest − Xi(iter − 1)); Xi(iter) = Xi(iter − 1) + vi(iter). |
6: If f(Xi(iter)) < Ji, best, Xi, best = Xi(iter) and Ji, best = f(Xi(iter)); End. |
7: If f(Xi(iter)) < Jbest, Xbest = Xi(iter) and Jbest = f(Xi(iter)); End. |
8: End |
9: End |
Simulation Parameters | Value | PSO Parameters | Value |
---|---|---|---|
Sampling time ΔT | 1 min | Number of particles N | 30 |
Allowable power fluctuation δ | 2.5 MW | Maximum iteration number iter | 100 |
Energy storage rated power Pbe | 5 MW | Inertia factor ω | 0.5 |
Energy storage battery capacity Q | 2 MWh | Learning factor k1 | 2 |
Energy storage initial value SOE | 0.5 | Learning factor k2 | 2 |
Energy storage upper limit SOEU | 0.9 | Markov state number K | 50 |
Energy storage lower limit SOEL | 0.1 | Wind power prediction length | 1 |
Parameters | Method 1 | Method 2 | Method 3 |
---|---|---|---|
Energy storage output level adjustment parameter α | 0 | 1 | 1 |
Future wind power output influence parameter β | 0 | 0 | 1 |
Energy Storage Battery Capacity | Method | (MW) | (MW) | (MWh) | (MWh) | (min) | |
---|---|---|---|---|---|---|---|
Q1 | Method 1 | 7.844 | 1.655 | 1278.75 | 11.222 | 45 | 0.217 |
Method 2 | 8.076 | 1.642 | 1278.69 | 12.120 | 5 | 0.173 | |
Method 3 | 6.278 | 1.632 | 1278.66 | 12.516 | 2 | 0.155 | |
Q2 | Method 1 | 8.235 | 1.645 | 1278.35 | 11.538 | 33 | 0.228 |
Method 2 | 5.079 | 1.631 | 1278.76 | 12.569 | 2 | 0.142 | |
Method 3 | 2.500 | 1.623 | 1278.74 | 12.607 | 0 | 0.120 | |
Q3 | Method 1 | 2.500 | 1.627 | 1277.62 | 11.882 | 0 | 0.235 |
Method 2 | 2.500 | 1.623 | 1278.52 | 12.343 | 0 | 0.107 | |
Method 3 | 2.500 | 1.624 | 1278.71 | 12.632 | 0 | 0.094 |
Allowable Power Fluctuation | Method | (MW) | (MW) | (MWh) | (MWh) | (min) | |
---|---|---|---|---|---|---|---|
δ1 | Method 1 | 6.891 | 0.899 | 1279.05 | 27.150 | 44 | 0.211 |
Method 2 | 7.107 | 0.876 | 1278.82 | 27.867 | 25 | 0.180 | |
Method 3 | 6.053 | 0.867 | 1278.78 | 27.952 | 7 | 0.167 | |
δ2 | Method 1 | 8.015 | 1.187 | 1278.94 | 20.508 | 21 | 0.191 |
Method 2 | 7.303 | 1.176 | 1278.94 | 21.144 | 10 | 0.164 | |
Method 3 | 6.142 | 1.159 | 1278.82 | 21.607 | 1 | 0.146 | |
δ3 | Method 1 | 8.235 | 1.655 | 1278.35 | 11.538 | 33 | 0.228 |
Method 2 | 5.079 | 1.631 | 1278.76 | 12.569 | 2 | 0.142 | |
Method 3 | 2.500 | 1.623 | 1278.74 | 12.607 | 0 | 0.120 |
Energy Storage Battery Capacity | Method | (MW) | (MW) | (MWh) | (MWh) | (min) | |
---|---|---|---|---|---|---|---|
Q1 | Method 1 | 7.748 | 1.655 | 618.47 | 13.72 | 56 | 0.227 |
Method 2 | 3.599 | 1.638 | 618.37 | 14.87 | 6 | 0.194 | |
Method 3 | 2.500 | 1.634 | 618.36 | 15.09 | 0 | 0.172 | |
Q2 | Method 1 | 3.980 | 1.640 | 618.15 | 13.98 | 21 | 0.191 |
Method 2 | 2.500 | 1.640 | 618.49 | 14.50 | 0 | 0.131 | |
Method 3 | 2.500 | 1.635 | 618.46 | 14.67 | 0 | 0.113 | |
Q3 | Method 1 | 2.500 | 1.637 | 617.97 | 14.09 | 0 | 0.117 |
Method 2 | 2.500 | 1.639 | 618.34 | 14.33 | 0 | 0.082 | |
Method 3 | 2.500 | 1.628 | 618.44 | 14.75 | 0 | 0.070 |
Allowable Power Fluctuation | Method | (MW) | (MW) | (MWh) | (MWh) | (min) | |
---|---|---|---|---|---|---|---|
δ1 | Method 1 | 6.582 | 0.890 | 618.76 | 31.34 | 62 | 0.226 |
Method 2 | 5.682 | 0.865 | 618.74 | 32.73 | 17 | 0.197 | |
Method 3 | 3.651 | 0.856 | 618.70 | 33.43 | 1 | 0.184 | |
δ2 | Method 1 | 3.987 | 1.164 | 618.84 | 24.11 | 31 | 0.220 |
Method 2 | 3.469 | 1.147 | 618.69 | 25.18 | 1 | 0.173 | |
Method 3 | 1.500 | 1.150 | 618.57 | 25.82 | 0 | 0.157 | |
δ3 | Method 1 | 3.980 | 1.640 | 618.15 | 13.98 | 21 | 0.191 |
Method 2 | 2.500 | 1.640 | 618.49 | 14.50 | 0 | 0.131 | |
Method 3 | 2.500 | 1.635 | 618.46 | 14.67 | 0 | 0.113 |
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Yun, P.; Ren, Y.; Xue, Y. Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method. Energies 2018, 11, 3393. https://doi.org/10.3390/en11123393
Yun P, Ren Y, Xue Y. Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method. Energies. 2018; 11(12):3393. https://doi.org/10.3390/en11123393
Chicago/Turabian StyleYun, Pingping, Yongfeng Ren, and Yu Xue. 2018. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method" Energies 11, no. 12: 3393. https://doi.org/10.3390/en11123393