The Frontiers of Society, Science and Technology, 2019, 1(4); doi: 10.25236/FSST.20190347.
Tong Xing, Zhongfu Tan, Liling Huang*
North China Electric Power University, Beijing, China, 102206
*Corresponding author: E-mail: [email protected]
In order to reduce the randomness of wind power and improve system consumption capacity, a jointly scheduling optimization model with energy storage systems (ESSs) and carbon emission trade (CET) is introduced. Firstly, the basic scheduling model for wind power and thermal power is established with the objective function of the maximum system benefit considering system operation constraints. Secondly, the carbon emission cost model for thermal power generation and the ESSs’ operation profit model are established, respectively. System comprehensive scheduling objective function considering CET and ESSs is also presented. Thirdly, the jointly scheduling model for wind power and thermal power considering ESSs’ operation condition, CET condition and newly system reserve condition are taken into consideration. Finally, the simulation system with 10 thermal power units and 2800MW wind power turbines is proposed. The results show the large-scale wind power grid connection relies on thermal power to provide reserve service, which could reduce unit utilization efficiency and the overall coal consumption rate. The cleaning characteristics of wind power could be transformed into economic benefits by CET, which will promote wind power consumption and reduce abandoned wind power. The charge-discharge characteristics of ESSs could smooth load curve and enlarge the grid-connected space of wind power. However, the overall benefit reduces due to ESS’s high fixed costs. The system overall benefit reaches the maximum when ESSs and CET are simultaneously introduced, which indicates ESS and CET have synergistic optimization effect.
Wind power; ESSs; CET; optimization
Tong Xing, Zhongfu Tan, Liling Huang. Low Carbon-based Scheduling Optimization Model for Wind Power and Thermal Power Considering Energy Storage Systems. The Frontiers of Society, Science and Technology (2019) Vol. 1 Issue 4: 299-323. https://doi.org/10.25236/FSST.20190347.
 Y Lin, JX. Johnson, JL. Mathieu. Emissions impacts of using energy storage for power system reserves[J]. Applied Energy, 2016, 168(15): 444-456
 KG Xie, JZ Dong, C Singh. Optimal capacity and type planning of generating units in a bundled wind–thermal generation system[J]. Applied Energy, 2016, 164(15): 200-210
 M. Jannati, S.H. Hosseinian, B. Vahidi. ADALINE (ADAptive Linear NEuron)-based coordinated control for wind power fluctuations smoothing with reduced BESS (battery energy storage system) capacity[J]. Energy, 2016, 101(15): 1-8
 Y. Zhu, Y.P. Li, G.H. Huang,et al. A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty[J]. Energy, 2015, 88(8): 636-649
 K. Geetha, V. Sharmila Deve, K. Keerthivasan. Design of economic dispatch model for Gencos with thermal and wind powered generators[J]. International Journal of Electrical Power & Energy Systems, 2015, 68(7): 222-232
 CH Peng, HJ Sun, JF Guo, et al. Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm[J]. International Journal of Electrical Power & Energy Systems, 2012, 42(1): 119-126
 SH Jiang, ZC Ji, Y Wang. A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability[J]. International Journal of Electrical Power & Energy Systems, 2015, 73(12): 1035-1050.
 J Ling, GH Huang, LC Huang, et al. Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with ind power under uncertainty[J]. Energy, 2016, 109(15): 920-932
 ZF Tan, LW Ju, B Reed, et al. The optimization model for multi-type customers assisting wind power consumptive considering uncertainty and demand response based on robust stochastic theory[J]. Energy Conversion and Management, 2015, 105(15): 1070-1081
 TKA Brekken, A Yokochi, A Von Jouanne, et al. Optimal energy storage sizing and control for wind power applications[J]．IEEE Trans Sustain Energy, 2011, 2(1): 69-77.
 MH Shan, CL Li, TT Lian, et al. A real-time optimal control strategy for battery energy storage system to smooth active output fluctuation of renewable energy sources[J]. Power System Technology, 2014, 38(2): 469-477
 J Lee, JH Kim, SK Joo. Stochastic Method for the Operation of a Power System With Wind Generators and Superconducting Magnetic Energy Storages (SMESs)[J]. IEEE Transactions on Applied Superconductivity, 2011, 21(3):2144-2148
 H Bludszuweit, J A Domínguez-Navarro. A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty[J]. IEEE Transactions on Power Systems, 2011, 26(3): 1651-1658
 S Gill, E Barbour, IAG Wilson, et al. Maximising revenue for non-firm distributed wind generation with energy storage in an active management scheme[J]. IT Renewable power generation, 2013, 7(5): 421-430
 M Dicrato, G Forte, M Pisani, et al. Planning and operating combined wind-storage system in electricity market[J]. IEEE Trans on Sustainable Energy, 2012, 3(2): 209-217
 LW Liu, CX Chen, YF Zhao, et al. China׳s carbon-emissions trading: Overview, challenges and future[J]. Renewable and Sustainable Energy Reviews, 2015, 49(9): 254-266
 W Li, ZJ Jia. The impact of emission trading scheme and the ratio of free quota: A dynamic recursive CGE model in China[J]. Applied Energy, 2016, 174(15): 1-14
 K Chang, C Zhang, H Chang. Emissions reduction allocation and economic welfare estimation through interregional emissions trading in China: Evidence from efficiency and equity[J]. Energy, 2016, 113(15): 1125-1135
 PE Morthorst. National environmental target and international emission reduction instrument[J]. Energy Policy, 2003, 31:72-83
 FR Aune, HM Dalen, C Hagem. Implementing the EU renewable target through green certificate markets[J]. Energy Economics, 2012(34): 922-1000
 V Colcelli, The problem of the legal nature of green certificates in the Italian legal system[J]. Energy Policy, 2012, 40: 301-306
 A Berrada, Khalid Loudiyi. Operation, sizing, and economic evaluation of storage for solar and wind power plants[J]. Renewable and Sustainable Energy Reviews, 2016, 59(6): 1117-1129
 KB Porate, KL Thakre, GL Bodhe. Impact of wind power on generation economy and emission from coal based thermal power plant[J]. International Journal of Electrical Power & Energy Systems, 2013, 44(1): 889-896
 A Panda, M Tripathy. Solution of wind integrated thermal generation system for environmental optimal power flow using hybrid algorithm[J]. Journal of Electrical Systems and Information Technology, 2016, 3(2)151-160
 M Kia, MS Nazar, M S Sepasian. Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system[J]. Energy, 2016, 19(11)
 LG Wang, M Lampe, P Voll, et al. Multi-objective superstructure-free synthesis and optimization of thermal power plants[J]. Energy, 2016, 116(1):1104-1116
 RG Cong, YM Wei. Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options[J]. Energy, 2010, 35(9): 3921-3931
 RM Dickinson, CA Cruickshank, SJ Harrison. Charge and discharge strategies for a multi-tank thermal energy storage[J]. Applied Energy, 2013, 109(9): 366-373
 X Jin, ZL Zhang, XQ Shi. A review on wind power industry and corresponding insurance market in China: Current status and challenges[J]. Renewable and Sustainable Energy Reviews, 2014, 38(10): 1069-1082