HYDRO-THERMAL-WIND CO-SCHEDULING MODEL AND SOLUTION METHOD CONSIDERING CONDITIONAL RISK OF WIND POWER

Zhang Binqiao, Zhang Songjia, Ran Yuanhang, Li Shuyu, Yang Wenjuan, Yu Zefa

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (4) : 394-403.

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (4) : 394-403. DOI: 10.19912/j.0254-0096.tynxb.2022-1875

HYDRO-THERMAL-WIND CO-SCHEDULING MODEL AND SOLUTION METHOD CONSIDERING CONDITIONAL RISK OF WIND POWER

  • Zhang Binqiao1,2, Zhang Songjia1, Ran Yuanhang1, Li Shuyu1, Yang Wenjuan1,3, Yu Zefa4
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Abstract

Under the background of the “dual carbon” strategy and the high proportion of renewable energy grid-connected policy, in order to accurately quantify the consumption cost of new energy such as wind power and the risk loss caused by its randomness to support power dispatching decisions, the CVaR(conditional value-at-risk) is used to model the conditional risk value of abandoned wind and abandoned load caused by the randomness of wind power, and the Copula function is used to calculate the continuous Markov chain wind speed model to predict wind power output, and then a short-term multi-objective optimal scheduling model of hydro-thermal-wind system(MOS-HTW) with minimum of uncertain risk loss, power generation cost and pollution emission is established. Meanwhile, strength Pareto evolutionary algorithm 2(SPEA2) is improved to solve the model efficiently in three aspects: variable external population size, enhanced local search ability and elite population elimination rule based on K-nearest neighbor distance(KND). The simulation results show that CVaR can well model the uncertain risk of wind power, and find a better Pareto optimal solution set by improving SPEA2.

Key words

multi-objective optimization / wind power / uncertainty / conditional value-at-risk(CVaR) / improved strength Pareto evolutionary algorithm 2(ISPEA2)

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Zhang Binqiao, Zhang Songjia, Ran Yuanhang, Li Shuyu, Yang Wenjuan, Yu Zefa. HYDRO-THERMAL-WIND CO-SCHEDULING MODEL AND SOLUTION METHOD CONSIDERING CONDITIONAL RISK OF WIND POWER[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 394-403 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1875

References

[1] 肖云鹏, 王锡凡, 王秀丽, 等. 面向高比例可再生能源的电力市场研究综述[J]. 中国电机工程学报, 2018, 38(3): 663-674.
XIAO Y P, WANG X F, WANG X L, et al.Review on electricity market towards high proportion of renewable energy[J]. Proceedings of the CSEE, 2018, 38(3): 663-674.
[2] 隋欣, 魏毅, 罗小林, 等. 面向“双碳”目标的脆弱区域生态光伏模式研究[J]. 太阳能学报, 2022, 43(7): 56-63.
SUI X, WEI Y, LUO X L, et al.Emergence of a new pattern of ecological solar photovoltaics (ECO-PV) in ecologically fragile areas driven by carbon peak and neutrality targets in China[J]. Acta energiae solaris sinica, 2022, 43(7): 56-63.
[3] MA X Y, SUN Y Z, FANG H L.Scenario generation of wind power based on statistical uncertainty and variability[J]. IEEE transactions on sustainable energy, 2013, 4(4): 894-904.
[4] 杨星, 李斌, 曾悦, 等. 非对称非线性平滑转换的广义自回归条件异方差算法的碳价格均值回归检验[J]. 控制理论与应用, 2019, 36(4): 622-628.
YANG X, LI B, ZENG Y, et al.Test of mean reversion of carbon price based on ANST-GARCH algorithm[J]. Control theory & applications, 2019, 36(4): 622-628.
[5] 陈光宇, 黄越辉, 张仰飞, 等. 基于博弈策略含风电和机组运行极限约束的多目标优化调度模型及求解[J]. 太阳能学报, 2020, 41(7): 372-384.
CHEN G Y, HUANG Y H, ZHANG Y F, et al.Multi-objective optimization scheduling model and solution method based on game strategy with wind power and unit operating limit constraints[J]. Acta energiae solaris sinica, 2020, 41(7): 372-384.
[6] INZUNZA A, MORENO R, BERNALES A, et al.CVaR constrained planning of renewable generation with consideration of system inertial response, reserve services and demand participation[J]. Energy economics, 2016, 59: 104-117.
[7] 田昊. 水火风电系统短期多目标优化调度与风险管理[D]. 武汉: 华中科技大学, 2016: 71-105.
TIAN H.Short-term multi-objective optimal scheduling and risk management of hydro-thermal-wind power system[D]. Hubei: Huazhong University of Science and Technology, 2016: 71-105.
[8] 吴薇, 刘俊勇, 张建明, 等. 计及风险影响的水电厂长期电量优化分配[J]. 电力自动化设备, 2012, 32(9): 44-49.
WU W, LIU J Y, ZHANG J M, et al.Optimal long-term hydropower generation allocation to reduce overall risk[J]. Electric power automation equipment, 2012, 32(9): 44-49.
[9] 李飞, 姚敏东, 李靖. 计及风电条件风险价值的鲁棒柔性超前调度[J]. 太阳能学报, 2022, 43(7): 356-365.
LI F, YAO M D, LI J.Robust flexible look-ahead dispatch considering CVaR of wind power[J]. Acta energiae solaris sinica, 2022, 43(7): 356-365.
[10] 陈皓勇, 王勇超, 禤培正, 等. 含高渗透率风电的微网系统鲁棒经济调度方法[J]. 控制理论与应用, 2017, 34(8): 1104-1111.
CHEN H Y, WANG Y C, XUAN P Z, et al.Robust economic dispatch method of microgrid containing high proportion of wind power[J]. Control theory & applications, 2017, 34(8): 1104-1111.
[11] 肖欣, 周渝慧, 何时有, 等. 含流域梯级水电的水火风互补发电系统联合运行优化[J]. 电力自动化设备, 2018, 38(2): 100-108.
XIAO X, ZHOU Y H, HE S Y, et al.Optimal joint operation of hydro-thermal-wind hybrid power system with cascaded hydro power[J]. Electric power automation equipment, 2018, 38(2): 100-108.
[12] 黄博南, 王勇, 李玉帅, 等. 基于分布式神经动态优化的综合能源系统多目标优化调度[J]. 自动化学报, 2022, 48(7): 1718-1736.
HUANG B N, WANG Y, LI Y S, et al.Multi-objective optimal scheduling of integrated energy systems based on distributed neurody-namic optimization[J]. Acta automatica sinica, 2022, 48(7): 1718-1736.
[13] 李铁, 李正文, 杨俊友, 等. 计及调峰主动性的风光水火储多能系统互补协调优化调度[J]. 电网技术, 2020, 44(10): 3622-3630.
LI T, LI Z W, YANG J Y, et al.Coordination and optimal scheduling of multi-energy complementary system considering peak regulation initiative[J]. Power system technology, 2020, 44(10): 3622-3630.
[14] YUAN X H, TIAN H, YUAN Y B, et al.An extended NSGA-Ⅲ for solution multi-objective hydro-thermal-wind scheduling considering wind power cost[J]. Energy conversion and management, 2015, 96: 568-578.
[15] CHEN F, ZHOU J Z, WANG C, et al.A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching[J]. Energy, 2017, 121: 276-291.
[16] 帅茂杭, 熊国江, 胡晓, 等. 基于改进多目标骨干粒子群算法的电力系统环境经济调度[J]. 控制与决策, 2022, 37(4): 997-1004.
SHUAI M H, XIONG G J, HU X, et al.Economic emission dispatch of power system based on improved bare-bone multi-objective particle swarm optimization algorithm[J]. Control and decision, 2022, 37(4): 997-1004.
[17] DASGUPTA K, ROY P K, MUKHERJEE V.Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm[J]. Electric power systems research, 2020, 178: 106018.
[18] ZITZLER E, LAUMANNS M, THIELE L.SPEA2: improving the strength Pareto evolutionary algorithm[R]. TIK-report, 2001: 103.
[19] WANG K Y, LUO X J, WU L, et al.Optimal coordination of wind-hydro-thermal based on water complementing wind[J]. Renewable energy, 2013, 60: 169-178.
[20] HONG L J, HU Z L, LIU G W.Monte carlo methods for value-at-risk and condi-tional value-at-risk: a review[J]. ACM transactions on modeling and computer simulation, 2014, 24(4): 1-37.
[21] AHMADI A, AGHAEI J, SHAYANFAR H A, et al.Mixed integer programming of multiobjective hydro-thermal self scheduling[J]. Applied soft computing, 2012, 12(8): 2137-2146.
[22] BASU M.An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling[J]. Electric power systems research, 2004, 69(2/3): 277-285.
[23] 赵传, 戴朝华, 周彤昕, 等. 基于Pareto折中解的风电装机规划多目标优化方法[J]. 太阳能学报, 2019, 40(6): 1763-1770.
ZHAO C, DAI C H, ZHOU T X, et al.Multi-objective optimization of wind power planning based on Pareto compromised solutions[J]. Acta energiae solaris sinica, 2019, 40(6): 1763-1770.
[24] AGRAWAL S, DASHORA Y, TIWARI M K, et al.Interactive particle swarm: a Pareto-adaptive metaheuristic to multiobjective optimization[J]. IEEE transactions on systems, man, and cybernetics - part A: systems and humans, 2008, 38(2): 258-277.
[25] TAVAKKOLI-MOGHADDAM R, AZARKISH M, SADEGHNEJAD-BARKOUSARAIE A.A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem[J]. Expert systems with applications, 2011, 38(9): 10812-10821.
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