Abstract：During the 13th Five-Year Plan period, the state implemented a continuous policy of cutting overcapacity in the coal industry, which led to a mismatch between coal production capacity and coal demand during the 14th Five-Year Plan period. As a result, coal prices continued to rise from 2021 to 2022. Meanwhile, the installed capacity of renewable energy in our country has been increasing year by year. The massive influx of “green electricity” has further compressed the profit margins of thermal power enterprises in the electricity energy market. Under the new round of coal market and power market environment, coal-fired power enterprises are confronted with multiple pressures such as rising coal prices, falling electricity prices and reduced power generation. Coal-fired power enterprises need to enhance their integrated management and control capabilities over both upstream coal market transactions and downstream power market transactions. How to formulate the optimal trading strategy and increase the profits of coal-fired power enterprises in both the coal market and the power market is the core issue in the cost control field of coal-fired power enterprises. The mutual influence between the decision-making results of the coal market and the clearing results of the electricity market, the pricing game process among power generators, and the settlement rules of futures trading are all issues that need to be urgently addressed. Therefore, studying the integrated futures and spot trading strategies in the coal-electricity market is of great significance for improving the market competitiveness of coal-fired power enterprises and reducing their operational risks. To this end, a futures-spot trading strategy for coal-fired power plants considering linked gaming in coal-electricity markets is developed. First, the worst-case trading environment for coal-fired power plants is obtained through a bidding decision model of rival power generators under marginal unit conditions. Next, the grey wolf optimization algorithm based on strengthening the hierarchy of wolves is used to solve the trading decision model of coal-fired power plants in coal-electricity markets. Finally, the effectiveness of the strategy is verified through case studies. The following conclusions are drawn: (1) Compared with the traditional single-domain market decision-making, the trading strategy of coal-fired power plants proposed in this paper is a more complex portfolio optimization problem. By solving the pricing game trading model of coal-fired power plants considering the linkage of the coal-electricity market, it can help coal-fired power plants obtain the integrated optimal trading strategy under the coal-electricity market, thereby enhancing the integrated operation and management capabilities of enterprises. (2) To obtain the most severe trading environment for coal-fired power plants, the counterparty power generator bidding decision model considering the marginal unit situation proposed in this paper can effectively narrow the decision domain of the counterparty power generator's quotation variable, reduce the difficulty of solving the master-slave game bidding model, and thereby help coal-fired power plants make robust decisions. (3) Considering the risks of spot trading, the futures-spot trading model in the coal-electricity market proposed in this paper can formulate more conservative trading strategies and enhance the risk management and control capabilities of coal-fired power plants by introducing futures trading in the electricity market trading section. It provides coal-fired power plants with revenue settlement tools and risk control tools in the decision-making process of coal-electricity market transactions.
[1] Zhang Zhi, Chen Yanbo, Ma Tianyang, et al.Multi-type energy storage expansion planning: a review for high-penetration renewable energy integration[J]. Renewable and Sustainable Energy Reviews, 2025, 219: 115853. [2] Wu Zhaoyuan, Zhou Ming, Zhang Zhi, et al.An incentive profit-sharing mechanism for welfare transfer in balancing market integration[J]. Renewable and Sustainable Energy Reviews, 2022, 168: 112762. [3] Chen Yanbo, Tian Haoxin, Zheng Guodong, et al.Operation strategy of rail transit green energy system considering uncertainty risk of photovoltaic power output[J]. Journal of Modern Power Systems and Clean Energy, 2024, 12(6): 1859-1868. [4] 陈艳波, 刘宇翔, 田昊欣, 等. 基于广义目标级联法的多牵引变电站光伏-储能协同规划配置[J]. 电工技术学报, 2024, 39(15): 4599-4612. Chen Yanbo, Liu Yuxiang, Tian Haoxin, et al.Collaborative planning and configuration of photovoltaic and energy storage in multiple traction substations based on generalized analytical target cascading method[J]. Transactions of China Electrotechnical Society, 2024, 39(15): 4599-4612. [5] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报, 2023, 38(6): 1662-1677. Wu Shan, Bian Xiaoyan, Zhang Jingxian, et al.A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1662-1677. [6] Wang Xinru, Long Ruyin, Chen Hong, et al.How to promote the trading in China's green electricity market? Based on environmental perceptions, renewable portfolio standard and subsidies[J]. Renewable Energy, 2024, 222: 119784. [7] Wang Benke, Li Chunhua, Ban Yongshuang, et al.A two-tier bidding model considering a multi-stage offer-carbon joint incentive clearing mechanism for coupled electricity and carbon markets[J]. Applied Energy, 2024, 368: 123497. [8] 姜欣, 刘萌, 王天梁, 等. 电网侧储能电站参与现货联合市场的竞价策略[J]. 电网技术, 2021, 45(9): 3398-3407. Jiang Xin, Liu Meng, Wang Tianliang, et al.Bidding strategy for grid-side energy storage power stations to participate in the spot joint market[J]. Power System Technology, 2021, 45(9): 3398-3407. [9] 李迁, 姜欣, 张钧钊, 等. 规模化储能参与电力现货市场的商业模式[J]. 上海交通大学学报, 2023, 57(12): 1543-1558. Li Qian, Jiang Xin, Zhang Junzhao, et al.Business models for large-scale energy storage systems to participate in electricity spot market[J]. Journal of Shanghai Jiao Tong University, 2023, 57(12): 1543-1558. [10] 李军徽, 安晨宇, 李翠萍, 等. 计及调峰市场交易的储能-新能源-火电多目标优化调度[J]. 电工技术学报, 2023, 38(23): 6391-6406. Li Junhui, An Chenyu, Li Cuiping, et al.Multi-objective optimization scheduling method considering peak regulating market transactions for energy storage-new energy-thermal power[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6391-6406. [11] Zhai Zhengyuan, Zhang Lei, Wang Yadong, et al.Optimization of power generation structure and electricity transmission pattern in China based on the electricity-carbon coordinated market[J]. Renewable Energy, 2025, 252: 123359. [12] 朱宗耀, 王秀丽, 吴雄, 等. 复合储能参与电能量及辅助服务市场的运行策略[J]. 电力系统自动化, 2023, 47(18): 80-90. Zhu Zongyao, Wang Xiuli, Wu Xiong, et al.Operation strategy for composite energy storage participating in electric energy and ancillary service markets[J]. Automation of Electric Power Systems, 2023, 47(18): 80-90. [13] Yi Zhongkai, Xu Yinliang, Wei Xuan, et al.Robust security constrained energy and regulation service bidding strategy for a virtual power plant[J]. CSEE Journal of Power and Energy Systems, 2025, 11(2): 692-704. [14] 王沛. 双碳背景下考虑不确定性的发电商多元市场协同优化决策研究[D]. 杭州: 浙江大学, 2023. Wang Pei.Research on the synergistically optimized bidding strategy of multiple markets of power generation company considering uncertainty under the background of carbon neutral[D]. Hangzhou: ZheJiang University, 2023. [15] Sun Sashuang, Liu Youbo, Tang Zhiyuan, et al.A hybrid stochastic-robust bidding model for wind-storage system in day-ahead market considering risk preference[J]. Electric Power Systems Research, 2025, 238: 111148. [16] 张文锦. 基于燃料成本优化的燃煤电厂购煤与竞价策略研究[D]. 广州: 华南理工大学, 2022. Zhang Wenjin.Research on coal purchase and bidding strategy of coalfired power plants based on fuel cost optimization[D]. Guangzhou: South China University of Technology, 2022. [17] 黄雷. 火力发电厂燃煤全过程成本分析与优化研究[D]. 北京: 华北电力大学, 2018. Huang Lei.Cost analysis and optimization of the whole process coal burning in thermal power plan[D]. Beijing: North China Electric Power University, 2018. [18] Prasad S K, Mangaraj B K.A multi-objective competitive-design framework for fuel procurement planning in coal-fired power plants for sustainable operations[J]. Energy Economics, 2022, 108: 105914. [19] 廖志伟, 郑广昱, 谢汛恺, 等. 基于上下游市场联动的燃煤电厂两阶段决策模型[J]. 中国电机工程学报, 2024, 44(8): 3036-3046. Liao Zhiwei, Zheng Guangyu, Xie Xunkai, et al.A two-stage decision model for coal-fired power plants based on upstream and downstream market linkages[J]. Proceedings of the CSEE, 2024, 44(8): 3036-3046. [20] 王辉, 孙学晶, 刘达, 等. 基于条件风险价值的时序运行燃煤采购决策模型[J]. 华北电力大学学报(自然科学版), 2024, 51(6): 104-114. Wang Hui, Sun Xuejing, Liu Da, et al.Time-series operational coal procurement decision model based on conditional value-at-risk[J]. Journal of North China Electric Power University, 2024, 51(6): 104-114. [21] Che Ping, Liu Yi, Che Liang, et al.Co-optimization of generation self-scheduling and coal supply for coal-fired power plants[J]. IEEE Access, 2020, 8: 110633-110642. [22] Huang Qiuli, Han Song, Rong Na, et al.Stochastic economic dispatch of hydro-thermal-wind-photovoltaic power system considering mixed coal-blending combustion[J]. IEEE Access, 2020, 8: 218542-218553. [23] 张恒基, 王海宁, 袁明珠, 等. 中国电力中长期市场交易机制分析与建议[J]. 电力系统自动化, 2024, 48(11): 11-23. Zhang Hengji, Wang Haining, Yuan Mingzhu, et al.Analysis and suggestions on trading mechanism of china's medium- and long-term electricity market[J]. Automation of Electric Power Systems, 2024, 48(11): 11-23. [24] Kallabis T, Pape C, Weber C.The plunge in German electricity futures prices-analysis using a parsimonious fundamental model[J]. Energy Policy, 2016, 95: 280-290. [25] 陶冶, 魏震波, 兰森林, 等. 考虑电力期货交易的发电企业全市场收益优化模型[J]. 电力建设, 2024, 45(5): 141-149. Tao Ye, Wei Zhenbo, Lan Senlin, et al.Market-wide revenue optimization model of power generation company considering electricity futures trading[J]. Electric Power Construction, 2024, 45(5): 141-149. [26] 王旭东, 高赐威. 中长期市场下发电企业发电与购煤安排两阶段决策模型[J]. 电网技术, 2021, 45(10): 3992-4001. Wang Xudong, Gao Ciwei.Two-stage decision-making model of power generation and coal purchase arrangement for power generation companies in medium-and long-term market[J]. Power System Technology, 2021, 45(10): 3992-4001. [27] 孙毅, 李飞, 胡亚杰, 等. 计及条件风险价值和综合需求响应的产消者能量共享激励策略[J]. 电工技术学报, 2023, 38(9): 2448-2463. Sun Yi, Li Fei, Hu Yajie, et al.Energy sharing incentive strategy of prosumers considering conditional value at risk and integrated demand response[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2448-2463. [28] 陈泽宇, 陈艳波. 计及循环寿命和电能量-调频市场出清的储能自调度策略[J]. 电力系统自动化, 2024, 48(14): 28-41. Chen Zeyu, Chen Yanbo.Self-scheduling strategy of energy storage considering cycle life and clearing of electric power energy-frequency regulation market[J]. Automation of Electric Power Systems, 2024, 48(14): 28-41. [29] 吴洋, 辛茹, 邹文滔, 等. 提升电力现货市场出清结果可解释性的综合分析方法[J]. 南方电网技术, 2022, 16(6): 113-123. Wu Yang, Xin Ru, Zou Wentao, et al.Comprehensive analysis method for enhancing the explainability of elec-tricityspot market clearing results[J]. Southern Power System Technology, 2022, 16(6): 113-123. [30] 陈艳波, 田昊欣, 刘宇翔, 等. 计及电动汽车需求响应的高速公路服务区光储充鲁棒优化配置[J]. 中国电机工程学报, 2025, 45(5): 1752-1766. Chen Yanbo, Tian Haoxin, Liu Yuxiang, et al.Robust optimization configuration of photovoltaic-storage- charging integrated system in expressway service area considering demand response of electric vehicles[J]. Proceedings of the CSEE, 2025, 45(5): 1752-1766. [31] 王怡, 杨知方, 余娟, 等. 从优化视角剖析电力市场的定价问题[J]. 电工技术学报, 2023, 38(17): 4729-4745. Wang Yi, Yang Zhifang, Yu Juan, et al.Analyzing pricing problem in electricity market from an optimization perspective[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4729-4745. [32] 于惠钧, 马凡烁, 陈刚, 等. 基于改进灰狼优化算法的含光伏配电网动态无功优化[J]. 电气技术, 2024, 25(4): 7-15, 58. Yu Huijun, Ma Fanshuo, Chen Gang, et al.Dynamic reactive power optimization of photovoltaic distribution network based on improved gray wolf optimization algorithm[J]. Electrical Engineering, 2024, 25(4): 7-15, 58. [33] 张新明, 涂强, 康强, 等. 强化狼群等级制度的灰狼优化算法[J]. 数据采集与处理, 2017, 32(5): 879-889. Zhang Xinming, Tu Qiang, Kang Qiang, et al.Grey wolf optimization algorithm based on strengthening the hierarchy of wolves[J]. Journal of Data Acquisition and Processing, 2017, 32(5): 879-889. [34] 陈艳波, 强涂奔, 田昊欣, 等. 考虑集中式与分布式新能源不确定性的输配协同负荷恢复方法[J]. 电工技术学报, 2026, 41(7): 2281-2299. Chen Yanbo, Qiang Tuben, Tian Haoxin, et al.Coordinated load restoration method of transmission and distribution networks considering the uncertainties of centralized and distributed renewable energy sources[J]. Transactions of China Electrotechnical Society, 2026, 41(7): 2281-2299. [35] 陈艳波, 田昊欣, 强涂奔, 等. 内嵌数据驱动的三相不平衡有源配电网两阶段动态最优潮流调度方法[J]. 电工技术学报, 2026, 41(9): 3005-3021, 3037. Chen Yanbo, Tian Haoxin, Qiang Tuben, et al.A two-stage dynamic optimal power flow dispatch method for three-phase unbalanced active distribution networks incorporating data-driven approaches[J]. Transactions of China Electrotechnical Society, 2026, 41(9): 3005-3021, 3037. [36] 陈艳波, 马嘉昊, 刘镇湘, 等. 计及阻尼惯量耦合振荡约束的多虚拟同步机馈入系统多层感知器动态控制策略[J]. 电工技术学报, 2026, 41(4): 1281-1297. Chen Yanbo, Ma Jiahao, Liu Zhenxiang, et al.Dynamic control strategy of multilayer perceptron for multiple virtual synchronous machine feed-in systems considering damped-inertia coupling oscillation constraints[J]. Transactions of China Electrotechnical Society, 2026, 41(4): 1281-1297. [37] 靳灿, 张晓燕, 孙本川. 基于海鸥优化算法改进深度极限学习机的锂电池健康状态预测[J]. 电气技术, 2025, 26(7): 40-45. Jin Can, Zhang Xiaoyan, Sun Benchuan, et al.State of health prediction for lithium batteries based on deep extreme learning machine improved by seagull optimization algorithm[J]. Electrical Engineering, 2025, 26(7): 40-45.
2026, Vol. 41 
