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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (9): 1891-1901    DOI: 10.3785/j.issn.1008-973X.2025.09.013
    
Day-ahead market economic dispatch considering energy storage providing flexible ramping products
Haijun XING1(),Qian YU1,Mingyang CHENG2,Qizhen GUO1,Chenghao HUANG1
1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. Lianyungang Power Supply Company, State Grid Jiangsu Electric Power Co. Ltd, Lianyungang 222000, China
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Abstract  

A day-ahead market economic dispatch model that incorporated the flexible ramping products (FRPs) provided by energy storage systems was proposed to fully harness the flexible regulatory capabilities of various resources to ensure real-time flexibility, thereby addressing the problem that conventional thermal power units cannot meet the power system’s flexibility demands against the backdrop of the construction of a new type of power system. The demand composition and the opportunity cost of FRPs were introduced. A decision tree model for FRPs participation in flexible ramping deployment in the day-ahead and real-time markets was obtained by conducting the probabilistic analysis of the decision-making schemes for FRPs in both the day-ahead and real-time markets, and the cost and revenue analyses were performed under the scenarios where FRPs are either abundant or scarce. Based on this, a day-ahead market economic dispatch model that considered energy storage providing FRPs was established. After the economic dispatch in the day-ahead market, the cost and FRPs revenue settlement was achieved based on the probability of accepting FRPs in the day-ahead market and the expected deployment probability in the real-time market. A case study analysis using an improved IEEE 30-bus system was conducted to validate the superiority of the model with energy storage participation, and the impact of the acceptance probability in the day-ahead market and the expected deployment probability in the real-time market on the system was discussed.

Key words： flexible ramping products      energy storage      day-ahead market acceptance probability      real-time market deployment probability      decision tree     
Received: 24 December 2024      Published: 25 August 2025
CLC:  TP 393  
Fund:  国家自然科学基金资助项目(52477106).
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Haijun XING
Qian YU
Mingyang CHENG
Qizhen GUO
Chenghao HUANG
Cite this article:

Haijun XING,Qian YU,Mingyang CHENG,Qizhen GUO,Chenghao HUANG. Day-ahead market economic dispatch considering energy storage providing flexible ramping products. Journal of ZheJiang University (Engineering Science), 2025, 59(9): 1891-1901.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.09.013     OR     https://www.zjujournals.com/eng/Y2025/V59/I9/1891


考虑储能提供灵活爬坡产品的日前市场经济调度

针对新型电力系统建设背景下常规火电机组不能满足系统灵活性需求的问题，为了充分挖掘各资源的灵活调节能力，保障系统实时灵活性，提出考虑储能提供灵活爬坡产品（FRPs）的日前市场经济调度模型. 介绍FRPs的需求构成和机会成本计算方法；通过对日前市场和实时市场的FRPs决策方案进行概率分析，得到FRPs在日前和实时市场参与灵活爬坡部署的决策树模型，在FRPs充足和短缺的情况下进行成本和收益分析. 在此基础上建立考虑储能提供FRPs的日前市场经济调度模型，并在日前市场进行经济调度后，根据FRPs的日前市场接受概率和实时市场预期部署概率来实现成本和FRPs收益结算. 通过改进的IEEE 30节点进行算例分析，验证了储能参与下该模型的优越性，并探讨了日前市场接受概率和实时市场预期部署概率对系统的影响.


关键词： 灵活爬坡产品,  储能,  日前市场接受概率,  实时市场部署概率,  决策树 
Fig.1 Ramping demand considering regulating capacity
机组$\lambda^{\mathrm{G}} /\left(\$ \cdot \mathrm{MW}^{-1} \cdot \mathrm{h}^{-1}\right)$$ {R}_{\text{up}}^{\text{G}}/{\mathrm{MW}} $$ {C}_{\text{op}}^{\text{G}} /\left(\$ \cdot \mathrm{MW}^{-1} \cdot \mathrm{h}^{-1}\right)$
G120200
G218202
G316104
G412108
G5101010
G62510—
Tab.1 Relationship between opportunity cost of FRPs and market quotation of electricity energy
Fig.2 Relationship between opportunity cost of FRPs and ramping demand
Fig.3 Decision tree for FRPs participation in day-ahead and real-time markets
Fig.4 Costs and revenues of FRPs participating in market settlement
Fig.5 Structure diagram of improved IEEE 30 node system
机组$ P_{i,\max }^{{\mathrm{G}}} $/
MW		$ P_{i,\min }^{{\mathrm{G}}} $/
MW		$ {R}_{i}^{\text{G}\text{ }} $/
(MW·h?1)		$ {\lambda }_{i}^{\text{G}} $/
($·MW?1)		$t_{{\text{on}}}^{\min }/{\mathrm{h}}$$t_{{\text{off}}}^{\min }/{\mathrm{h}}$$t_{\text{run}}^{\;}/{\mathrm{h}}$
G12001002516101010
G2100403020888
G38040202466?6
G440203026444
G550202028333
G62010403011?1
Tab.2 Thermal power unit parameters
Fig.6 Forecast results of day-ahead wind power output and real-time wind power output
Fig.7 Day-ahead load and real-time load Forecast results
Fig.8 Electricity prices in day-ahead market and real-time market
场景$ C $/(104 $)$ {C_{{\text{FRPs}}}} $/(104 $)$ {R_{{\text{FRPs}}}} $/(104 $)$ {C_{\text{p}}} $/(104 $)
119.410.000.004.57
216.041.251.781.79
314.561.412.540.45
Tab.3 Comparison of operation costs under different scenarios
Fig.9 Energy storage output analysis in scenario 2
Fig.10 Energy storage output analysis in scenario 3
Fig.11 Upward and downward awarded quantities of FRPs in day-ahead market
$ \alpha $$ P_{{{\mathrm{up}}}}^{{\mathrm{G}}}/{\text{MW}} $$ P_{{\text{dn}}}^{{\mathrm{G}}}/{\text{MW}} $$ P_{{\text{up}}}^{{{\mathrm{E}}}{\text{d}}}/{\text{MW}} $$ P_{{\text{dn}}}^{{{\mathrm{E}}}{\text{c}}}/{\text{MW}} $$ {P_{{\text{up}}}}/{\text{MW}} $$ {P_{{\text{dn}}}}/{\text{MW}} $
0.00.00.00.00.00.00.0
0.133.546.511.60.045.146.5
0.265.693.124.70.090.393.1
0.399.0125.636.212.2135.2138.2
0.4130.5151.050.635.0181.1186.0
0.5154.1176.971.555.8225.6232.8
0.6179.2213.890.165.4270.1279.2
0.7195.7244.6120.182.2315.8325.8
0.8202.1264.5158.0107.8360.1372.3
0.9197.6264.5208.4154.4406.0418.9
1.0184.5264.5266.7200.5451.2465.0
Tab.4 Relationship between acceptance probability of day-ahead market and capacity
Fig.12 Impact of acceptance probability of day-ahead market on costs
Fig.13 Impact of expected deployment probability in real-time market on costs and revenues
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