|
|
|
| Joint Market Clearing Model and Uncertainty Pricing Approach Based on Distributed Robust Bilateral Chance Constraint |
| Cai Qinqin, Xu Ying, Yi Zhongkai, Tu Zhenghong, Zhou Yuhao |
| School of Electrical Engineering and automation Harbin Institute of Technology Harbin 150001 China |
|
|
|
Abstract The continuous grid connection of wind and solar energy has significantly increased the uncertainty of the power system. Due to the inaccurate prediction of new energy and load power, the imbalance power results in insufficient flexible adjustment capacity, further leading to the continuous increase in demand for reserve and frequency regulation capacity, which brings huge challenges to the clearing of the power market.
The traditional clearing model of independent markets of electric energy and ancillary service is established based on the predicted power. However, the clearing results of the traditional market have a lower execution rate since the increasing strong uncertainty of the system net load power. Therefore, this paper proposes a joint market clearing model of electric energy and ancillary services constructed by distributed robust bilateral chance constraints (DRBCC) approach that is based on two types fuzzy sets of uncertain power. For the model solving, DRBCC are converted into mixed integer second-order cone programming for solving through convex reconstruction. Considering that the prediction accuracy of new energy power is much lower than that of load power, an ellipsoid fuzzy set is constructed to sketch the uncertainty of new energy power, while uncertainty of load power is described by deterministic fuzzy sets based on the statistical moment information. In order to classify different market participants, participants are divided into stochastic market entities (SME) and deterministic market entities (DME). Besides, DME are divided into two categories: power constraint entities (PCE) and energy constraint entities (ECE).
A scientific and reasonable electricity price mechanism and is the key to the construction of the future power market, which will guide market players to reduce uncertainty, reflecting the scarcity of flexible adjustment resources. Based on the marginal pricing principle, the node marginal prices of electric energy, reserve, frequency regulation and uncertain power are derived. The proposed USMP and ULMP clarify the market cost of uncertainty sources, resulting in the penalty cost of SME generating uncertain power mainly flowing to PCE and ECE enterprises that provide backup capacity and frequency regulation power. To ensure that both DME and SME obtain equal benefits based on their own value, it is necessary to distinguish the prices of deterministic power provided by DME and uncertain power generated by SME. Therefore, this paper advocates pricing the two types of power separately, which helps guide the matching of flexible adjustment resources and uncertain power in the system. The main contributions are illustrated as follows:
(1) A multi-category coordinated pricing mechanism is proposed based on the marginal pricing principle, the locational marginal prices (LMP) of electric energy, reserve and frequency regulation capacity are derived. Moreover, the uncertainty system marginal prices (USMP) and uncertainty locational marginal prices (ULMP) is designed to provide price signal for market operation.
(2) The market fairness is quantified by market returns, which is reflected in the SMEs’ market penalty cost for generating uncertain power, and the PCEs’ and ECEs’ market rewards for participating in regulating system power balance. The penalty cost encourage SMEs to reduce uncertainty, while the rewards promote DMEs to participate in the joint market.
(3) The proposed joint market clearing model is constructed by DRBCC based on two types fuzzy sets of uncertain power with consideration of the differences in prediction accuracy of new energy and load. Compared with the traditional model, the proposed model reduce the impact of strong uncertainty and volatility of power on the market.
Finally, case analysis verify the feasibility and practical application value of the proposed joint clearing model and uncertain power pricing mechanism the IEEE 39-node system. Compared with the traditional market clearing model, SMEs reduce their profits by 18.5% for uncertain payment, ECEs increase their profits by 26% for providing the reserve and frequency regulation service and the total operation costs are reduced by 25.5% for market cooperation. In conclusion, the proposed joint market clearing and pricing model provides technical support and incentives for the long-term operation of the strong uncertainty market.
|
|
Received: 29 October 2024
|
|
|
|
|
|
[1] 刘映尚, 马骞, 王子强, 等. 新型电力系统电力电量平衡调度问题的思考[J]. 中国电机工程学报, 2023, 43(5): 1694-1705.
Liu Yingshang, Ma Qian, Wang Ziqiang, et al.Cogitation on power and electricity balance dispatching in new power system[J]. Proceedings of the CSEE, 2023, 43(5): 1694-1705.
[2] 向明旭, 陈泓霏, 曹晓峻, 等. 省间电力中长期交易出清问题多解顺次寻优的高效计算方法[J]. 电工技术学报, 2025, 40(11): 3545-3559.
Xiang Mingxu, Chen Hongfei, Cao Xiaojun, et al.Efficient sequential optimization method for inter-provincial medium-and long-term power transaction clearing problem under multiple-solution scenarios[J]. Transactions of China Electrotechnical Society, 2025, 40(11): 3545-3559.
[3] Fang Xichen, Guo Hongye, Zhang Xian, et al.An efficient and incentive-compatible market design for energy storage participation[J]. Applied Energy, 2022, 311: 118731.
[4] Thomas D, Kazempour J, Papakonstantinou A, et al.A local market mechanism for physical storage rights[J]. IEEE Transactions on Power Systems, 2020, 35(4): 3087-3099.
[5] 石剑涛, 郭烨, 孙宏斌, 等. 备用市场机制研究与实践综述[J]. 中国电机工程学报, 2021, 41(1): 123-134, 403.
Shi Jiantao, Guo Ye, Sun Hongbin, et al.Review of research and practice on reserve market[J]. Proceedings of the CSEE, 2021, 41(1): 123-134, 403.
[6] 肖云鹏, 张兰, 张轩, 等. 包含独立储能的现货电能量与调频辅助服务市场出清协调机制[J]. 中国电机工程学报, 2020, 40(增刊1): 167-180.
Xiao Yunpeng, Zhang Lan, Zhang Xuan, et al.Coordination mechanism of spot electric energy with independent energy storage and market clearing of FM auxiliary service[J]. Proceedings of the CSEE, 2020, 40(S1): 167-180.
[7] 周安平, 杨明, 翟鹤峰, 等. 计及风电功率矩不确定性的分布鲁棒实时调度方法[J]. 中国电机工程学报, 2018, 38(20): 5937-5946.
Zhou Anping, Yang Ming, Zhai Hefeng, et al.Distributionally robust real-time dispatch considering moment uncertainty of wind generation[J]. Proceedings of the CSEE, 2018, 38(20): 5937-5946.
[8] 董雷, 李扬, 陈盛, 等. 考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度[J]. 电工技术学报, 2024, 39(9): 2635-2651.
Dong Lei, Li Yang, Chen Sheng, et al.Multi-microgrid cooperative game optimization scheduling considering multiple uncertainties and coupled electricity-carbon transactions[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2635-2651.
[9] Fang Xin, Sedzro K S, Yuan Haoyu, et al.Deliverable flexible ramping products considering spatiotemporal correlation of wind generation and demand uncertainties[J]. IEEE Transactions on Power Systems, 2020, 35(4): 2561-2574.
[10] Fang Xin, Hodge B M, Li Fangxing, et al.Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty[J]. Journal of Modern Power Systems and Clean Energy, 2019, 7(3): 658-664.
[11] 房欣欣, 杨知方, 余娟, 等. 节点电价的理论剖析与拓展[J]. 中国电机工程学报, 2020, 40(2): 379-390.
Fang Xinxin, Yang Zhifang, Yu Juan, et al.Theoretical analysis and extension of locational marginal price[J]. Proceedings of the CSEE, 2020, 40(2): 379-390.
[12] Fang Xin, Hodge B M, Du Ershun, et al.Introducing uncertainty components in locational marginal prices for pricing wind power and load uncertainties[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2013-2024.
[13] Majumder S, Khaparde S A, Agalgaonkar A P, et al.Chance-constrained pre-contingency joint self-scheduling of energy and reserve in VPP[J]. IEEE Transactions on Power Systems, 2024, 39(1): 245-260.
[14] Zhong Weifeng, Xie Kan, Liu Yi, et al.Chance constrained scheduling and pricing for multi-service battery energy storage[J]. IEEE Transactions on Smart Grid, 2021, 12(6): 5030-5042.
[15] Wei Wei, Liu Feng, Mei Shengwei.Distributionally robust co-optimization of energy and reserve dispatch[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1): 289-300.
[16] Wang Haoyuan, Bie Zhaohong, Ye Hongxing.Locational marginal pricing for flexibility and uncertainty with moment information[J]. IEEE Transactions on Power Systems, 2023, 38(3): 2761-2775.
[17] 陈中飞, 荆朝霞, 陈达鹏, 等. 美国调频辅助服务市场的定价机制分析[J]. 电力系统自动化, 2018, 42(12): 1-10.
Chen Zhongfei, Jing Zhaoxia, Chen Dapeng, et al.Analysis on pricing mechanism in frequency regulation ancillary service market of United States[J]. Automation of Electric Power Systems, 2018, 42(12): 1-10.
[18] 宋永华, 包铭磊, 丁一, 等. 新电改下我国电力现货市场建设关键要点综述及相关建议[J]. 中国电机工程学报, 2020, 40(10): 3172-3187.
Song Yonghua, Bao Minglei, Ding Yi, et al.Review of Chinese electricity spot market key issues and its suggestions under the new round of Chinese power system reform[J]. Proceedings of the CSEE, 2020, 40(10): 3172-3187.
[19] Xie Weijun, Ahmed S.Distributionally robust chance constrained optimal power flow with renewables: a conic reformulation[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1860-1867.
[20] 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. |
|
|
|
2025, Vol. 40 
