基于信息间隙决策理论的含碳捕集-电转气综合能源系统优化调度

doi:10.12096/j.2096-4528.pgt.24021

摘要/Abstract

摘要：

目的为科学统筹综合能源系统运行经济性、稳定性和低碳性优化目标，采用何种技术手段以提升能源转化效率，减少系统能源浪费和区域环境污染，是当下综合能源系统合理优化的主要问题。为此，提出一种基于场景生成与信息间隙决策理论的含碳捕集与封存(carbon capture and storage, CCS)—两段式电转气(power to gas, P2G)综合能源系统低碳优化策略。方法在技术层面，通过对电P2G两阶段精细化建模，提高氢能利用效率，建立热电联产(combined heating and power，CHP)-CCS-P2G耦合模型；在市场机制层面，引入阶梯型碳交易模型以降低系统中CO₂排放量。最终，基于信息间隙决策理论(IGDT)构建不同风险偏好下的优化调度模型。结果以典型综合能源系统进行算例分析，仿真结果表明所提模型可提高风光消纳率，实现系统低碳、经济、稳定运行。结论该优化策略可有效帮助决策者根据其风险偏好制定风险规避与风险追求策略下的调度方案，实现系统不确定性与经济性的平衡。

关键词: 综合能源系统, 场景生成, 信息间隙决策理论(IGDT), 碳捕集与封存(CCS), 电转气(P2G), 阶梯型碳交易

Abstract:

Objectives The main issue in the current rational optimization of integrated energy systems is to adopt technological means to improve energy conversion efficiency, reduce system energy waste and regional environmental pollution, in order to scientifically coordinate the optimization goals of economic, stability, and low-carbon operation of the integrated energy system. To this end, a low-carbon optimization strategy for a carbon capture and storage (CCS)two-stage power to gas (P2G)integrated energy system based on scenario generation and information gap decision theory (IGDT) was proposed. Methods At the technical level, by finely modeling the two-stage conversion from power to gas, the efficiency of hydrogen energy utilization was improved, and a combined heating and power (CHP)-CCS-P2G coupling model was established. At the market mechanism level, a tiered carbon trading model was introduced to reduce CO₂ emissions in the system. Finally, based on the IGDT, an optimization scheduling model was constructed for different risk preferences. Results Taking a typical integrated energy system as an example, the simulation results show that the proposed model can improve the wind and solar energy consumption rate, achieve low-carbon, economic, and stable operation of the system. Conclusions This optimization strategy can effectively help decision-makers develop scheduling plans under risk avoidance and risk pursuit strategies based on their risk preferences, achieving a balance between system uncertainty and economy.

Key words: integrated energy system, scenario generation, information gap decision theory(IGDT), carbon capture and storage(CCS), power to gas(P2G), stepped carbon trading

中图分类号:

TK 01

赵振宇, 包格日乐图, 李炘薪. 基于信息间隙决策理论的含碳捕集-电转气综合能源系统优化调度[J]. 发电技术, 2024, 45(4): 651-665.

Zhenyu ZHAO, Geriletu BAO, Xinxin LI. Optimization and Scheduling of Integrated Energy Systems With Carbon Capture and Storage-Power to Gas Based on Information Gap Decision Theory[J]. Power Generation Technology, 2024, 45(4): 651-665.

图/表 18

图1 IES运行结构图

Fig. 1 IES operation structure diagram

图2 逆变换示意图

Fig. 2 The diagram of inverse transform

图3 风光出力和负荷预测曲线

Fig. 3 Wind and solar power output and load prediction curves

图4 风电场景削减图

Fig. 4 Wind power scenario reduction map

图5 光伏场景削减图

Fig. 5 Photovoltaic scenario reduction map

表1 分时电价

Tab. 1 Time of use

价格/[元/(MW⋅h)]	时段
310	01:00—07:00, 23:00—24:00
520	08:00—11:00, 15:00—18:00, 21:00—22:00
820	12:00—14:00, 19:00—20:00

图6 各场景碳排放量及碳交易成本

Fig. 6 Carbon emissions and carbon trading costs in various scenarios

表2 各类场景下运行费用

Tab. 2 Operating costs under various scenarios

场景	碳排放量/t	碳交易成本/元	投资成本/元	购电购气成本/元	弃风光成本/元	运行成本/元	总成本/元
1	513.81	41 566.11	128 532.10	230 641.08	7 502.90	33 902.04	442 144.23
2	458.83	17 354.87	156 340.18	215 067.56	3 293.61	39 040.39	431 096.62
3	387.14	11 752.87	178 589.42	191 842.27	1 317.39	39 571.25	423 073.20
4	351.40	12 096.76	178 589.42	183 619.83	0	40 342.90	414 648.92

图7 场景4电功率调度结果

Fig. 7 Power scheduling results of scenario 4

图8 场景4热功率调度结果

Fig. 8 Thermal power scheduling results of scenario 4

图9 场景4气功率调度结果

Fig. 9 Natural gas power scheduling results of scenario 4

图10 场景4氢功率调度结果

Fig. 10 Hydrogen power scheduling results of scenario 4

图11 区间长度分析

Fig. 11 Interval length analysis

图12 价格增长率分析

Fig. 12 Analysis of price growth rate

图13 碳交易基础价格分析

Fig. 13 Analysis of basic carbon trading prices

图14 IES各时刻碳平衡

Fig. 14 Carbon balance of IES at each time

图15 IGDT优化调度结果

Fig. 15 IGDT optimization scheduling results

表3 风险偏好敏感度分析

Tab. 3 Sensitivity analysis of risk preference

风险规避策略		偏差因子	风险追求策略
运行成本/元	不确定度	偏差因子	运行成本/元	不确定度
414 648.92	0	0	414 648.92	0
423 577.77	0.018 4	0.025	407 721.28	0.018 5
433 494.96	0.030 0	0.050	397 778.94	0.029 6
442 418.25	0.050 5	0.075	387 827.61	0.048 5
450 103.51	0.067 7	0.100	381 896.94	0.062 6
459 955.73	0.081 9	0.125	374 949.76	0.077 0
467 199.98	0.108 5	0.150	367 027.02	0.099 7

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