PDF(5257 KB)
Research on Distributed Robust Planning of Electric-Hydrogen-Gas Integrated Energy System Considering Demand Response
ZHANG Wenxuan, SU Jia, DU Xinhui, ZHANG Zhishuo, WANG Qianchun, JIANG Haipeng
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (7) : 108-122.
PDF(5257 KB)
PDF(5257 KB)
Research on Distributed Robust Planning of Electric-Hydrogen-Gas Integrated Energy System Considering Demand Response
[Objective] To completely exploit the coupling flexibility of the electric-hydrogen-gas-storage-demand response， a data-driven two-stage distributed robust collaborative planning model for integrated energy systems is proposed. [Methods] To address the problems of model inaccuracy and low solving efficiency of existing equipment modeling methods， a refined modeling method for an integrated energy system was proposed， which considered a refined model of distributed power supply， energy coupling equipment， hybrid energy storage， and demand response mechanism. A demand response incentive mechanism considering baseline uncertainty was developed. [Results] The MATLAB simulation results showed that the baseline load prediction model based on Gaussian process regression can calculate the baseline load more accurately and rapidly while simultaneously considering the response uncertainty. In addition， the equipment refinement model proposed in this study effectively reduced the comprehensive planning cost of the system， in which the operation， planning， carbon trading， and demand response costs were reduced by 2.55%， 10.78%， 1.08%， and 2.55%， respectively. Simultaneously， through the collaborative optimization of carbon trading and demand response mechanisms， the system could reduce the power purchased by the upper power grid and use flexible loads and distributed power sources to achieve a low-carbon and stable operation of the integrated energy system. The example showed that compared with the SO and RO methods， the proposed DRO planning method had more advantages in terms of the balance of economy and robustness and verified its applicability in integrated energy system planning. [Conclusions] The integrated energy system planning model based on demand response can significantly reduce the annual comprehensive cost of the system， improve the utilization rate of renewable energy， and reduce carbon emissions， providing ideas for subsequent research on the planning of the electric-hydrogen-gas integrated energy systems.
demand response / electric-hydrogen-gas integrated energy system planning / refined modeling / distributed robust
| [1] |
张沈习, 王丹阳, 程浩忠, 等. 双碳目标下低碳综合能源系统规划关键技术及挑战[J]. 电力系统自动化, 2022, 46(8): 189-207.
|
| [2] |
谭青博, 潘伟, 王竹宁, 等. 新型电力系统下综合能源系统的投资决策模型[J]. 智慧电力, 2023, 51(8): 46-52.
|
| [3] |
郑婉婷, 赵倩宇, 王璇, 等. 绿证-碳交易机制下新型电力系统电-氢-气混合储能容量优化配置方法[J]. 供用电, 2024, 41(3): 24-31, 41.
|
| [4] |
张冠宇, 付炜, 陈晨, 等. 面向电-气-热综合能源系统的恢复力研究现状与展望[J]. 智慧电力, 2023, 51(1): 69-77.
|
| [5] |
刘珊珊, 李柯睿, 刘柏康, 等. 绿证—碳联合机制下含多类型需求响应和氢能多元利用的综合能源系统优化调度[J]. 电力科学与技术学报, 2024, 39(5): 203-215, 225.
|
| [6] |
周亦洲, 李想, 孙国强, 等. 考虑余热回收的电-氢-热综合能源系统随机分布鲁棒韧性规划[J/OL]. 电网技术, 2024.(2024-07-15)[2024-10-13]. https://doi.org/10.13335/j.1000-3673.pst.2024.1178.
|
| [7] |
王艳松, 王毓铎. 计及电制氢和碳捕集的园区综合能源系统动态规划[J]. 中国石油大学学报(自然科学版), 2024, 48(2): 142-150.
|
| [8] |
牛启帆, 武鹏, 张菁, 等. 考虑电转气的电-气耦合系统协同优化规划方法[J]. 电力系统自动化, 2020, 44(3): 24-31.
|
| [9] |
孙惠娟, 阙炜新, 彭春华. 考虑电氢耦合和碳交易的电氢能源系统置信间隙鲁棒规划[J]. 电网技术, 2023, 47(11): 4477-4490.
|
| [10] |
李亚峰, 王维庆. 考虑阶梯碳交易机制的含混氢天然气综合能源系统容量配置[J]. 电力科学与技术学报, 2023, 38(6): 237-247.
|
| [11] |
郑丁园, 崔双喜, 樊小朝, 等. 计及风电不确定性的综合能源系统多目标分布鲁棒优化调度[J]. 智慧电力, 2024, 52(8): 1-8, 18.
|
| [12] |
王世豪, 吕家君, 李更丰, 等. 考虑氢混燃机动态混氢特性的电-气互联系统优化调度[J]. 电网技术, 2024, 48(5): 1896-1906.
|
| [13] |
李志伟, 赵雨泽, 吴培, 等. 基于制氢设备精细建模的综合能源系统绿氢蓝氢协调低碳优化策略[J]. 电网技术, 2024, 48(6): 2317-2326.
|
| [14] |
|
| [15] |
闫博阳, 韩肖清, 李廷钧, 等. 计及能量-备用联合市场交易的含储能主动配电网运营策略[J]. 电力系统自动化, 2023, 47(23): 131-140.
|
| [16] |
|
| [17] |
杨欢红, 赵峰, 黄文焘, 等. 考虑设备变工况特性的园区综合能源系统双层优化[J]. 电力系统保护与控制, 2024, 52(17): 115-127.
|
| [18] |
王浩丞, 高红均, 王仁浚. 计及需求响应的虚拟电厂日前市场交易策略研究[J]. 智慧电力, 2024, 52(7): 64-71.
|
| [19] |
周步祥, 黄伟, 臧天磊. 计及共享储能与柔性负荷的微电网鲁棒优化调度[J]. 电力科学与技术学报, 2023, 38(2): 48-57.
|
| [20] |
张庆, 王涛, 李川. 计及柔性负荷和碳流的园区综合能源系统优化运行模型研究[J]. 智慧电力, 2024, 52(6): 54-61.
|
| [21] |
梁俊鹏, 张高航, 李凤婷, 等. 计及氢储能与需求响应的路域综合能源系统规划方法[J]. 电网技术, 2024, 48(12):4918-4927.
|
| [22] |
曾艾东, 邹宇航, 郝思鹏, 等. 考虑阶梯式碳交易机制的园区工业用户综合需求响应策略[J]. 高电压技术, 2022, 48(11): 4352-4363.
|
| [23] |
崔杨, 邓贵波, 赵钰婷, 等. 考虑源荷低碳特性互补的含风电电力系统经济调度[J]. 中国电机工程学报, 2021, 41(14): 4799-4815.
|
| [24] |
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/OL]. 中国电机工程学报, 2023.(223-11-20)[2024-10-13]. https://doi.org/10.13334/j.0258-8013.pcsee.231850.
|
| [25] |
|
| [26] |
陈湘元, 吴公平, 龙卓, 等. 考虑源荷不确定性及用户侧需求响应的综合能源系统多时间尺度优化调度[J]. 电力科学与技术学报, 2024, 39(3): 217-227.
|
| [27] |
钱科军, 沈杰, 刘乙, 等. 基于负荷聚类的居民需求响应积分精准激励机制[J]. 智慧电力, 2019, 47(7): 29-35.
|
| [28] |
刘春阳, 李康平, 纪陵, 等. 基于聚类-估计联动的需求响应集群基线负荷估计方法[J]. 电力系统自动化, 2023, 47(2): 79-87.
|
| [29] |
付文杰, 王喻玺, 申洪涛, 等. 基于拉丁超立方抽样和场景消减的居民用户基线负荷估计方法[J]. 电网技术, 2022, 46(6): 2298-2307.
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
刘迎迎, 张孝远, 刘梦楠, 等. 基于自适应最优组合核函数高斯过程回归的锂电池健康状态区间估计[J]. 储能科学与技术, 2025, 14(1):346-357.
锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程，使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression, GPR)，因可输出估计结果的不确定性，近年来在锂电池SOH区间估计中得到广泛应用。然而，GPR的性能很大程度上取决于其核函数的选择，当前研究多凭借经验选用固定单一核函数，无法适应不同的数据集。为此，本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF)，并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后，在当前常用的7个核函数集合上，通过两两随机组合构造新的组合核函数，并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证，结果表明：本文方法具有出色的SOH区间估计性能。在3个公开数据集上，平均区间宽度指标在0.0509以内，平均区间分数大于-0.0004，均方根误差小于0.0181。
The degradation of lithium battery state of health (SOH) is to some extent a nonsmooth stochastic process, which makes most of the current point estimation machine learning approaches limited in practical applications. In recent years, Gaussian process regression (GPR), which is based on the Bayesian theory, has been widely used in lithium battery SOH interval estimation due to its ability to quantify uncertainty in the estimation results; however, the performance of GPR significantly depends on the selection of its kernel function. Current studies typically rely on empirically selecting a fixed single kernel function, which may not be suitable for diverse datasets. To address this limitation, this study introduces an SOH interval estimation method for lithium batteries based on an adaptive optimal combination of kernel functions in GPR. The proposed method first extracts multiple health factors from the battery's charge/discharge data and uses the Pearson correlation coefficient method to optimize six health factors that are strongly correlated with SOH as inputs to the model. Subsequently, with a set of seven commonly used kernel functions, new kernel function combinations were created by two-by-two random combinations. Cross-validation was then used to adaptively optimize the optimal kernel function combinations. The proposed approach was validated using three different datasets, and the results indicate its excellent performance in SOH interval estimation. For the three publicly available datasets, the average interval width index is within 0.0530, the average interval score is greater than -0.0004, and the root mean square error is less than 0.0181. |
| [37] |
王强, 张津, 李上杨. 抗噪性高斯过程用于风电系统暂态电压稳定性的评估[J]. 南方电网技术, 2024, 18(9):126-137.
|
| [38] |
邓鸿枥, 吴松荣, 刘齐, 等. 基于改进高斯过程回归的锂离子电池健康状态估计[J/OL]. 电源学报, 2024. (2024-04-26)[2024-10-13]. https://link.cnki.net/urlid/12.1420.TM.20240426.0915.008.
|
| [39] |
|
| [40] |
胥洪远, 龙太聪, 赵启道, 等. 考虑电转气消纳水电的水-电-气系统低碳鲁棒优化调度[J]. 中国电力, 2022, 55(11): 163-174.
|
| [41] |
李嘉森, 王进, 杨蒙, 等. 基于随机优化的虚拟电厂热电联合经济优化调度[J]. 太阳能学报, 2023, 44(9): 57-65.
针对三北地区现有能源结构调节能力不足而导致的弃风问题,将风电场、光热电站、火电机组和热电联产机组聚合为虚拟电厂。采用随机优化处理风光不确定性问题,通过拉丁超立方抽样生成大量随机风光场景,并在充分考虑风光相关性和分布随机特性的基础上,利用Kantorovich距离削减与K-均值聚类算法对随机场景进行降维优化,获得风电、太阳直接辐照度典型预测场景。结合光热电站的灵活性与供能惯性,构建含光热虚拟电厂热电联合优化调度模型,并建立系统总运行成本最小的目标函数。最后在算例部分验证所提随机优化方法在计算效率、预测精度和处理风光随机问题的优越性;对不同运行模式下的目标函数进行求解,验证所提出的优化调度策略能够在满足系统运行经济性的同时实现风电的最大消纳。
Aiming at the problem of wind curtailment caused by the energy structure lacked the adjustment ability in the three north area, this paper aggregated wind farm, concentrating solar power plant（CSPP）, thermal power units and combined heat and power（CHP） plant into virtual power plant（VPP）. Using stochastic optimization to deal with the uncertainty of wind-solar, Latin hypercube sampling （LHS） was used to generated a large number of random scenes, and based on considering the random characteristics and correlation of wind-solar distribution fully,Kantorovich distance reduction and <em>K</em>-means clustering algorithm were used to optimized and reduced the dimension of random scenes, for obtaining typical prediction wind-solar scenes. Combined with the flexibility and energy supply inertia of CSPP, the optimal dispatching model of the VPP contained photothermal was constructed, and the objective function of minimizing the total operation cost of the system was established. Finally, an example was given to verify the superiority of the proposed stochastic optimization method in computational efficiency and prediction accuracy; The objective functions under different operation scenarios were solved to verify that the optimal dispatching model could improve the wind power consumption capacity while reducing the system operation cost effectively.
|
/
| 〈 |
|
〉 |
