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计及需求响应的电-氢-气综合能源系统分布式鲁棒规划
Research on Distributed Robust Planning of Electric-Hydrogen-Gas Integrated Energy System Considering Demand Response
【目的】 为充分挖掘电-氢-气-储-需求响应之间的耦合灵活性，提出一种基于数据驱动的综合能源系统两阶段分布式鲁棒协同规划模型。【方法】 针对现有的设备建模方法存在模型不精确、求解效率低等问题，提出了一种考虑分布式电源、能源耦合设备、混合储能的精细化模型以及需求响应机制的综合能源系统精细化建模方法，并制定了考虑基线不确定性的需求响应激励机制。【结果】 Matlab仿真结果表明，基于高斯过程回归的基线负荷预测模型可以更精确、更快速地计算基线负荷，同时能够考虑响应不确定性。提出的设备精细化模型有效降低了系统综合规划成本，其中运行、规划、碳交易和需求响应成本分别减少了2.55%、10.78%、1.08%和2.55%。同时，通过碳交易与需求响应机制协同优化，系统可以减少向上层电网购电量，并使用柔性负荷和分布式电源实现综合能源系统低碳稳定运行。算例表明，相比随机优化（stochastic optimization，SO）和鲁棒优化（robust optimization，RO）方法，所提分布式鲁棒优化（distributionally robust optimization， DRO）方法在经济性与稳健性平衡方面更具优势，验证了其在综合能源系统规划中的适用性。【结论】 所构建的计及需求响应的综合能源系统规划模型可以显著降低系统的年综合成本，提高可再生能源利用率，降低碳排放，为后续电-氢-气综合能源系统规划研究提供了思路。
[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.
需求响应 / 电-氢-气综合能源系统规划 / 精细化建模 / 分布式鲁棒
demand response / electric-hydrogen-gas integrated energy system planning / refined modeling / distributed robust
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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. |
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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.
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