构建以新能源为主体的新型电力系统，既是我国电力系统转型升级的重要方向，也是实现碳达峰、碳中和目标的关键途径。本文分析了电力系统转型带来的变化、问题及挑战，阐述了新型电力系统的内涵、构建原则与思路；根据电力系统发展的技术特征、新能源接入规模，合理划分新型电力系统的发展阶段并针对性提出各阶段的发展建议。研究认为，新型电力系统以新能源为电能供给主体，可满足不断增长的清洁用电需求，兼具高度的安全性、开放性、适应性；相关系统构建是一项系统性工程，应遵循电力系统的技术演进规律与特征，充分利用成熟技术、存量系统并深入挖掘潜力，同步着力研发新兴技术，积极稳妥并循序渐进实施重大转型。

Building a new electric power system that is based on new energy sources is an important direction for power system transformation and upgrading in China, and it is critical for peaking carbon emissions and achieving carbon neutrality. In this study, we analyze the changes and challenges that are brought by power system transformation and elaborate on the connotation and building principles of a new electric power system. Moreover, we categorize the development of the new system into stages and propose development suggestions for each stage considering the technical features of the system and the new energy access scale. The new electric power system proposed in this study can satisfy the increasing demand for clean power as it primarily uses new energy sources and it has the features of high safety, openness, and adaptability. Building the new electric power system should follow the technical evolution law and characteristics of power systems; it should further exploit the potentials of mature technologies and current power systems. Meanwhile, emerging technologies should be researched and developed.

随着新型电力系统加速构建,可再生能源发电装机与电量占比不断攀升。如何在源荷双重强不确定性下实现准确、高效、实用的电力电量平衡分析,是新型电力系统调控与规划中的基础与关键问题。通过对电力电量平衡分析在研究对象、分析模型与方法、评价指标与应用等方面的综述,总结了现有研究在平衡状态刻画和分析计算方面的不足。针对“强随机性与策略性下的电力电量平衡状态刻画”与“集中调控-协调自治平衡机制下的电力电量平衡分析与计算”两大关键科学问题,首先提出了涵盖“平衡边界生成-平衡分析计算-平衡状态评估-平衡预警-平衡能力提升”的新型电力系统电力电量平衡分析研究内容体系,接着阐述随机性平衡场景生成、多周期协调解耦的平衡分析、平衡状态评估指标体系设计、多时空分级平衡能力预警和新型平衡协作机制的研究思路与技术方案。最后总结并展望了新型电力系统电力电量平衡分析研究中需要重点突破的关键技术。

With the accelerated construction of new types of power systems, the proportion of integrated renewable energy sources has increased rapidly. Achieving an accurate, efficient, and practical power balance analysis under strong uncertainty of the source and load is a basic theme in the dispatching, controlling, and planning of new power systems. Hence, the shortcomings of current research in the description and calculation of the balance state are reviewed by summarizing the research objects, analytical models and methods, evaluation indices, and applications of power and quantity balance analyses. Based on the two key scientific issues of “electric power and energy balance state description under strong randomness and strategy” and “electric power and energy balance analysis and calculation under the centralized regulation-coordination autonomous balancing mechanism,” a research theoretical system for electric power and energy balance is proposed. First, this system covers “balance boundary generation-balance analysis and calculation-balance state assessment- balance early warning-balance capacity improvement.” Then, research ideas and technical schemes are proposed, including random balanced scenario generation, multi-period coordination decoupling balanced analysis, balanced state evaluation index system design, multi-spatial-temporal hierarchical balanced early warning, and balanced cooperation mechanism construction. Finally, some key technologies are worthy of consideration in further research on electric power and energy balance analyses in new types of power systems.

高比例新能源接入区域系统强度弱,已经逐步成为制约新能源消纳的重要因素,因此亟需提出一种适用于新能源集群弱电网系统的新能源消纳能力评估方法。首先基于容量短路比指标计算方法,并以临界短路比指标约束建立了新能源消纳能力与新能源并网点系统强度之间的关联,阐述了弱电网系统制约新能源消纳的机理,并分析了无功补偿方式对弱电网系统新能源消纳的提升机理;然后,在传统时序生产模拟算法基础上,加入短路比约束,提出一种考虑短路比约束的时序生产模拟模型,以准确评估弱电网系统新能源消纳能力。最后,结合我国“三北”地区某新能源汇集送端系统工程,开展了基于短路比约束的弱电网系统新能源消纳评估应用研究,并进一步量化分析了各无功补偿方式对新能源消纳提升的效果。

The high proportion of new energy access to regional systems is weak and has gradually become a critical factor restricting the consumption of new energy. Therefore, there is an urgent need to propose a new energy consumption capacity evaluation method suitable for new weak energy cluster grid systems. First, based on the calculation method of the capacity short-circuit ratio (SCR-S) index and the constraint of the critical short-circuit ratio (CSCR) index, the relationship between the new energy consumption capacity and the strength of the grid-connected system is established. The mechanism of the weak grid system that restricts new energy consumption is expounded, and the improvement mechanism of reactive power compensation for the new energy consumption of the weak grid system is analyzed. Second, based on the traditional time-series production simulation algorithm, a short-circuit ratio (SCR) constraint is added, and a time-series production simulation model considering the SCR constraint is proposed to accurately evaluate the new energy consumption capacity of the weak grid system. Finally, combined with anew energy collection and transmission system project in the three northern regions of China, the application research of new energy consumption evaluation of weak grid systems based on the SCR constraint is conducted, and the effect of reactive power compensation methods on new energy consumption is further analyzed quantitatively.

能源互联网的建设，将物联网、人工智能、云计算等技术融入电网。虚拟电厂作为能源互联网的基本单元，其聚合、运行方式也将迎来改变。针对虚拟电厂如何有效参与电网运行，提出一种基于主成分分析降维和凝聚层次聚类与k-means聚类相结合的虚拟电厂负荷曲线聚类方法，并对聚类结果的应用进行了研究。首先，结合信息物理网络所获数据，采用主成分分析方法对参与虚拟电厂聚合的不同负荷的特征进行分析，对数据进行标准化处理并降低维度；然后， 利用凝聚层次聚类和k-means聚类相结合的算法，对所有参与聚合的负荷出力曲线进行聚类，得到同类别的负荷曲线簇并找出聚类中心；最后，分析聚类结果，建立与之匹配的评价体系，通过综合评价选取合适的负荷组合参与虚拟电厂聚合。

The construction of energy internet integrates the Internet of Things， artificial intelligence， cloud computing and other technologies into the power grid. As the basic unit of energy internet， virtual power plant (VPP) will change its aggregation and operation mode. In view of how virtual power plants can effectively participate in power grid operation， this paper proposes a VPP load curve clustering method based on principal-component dimension-reduced analysis， aggregation level clustering and k-means clustering， and studies the application of the clustering results. Firstly， combined with the data obtained from the information physical network， the principal-component analysis method is adopted to analyze the characteristics of different loads participating in the VPP aggregation， so as to standardize the data and reduce the dimension. Then， the algorithm combining aggregation hierarchical clustering and k-means clustering is used to cluster all load output curves participating in the aggregation， to obtain load curve clusters of the same class and find out the clustering center. Finally， the clustering results are analyzed， and the corresponding evaluation system is established. Through comprehensive evaluation， appropriate load combinations are selected to participate in the VPP aggregation.

In renewable energy generation, uncertainties mainly refer to power output fluctuations caused by the intermittency, variability, and forecasting errors of wind and photovoltaic power. These uncertainties have adverse effects on the secure operation of the power systems. Probabilistic load supply capability (LSC) serves as an effective perspective for evaluating power system security under uncertainties. Therefore, this paper studies the influence of renewable energy generation on probabilistic LSC to quantify the impact of these uncertainties on the secure operation of the power systems. Global sensitivity analysis (GSA) is introduced for the first time into probabilistic LSC evaluation. It can quantify the impact of renewable energy generation on the system’s LSC and rank the importance of renewable energy power stations based on GSA indices. GSA necessitates multiple rounds of probabilistic LSC evaluation, which is computationally intensive. To address it, this paper introduces a novel probabilistic repeated power flow (PRPF) algorithm, which employs a basis-adaptive sparse polynomial chaos expansion (BASPCE) model as a surrogate model for the original repeated power flow model, thereby accelerating the probabilistic LSC evaluation. Finally, the effectiveness of the proposed methods is verified through case studies on the IEEE 39-bus system. This study provides a practical approach for analyzing the impact of renewable generation uncertainties on power system security, contributing to more informed planning and operational decisions.

针对当前含风电电力系统中大规模新能源难以消纳及钢铁企业缺乏参与调度积极性的问题，提出计及钢铁企业参与的含风电系统协同调度策略。分析钢铁企业参与风电消纳的模式，基于电弧炉的调度特性对其进行建模；采用数据驱动方法进行大规模场景生成，分析电弧炉与风电出力的不确定性叠加影响，确定电力系统应对不确定性波动所需的备用容量；基于生成的场景集，提出计及风电出力变化与源荷波动的钢铁企业自适应电价机制，激励其参与需求响应；建立兼顾多方利益的优化调度模型并采用人工蜂群算法求解；通过算例对所提策略进行仿真验证。结果表明，所提方法可以在保证钢铁企业调度积极性的同时提高系统的经济性与风电消纳。

To address the challenges of large-scale renewable energy consumption in the current wind power integrated power systems and the lack of enthusiasm among steel enterprises to participate in scheduling， a coordinated scheduling strategy of wind power systems considering the participation of steel enterprises was proposed. The participation modes of steel enterprises in wind power consumption were analyzed， and modeling was conducted based on the scheduling characteristics of electric arc furnace. The data-driven method was used to generate large-scale scenarios， analyze the combined effect of uncertainties of electric arc furnace and wind power output， and determine the reserve capacity required by the power system to cope with uncertainty fluctuations. Based on the generated scenario set， an adaptive electricity price mechanism for steel enterprises was proposed， which considered the variation of wind power output and source-load fluctuation to encourage their participation in demand response. An optimal scheduling model considering the interests of multiple stakeholders was established and solved by the artificial bee colony algorithm. Numerical examples were then provided to simulate and verify the proposed method. The results showed that the proposed method could improve the economic efficiency and wind power consumption of the system while ensuring the scheduling enthusiasm of steel enterprises.

针对综合能源系统负荷预测存在的多源异构数据融合与不确定性量化机制不足问题，利用卷积神经网络（CNN）提取负荷数据局部特征，通过双向长短期记忆网络（BiLSTM）捕捉双向时序依赖，采用随机森林（RF）处理高维非线性关系并借助核密度估计（KDE）量化预测不确定性，从而建立CNN-BiLSTM-RF-KDE混合模型；同时，构建电-热-气多能流耦合模型，分析不同碳价区间对调度策略的影响。算例分析显示：训练集上电、热负荷预测的决定系数分别为0.93，0.96；测试集上电、热负荷预测的决定系数分别为0.79，0.84。预测出的各设备发电、发热量与均值趋势高度吻合，表明运用该模型能够得出更接近准确值的负荷量，以此为基础数据，可以对综合能源系统进行更为可靠的分析与调度。

To address the challenges of insufficient multi-source heterogeneous data fusion and uncertainty quantification in load forecasting for integrated energy systems， a hybrid CNN-BiLSTM-RF-KDE model was proposed. The model utilized convolutional neural network（CNN） to extract local features of load data， bidirectional long short-term memory （BiLSTM） to capture bidirectional temporal dependencies， random forest（RF） to handle high-dimensional nonlinear relationships， and kernel density estimation（KDE） to quantify prediction uncertainty. Additionally， an electricity-heat-gas multi-energy flow coupling model was established to analyze the influence of different carbon price intervals on scheduling strategies. Case studies demonstrated that the coefficient of determination（ R²） for electrical and heating load forecasting reached 0.93 and 0.96 on the training set， and 0.79 and 0.84 on the test set， respectively. The predicted power generation and heat output of each device closely aligned with the mean trend， indicating that the model provided more accurate load predictions. Based on this data， more reliable analysis and scheduling of integrated energy systems could be achieved.

针对含有风电、光伏及电动汽车充电站的新能源电力系统，提出了一种考虑新能源出力相关性的电网电压暂降随机预估方法。首先构建了故障信息随机模型及新能源出力随机模型，采用拉丁超立方采样（Latin hypercube sampling，LHS）得到故障信息样本，利用皮尔森相关分析法确定同一类型新能源出力的相关系数后，采用Nataf变换及LHS法得到含有相关性的新能源随机出力样本；然后通过故障仿真模拟，得到一系列暂降事件，计算各节点电压暂降指标，预估各节点电压暂降情况；最后以IEEE 30节点为例进行了仿真分析，研究相关系数变化下的新能源出力及不同类型新能源的接入对电网电压暂降的影响。

For the electric power system with renewable energy sources such as wind power and photovoltaic power, a stochastic estimation method for voltage sag considering output correlation of renewable energy sources is proposed. Firstly, the stochastic models of short circuit fault information and the output stochastic models of renewable energy sources are established. Secondly, the fault information samples are obtained using the Latin hypercube sampling （LHS）. The correlation coefficient of the renewable energy sources output of the same type is determined by the Pearson correlation analysis method, and then the output samples of renewable energy sources are obtained through the Nataf transformation and LHS. Thirdly, through the fault simulation, a series of voltage sag events can be got, the sag indices of each node can be calculated, and sags at each node can be estimated. The IEEE 30-bus system is taken as an example, the influences of the renewable energy sources output under the change of correlation coefficient and different types of renewable energy sources on the voltage sag are studied.

精准的多元负荷短期预测是综合能源系统调度和运行的基础。综合能源系统中的多种负荷之间存在较强的耦合作用,目前已有的单一负荷预测难以挖掘不同负荷之间复杂的内在联系。对此,提出一种基于多头概率稀疏自注意力模型的多元负荷短期预测方法。首先,采用皮尔逊相关系数分析多元负荷之间的相关性,并提取多元负荷之间的耦合特征;然后,使用改进位置编码的多头概率稀疏自注意力机制学习长序列输入的依赖关系,并且采用多元预测任务的参数软共享机制,通过不同子任务对共享特征的差异化选择,实现多元负荷的联合预测;最后,在亚利桑那州立大学Tempe校区的多元负荷数据集上对所提模型的性能进行验证,结果表明所提预测方法相较于其他预测模型能够有效提高预测精度。

Accurate short-term forecasting of multienergy loads is the basis for the dispatch and operation of integrated energy systems. There is a strong coupling between multiple loads in an integrated energy system, and the existing single load forecasting is challenging to explore the complex internal relationship between multiple loads. Therefore, a short-term forecasting method for multienergy loads based on a multihead probabilistic sparse self-attention (MPSS) model was proposed. First, the Pearson correlation coefficient was used to analyze the correlation between multiple loads, the coupling features between multiple loads were extracted, a multihead probabilistic sparse self-attention mechanism with improved location coding was used to learn the dependencies of long-sequence inputs, and the parameter soft sharing mechanism of multivariate prediction tasks was adopted. The sharing mechanism realizes the joint prediction of multiple loads through a differentiated selection of shared features using different subtasks. Finally, the performance of the proposed model was verified using the multiple-load dataset of the Tempe Campus of Arizona State University. Compared with other forecasting models, the results show that the proposed multivariate load forecasting method can effectively improve forecasting accuracy.

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset's meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.© 2024. The Author(s).

This paper proposes a distance-based distributionally robust energy and reserve (DB-DRER) dispatch model via Kullback–Leibler (KL) divergence, considering the volatile of renewable energy generation. Firstly, a two-stage optimization model is formulated to minimize the expected total cost of energy and reserve (ER) dispatch. Then, KL divergence is adopted to establish the ambiguity set. Distinguished from conventional robust optimization methodology, the volatile output of renewable power generation is assumed to follow the unknown probability distribution that is restricted in the ambiguity set. DB-DRER aims at minimizing the expected total cost in the worst-case probability distributions of renewables. Combining with the designed empirical distribution function, the proposed DB-DRER model can be reformulated into a mixed integer nonlinear programming (MINLP) problem. Furthermore, using the generalized Benders decomposition, a decomposition method is proposed and sample average approximation (SAA) method is applied to solve this problem. Finally, simulation result of the proposed method is compared with those of stochastic optimization and conventional robust optimization methods on the 6-bus system and IEEE 118-bus system, which demonstrates the effectiveness and advantages of the method proposed.

我国风能、太阳能资源富集地与负荷存在时空错配问题，利用同一区域内风能和太阳能之间的相关性与互补性，采用电-氢协同储能模式，是缓解可再生能源出力对电网不良影响的有效技术路径。使用非参数核密度估计法拟合风、光出力数据的概率分布规律，结合Copula理论生成计及风光时空相关性的时序场景；考虑风、光发电的相关性，进一步建立了电-氢协同储能综合能源系统的经济性日前优化调度模型，并使用自适应模拟退火粒子群优化（ASA-PSO）算法求解。仿真结果表明：相比于基本PSO算法，ASA-PSO算法具有更高的求解速度与精度；电-氢协同储能系统的日前经济优化调度方案节省了约19%的日运行成本，可以避免系统在电价高峰时段大量购入电力并实现了波动性新能源的就地消纳，可为友好型规模化电网的构建提供电-氢柔性匹配方法。

In China， there is a spatiotemporal mismatch between the areas rich in wind and solar energy resources and the load centers. By leveraging the correlation and complementarity between wind and solar energy within the same region， an electricity-hydrogen coordinated energy storage model was proposed， which was an effective technical approach to mitigate the adverse impact of renewable energy output on the power grid. The probability distribution patterns of wind and photovoltaic power output data were fitted using the nonparametric kernel density estimation method， and time-series scenarios considering the spatiotemporal correlation of wind and photovoltaic power were generated using Copula theory. By considering the correlation between wind and photovoltaic power generation， an economic day-ahead optimal scheduling model for the integrated electricity-hydrogen coordinated energy system was further established， and solved using the adaptive simulated annealing particle swarm optimization （ASA-PSO） algorithm. The simulation results showed that compared to the basic PSO algorithm， the ASA-PSO algorithm demonstrated superior solving speed and accuracy. The economic day-ahead optimal scheduling scheme for the electricity-hydrogen coordinated energy storage system reduced daily operating costs by approximately 19%， avoided large-scale electricity purchases during peak price periods， and enabled local consumption of fluctuating renewable energy. It provides a flexible electricity-hydrogen matching approach for constructing a grid-friendly and large-scale power system.

面对日益严峻的短路电流超标、交直流交互影响、第三道防线难以有效设置等问题,广东等多直流馈入受端电网分区构网需求迫切。为协调分区与系统稳定、网架可靠性等方面的矛盾,支撑区内电力消费增长和大规模清洁能源接入,提出了建设大湾区外环的设想,并对广东电网大湾区外环覆盖范围、分区划分和外环容量选择等关键问题进行了具体论证,形成了基于大湾区外环的柔性直流互联构网方案。经仿真分析,该方案符合安全标准,可提升广东电网容量至预期水平且有效应对了短路电流超标、交直流交互影响突出、第三道防线难以有效设置等问题。

Facing the increasingly severe problems like short circuit current exceeding limit, serious AC-DC interaction and difficulty in setting up the third defensive line, system partition in multi-DC infeed receiving power grids like Guangdong power system is urgently required. In order to solve the contradiction between system partition and system stability and reliability, and support regional power consumption growth and large-scale clean energy access, an idea of building the outer ring of the Guangdong-Hong Kong-Macao Greater Bay Area is proposed. Key issues like coverage of the outer ring of the Bay, partition scheme of Guangdong power grid and capacity selection of the outer ring are specifically discussed. Then the flexible DC interconnection network scheme based on the outer ring of the Guangdong-Hong Kong-Macao Greater Bay Area is formed. Simulation analysis indicate that the scheme could meet the relevant safety standards, the capacity of Guangdong power grid could be increased to the expected level and problems such as short circuit current exceeding standards, serious AC-DC interaction and difficulty in setting up the third defensive line are effectively dealt with.