随着新能源发电技术的高速发展,电网新能源消纳压力日益凸显。与此同时,随着多能转换技术发展,电网与其他类型能源网络的耦合程度不断提高,如何利用不同能源网络灵活性资源消纳受阻新能源,成为当前亟待研究的问题。为此,提出了计及综合需求响应参与消纳受阻新能源的多时间尺度优化调度策略。首先,充分考虑综合能源系统(integrated energy system,IES)特性建立了冷、热、电负荷的多类型需求响应模型。其次,在日前时间尺度,考虑新能源消纳过程中各方利益均衡,建立了基于主从博弈理论的价格型综合需求响应(integrated demand response, IDR)日前优化调度模型;在日内时间尺度,针对新能源日前预测偏差对系统优化影响,建立了考虑激励型IDR日内滚动优化调度模型。最后,通过算例仿真证明了所提策略可深入挖掘多类型负荷需求响应能力,有效提高新能源消纳量。

<p id="p00010">With the rapid development of new energy generation technology, the pressure of new energy consumption in the power grid is increasingly prominent. At the same time, with the development of multi-energy conversion technology, the degree of coupling between power grids and other types of energy networks is increasing. How to use the flexibility resources of different energy networks to dissipate the blocked new energy has become an urgent issue to be studied. This paper proposes a multi-timescale trading strategy that takes into account the multi-energy demand response to participate in the consumption of blocked new energy sources. First, a multi-type demand response model for cold, heat and electric loads is established with full consideration of the characteristics of integrated energy system (IES). Secondly, a price-based integrated demand response (IDR) day-ahead optimal scheduling model based on master-slave game theory is established in the day-ahead time scale, considering the equilibrium of interests of all parties in the process of new energy consumption. At the intra-day time scale, an incentive-based IDR intra-day rolling optimization scheduling model is developed to address the impact of the deviation from the new energy day-ahead forecast on system optimization. </p> <p>Finally, the effectiveness of the strategy proposed in this paper is verified by case simulation. This work is supported by National Key R&#x00026;D Program of China (No. 2018YFE0208400).</p>

在清洁能源大规模接入和电网调峰压力日益加剧的背景下,储能装置和深度调峰火电机组作为促进新能源消纳和平抑电网峰谷差的重要调节资源,已受到学术界和工业界的广泛关注。文章综合考虑了多类型储能装置的自身特点,并结合了火电机组的深度调峰特性,对不同类型储能装置参与调峰的经济效益进行了深入分析。首先,选取并建立了最具代表性的3种能量型储能系统(抽水蓄能、压缩空气储能和锂离子电池储能)的运行模型;其次,考虑火电机组的深度调峰特性,建立了火电机组的运行模型和经济性模型;再次,考虑多类型储能系统的全寿命周期成本,构建了考虑火电机组深度调峰的多类型储能系统日前经济调度模型,并据此开展了多类型储能经济性对比分析;最后,基于某地区的典型日数据开展了算例仿真,对比分析了各类型储能电站的经济效益并给出了储能容量配置建议。

In the context of the large-scale access of clean energy and the increasing pressure of peak-shaving power grids, energy storage devices and deep peak-shaving thermal power units, as important adjustment resources to promote the absorption of new energy and suppress the peak-valley difference of the power grid, have been widely concerned by academia and industry. Considering the characteristics of multi-type energy storage devices and combined with the deep peak-shaving characteristics of thermal power units, this paper compares and analyzes the economic benefits of different types of energy storage devices assisting deep peak-shaving of thermal power units. Firstly, by fully considering the technical, economic characteristics and development prospects of multi-type energy storages, the most representative three kinds of energy storage systems: pumped storage, compressed-air energy storage and lithium-ion battery energy storage are selected, and the relevant system operation models are established. Secondly, considering the deep peak-shaving characteristics of thermal power units, the operation model and economic model of thermal power units are established. Thirdly, considering the life-cycle cost of multiple-type energy storage systems, a day-ahead economic dispatch model of multi-type energy storage systems considering the deep peak-shaving of thermal power units is constructed, and a comparative analysis of the economics of multi-type energy storage is carried out accordingly. Finally, on the basis of the typical daily data in a certain area, a simulation example is carried out to compare and analyze the economic benefits of multi-type energy storage power stations, and the recommendations of energy storage capacity configuration are put forward.

随着可再生能源在电力系统中所占的比例日益提高,电力系统的电源结构发生了巨大变化,可再生能源的高渗透性、出力的随机性和不确定性,使得电源侧、负荷侧对电力系统调峰资源需求问题逐渐凸显。首先,总结可再生能源时序发电特性和空间分布特性,对高比例可再生能源电力系统的调峰问题进行了说明。然后,对多种调峰储能类型的配备、特点、发展趋势等研究成果进行总结与归纳,对需求侧参与电力系统调峰方式进行归纳总结,包括需求响应建设、发展电力市场和辅助调峰服务市场以及增强电力传输能力。最后,对未来高比例可再生能源电力系统调峰问题研究前景进行了讨论与展望。

Power supply structure has undergone tremendous changes with the increasing proportion of renewable energy in the power system. The high permeability, randomness and uncertainty of the output of renewable energy cause problems increasingly prominent, such as demand of power system peak-shaving resources on both power supply and load side. Firstly, this paper summarizes temporal and spatial distribution characteristics of renewable energy, and explains the peak-shaving problem of power system with high proportion of renewable energy. Then, the equipment, characteristics and development trend of various peak-shaving energy storage types are summarized. The paper also elaborates the peak-shaving modes of the demand side in the power system, including demand response construction, power market and auxiliary peak-shaving service market, and enhancing power transmission capacity. Finally, the future research prospects of peak-shaving of power system with high proportion of renewable energy are discussed and prospected.

High voltage direct current (HVDC) transmission is an economical option for transmitting a large amount of power over long distances. Initially, HVDC was developed using thyristor-based current source converters (CSC). With the development of semiconductor devices, a voltage source converter (VSC)-based HVDC system was introduced, and has been widely applied to integrate large-scale renewables and network interconnection. However, the VSC-based HVDC system is vulnerable to DC faults and its protection becomes ever more important with the fast growth in number of installations. In this paper, detailed characteristics of DC faults in the VSC-HVDC system are presented. The DC fault current has a large peak and steady values within a few milliseconds and thus high-speed fault detection and isolation methods are required in an HVDC grid. Therefore, development of the protection scheme for a multi-terminal VSC-based HVDC system is challenging. Various methods have been developed and this paper presents a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multi-terminal network configurations.

针对传统DBSCAN算法需要人工输入[Eps]和[MinPts]参数，且参数选择不合理导致聚类准确率低的问题，提出了一种改进的自适应参数密度聚类算法。采用核密度估计确定[Eps]和[MinPts]参数的合理区间，通过分析数据局部密度特点确定簇数，根据合理区间内的参数值进行聚类，计算满足簇数条件时的轮廓系数，最大轮廓系数对应的参数即为最优参数。在4种经典数据集上进行对比实验，结果表明，该算法能够自动选择最优的[Eps]和[MinPts]参数，准确率平均提高6.1%。

Aiming at the problem that traditional DBSCAN algorithm needs to input [Eps] and [MinPts] parameters manually, and improper parameter selection leads to low clustering accuracy, an improved adaptive parameter density clustering algorithm is proposed. Firstly, the kernel density estimation is used to determine the reasonable interval of [Eps] and [MinPts] parameters, and the cluster number is determined by analyzing the local density characteristics of the data. Then, the clustering is performed according to the parameter values within the reasonable interval. Finally, the contour coefficients satisfying the cluster number condition are calculated, and the parameter corresponding to the maximum contour coefficient is the optimal parameter. The comparison experiments on four classical datasets show that the algorithm can automatically select the optimal [Eps] and [MinPts] parameters, and the accuracy is improved by 6.1% on average.

随着我国新型电力系统建设进程的不断推进,电力系统运行工况的不确定性大大增加,抗扰动能力更低,连锁故障发生率更高,演化时间更短。传统的基于潮流计算的方法依赖对场景的枚举考虑随机因素,时效性和准确率都难以应对高占比新能源电网复杂的运行工况,因此,提出一种挖掘历史数据规律的基于多尺度特征集的高占比新能源电网连锁故障数据驱动辨识方法,充分考虑历史运行数据中包含的随机信息。从宏观、中观、微观3个层次提取能够表征复杂网络拓扑特征与系统运行状态的特征指标,构成特征指标集;基于双向长短期记忆神经网络学习历史连锁故障过程中复杂网络特征指标与系统运行状态间的映射关系,构建高占比新能源电网连锁故障辨识模型,并通过新疆电网拓扑验证了所提模型的有效性。

With the continuous advancement in the renewable power system construction process, the uncertainty of system operating conditions has significantly increased, resulting in a lower antidisturbance ability, higher cascading failure occurrence rate, and shorter evolution time. The traditional power-flow calculation method relies on the enumeration of scenarios to consider random factors, making it difficult to cope with the complex operating conditions experienced with a high percentage of new energy grids in terms of timeliness and accuracy. Therefore, a multi-scale feature-set-based data-driven method for identifying cascading faults under conditions of having a high percentage of new energy grids is proposed by mining historical data patterns and fully considering the stochastic information contained in historical operation data. Indexes that can describe the topological characteristics of a complex network and the operation state of the system are extracted from the macro-, meso-, and micro-levels to form the index set. Based on a bidirectional long-term and short-term memory neural network, the mapping relationship between the index set and the system operation state is studied using the historical cascading failure dataset. This enables a cascading fault prediction model of a high-proportion renewable power system to be constructed. The effectiveness of the proposed model is verified using the topology of the Xinjiang power-grid system.

发展新能源是应对环境污染和能源危机的根本性措施,有助于推动“双碳”目标的实现。可再生能源发电固有的间歇性、波动性给电网规划和运行带来严峻挑战。针对高比例新能源电力系统,分析了以最小功率波动为目标的新能源和储能容量最佳配比。立足风光资源的互补性,构建了最小化系统功率波动的风光最优配比模型,并提出了基于线性规划的求解算法。最后建立了面向平滑新能源出力的储能容量配置参数线性规划模型,得到波动性指标关于储能容量的解析表达式,并根据成本确定了最优储能容量。所提方法为政策制定提供了可视化工具以及比单一最优解更加丰富的信息。

The development of renewable energy is a fundamental measure to resolve environmental pollution and energy crisis, and achieve the carbon peaking and carbon neutrality goals. However, the inherent volatility and fluctuation of renewable energy output bring unprecedented challenges to the planning and operation of the power grid. This paper studies the optimal ratio of renewable energy and energy storage, aiming to minimize power fluctuation. According to the complementary nature of wind and solar resources, the mode of optimal ratio of wind and solar power that leads to minimal power fluctuation is established and is further transformed into linear programming. The optimization problem of energy storage capacity aiming to smooth the renewable energy output is formulated as a multi-parameter linear program, where storage charging power and energy capacities are parameters. The power fluctuation index is expressed as an analytical function in storage parameters, which is convex and piecewise linear. On the basis of the fluctuation index function, the optimal storage capacities can be determined according to the costs. The proposed method provides an illustrative tool and more abundant information for policy and decision-making.

随着&#x0201c;双碳目标&#x0201d;的提出和可再生能源渗透率的增加,确保电力系统灵活运行变得愈加重要。针对源、荷、储等多类分布式灵活性资源,构建了电力市场环境下计及风光不确定性和共享储能的虚拟电厂(virtual power plant,VPP)运行优化和双层效益分配模型。首先,构建了含分布式风光机组、共享储能、柔性负荷的VPP系统结构,并建立了两级电力市场交易机制。其次,利用场景生成与削减的方法处理了风光出力不确定性,在此基础上以VPP运行收益最大化为目标,构建了考虑日前-实时交易的VPP运行优化模型。再次,为保障参与主体收入公平性和合理性,基于双层合作博弈Owen值法,分别建立了VPP参与主体收益分配策略和共享储能投资主体收益分配策略。最后,通过算例分析验证了所提模型的有效性。算例结果表明含共享储能的VPP能够有效降低风光不确定性造成的干扰,提高VPP在日前-实时两级市场中的收益;通过Owen值法对各投资主体进行效益分配,有助于保障系统的公平合理性。

With the proposal of "double carbon target" and the increase of renewable energy penetration rate, it becomes more and more important to ensure the flexible operation of power system. In order to aggregate multiple types of distributed flexible resources such as power generators, load and storage, this paper constructs an operation optimization and two-layer benefit allocation models for virtual power plants (VPP), which considers the uncertainty of wind and solar power and shared energy storage under the environment of electricity market. Firstly, a VPP system structure including distributed PV and wind power generators, shared energy storage and flexible load is constructed, and a two-level power market trading mechanism is established. Secondly, the method of scenario generation and reduction is used to deal with the uncertainty of wind and solar power output. On this basis, the VPP operation optimization model considering day-ahead and real-time trades is constructed with the goal of maximizing VPP operation income. Thirdly, in order to guarantee the fairness and rationality of participants' income, the income distribution strategies for VPP participants and shared energy storage investors are established on the basis of the Owen value method of two-layer cooperative game. Finally, an example analysis is carried out to verify the effectiveness of the proposed model. The example results show that VPP with shared energy storage can effectively reduce the interference caused by wind and solar power uncertainty and increase the income of VPP in two-level market. The income distribution of each investment subject through Owen value method is conducive to ensuring the fairness and rationality of the system.