In the transient stability analysis,the grid-forming converter considering current limiting will appear as a switching system,and its switching mechanism is still unclear.Aiming at a dual power synchronization loop(PSL)system considering current limiting,where the current limiting measure uses a current saturation algorithm.The switching boundaries of the dual-machine system in terms of phase angle are derived.It is found that the dual-machine system considering current limiting has four working states and four switching boundaries within a phase angle period,and the positions of two switching boundaries are related to the saturation current angle settings of the two converters.Based on this,the transient stability of the studied system is further analyzed,obtaining the stable equilibrium points of the four working states.The stable equilibrium points are configured by changing the current saturation angle set values of the two converters,which can avoid the situation that one converter is locked in the current limiting control mode.The simula-tion verifies the correctness and effectiveness of the proposed theory.
A dual-loop current control is discussed for LCL -type shunt active power filters (APFs), where inverter-side current feedback and point of common coupling (PCC) voltage feedforward both provide active damping (AD) effects for LCL resonance. However, two ADs are equivalently applied to different elements of LCL filter with narrow damping boundaries, which causes less robustness against grid impedance variation. Hence, this paper has presented a comprehensive approach to strengthen the system robustness against grid impedance variation. On the basis of phase compensated resonant unit, a delay-compensation scheme is firstly proposed to extend the damping boundary of inverter-side current feedback up to almost Nyquist frequency ( $f_{s}/2$ ). Theory analysis considering PCC voltage feedforward indicates that the control system can keep robust to grid impedance when the initial LCL resonance frequency falls between $f_{s}/3$ and the extended damping boundary. Moreover, a typical weighted proportional-resonant feedforward scheme is suggested to prevent high-frequency resonant units from inducing system instability under a large grid impedance. We finally validate the effectiveness of the proposed approach through experiments.
Under inverter operating conditions, significant loss is incurred by the insulated gate bipolar transistor (IGBT) (referred to as T2 tube) in the lower part of the half-bridge submodule. The reduction of loss is beneficial for the improvement of equipment operation reliability. At the same time, the suppression of capacitor ripple voltage has the advantage for reducing capacitor demand and enhancing power density. However, attention is not given by existing optimization control strategies to the contradiction between loss distribution optimization and capacitor ripple voltage, making it difficult to balance equipment operation reliability and power density. Therefore, a comprehensive optimization method that combines the reduction of T2 transistor losses and the suppression of capacitor voltage ripple is proposed in this article. Firstly, the inherent contradiction between reducing the on-state loss of the T2 transistor and suppressing capacitor voltage ripple is explained by analyzing the impact path of charge on device loss and capacitor ripple voltage. Then, by introducing a penalty function, a comprehensive objective function is established that takes into account T2 transistor losses and capacitor voltage ripple. Subsequently, using the active bypass strategy as an example, a comprehensive optimization method based on the injection of second harmonic current and third harmonic voltage is proposed by analyzing the impact of the second harmonic current and third harmonic voltage injection on T2 transistor loss and capacitor voltage ripple. Finally, a simulation model is built in MATLAB/Simulink and PLECS for verification. The simulation results suggest that the reliability and power density of the device increase by the comprehensive optimization method, considering both T2 transistor losses and capacitor voltage ripple.
With the proposal of the “dual-carbon” target and the rapid development of distributed generation technology, distribution areas (DAs) are faced with the problems of difficult to expand capacity, difficult to consume distributed generation, unable to effectively use the remaining capacity of each DA, etc. The flexible interconnection system (FIS) has gradually become a solution to improve the power supply level of DAs. To fully utilize the advantages of the flexible power flow control of FIS, a $v_{\mathrm {{dc}}}-P_{\mathrm {{grid}}}$ droop control strategy is proposed, which takes into account load balancing. This strategy balances the AC source output of each DA during normal operation. Subsequently, a simplified circuit model of the system is developed, and the stability of the system is analyzed under large disturbances when a constant power load is connected to the DC bus, using the mixed potential function theory. The stability criteria of the system is obtained, and verified by the real-time hardware-in-loop (HIL) tests.
In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.
Quickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state recognition method for renewable energy units based on sparse stacked auto-encoder(SSAE) feature extraction and improved k-nearest neighbor (KNN) algorithm. The characteristics of this method is that the electrical parameters of the unit port are collected directly without relying on the unit's supervisory control and data acquisition (SCADA) system, whose acquisition speed is too slow to meet the recognition accuracy requirement, and that the unit operation states can be recognized quickly and accurately. Firstly, operation states of renewable energy unit are divided, and the framework for the unit's state recognition is proposed. Moreover, improved strategies for state recognition of renewable energy unit are proposed. Finally, the power system analysis software package (PSASP) is used to obtain the electrical parameters of renewable energy units and the improved KNN algorithm is used to recognize operation states after extracting features based on SSAE. By comparing the method proposed with the traditional KNN algorithm, the effect of the proposed method for states recognition is shown to be the best, with an accuracy of 98.16% and computing time of 50ms. The results show the validity of the proposed method.
Integrated electricity and natural gas system (IEGS) plays an important role in increasing efficiency of energy usage. Uncertain power flow calculation is helpful to system optimization and scheduling with increasingly prominent uncertainty. In this paper, an affine energy flow algorithm for integrated electricity and natural gas system is proposed to analyze the system uncertainty. First, the affine models and energy flow equations of the system are established. Secondly, the prediction-correction affine energy flow algorithm is proposed. The algorithm predicts the state variable according to the sensitivity analysis, and introduces correction coefficients to compress the predicted state variable. At the same time, an improved decomposition method of multi energy flow is adopted, without considering the calculation order of multi energy flow in the complex coupling network. Finally, the case simulation verifies the advantages of the affine power flow method in conservatism, computational efficiency and tracking the source of uncertainty.
There are inevitable multiple uncertainties in multi-energy microgrid. In order to formulate a reasonable dispatching scheme under the uncertainties, this paper proposed an affine-model predictive control optimal dispatching method for multi-energy microgrid. Firstly, the uncertainties in multi-energy microgrid are represented by affine algorithm, and the interval expansion problem is solved by using the correlation between affine variables. Further, an affine optimal dispatching model of multi-energy microgrid is constructed to ensure the economy and decrease the conservatism of the dispatching scheme. Finally, the model predictive control method is used to update the predictive information, and the affine optimal dispatching model of multi-energy microgrid is solved by rolling. The simulation results show that the proposed method can ensure the economy and decrease the conservatism of the dispatching scheme. Under the premise of ensuring the economic operation of multi-energy microgrid, it can effectively deal with the impact of uncertainties and provide a reasonable optimal dispatching scheme.
When asynchronous motors, especially double-fed asynchronous motors in large capacity pump storage are the main loads in the high voltage direct current (HVDC) receiving end power grid, the increase of the equivalent slip of asynchronous motor load may cause transient voltage instability. In order to recover the voltage rapidly in the grid, the emergency reactive power support needs to be quick and accurate. A method for transient voltage stability emergency control by temporarily reducing DC current is proposed, the inverter station is used as emergency reactive power source for the HVDC receiving end power grid. In detail, firstly, aiming at the quantitative calculation of DC current, a nonlinear optimization model with the optimization variable of DC current and the objective of minimizing energy transmission reduction of HVDC is established. Further, in order to achieve fast solution and meet the accuracy requirements, global orthogonal collocation (GOC) is incorporated into the optimization model to transform the differential equations of both objective function and constraints into algebraic equations, thus the optimization is transformed into a nonlinear programming (NLP) problem, by which the emergency control strategy, in specific, the optimal DC current control scheme is obtained. Finally, the modified IEEE 14 benchmark is used to verify the effectiveness and superiority of the proposed strategy.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Energy storage (ES) can mitigate the pressure of peak shaving and frequency regulation in power systems with high penetration of renewable energy (RE) caused by uncertainty and inflexibility. However, the demand for ES capacity to enhance the peak shaving and frequency regulation capability of power systems with high penetration of RE has not been clarified at present. In this context, this study provides an approach to analyzing the ES demand capacity for peak shaving and frequency regulation. Firstly, to portray the uncertainty of the net load, a scenario set generation method is proposed based on the quantile regression analysis and Gaussian mixture model clustering. Then, a multi-scenario and multi-time scale optimal operation model is established to handle the uncertainty of net load, and the power correction model for ES operations is established to accommodate the balance of ES charging/discharging and optimization of system operation cost. Finally, based on the solution results of the above models, the method for determining the system's demand for ES capacity is proposed, and the relationship between the penetration of RE, ES power and capacity, and the confidence level of meeting demand is obtained. Numerical studies show that with a confidence level of 90% for satisfying demand, the 49.5% RE penetration system (the maximum load is 9896.42 MW) needs ES power and capacity of 1358 MW and 4122 MWh for peaking and ES power and capacity of 478 MW and 47 MWh for frequency regulation. Further, as the penetration of RE increases, the proportion of ES demand power to the system's power supply capacity and duration demand of ES also increase.
Because the traditional fault location method cannot be applied to overhead and cable hybrid transmission line in the VSC-HVDC system, this paper proposes a novel fault location method, which primarily uses the principle of first determining the fault segment and then locating the fault point in the fault segment. Two voltage symbolic functions are defined, and the fault segment is determined according to the different numerical combinations of the two voltage symbolic functions. Through combining adaptive characteristic scale decomposition with improved general local frequency decomposition to adaptively extract the fault characteristic component, the intra-segment location equation is established based on the characteristic component to locate the fault point. The simulation results verify that the proposed method can accurately locate the fault point for the overhead and cable hybrid transmission line in the VSC-HVDC system.
When a serious grid fault occurs, a virtual synchronous generator (VSG) tends to lose transient power angle stability (TPAS) and cause the fault current to exceed the limit. Most existing research usually neglects the inherent relationship between transient power angle instability (TPAI) and fault over-current (FOC), which leads to the two problems being solved separately rather than simultaneously. In this paper, the transient power angle and fault current characteristics of VSG are first studied, and the causes and influencing factors of TPAI and FOC are explored. Then, the TPAS analysis is carried out based on the phase portrait theory. A TPAS control method for VSG considering fault current suppression (FCS) is proposed. This method is fulfilled by the combined regulation of active power reference and reactive voltage regulation coefficient, in which the reference and coefficient can be adjusted adaptively according to different fault degrees and application scenarios. In addition, a quasi-static approximate (QSA)virtual inductance is introduced to limit the instantaneous inrush current (IIC). The proposed method achieves both the TPAS and FCS at the same time during the fault. Finally, simulations and experiments verify the correctness of the theoretical analysis and the proposed method.
通过建模理解电力用户行为，有助于在需求响应中有效引导用户灵活用电，从而挖掘负荷侧的灵活性。面向需求响应场景的实际需求，系统梳理电力用户行为建模的研究现状与应用。分析并总结电力用户行为建模的研究进展，包括用户行为特性描绘、用户行为解析和用户行为定量建模3个不同层次。梳理电力用户行为建模在需求响应中的主要应用方向，包括需求响应潜力评估、考虑需求响应的能源系统协同优化、负荷零售商定价和考虑需求响应的市场机制设计。讨论现有电力用户行为建模研究所面临的不完全信息条件、用户的有限理性等实际问题，并对未来的研究方向进行展望。
压缩空气储能(CAES)是一种大规模物理储能技术,可广泛应用于电网削峰填谷和大规模新能源消纳.当前我国CAES正处于由示范项目向产业化发展的关键阶段,呈现出良好的发展态势.系统总结了国内外CAES工程现状,介绍了已投运的商业电站,并对其在新能源侧的应用前景进行了阐述.进一步,从装机规模、系统效率、应用场景、建设成本等多个方面对其发展趋势进行了介绍.针对CAES发展中遇到的挑战,从电站建设、核心装备、标准体系、价格机制4个角度进行阐述,给出了推动CAES发展的建议,推动其向多元化、规模化、产业化方向发展,成为支撑我国"双碳"目标的关键技术.
给出一种电制燃料设施与煤电机组联合运行的方案,可在产生电力、甲醇等重要二次能源的同时,提高发电系统灵活性.为了对联合机组进行合理调度,实现提升灵活性的同时确保经济性最优,提出了电制燃料设施联合煤电机组参与调频服务的优化经济调度方法.在充分考虑调频服务市场规则的情况下,构建了电制燃料设施与煤电机组联合参与单次调频的优化经济调度模型,相比已有研究该模型更加准确;针对含max函数的NP-hard非线性模型求解困难的问题,提出了采用分类转换并利用半正定松弛对原问题进行松弛,通过高斯随机化求解的方案.算例分析表明所提方法具有准确、高效的优势,为灵活能源设施与煤电机组联合运行的优化经济调度提供了参考.
随着以风光为代表的可再生能源发电比例迅速提升,风电场、光伏电站出力的不确定性和波动性给电力实时平衡带来了极大的挑战,配置合理规模的储能可以保障经济性并提高新能源利用率.为此,针对容量已知的新能源电站,提出了一种配套储能和传输线路容量协调优化配置的机会约束规划模型,以储能和传输线路建设成本最小化为目标,以年新能源弃电率不超过规定的指标为机会约束.由于机会约束非凸且缺乏显式表达,基于条件风险价值将机会约束对应的可行域保守转换为线性约束,得到易于求解的线性规划问题,并量化分析了规划方案对新能源场景概率分布的鲁棒性.算例分析结果表明,所提规划模型可以有效地解决考虑新能源消纳能力的储能和传输线路容量配置问题,通过合理配置储能可以降低传输线路的容量从而节约总投资成本.
The construction of electricity spot market is the key point of China’s electricity system further reform. The first batch of eight pilot provinces have carried out beneficial exploration on the construction of electricity spot market,but they have also encountered some problems such as unclear price signals,no involvement of users in some provinces,unbalance of settlement funds,and so on. As a non-pilot province,Heilongjiang has unique difficulties,such as single type of power supply,lack of flexible resources,high proportion of new energy and heating units,prominent peak shaving issue,and so on. Based on the experience of pilot provinces in constructing electricity spot market,the advantages and disadvantages of different market modes are analyzed,and combining with the actual situation of electric power production in Heilongjiang province,some suggestions on market system design and participation of users and new energy sources in the electricity spot market are given. The different clearing methods of day-ahead market are compared to derive the construction path for Heilongjiang day-ahead market. The generation mechanism of the two-track unbalanced funds and the market-oriented unbalanced funds in the pilot provinces is analyzed,the method of decoupling plan and market and the distribution method of base electricity are designed,and an improved settlement mechanism based on the pilot province is obtained,so as to promote the healthy development of the construction of Heilongjiang electricity spot market.
概率最优潮流需要对非线性最优潮流问题进行重复求解,计算量较大,从而限制了其应用.提出一种基于特征降维、分块和深度神经网络辅助预测的最优潮流两阶段求解方法.在第一阶段,提出基于深度神经网络的最优潮流部分关键决策变量的优先辨识策略,以解决深度学习中因特征维度过高而导致的数值湮没问题,进而以最优潮流的结果特征为导向,基于关联性分析和聚类分析挖掘最优潮流输入与输出特征的关联性匹配度,并构建样本数据的分块特征库,以降低学习难度.在第二阶段,利用深度神经网络完成部分关键决策变量的分块映射,基于潮流模型恢复剩余状态变量,并对计算结果不收敛、不满足约束的情况进行修正,以恢复可行性.根据最优潮流两阶段求解方法构建概率最优潮流求解方法.仿真结果表明所提方法在最优潮流、概率最优潮流的求解速度和求解精度上均有较好的表现.
In order to solve the “curse of dimensionality” problem of unit commitment in large-scale power system,a two-stage unit commitment decision-making method based on Transformer neural network is proposed,which considers both the solving accuracy and speed. In the first stage,considering the coupling characteristic of unit commitment periods,a feature vector construction method based on multi-Attention mechanism is proposed,further an improved Transformer neural network is proposed to predetermine the unit start-stop values based on the advantages of global view and parallelization of Transformer neural network. In the second stage,the credibility threshold is designed based on the predetermined unit states,and the unit start-stop determination credibility is defined as start-stop credible and start-stop incredible states,the state of start-stop credible unit is determined directly,while the state of start-stop incredible state unit is solved by the unit commitment physical model to ensure the solving feasibility. The simulative results of IEEE 30-bus and IEEE 2 383-bus systems verify the effectiveness of the proposed method.
为了实现"双碳"目标以及适应新能源快速发展的形势,基于参数规划及工程博弈论提出了一种含储能和风电电力系统的多目标低碳经济调度方法.以系统发电成本、碳排放量、储能寿命折损为目标构建多目标优化模型,采用系数约束法将模型转化为参数线性规划模型,求解参数规划模型可获得Pareto前沿的精确解析表达式,从而进一步构建工程博弈问题,精炼得到对多目标具有公平性的唯一Pareto最优解,为决策者提供参考.算例分析结果表明,所提调度方法能够充分兼顾各目标的优化程度,保证电力系统调度的环保性与经济性,可有效应用于新能源电力系统的低碳经济调度.
