基于滑模控制的含风储多域电力系统负荷频率控制

建立含风储多域互联电力系统负荷频率控制(LFC)模型,同时考虑系统参数不确定性、储能系统和传统机组控制信道延时问题.为提高系统鲁棒性,降低储能系统的容量配置,针对含风储的LFC模型,设计滑模负荷频率控制器,并提出滑模负荷频率控制器和储能协调的控制策略.算例分析表明,所提出的协调控制策略在新能源大规模渗透和系统负荷波动情况下能够有效减小系统频率偏差和区域控制偏差,同时降低储能系统的配置容量,提高电力系统安全稳定运行的经济性.     

A coordinated dispatch method for energy storage power system considering wind power ramp event

In response to the impact of wind power ramp events on power system, a forecast and coordinated dispatch method for wind power ramp events is proposed. Firstly, the LSTM neural network is utilized to multi-step forecast the wind power, which can identify the features such as amplitude and duration of wind power ramp event in advance. Then, an ahead-delay adjustment method (ADAM) for wind power ramp events is proposed, which coordinates the thermal power generation units (referred to as units) and the energy storage system (referred to as ESS) to make two-step adjustments during the period before and after wind power ramp event. Thereby reducing the ramp rate of wind power, while reducing the requirement for the output power and capacity of ESS. Based on the above methods, a coordinated dispatch model of units and ESS considering wind power ramp events is established. The units are coordinated in advance to ensure sufficient adjustable reserves during wind power ramp events. A more economical and reliable dispatch plan can be achieved by coordinating the units and ESS according to the judging conditions. Finally, the forecast performance test and the ramp dispatch simulation are carried out in case study to verify the effectiveness of the forecast and dispatch method.     

An efficient on-line load-modeling algorithm for short-term forecasting of interconnected power systems

This note addresses itself to the problem of one-step-ahead forecasting of the load demand at all the loading nodes of an interconnected power system. A multivariable state-space combined parameter and state estimation technique is proposed. A normalized stochastic approximation method-which is computationally simple and exhibits good initial convergence-is used to generate estimates of the model parameters and the optimum filter gain matrix. The estimated parameters are then utilized to obtain a one-step-ahead forecast of the load demand. Some results of application of the proposed algorithm to real load pertaining to the Eastern Saudi Arabian power grids are presented.     

Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach

Optimizing reactive power flow in electrical network is an important aspect of system study as the reactive power supports network voltage which needs to be maintained within desirable limits for system reliability. A network consisting of only conventional thermal generators has been extensively studied for optimal active and reactive power dispatch. However, increasing penetration of renewable sources into the grid necessitates power flow studies incorporating these sources. This paper presents a formulation and solution procedure for stochastic optimal reactive power dispatch (ORPD) problem with uncertainties in load demand, wind and solar power. Appropriate probability density functions (PDFs) are considered to model the stochastic load demand and the power generated from the renewable energy sources. Numerous scenarios are created running Monte-Carlo simulation and scenario reduction technique is implemented to deal with reduced number of scenarios. Real power loss and steady state voltage deviation of load buses in the network are set as the objectives of optimization. Success history based adaptive differential evolution (SHADE) is adopted as the basic search algorithm. SHADE has been successfully integrated with a constraint handling technique, called epsilon constraint (EC) handling, to handle constraints in ORPD problem. The effectiveness of a proper constraint handling technique is substantiated with case studies for deterministic ORPD on base configurations of IEEE 30-bus and 57-bus systems using SHADE-EC algorithm. The single-objective and multi-objective stochastic ORPD cases are also solved using the SHADE-EC algorithm. The results are discussed, compared and critically analyzed in this study.     

A robust stochastic stability analysis approach for power system considering wind speed prediction error based on Markov model

This paper proposes a robust stochastic stability analysis approach with partly unknown transition probability by considering the wind speed prediction error in power system. Firstly, taking this prediction error into account, based on Markov modeling theory, the stochastic dynamic model of wind power system with uncertain transition probability is developed. Secondly, according to the stochastic stability theory of Markov jump system, the transition probability of wind power system mode is divided into three cases: fully known, only known upper and lower bounds, and completely unknown. Then, by using linear matrix inequality (LMI) technology, a robust stochastic stability criterion with disturbance attenuation is obtained. Finally, test results show that the proposed analysis approach does not need to obtain the trajectory of the actual system operation parameters, and has the advantages of high computational efficiency.     

A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted. This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.      

计及激励型需求响应的微电网可靠性分析

鉴于激励型需求响应能够引导用户调整用电行为、主动参与负荷削减,而微电网中的分布式电源及储能能为实施激励型需求响应提供响应时间,文中对计及激励型需求响应的微电网可靠性进行了重点研究。首先分析了激励型需求响应的机理及其对供电可靠性的影响,接着对用户进行分级、建立了需求响应的调度模型,然后以用户收益最大为目标建立用户侧的响应模型,并提出了计及激励型需求响应的微电网负荷削减策略,最后基于蒙特卡洛模拟方法对改进的RBTS Bus6测试系统进行可靠性评估和经济性分析。算例结果验证了所建模型的有效性,证明了激励型需求响应能有效提高微电网的可靠性和经济性。采用随机优化理论,研究具有提前通知选择时供电公司对于可中断负荷的最优决策模型,并给出模型的解析解。     

基于数据关联狄利克雷混合模型的电网净负荷不确定性表征研究EI北大核心CSCD

针对电网净负荷时序数据关联的特点,提出基于数据关联的狄利克雷混合模型(Data-relevance Dirichlet process mixture model,DDPMM)来表征净负荷的不确定性.首先,使用狄利克雷混合模型对净负荷的观测数据与预测数据进行拟合,得到其混合概率模型;然后,提出考虑数据关联的变分贝叶斯推断方法,改进后验分布对该混合概率模型进行求解,从而得到混合模型的最优参数;最后,根据净负荷预测值的大小得到其对应的预测误差边缘概率分布,实现不确定性表征.本文基于比利时电网的净负荷数据进行检验,算例结果表明:与传统的狄利克雷混合模型和高斯混合模型(Gaussian mixture model,GMM)等方法相比,所提出的基于数据关联狄利克雷混合模型可以更为有效地表征净负荷的不确定性.     

Optimal wind turbines placement within a distribution market environment

This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines genetic algorithm (GA) and market-based optimal power flow (OPF). The method jointly maximizes net present value (NPV) related to WTs investment made by WTs’ developers and social welfare (SW) considering different combinations of wind generation and load demand over a year. The GA is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis. The effectiveness of the method is demonstrated with an 84-bus 11.4 kV radial distribution system.     

可再生能源分布式微网电源规划方法及应用

近年来, 可再生能源分布式发电微网技术研究引起国内外广泛关注. 本论文将遗传算法应用到风–光–柴–蓄组成的可再生能源分布式微网电源规划中, 建立微网电源规划模型及相关约束条件, 以满足能量平衡控制、费效率等为最优原则, 给出了算法的实现流程. 最后, 结合案例说明了算法的应用.     

Capacity expansion of stochastic power generation under two-stage electricity markets

Energy imbalances due to power forecast errors have a significant impact on both the cost of operating the power system and the profitability of stochastic power generating units. In this paper, we propose a modeling framework to analyze the effect of the costs of these imbalances on the expansion of stochastic power generating units. This framework includes the explicit representation of a day-ahead and a balancing market-clearing mechanisms to properly capture the impact of forecast errors of power production on the short-term operation of a power system. The proposed generation expansion problems are first formulated from the standpoint of a social planner to characterize a perfectly competitive market. We investigate the effect of two paradigmatic market designs on generation expansion planning: a day-ahead market that is cleared following a conventional cost merit-order principle, and an ideal market-clearing procedure that determines day-ahead dispatch decisions accounting for their impact on balancing operation costs. Furthermore, we reformulate the proposed models to determine the optimal expansion decisions that maximize the profit of a collusion of stochastic power producers in order to explore the effects of competition at the investment level. The proposed models are first formulated as multi-level programming problems and then recast, under certain assumptions, as single-level mixed-integer linear or non-linear optimization problems using duality theory. The variability of the forecast of the stochastic power production and the demand level throughout the planning horizon is modeled using yearly duration curves. Likewise, the uncertainty pertaining to power forecast errors is characterized through scenario sets. The main features and results of the proposed models are discussed using an illustrative example and a more realistic case study based on the Danish power system.     

计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法;对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。     

Active power control design for supporting grid frequency regulation in wind farms

Among renewable energy sources, wind power is expected to contribute a larger and rapidly growing portion of the world's energy portfolio. However, the increased penetration of wind power into the power grid has challenged the reliable and stable operation of the grid. This motivates new opportunities in the design and development of novel control schemes capable of actively maintaining the necessary balance between power generation and load, which in turn regulates the grid frequency when plenty of winds are available. This paper presents two active power control schemes that are developed based on adaptive pole placement control and fuzzy gain-scheduled proportional-integral control approaches. The active power control is conducted collectively across a wind farm to provide rapid power response while maintaining safe structural loading on turbines’ components. The proposed active power control schemes are evaluated and compared by a series of simulations on an advanced wind farm benchmark model in the presence of wind turbulences, measurement noises, and grid load variations. It is further demonstrated that the mentioned schemes are able to tolerate probable occurrence of sudden imbalance between generation and load due to relevant faults/failures in the wind farm or electric grid.     

Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization

Uncertainties from deepening penetration of renewable energy resources have posed critical challenges to the secure and reliable operations of future electric grids. Among various approaches for decision making in uncertain environments, this paper focuses on chance-constrained optimization, which provides explicit probabilistic guarantees on the feasibility of optimal solutions. Although quite a few methods have been proposed to solve chance-constrained optimization problems, there is a lack of comprehensive review and comparative analysis of the proposed methods. We first review three categories of existing methods to chance-constrained optimization: (1) scenario approach; (2) sample average approximation; and (3) robust optimization based methods. Data-driven methods, which are not constrained by any particular distributions of the underlying uncertainties, are of particular interest. Key results of the analytical reformulation approach for specific distributions are briefly discussed. We then provide a comprehensive review on the applications of chance-constrained optimization in power systems. Finally, this paper provides a critical comparison of existing methods based on numerical simulations, which are conducted on standard power system test cases.     

考虑源荷随机性的跨区互联电网直流联络线调度学习优化

在跨区互联电网中,充分利用直流联络线调度能力可以有效地平衡电力资源的配置,促进新能源的消纳.本文针对源荷不确定性的跨区互联电网直流联络线调度问题,首先用连续马尔科夫过程模型描述互联电网中风电出力与负荷需求随机动态特性;然后在功率平衡及联络线日交易电量约束等实际运行要求前提下,将直流联络线调度优化问题建立成离散马尔科夫决策过程模型.在该模型下,调度机构根据互联电网系统各时段源荷的功率情况,动态调整联络线输电计划和配套的柔性负荷调节方案,以达到提升系统运行效益的优化目标;最后引入强化学习方法对调度策略进行优化求解.通过学习优化,系统平均日运行代价显著下降且最终收敛.实验结果表明考虑源荷随机性的直流联络线动态调整方法可有效地提高互联电网发输电系统的运行效益.     

含电动汽车充电站的多端直流微电网研究

为未来大规模能源互联网的形成以及多种新能源的接入提供技术支撑,设计直流微电网的拓扑结构,提出了一种四端口环网的直流电网拓扑结构,实现交流电网、储能单元、直流负荷、风力发电和光伏发电与直流电网的互联。首先研究了交流电网与直流电网的接口方式和相关技术参数,提出了光伏发电、风力发电和储能单元等接口的技术配置。其次,研究了整个直流微电网的启停时序,设计了直流电网的接线方式、电压等级和容量,最后基于MATLAB Simulink平台搭建了直流园区系统仿真模型,然后对典型工况进行了仿真分析：（1）储能单元由放电到能量为零;（2）储能单元由充电到能量充满;（3）VSC1变换器功率反转;（4）负荷跳变;（5）储能单元由放电到充电。这些工况基本包涵了直流微电网可能出现的运行状态,对直流微电网的运行管理有较高的参考价值。     

含风电电力系统的主从博弈经济调度

风力发电具有显著的随机性和波动性,对电力系统原有调度模式提出挑战.采用鲁棒优化处理风电不确定性,利用鲁棒优化蕴含的博弈思想,将风电场看作调度中心的一个虚拟博弈者,利用双层规划法建立了二者的主从博弈模型,将调度中心看作领导层,其决策目标为电网运行的成本最低,将风电场看作下属层,其决策目标是能保证系统实时安全运行的最大风电出力区间.由于考虑了火电机组的阀点效应,主从博弈模型呈现出非线性双层规划的数学特点,提出一种改进教与学算法与线性规划相嵌套的求解方法.最后,采用改进的10机39节点系统对模型以及求解方法的有效性进行了验证.     

基于热调度消纳风电的户式相变蓄热电采暖热容量研究

传统研究方法在研究户式相变蓄热电采暖热容量时,准确性很低。为了解决这一问题,基于热调度消纳风电提出了一种新的户式相变蓄热电采暖热容量研究方法,首先通过系统备用功率分析了热电模式下户式相变蓄热系统接纳风电能力,进而给出了风电供热调度模型,该模型根据显热蓄热热容量,潜能蓄热热容量和化学反应蓄热三种户式相变蓄热方式进行电采暖热容量研究。与传统研究方法进行实验对比,结果表明,给出的方法准确率可以达到99.82%,传统的研究方法准确率为90.53%,所提方法误差率更小,具有更广阔的发展空间。     

A dynamic state estimator for power system dynamic security assessment

A global modularized dynamic state estimator is formulated to provide the data which will be required for future dynamic security assessment and dynamic security enhancement applications. The dynamic state estimator is global because it is capable of estimating small and large dynamic fluctuations in voltage angle and frequency for an entire area. The dynamic state estimator is composed of the sum of the static state estimate, obtained by using present hardware and algorithms and a modularized dynamic state estimate based on a linearized classical transient stability model with a stochastic load model. This dynamic state estimate component is modularized to (1) eliminate the need to measure or model external system generation and (2) to permit a reduction in computation requirements for (a) updating the linearized power system dynamic model and (b) for computing the state estimate. The modularization, which is accomplished by decoupling the linearized dynamic model for each subregion by measuring the power injections on lines connecting the subregion to the rest of the power system, causes the dynamic state estimate to be locally referenced. A global referencing procedure is proposed and discussed. A linearized stochastic model for the Michigan Electric Coordinated System is developed to illustrate the procedures proposed for developing the stochastic load model and determining the constant gain approximation for the governor turbine energy system dynamics. A summary of results on the performance of the Kalman state estimator is presented.     

基于随机模型预测控制的含大规模风电接入的电力系统优化调度

风电是重要的清洁可再生能源,将其引入智能电网中对节能减排有着重要的意义.为降低大规模风电不确定性给电网调度带来的影响,提出一种基于随机模型预测控制的风电与传统机组协调调度方法.考虑了部分传统机组需要人工调度而无法频繁、连续操作的情况,并引入可调负荷以增加系统可调度能力.构建基于混合整数二次规划(MIQP)的风电调度目标函数,以及包括机组最大可调节次数、最小运行/停机时间、可调度负荷总能量需求一致性、风电切负荷比例等约束.提出两阶段场景缩减方法以实现典型场景的快速筛选.通过与传统开环调度方法的性能对比表明所提出方法的可行性与有效性,并在此基础上进一步分析机组启停次数和可调度负荷对系统运行的影响.