基于帝国竞争算法考虑运行风险的电力系统随机无功备用优化

针对目前电力系统中的随机无功备用优化不能控制系统总无功备用风险,从而导致对系统的安全水平评估不准确的问题,首先建立考虑目标函数置信水平的随机无功备用优化模型,采用Nataf变换重构生成风速样本,然后采用蒙特卡洛法将原问题转化为多次的确定型优化运算,最后采用帝国竞争算法对问题进行求解。算例分析结果表明,相比于传统基于期望值目标函数的随机无功备用优化,所提方法可有效控制目标函数的风险;其中帝国竞争算法的采用是该算法效率提升的关键因素。     

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.     

Novel power capture optimization based sensorless maximum power point tracking strategy and internal model controller for wind turbines systems driven SCIG

Under the trends to using renewable energy sources as alternatives to the traditional ones, it is important to contribute to the fast growing development of these sources by using powerful soft computing methods. In this context, this paper introduces a novel structure to optimize and control the energy produced from a variable speed wind turbine which is based on a squirrel cage induction generator (SCIG) and connected to the grid. The optimization strategy of the harvested power from the wind is realized by a maximum power point tracking (MPPT) algorithm based on fuzzy logic, and the control strategy of the generator is implemented by means of an internal model (IM) controller. Three IM controllers are incorporated in the vector control technique, as an alternative to the proportional integral (PI) controller, to implement the proposed optimization strategy. The MPPT in conjunction with the IM controller is proposed as an alternative to the traditional tip speed ratio (TSR) technique, to avoid any disturbance such as wind speed measurement and wind turbine (WT) characteristic uncertainties. Based on the simulation results of a six KW-WECS model in Matlab/Simulink, the presented control system topology is reliable and keeps the system operation around the desired response.     

Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search

In this paper, an optimization method for the reactive power dispatch in wind farms (WF) is presented. Particle swarm optimization (PSO), combined with a feasible solution search (FSSPSO) is applied in order to optimize the reactive power dispatch, taking into consideration the reactive power requirement at point of common coupling (PCC), while active power losses are minimized in a WF. The reactive power requirement at PCC is included as a restriction problem and is dealt with feasible solution search. Finally an individual set point, particular for each wind turbine (WT), is found. The algorithm is tested in a WF with 12 WTs, taking into consideration different control options and different active power output levels.     

Short‐term hydropower planning coordinated with wind power in areas with congestion problems

In this paper, a day‐ahead planning algorithm for a multi‐reservoir hydropower system coordinated with wind power is developed. Coordination applies to real situations, where wind power and hydropower are owned by different utilities, sharing the same transmission lines, although hydropower has priority for transmission capacity. Coordination is thus necessary to minimize wind energy curtailments during congestion situations. The planning algorithm accounts for the uncertainty of wind power forecast. Only planning for the spot market is considered. Once the production bid is placed on the market, it cannot be changed. The solution of the stochastic optimization problem should, therefore, fulfill the transmission constraints for all wind power production scenarios. An evaluation algorithm is also developed to quantify the impact from the coordinated planning in the long run. The developed planning algorithm and the evaluation algorithm are applied in a case study. The results are compared with uncoordinated operation. The results of the case study show that coordination with wind power brings additional income to the hydropower utility and leads to significant reduction of wind energy curtailments. Copyright © 2007 John Wiley & Sons, Ltd.     

Application of a control algorithm for wind speed prediction and active power generation

The main objective of the work described in this paper is to offer a new method of prediction of wind speeds, whilst aware that the method develops predictions in time-scales that can vary from a few minutes to an hour. This is needed because wind energy generation is increasing its participation in energy distribution and has to compete with other energy sources that are not so variable in terms of generated active power. It is important to consider that active power demand can vary quite rapidly and different sources of electricity generation must be available. In the case of wind energy, wind speed predictions are an important tool to help producers make the best decisions when selling the energy produced. These decisions are crucial in the electricity market, because of the economic benefits for producers and consequently their profitability, depends on them. The algorithm presented in this paper is based on an artificial neural network and two types of wind data have been used to test the algorithm. In the first, data was collected from a not very windy area; in the second data was collected from a real wind farm located in Navarre (North of Spain), and the values vary from very low to high speeds. Although the algorithm was not tested with typical wind speed values measured on offshore wind farm applications, it can be concluded from the first set of results presented in this paper that the algorithm is valid for estimating average speed values. Finally, a generic algorithm for the active power generation of a wind farm is presented.     

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post‐process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non‐linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non‐linear non‐parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non‐linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non‐parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power‐to‐wind) transformation, simpler linear regression models with censoring perform equally or better than non‐linear models with or without the frequently used wind‐to‐power transformation. © 2013 The Authors. Wind Energy published by John Wiley & Sons Ltd.     

Dampening variations in wind power generation—the effect of optimizing geographic location of generating sites

This paper presents a method to dampen the variations in the output of aggregated wind power through geographic allocation of wind power generation sites. The method, which is based on the sequential optimization of site localization, is applied to the Nordic countries and Germany, using meteorologic wind speed data as the input. The results show that the variability in aggregated wind power output mitigates by applying sequential optimization. For the data used in this work, the coefficient of variation (standard deviation/mean) was 0.54 for the optimized aggregation of sites, as compared with 0.91 for the present day installation. An optimal allocation of wind power generation site reduces the need for dispatch and other measures to deal with the intermittent nature of wind power. Copyright © 2013 John Wiley & Sons, Ltd.     

Displacement of the maximum power point caused by losses in wind turbine systems

The energy yield of wind turbines is to a large extent determined by the performance of the Maximum Power Point Tracking (MPPT) algorithm. Conventionally, they are programmed to maximize the turbines power coefficient. However, due to losses in the generator and converter, the true optimal operating point of the system shifts. This effect is often overlooked, which results in a decreased energy yield. Therefore, in this paper, the wind turbine system is modeled including the dominant loss components to investigate this effect in detail. By simulations and experiments on a wind turbine emulator, it is shown that the location of the maximum power point is significantly affected for low wind speeds. For high wind speeds, the effect is less pronounced. The parameter of interest is the increase in yearly energy output with respect to the classical MPPT method, which is calculated in this paper by including a Rayleigh wind speed distribution. For typical average wind speeds, the energy yield can increase with 1–2%. There is no cost associated with operating the turbine in the overall MPP, making it worthwhile to include this effect. The findings are implemented in an MPPT algorithm to validate the increased performance in a dynamic situation.     

Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet‐PSO‐NNs

This paper describes the problem of short‐term wind power production forecasting based on meteorological information. Aggregated wind power forecasts are produced for multiple wind farms using a hybrid intelligent algorithm that uses a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on neural network (NN), which is optimized by using particle swarm optimization (PSO) algorithm. To demonstrate the effectiveness of the proposed hybrid intelligent WT + NNPSO model, which takes into account the interactions of wind power, wind speed, wind direction, and temperature in the forecast process, the real data of wind farms located in the southern Alberta, Canada, are used to train and test the proposed model. The test results produced by the proposed hybrid WT + NNPSO model are compared with other SCMs as well as the benchmark persistence method. Simulation results demonstrate that the proposed technique is capable of performing effectively with the variability and intermittency of wind power generation series in order to produce accurate wind power forecasts. Copyright © 2014 John Wiley & Sons, Ltd.     

Environmental/economic power dispatch with wind power

Economic environmental dispatch (EED) is a significant optimization problem in electric power system. With more wide spread use of wind power, it is necessary to include wind energy conversion system (WECS) in the EED problem. This paper presents a model to solve the EED problem incorporating wind power. In addition to the classic EED factors, the factors accounting for overestimation and underestimation of available wind power in both economic and environmental aspects are also considered. In order to obtain some quantitative results, the uncertain characteristic of available wind power and the performance of WECS are determined on the basis of the statistical characteristic of wind speed. The optimization problem is numerically solved by a scenario involving two conventional generators and two wind-powered generators. The results demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the cost for overestimation and underestimation of available wind power, and by the multiplier related to the emissions for underestimation of available wind power. Nevertheless, the multiplier related to the emissions for overestimation of available wind power has little impact on the allocation. Taking account of economic factors, environmental factors and impacts of wind power penetration, the proposed EED model is beneficial to finding the right balance between radical and conservative strategy for wind power development.     

基于改进遗传算法的风/光互补发电系统恒压控制

针对带有约束条件的风/光互补发电系统恒压控制这个多极值非线性组合优化问题,采用一种改进遗传算法,以系统发电成本最低为目标,根据拧制规则对一风/光互补发电系统的电压进行了优化.仿真结果表明.该算法能显著地提高收敛速度和计算精度,有效地提高了风/光互补发电系统的电压稳定性.     

Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution

Considering the inevitable prediction errors in the traditional point predictions of wind power, in this paper, a new ultra short‐term probability prediction method for wind power is proposed, in which the long short‐term memory (LSTM) network, wavelet decomposition (WT), and principal component analysis (PCA) are combined together for ultra short‐term probability prediction of wind power, a conditional normal distribution model that is developed to describe the uncertainty of prediction errors. First, WT and PCA are jointly used to smooth the original time series, then the point prediction model for subsequence data based on LSTM network is proposed. It is worth pointing out that the input matrix of the model includes many features, such as wind power and wind speed, which will be helpful for improving prediction performance. After optimizing the index of the ultra short‐term probability prediction interval (PI) of wind power by particle swarm optimization (PSO), the conditional normal distribution model of prediction errors is developed. Thus, the ultra short‐term PIs for wind power are obtained. Finally, based on the data of two wind farms in China, simulation results are provided to illustrate the usefulness of the proposed prediction model. It follows from those results that the proposed method can improve the accuracy of prediction, and the reliability of probability prediction for wind power is also improved.     

Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system

To achieve maximum power point tracking (MPPT) for wind power generation systems, the rotational speed of wind turbines should be adjusted in real time according to wind speed. In this paper, a Wilcoxon radial basis function network (WRBFN) with hill-climb searching (HCS) MPPT strategy is proposed for a permanent magnet synchronous generator (PMSG) with a variable-speed wind turbine. A high-performance online training WRBFN using a back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller is designed for a PMSG. The MPSO is adopted in this study to adapt to the learning rates in the back-propagation process of the WRBFN to improve the learning capability. The MPPT strategy locates the system operation points along the maximum power curves based on the dc-link voltage of the inverter, thus avoiding the generator speed detection.     

Stochastic reactive power market with volatility of wind power considering voltage security

While wind power generation is growing rapidly around the globe; its stochastic nature affects the system operation in many different aspects. In this paper, the impact of wind power volatility on the reactive power market is taken into account. The paper presents a novel stochastic method for optimal reactive power market clearing considering voltage security and volatile nature of the wind. The proposed optimization algorithm uses a multiobjective nonlinear programming technique to minimize market payment and simultaneously maximize voltage security margin. Considering a set of probable wind speeds, in the first stage, the proposed algorithm seeks to minimize expected system payment which is summation of reactive power payment and transmission loss cost. The object of the second stage is maximization of expected voltage security margin to increase the system loadability and security. Finally, in the last stage, a multiobjective function is presented to schedule the stochastic reactive power market using results of two previous stages. The proposed algorithm is applied to IEEE 14-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the actual market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

永磁直驱风力发电系统MPPT控制的研究

文章首先介绍了风力机模型。然后介绍了一种改进型的变步长爬山算法,通过该算法改变直驱风力发电系统三重交错并联Boost变换电路的占空比,从而实现最大功率跟踪,获取最大风能。最后,利用MATLAB/SIMULINK建立直驱永磁风力发电系统仿真模型并进行研究。试验结果表明,改进型变步长爬山算法比传统爬山算法能更快跟踪最大功率点,控制系统具有较好的控制精度和稳定性。     

计及风电条件风险价值的鲁棒柔性超前调度

提出一种考虑大规模风电并网的超前优化调度方法,引入风电条件风险价值来评估风电消纳风险。建立基于鲁棒优化的柔性超前调度模型,以平衡运行成本与风电条件风险价值。根据该模型,对AGC机组的基点功率、参与因子、柔性容量进行协同优化,还可得到各风电场输出功率的可容许区域。提出一种基于大M法和分解法的求解双线性规划模型的高效算法。所提模型及算法结合鲁棒优化与随机优化的优点,在保证计算效率的同时,可避免鲁棒优化的过度保守。仿真结果验证了所提模型及算法的有效性。     

A review on reliability assessment for wind power

The application of wind energy in electric power systems is growing rapidly due to enhanced public concerns to adverse environmental impacts and escalation in energy costs associated with the use of conventional energy sources. Electric power from wind energy is quite different from that of conventional resources. The fundamental difference is that the wind power is intermittent and uncertain. Therefore, it affects the reliability of power system in a different manner from that of the conventional generators. This paper, from available literatures, presents the model of wind farms and the methods of wind speed parameters assessment. Two main categories of methods for evaluating the wind power reliability contribution, i.e., the analytical method and the Monte Carlo simulation method have been reviewed. This paper also summarizes factors affecting the reliability of wind power system, such as wake effect, correlation of output power for different windturbines, effect of windturbine parameters, penetration and environment. An example has been used to illustrate how these factors affect the reliability of wind power system. Finally, mainstream reliability indices for evaluating reliability are introduced. Among these reliability indices, some are recently developed, such as wind generation interrupted energy benefit (WGIEB), wind generation interruption cost benefit (WGICB), Equivalent Capacity Rate (ECR), load carrying capacity benefit ratio (LCCBR).     

A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research.     

Characterizing future large,rapid changes in aggregated wind power using Numerical Weather Prediction spatial fields

A critical limiting factor to the successful deployment of a large proportion of wind power in power systems is its predictability. Power system operators play a vital role in maintaining system security, and this task is greatly aided by useful characterizations of future system operations. A wind farm power forecast generally relies on the forecast output from a Numerical Weather Prediction (NWP) model, typically at a single grid point in the model to represent the wind farm's physical location. A key limitation of this approach is the spatial misplacement of weather features often found in NWP forecasts. This paper presents a methodology to display wind forecast information from multiple grid points at hub height around the wind farm location. If the raw forecast wind speeds at hub height at multiple grid points were to be displayed directly, they would be misleading as the NWP outputs take account of the estimated local surface roughness and terrain at each grid point. Hence, the methodology includes a transformation of the wind speed at each grid point to an equivalent value that represents the surface roughness and terrain at the chosen single grid point for the wind farm site. The chosen‐grid‐point‐equivalent wind speeds for the wind farm can then be transformed to available wind farm power. The result is a visually‐based decision support tool which can help the forecast user to assess the possibilities of large, rapid changes in available wind power from wind farms. A number of methods for displaying the field for multiple wind farms are discussed. The chosen‐grid‐point‐equivalent transformation also has other potential applications in wind power forecasting such as assessing deterministic forecast uncertainty and improving downscaling results. Copyright © 2008 John Wiley & Sons, Ltd.