A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing,Malaysia

Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R² determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m², and SSW is the most common wind direction for this data.     

Optimization of a Wind/PV Hybrid Power Generation System

This study investigates the wind and solar electricity generation availability at the Solar Energy Institute of Ege University, Izmir, Turkey. The main purpose of this study is to design an appropriate wind-PV hybrid system to cover the electricity consumption of the Institute. In order to do this, monthly average solar irradiation and wind speed data are used, which were measured, consisting of hourly records over an eight-year period from 1995–2002. Simple models were developed to determine wind, solar, and hybrid power resources per unit area. Correlations between the solar and wind power data were carried out on an hourly, daily, and monthly basis. It is shown that the hybrid system can be applied for the efficient and economic utilization of these resources.     

A stochastic approach for the operation of a wind and pumped storage plant under a deregulated environment

To improve the competiveness in the energy market, it is necessary that the wind power plants provide guaranteed power generation, although, it is not possible to forecast power availability from wind power plant accurately. This paper presents a stochastic model and solution technique for the combined operation of wind and pumped storage power plants to improve the power availability and increasing the profit considering uncertainties of wind power generation. In this model, uncertainties in wind data have been forecasted for grid connected day-ahead market using Weibull distribution model. The imbalances in the forecasted wind data and the market demand have been reduced by operating the pumped storage power plant. In this stochastic mixed integer problem, pumped storage plant can take the supply either from the grid or from the wind power plant for the pumping operation to store the energy in order to utilize this energy during peak hours for increasing the overall revenue. The reliability of the pumped storage is improved by replacing the conventional unit with the adjustable speed type pumped storage unit. In order to prove the optimality of the solution, two case studies were considered. In case study–I, scheduling is provided by operating the conventional pumped storage unit, whereas in case study–II, adjustable speed pumped storage unit has been used. It has been found that the adjustable speed pumped storage unit has further reduced the imbalance between generated power and demand. The complete approach has been formulated and implemented using AMPL software.     

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

The drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

Availability estimation of a multi-state wind farm in fuzzy environment

Wind is one of the fastest growing renewable energy resources in the electric power system. Availability of wind energy is volatile in nature due to the stochastic behavior of wind speed and non-linear variation of the wind power curve of wind turbine generator. Because of this impression and uncertainty, the availability estimation of wind power has become a challenging issue. In this paper, Markov Fuzzy Reward technique has been proposed for finding out the reliability of wind farm by assessing the availability of wind power. According to this technique, availability of the wind power has been estimated considering wind farm and demand both as a multi-state system. In addition to the availability, different reliability indices such as the number of absolute failures, mean time to deficiency, and probability of failures of a wind farm have been assessed in a time horizon, which can provide useful information for the power system planner at wind farm installing stage. A comparison of this study reveals the efficacy of the proposed Markov Fuzzy Reward approach over the conventional Markov Reward approach.     

Improving the dynamic response of frequency and power in a wind integrated power system by optimal design of compensated superconducting magnetic energy storage

The frequency deviation and power fluctuation need to be controlled in a wind-integrated power system (WIPS) for keeping the balance between system power generation and demand, which support the quality and stability of overall power system. The present paper addresses this problem while concerning the integration of intermittent wind power and load disturbance into the WIPS. With this intent, it proposes the compensated superconducting magnetic energy storage (CSMES) system with proportional integral derivative (PID) controller for improving the frequency and power deviation profile. A novel swarm intelligence-based artificial bee colony (ABC) algorithm is used for optimal design of PID-CSMES system. Robustness of the proposed ABC-based PID-CSMES control strategy is tested in WIPS under various disturbance patterns of load and wind power. To demonstrate the improved dynamic response, their simulation results are compared with particle swarm optimization-based PID-CSMES, PID with SMES, and only PID controller technique. The performance indices and transient response characteristics of frequency and power deviation are used to evaluate and compare the accuracy and efficiency of each controller. Stability of various system configurations is analyzed using eigenvalue location. Comparing the results of different controller in WIPS indicates a substantial improvement in the dynamic response of system frequency and power deviations by utilizing the proposed control strategy.     

Wind energy potential assessment and techno-economic performance of wind turbines in coastal sites of Buenos Aires province,Argentina

An assessment of wind energy potential was carried out in five sites (four onshore and one offshore) in South-West (SW) of Buenos Aires province (Argentina). We use high-resolution wind data (2 and 5 min) for the period 2009–2012. The power law was used to estimate the wind speed at 30, 40, and 60 m height from the anemometer position. Turbulence intensity and wind direction were analyzed. Statistical analyses were conducted using two-parameter Weibull distribution. A techno-economic analysis based on a set of commercial wind turbines was performed in those sites. The results derived from this work indicate that the SW of Buenos Aires province represents a promising area for the wind energy extraction, which would encourage the construction of wind farms for electricity generation.     

Research on Coupling Mechanism of China’s Wind Power Industry Chain

ABSTRACT

Firstly, on the basis of literature research, sort out and summarize the critical coupling relationship among the upstream, middle, and downstream enterprises in the wind power industry chain. Secondly, the evaluation index system of coupling coordination degree of China’s wind power industry chain was established. Based on entropy weight method and subsystem efficiency function, the capacity coupling (CC) coefficient model of wind power industry chain subsystem was established. The coupling coordination degree between the upstream subsystem and the midstream subsystem of the wind power industry chain, and between the midstream subsystem and the downstream subsystem is dynamically evaluated, and the coupling coordination degree evaluation model of the wind power industry chain in China is proposed. Thirdly, according to the relevant statistical data of China from 2010 to 2017, this paper conducts an empirical study on the coupling of the upstream, middle and downstream subsystems of the wind power industry chain. Finally, based on the collaborative coupling study of China’s wind power industry chain, this paper analyzes the key factors influencing the collaborative development of wind power industry chain, and puts forward Suggestions on the optimization of the collaborative development of China’s wind power industry chain.     

Performance evaluation of a horizontal axis wind turbine in operation

The operation of modern horizontal axis wind turbine (HAWT) includes a number of important factors, such as wind power (P), power coefficient (C_P), axial flow induction factor (a), rotational speed (Ω), tip speed ratio (λ), and thrust force (T). The aerodynamic qualities of these aspects are evaluated and discussed in this study. For this aim, the measured data are obtained from the Sebenoba Wind Energy Power Plant (WEPP) that is located in the Sebenoba region in Hatay, Turkey, and a wind turbine with a capacity of 2 MW is selected for evaluation. According to the results obtained, the maximum turbine power output, maximum power coefficient, maximum axial flow induction factor, maximum thrust force, optimum rotational speed, probability density of optimum rotational speed, and optimum tip speed ratio are found to be 2 MW, 30%, 0.091, 140 kN, 16.11 rpm, 46.76%, and 7, respectively. This study has revealed that wind turbines must work under optimum conditions in order to extract as much energy as possible for approaching the ideal limit.     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.     

Offshore wind power potential of the Black Sea region in Turkey

Majority of the studies on offshore wind power potential assessment is limited to the examination of the wind speed only. This study examines the offshore wind power potential of the Black Sea coastal region in Turkey based on location selection criteria including territorial waters, military areas, civil aviation, shipping routes, pipelines and underground cables, social, and environmental concerns. Wind Atlas Analysis and Application Program (WAsP) is used to do the statistical analysis of wind speed and wind direction data for 20 locations in the Black Sea coastal region. WAsP results are then elaborated based on the location selection criteria for better assessment of offshore wind power utilization. The study reveals that there are limited numbers of locations for offshore wind power generation in the Black Sea region in spite of its long coastline. Moreover, there is a high need for a zoning change for Amasra shores in order to utilize high offshore wind power potential of Amasra. Our finding suggests that location selection criteria other than wind speed should definitely be considered for better assessment of the wind power potential of a region.     

Electricity systems with near-zero emissions of CO2 based on wind energy and coal gasification with CCS and hydrogen storage

Emissions from electricity generation will have to be reduced to near-zero to meet targets for reducing overall greenhouse gas emissions. Variable renewable energy sources such as wind will help to achieve this goal but they will have to be used in conjunction with other flexible power plants with low-CO₂ emissions. A process which would be well suited to this role would be coal gasification hydrogen production with CCS, underground buffer storage of hydrogen and independent gas turbine power generation. The gasification hydrogen production and CO₂ capture and storage equipment could operate at full load and only the power plants would need to operate flexibly and at low load, which would result in substantial practical and economic advantages. This paper analyses the performances and costs of such plants in scenarios with various amounts of wind generation, based on data for power demand and wind energy variability in the UK. In a scenario with 35% wind generation, overall emissions of CO₂ could be reduced by 98–99%. The cost of abating CO₂ emissions from the non-wind residual generation using the technique proposed in this paper would be less than 40% of the cost of using coal-fired power plants with integrated CCS.     

基于ZigBee技术的电力环境监测系统设计

为了解决电力环境监测中的数据传输问题,提出了基于ZigBee技术的电力环境监测系统设计方案。该系统通过ZigBee节点采集各种电力环境数据,并将ZigBee网络和GPRS相结合实现远程监测。方案采用TI公司CC2430芯片设计传感器节点电路,在ZigBee协议栈的基础上设计了协调控制器节点,提高了系统的可扩展性,确保了数据传输的实时性和可靠性。     

A case study of an isolated wind powered electricity system meeting 100% of local demand

Using meteorological and electricity demand data for a 4-year period, electricity demand in Shetland was modeled to provide an estimate of the demand over a 30-year period from 1 January 1981. That modeled demand was then compared to estimated wind power output over the same period using the WAsP model. The wind farm output was estimated for a range of sizes of wind farm up to the consented 370 MW Viking Wind Farm in Shetland. Some wind power was available for 94% of the time and the 370 MW wind farm would meet 100% of demand for nearly 80% of the time. The statistics of single and accumulated deficits were calculated for a range of wind farms and estimates of the amount of additional generation capacity and additional power requirements were assessed. The study suggests that with storage, wind power in Shetland could meet all electricity demand in Shetland at around £130 to £150/MWh (excluding subsidy) and with a grid connection allowing the sale of excess power, those costs could be reduced.     

A comparative analysis of wind resource parameters using WAsP and windPRO

This paper investigates the accuracy of the wind resource estimation for a site in a central India region using a latest licensed version of WAsP 11 and windPRO 3.1. Whole one year measured met mast wind data has been taken using anemometer and wind vane at 10 m and 25 m height, respectively above ground level. The digitized elevation and roughness model of the corresponding site shows the roughness class 4 (roughness length 1.2525 m). The wind data has been extrapolated up to 80 m height by using power and log law models which provide the power density near about 120 W/m². As per the micro sitting guidelines for the virtual wind farm installation 5D X 7D mapping has been selected which Indicates the total power output by installing 8 Vestas V-90 1.8 MW wind turbine from WAsP is 31.561 GWh and from windPRO is 28.083 GWh.     

Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study

Scientific literature discussed various types of mixture models and models derived from maximum entropy principle using short-term wind speed data for their relative assessment. The literature on suitability of these mixture models for long-term data is rarely available. However, for correct assessment of wind power potential both wind speed and wind direction are equally important. Therefore, in this paper, both wind speed and wind direction are simultaneously analyzed using several types of mixture distribution and compared the same with conventional Weibull distribution. For wind speed and wind power density assessment, the mixture distributions such as Weibull--Weibull distribution, Gamma--Weibull distribution, Truncated Normal--Weibull distribution, Truncated Normal--Normal distribution, proposed Truncated Normal--Gamma distribution and Gamma--Gamma distribution along with MEP-distribution are compared with conventional 2-parameter Weibull distribution. Similarly, for wind direction analysis, the finite mixtures of von-Mises distribution are compared with conventional von-Mises distribution. Judgment criteria include R², RMSE, Kolmogorov--Smirnov test and relative percentage error in wind power density. The sites selected are the three onshore locations of India, viz., Calcutta, Trivandrum, and Ahmedabad. The results show that for wind speed assessment, mixture distribution performs better than the conventional Weibull distribution for analyzing wind power density. However, location wise comparison of all mixture distribution is of prime importance. For wind direction analysis, finite mixture of two von-Mises distributions proved to be a suitable candidate for Indian climatology.     

Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function

The present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

全国碳市场启动后北京热电行业低碳运行管理方式研究

随着四大热电中心的建成投产,北京市完成了由燃机替代煤机的转变,实现了更加智能化的清洁能源发电供热。2013年北京市碳市场启动运行,给刚起步的燃气供热机组运行赋予了更广阔和更深远的意义;2017年12月19日,以发电行业为突破口,全国碳排放交易体系正式启动,北京市热电行业低碳运行管理意义重大而深远。本文以燃气热电联产机组运行数据为基础,通过计算分析,提出燃气热电联产机组清洁、高效、低碳运行方式,为全国碳市场启动后,北京乃至全国热电行业低碳运行管理提供经验借鉴。