Effects of Large Scale Wind Power on Total System Variability and Operation: Case Study of Northern Ireland

2003 ◽  
Vol 27 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Shashi Persaud ◽  
Brendan Fox ◽  
Damian Flynn
Keyword(s):  
Power Generation ◽  
Northern Ireland ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Wind Generation ◽  
Total System ◽  
Power Capacity ◽  
Spinning Reserve

The paper simulates the potential impact of significant wind power capacity on key operational aspects of a medium-sized grid-power system, viz. generator loading levels, system reserve availability and generator ramping requirements. The measured data, from Northern Ireland, consist of three years of 1/2 hourly metered records of (i) total energy generation and (ii) five wind farms, each of 5 MW capacity. These wind power data were scaled-up to represent a 10% annual energy contribution, taking account of diversity on the specific variability of total wind power output. The wind power generation reduced the system non-wind peak-generation. This reduction equalled 20% of the installed wind power capacity. There was also a reduction in the minimum non-wind generation, which equalled 43% of the wind power capacity. The analysis also showed that the spinning-reserve requirement depended on the accuracy of forecasting wind power ahead of scheduling, i.e. on the operational mode. When wind power was predicted accurately, (i) it was possible to reduce non-wind generation without over-commitment, but, (ii) the spinning-reserve non-wind conventional generation would usually have to be increased by 25% of the wind power capacity, unless quick-start gas generation was available. However, with unpredicted wind power generation, (i) despite reductions in non-wind generation, there was frequent over-commitment of conventional generation, but (ii) usually the spinning-reserve margin could be reduced by 10% of the wind power capacity with the same degree of risk. Finally, it was shown that wind power generation did not significantly increase the ramping duty on the system. For accurately predicted and unpredicted wind power the increases were only 4% and 5% respectively.

scholarly journals Two methods for estimating limits to large-scale wind power generation

2015 ◽  
Vol 112 (36) ◽  
pp. 11169-11174 ◽  
Author(s):  
Lee M. Miller ◽  
Nathaniel A. Brunsell ◽  
David B. Mechem ◽  
Fabian Gans ◽  
Andrew J. Monaghan ◽  
...  
Keyword(s):  
Power Generation ◽  
Kinetic Energy ◽  
Wind Power ◽  
Energy Flux ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Flux Method ◽  
Free Atmosphere

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way.

scholarly journals Impacts of Integration of Wind Farms on Power System Transient Stability

2018 ◽  
Vol 8 (8) ◽  
pp. 1289 ◽  
Author(s):  
Shiwei Xia ◽  
Qian Zhang ◽  
S.T. Hussain ◽  
Baodi Hong ◽  
Weiwei Zou
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Power System ◽  
Wind Power ◽  
Transient Stability ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Wind Generation ◽  
System Operation ◽  
Power System Operation

To compensate for the ever-growing energy gap, renewable resources have undergone fast expansions worldwide in recent years, but they also result in some challenges for power system operation such as the static security and transient stability issues. In particular, as wind power generation accounts for a large share of these renewable energy and reduces the inertia of a power network, the transient stability of power systems with high-level wind generation is decreased and has attracted wide attention recently. Effectively analyzing and evaluating the impact of wind generation on power transient stability is indispensable to improve power system operation security level. In this paper, a Doubly Fed Induction Generator with a two-lumped mass wind turbine model is presented firstly to analyze impacts of wind power generation on power system transient stability. Although the influence of wind power generation on transient stability has been comprehensively studied, many other key factors such as the locations of wind farms and the wind speed driving the wind turbine are also investigated in detail. Furthermore, how to improve the transient stability by installing capacitors is also demonstrated in the paper. The IEEE 14-bus system is used to conduct these investigations by using the Power System Analysis Tool, and the time domain simulation results show that: (1) By increasing the capacity of wind farms, the system instability increases; (2) The wind farm location and wind speed can affect power system transient stability; (3) Installing capacitors will effectively improve system transient stability.

Modeling Large-Scale Wind Farms for Reliability Analysis Considering Wake Effect

2014 ◽  
Vol 543-547 ◽  
pp. 647-652
Author(s):  
Ye Zhou Hu ◽  
Lin Zhang ◽  
Pai Liu ◽  
Xin Yuan Liu ◽  
Ming Zhou
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Wake Effect ◽  
Effect Model ◽  
The Individual

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yerzhigit Bapin ◽  
Vasilios Zarikas
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Unit Commitment ◽  
Wind Power Generation ◽  
Total System ◽  
Spinning Reserve ◽  
Content Type ◽  
Novel Approach ◽  
Spinning Reserves ◽  
Actual Unit

Purpose This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

scholarly journals Weather dependent estimation of continent-wide wind power generation based on spatio-temporal clustering

2017 ◽  
Vol 14 ◽  
pp. 131-138 ◽  
Author(s):  
Bruno U. Schyska ◽  
António Couto ◽  
Lueder von Bremen ◽  
Ana Estanqueiro ◽  
Detlev Heinemann
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Stable Operation ◽  
Temporal Clustering ◽  
Renewable Power ◽  
Spatio Temporal

Abstract. Europe is facing the challenge of increasing shares of energy from variable renewable sources. Furthermore, it is heading towards a fully integrated electricity market, i.e. a Europe-wide electricity system. The stable operation of this large-scale renewable power system requires detailed information on the amount of electricity being transmitted now and in the future. To estimate the actual amount of electricity, upscaling algorithms are applied. Those algorithms – until now – however, only exist for smaller regions (e.g. transmission zones and single wind farms). The aim of this study is to introduce a new approach to estimate Europe-wide wind power generation based on spatio-temporal clustering. We furthermore show that training the upscaling model for different prevailing weather situations allows to further reduce the number of reference sites without losing accuracy.

scholarly journals A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2442 ◽  
Author(s):  
Jussi Ekström ◽  
Matti Koivisto ◽  
Ilkka Mellin ◽  
Robert Millar ◽  
Matti Lehtonen
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Statistical Modeling ◽  
Renewable Energy Sources ◽  
Measurement Data ◽  
Wind Power Generation ◽  
System Level ◽  
Wind Generation ◽  
The Impact

In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

scholarly journals Combining QuickSCAT wind data and Landsat ETM+ images to evaluate the offshore wind power resource of East Vietnam Sea

2020 ◽  
Vol 20 (2) ◽  
pp. 143-153
Author(s):  
Nguyen Xuan Tung ◽  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Thi Nhan ◽  
Tran Quang Son
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Geographical Location ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wind Data ◽  
Power Capacity ◽  
Wind Power Density

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.

scholarly journals A secondary option market design for reserve procurement by renewable energy sources.

2021 ◽  
Author(s):  
Reza Ghaffari
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Real Time ◽  
Wind Power ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Wind Power Generation ◽  
Reliable Operation ◽  
Actual Load ◽  
Balancing Services

Wind power generation is uncertain and intermittent accentuating variability. Currently in many power systems worldwide, the total generation-load unbalance caused by mismatch between forecast and actual wind power output is handled by automatic governor control and real-time 5-minute balancing markets, which are operated by the independent system operators for maintaining reliable operation of power systems. Mechanisms such as automatic governor control and real-time 5-minute balancing markets are in place to correct the mismatch between the load forecast and the actual load. They are not designed to address increased uncertainty and variability introduced by large-scale wind power or solar power generation expected in the future. Thus, large-scale wind power generation with increased uncertainty and intermittency causing variability poses a techno-economic challenge of sourcing least cost load balancing services (reserve).

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