scholarly journals The smoothing effect for renewable resources in an Afro-Eurasian power grid

2017 ◽  
Vol 14 ◽  
pp. 253-260 ◽  
Author(s):  
Maria Krutova ◽  
Alexander Kies ◽  
Bruno U. Schyska ◽  
Lueder von Bremen

Abstract. Renewable power systems have to cope with highly variable generation. Increasing the spatial extent of an interconnected power transmission grid smooths the feed-in by exchange of excess energy over long distances and therefore supports renewable power integration. In this work, we investigate and quantify the balancing potential of a supergrid covering Europe, Africa and Asia. We use ten years of historical weather data to model the interplay of renewable generation and consumption and show that a pan-continental Afro-Eurasian supergrid can smooth renewable generation to a large extent and reduce the need for backup energy by around 50 %. In addition, we show that results for different weather years vary by up to approximately 50 %.

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3365 ◽  
Author(s):  
Lukas Wienholt ◽  
Ulf Müller ◽  
Julian Bartels

The paradigm shift of large power systems to renewable and decentralized generation raises the question of future transmission and flexibility requirements. In this work, the German power system is brought to focus through a power transmission grid model in a high spatial resolution considering the high voltage (110 kV) level. The fundamental questions of location, type, and size of future storage units are addressed through a linear optimal power flow using today’s power grid capacities and a generation portfolio allowing a 66% generation share of renewable energy. The results of the optimization indicate that for reaching a renewable energy generation share of 53% with this set-up, a few central storage units with a relatively low overall additional storage capacity of around 1.6 GW are required. By adding a constraint of achieving a renewable generation share of at least 66%, storage capacities increase to almost eight times the original capacity. A comparison with the German grid development plan, which provided the basis for the power generation data, showed that despite the non-consideration of transmission grid extension, moderate additional storage capacities lead to a feasible power system. However, the achievement of a comparable renewable generation share provokes a significant investment in additional storage capacities.


2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu

<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>


Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 795 ◽  
Author(s):  
Amir Heidary ◽  
Hamid Radmanesh ◽  
Ali Moghim ◽  
Kamran Ghorbanyan ◽  
Kumars Rouzbehi ◽  
...  

Current power systems will suffer from increasing pressure as a result of an upsurge in demand and will experience an ever-growing penetration of distributed power generation, which are factors that will contribute to a higher of incidence fault current levels. Fault current limiters (FCLs) are key power electronic devices. They are able to limit the prospective fault current without completely disconnecting in cases in which a fault occurs, for instance, in a power transmission grid. This paper proposes a new type of FCL capable of fault current limiting in two steps. In this way, the FCLs’ power electronic switches experience significantly less stress and their overall performance will significantly increase. The proposed device is essentially a controllable H bridge type fault current limiter (HBFCL) that is comprised of two variable inductances, which operate to reduce current of main switch in the first stage of current limiting. In the next step, the main switch can limit the fault current while it becomes open. Simulation studies are carried out using MATLAB and its prototype setup is built and tested. The comparison of experimental and simulation results indicates that the proposed HBFCL is a promising solution to address protection issues.


Author(s):  
S. Richards ◽  
H. Perez-Blanco

Renewable power production is both variable and difficult to forecast accurately. These facts can make its integration into an electric grid problematic. If an area’s demand for electricity can be met without using renewable generation, the addition of renewable generation would not warrant a further increase in generation capacity. However, to effectively integrate large amounts of additional renewable generation, it is likely that a more flexible generation fleet will be required. One way of increasing a generation fleet’s flexibility is through the adoption of pumped hydroelectric storage (PHS, see the glossary for definitions of select terms). Like traditional hydropower generation, PHS is capable of quickly varying its power output but it is also capable of operating in reverse to store excess energy for later use. This paper will address many of the operational aspects of combining pumped hydroelectric storage (PHS), which is currently used to store excess energy from traditional generators, with wind and solar power generation. PJM, a grid operator in the Middle Atlantic States, defines capacity value for renewable generation as the percent of installed generating capacity that the generator can reliably contribute during summer peak hours. Existing wind generators inside PJM have an average capacity value of 13% and existing solar generators have a capacity value of 38%. The chief reason for these capacity values is that the renewable power production does not usually coincide with the hours of peak electricity demand during the summer. If PHS were used to firm renewable power generation, it would translate into increased utilization of the renewable generation that would displace the least efficient/most costly generators. A computer model with one minute granularity is constructed in order to study the operational requirements of PHS facilities. PJM electricity demand, power prices, and wind power production data for 2010 were used in conjunction with NREL simulated solar power production as input to the model. Currently, various PHS operational strategies are being tested to ascertain their effectiveness at firming and time shifting renewable generation. Preliminary results show the profound effects of increased penetration of renewable energy on an electric grid. The results also demonstrate a niche for even greater PHS operational flexibility, i.e. variable speed or unidirectional ternary machine (UTM) PHS.


2021 ◽  
Vol 10 (2) ◽  
pp. 104
Author(s):  
Reinhold Lehneis ◽  
David Manske ◽  
Daniela Thrän

Wind power has risen continuously over the last 20 years and covered almost 25% of the total German power provision in 2019. To investigate the effects and challenges of increasing wind power on energy systems, spatiotemporally disaggregated data on the electricity production from wind turbines are often required. The lack of freely accessible feed-in time series from onshore turbines, e.g., due to data protection regulations, makes it necessary to determine the power generation for a certain region and period with the help of numerical simulations using publicly available plant and weather data. For this, a new approach is used for the wind power model which utilizes a sixth-order polynomial for the specific power curve of a turbine. After model validation with measured data from a single wind turbine, the simulations are carried out for an ensemble of 25,835 onshore turbines to determine the German wind power production for 2016. The resulting hourly resolved data are aggregated into a time series with daily resolution and compared with measured feed-in data of entire Germany which show a high degree of agreement. Such electricity generation data from onshore turbines can be applied to optimize and monitor renewable power systems on various spatiotemporal scales.


2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu

<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>


2021 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu

<p>With the increasing penetration of renewable resources such as wind and solar, especially in terms of large-scale integration, the operation and planning of power systems are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised distribution learning method for renewable scenario forecasts that considers spatiotemporal correlation based on generative adversarial network (GAN), which has been shown to generate realistic time series for stochastic processes. We first utilize an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generate a large number of forecasted scenarios using stochastic constrained optimization. For validation, we use power generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The simulation results show that the generated trajectories not only reflect the future power generation dynamics, but also correctly capture the temporal, spatial, and fluctuant characteristics of the real power generation processes. The experimental comparisons verify the superiority of the proposed method and indicate that it can reduce at least 50% of the training iterations of the generative model for scenario forecasts.<br></p>


Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4150
Author(s):  
Lluís Monjo ◽  
Luis Sainz ◽  
Juan José Mesas ◽  
Joaquín Pedra

Photovoltaic (PV) power systems are increasingly being used as renewable power generation sources. Quasi-Z-source inverters (qZSI) are a recent, high-potential technology that can be used to integrate PV power systems into AC networks. Simultaneously, concerns regarding the stability of PV power systems are increasing. Converters reduce the damping of grid-connected converter systems, leading to instability. Several studies have analyzed the stability and dynamics of qZSI, although the characterization of qZSI-PV system dynamics in order to study transient interactions and stability has not yet been properly completed. This paper contributes a small-signal, state-space-averaged model of qZSI-PV systems in order to study these issues. The model is also applied to investigate the stability of PV power systems by analyzing the influence of system parameters. Moreover, solutions to mitigate the instabilities are proposed and the stability is verified using PSCAD time domain simulations.


Author(s):  
Omar J Guerra ◽  
Joshua Eichman ◽  
Paul Denholm

Achieving 100% carbon-free or renewable power systems can be facilitated by the deployment of energy storage technologies at all timescales, including short-duration, long-duration, and seasonal scales; however, most current literature...


Processes ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 54 ◽  
Author(s):  
Aikaterini Anastasopoulou ◽  
Sughosh Butala ◽  
Bhaskar Patil ◽  
John Suberu ◽  
Martin Fregene ◽  
...  

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