Hybrid hydropower-connected floating solar PV plants: impact of the downstream water release constraint

2021 ◽  
Author(s):  
Stanislas Merlet ◽  
Magnus Korpås ◽  
Bjørn Thorud
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Large Scale ◽  
Solar Power ◽  
Hydropower Plant ◽  
Solar Pv ◽  
Reservoir Operations ◽  
Water Release ◽  
Renewable Power ◽  
Scale Integration

<p>Solar and wind power continue to dominate the renewable energy expansion, jointly accounting for more than 90% of the new capacity installed in 2019. Hydropower, however, still accounts for 47% of the 2,537 GW of global renewable power in operation. Solar power continued to lead the yearly expansion, for the fourth year in a row, with an annual increase of +20% while hydropower capacity increased by +1%. However, the inherent intermittency and stochastic nature of solar PV is a well-known obstacle to the further large-scale integration of the technology in existing power systems. Large-scale reservoir hydropower offers a cost-competitive, mature and dispatchable alternative that can provide both production flexibility and storage. Nonetheless, the costs of large hydropower are highly site-specific and new capacity development has been more and more constrained by substantial environmental and social impacts in many places worldwide. Solar power and hydropower resources have been identified to be quite complementary and hybrid plants could have many flexibility benefits in addition to the increase of renewable energy production. In this context, floating solar PV (FPV) on hydropower reservoirs is emerging as a relevant solution to accommodate both energy sources at the same location.</p><p>Adding FPV to an existing hydropower plant, aiming at hybridizing the output, might impact its reservoir operations and water-related constraints need to be carefully considered. Solar PV can contribute to saving water on mid- to long-term scheduling considering that solar energy generation corresponds in some extent to non-turbined water, i.e. saved energy. Besides, on the short-term time scale, one of the main benefits is that hydropower could, in some extent, compensate for the variability of PV generation by its rapidly adjustable output. In practice, a utility-scale solar PV plant could lose several MW of generation in seconds, if a large cloud passes, for example. To avoid consequences on the power grid, this energy loss would need to be translated almost immediately (according to available capacity and ramp rates capabilities) to hydropower generation, meaning substantial (and potentially more frequent) surges in released water downstream.</p><p>The presentation investigates these opportunities and challenges linked to reservoir operations of hybrid hydropower-connected floating solar PV plants and provide inputs on optimal solutions.</p>

scholarly journals Day-ahead renewable scenario forecasts based on generative adversarial networks

2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu
Keyword(s):  
Power Systems ◽  
Renewable Resources ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Power Sources ◽  
Renewable Power ◽  
Adversarial Networks ◽  
Future Behavior ◽  
Large Scale Integration ◽  
Scale Integration

<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>

scholarly journals Exploring Wind and Solar PV Generation Complementarity to Meet Electricity Demand

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4132 ◽  
Author(s):  
António Couto ◽  
Ana Estanqueiro
Keyword(s):  
Power Systems ◽  
Power System ◽  
Solar Power ◽  
Extreme Values ◽  
System Development ◽  
Power Source ◽  
Electricity Demand ◽  
Energy Deficit ◽  
Solar Pv ◽  
Renewable Power

Understanding the spatiotemporal complementarity of wind and solar power generation and their combined capability to meet the demand of electricity is a crucial step towards increasing their share in power systems without neglecting neither the security of supply nor the overall cost efficiency of the power system operation. This work proposes a methodology to exploit the complementarity of the wind and solar primary resources and electricity demand in planning the expansion of electric power systems. Scenarios that exploit the strategic combined deployment of wind and solar power against scenarios based only on the development of an individual renewable power source are compared and analysed. For each scenario of the power system development, the characterization of the additional power capacity, typical daily profile, extreme values, and energy deficit are assessed. The method is applied to a Portuguese case study and results show that coupled scenarios based on the strategic combined development of wind and solar generation provide a more sustainable way to increase the share of variable renewables into the power system (up to 68% for an annual energy exceedance of 10% for the renewable generation) when compared to scenarios based on an individual renewable power source. Combined development also enables to reduce the overall variability and extreme values of a power system net load.

scholarly journals A Self Monitoring and Analyzing System for Solar Power Station using IoT and Data Mining Algorithms

2021 ◽  
Vol 3 (2) ◽  
pp. 96-109
Author(s):  
Subarna Shakya
Keyword(s):  
Data Mining ◽  
Renewable Energy ◽  
Power Systems ◽  
Large Scale ◽  
Solar Power ◽  
Renewable Energy Sources ◽  
Large Area ◽  
Power Station ◽  
Research Attention ◽  
Data Mining Algorithms

Renewable energy sources are gaining a significant research attention due to their economical and sustainable characteristics. In particular, solar power stations are considered as one of the renewable energy systems that may be used in different locations since it requires a lower installation cost and maintenance than conventional systems, despite the fact that they require less area. In most of the small generating stations, space occupancy is controlled by placing the equipment on an open terrace. However, for large-scale power generating stations, acres of land are required for installation. Human employers face a challenging task in maintaining such a large area of power station. Through IoT and data mining techniques, the proposed algorithm would aid human employers in detecting the regularity of power generation and failure or defective regions in solar power systems. This allows performing a quick action for the fault rectification process, resulting in increased generating station efficiency.

scholarly journals Optimising the Concentrating Solar Power Potential in South Africa through an Improved GIS Analysis

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3258
Author(s):  
Dries. Frank Duvenhage ◽  
Alan C. Brent ◽  
William H.L. Stafford ◽  
Dean Van Den Heever
Keyword(s):  
South Africa ◽  
Renewable Energy ◽  
Spatial Data ◽  
Large Scale ◽  
Solar Power ◽  
Solar Irradiation ◽  
Solar Pv ◽  
Concentrating Solar Power ◽  
Generating Capacity ◽  
The Impact

Renewable Energy Technologies are rapidly gaining uptake in South Africa, already having more than 3900 MW operational wind, solar PV, Concentrating Solar Power (CSP) and biogas capacity. CSP has the potential to become a leading Renewable Energy Technology, as it is the only one inherently equipped with the facility for large-scale thermal energy storage for increased dispatchability. There are many studies that aim to determine the potential for CSP development in certain regions or countries. South Africa has a high solar irradiation resource by global standards, but few studies have been carried out to determine the potential for CSP. One such study was conducted in 2009, prior to any CSP plants having been built in South Africa. As part of a broader study to determine the impact of CSP on South Africa’s water resources, a geospatial approach was used to optimise this potential based on technological changes and improved spatial data. A tiered approach, using a comprehensive set of criteria to exclude unsuitable areas, was used to allow for the identification of suitable areas, as well as the modelling of electricity generation potential. It was found that there is more than 104 billion m2 of suitable area, with a total theoretical potential of more than 11,000 TWh electricity generating capacity.

scholarly journals Day-ahead renewable scenario forecasts based on generative adversarial networks

2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu
Keyword(s):  
Power Systems ◽  
Renewable Resources ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Power Sources ◽  
Renewable Power ◽  
Adversarial Networks ◽  
Future Behavior ◽  
Large Scale Integration ◽  
Scale Integration

<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>

scholarly journals Short-Term Scheduling of Expected Output-Sensitive Cascaded Hydro Systems Considering the Provision of Reserve Services

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2477
Author(s):  
Ruhong Zhong ◽  
Chuntian Cheng ◽  
Shengli Liao ◽  
Zhipeng Zhao
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Solar Power ◽  
Short Term ◽  
Iterative Approximation ◽  
Peak Shaving ◽  
Renewable Power ◽  
Optimality Algorithm ◽  
Output Characteristics ◽  
Iterative Approximation Method

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

scholarly journals Day-ahead renewable scenario forecasts based on generative adversarial networks

2021 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Renewable Resources ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Power Sources ◽  
Renewable Power ◽  
Adversarial Network ◽  
Scale Integration

<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>

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