Joint optimisation of generation and storage in the presence of wind

Liu, Fei; Giulietti, Monica; Chen, Bo

doi:10.1049/iet-rpg.2015.0547

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…If left unaddressed, the fluctuating nature of RESs may lead to power outages, associated with severe economical, healthcare, and other consequences. While energy storage provides a quick solution, the investment in batteries per MW of wind power can actually exceed the cost of the wind turbine (WT) [6, 7]. The uneven electricity production is, therefore, better off mitigated using real‐time load balancing capabilities of smart grids [8], made possible by accurate and elaborate RES forecasting techniques [9].…”

Section: Introductionmentioning

confidence: 99%

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Marinšek

Bajt

2020

IET Renewable Power Generation

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Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open‐source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short‐term memory (LSTM) for short‐term wind power forecasting based on the publicly accessible ERA5‐Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3‐hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.

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Section: Introductionmentioning

confidence: 99%

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Marinšek

Bajt

2020

IET Renewable Power Generation

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“…Therefore, there is a tradeoff between system reliability and costs, which can only be addressed by the development of efficient capacity estimation techniques for the BESS. Numerous research works have been carried out in this regard for optimal sizing of storage systems along with the generation units such as wind and solar PV plants [16][17][18][19][20]. The complementary nature of these resources is utilised for determining the optimal capacity of a BESS.…”

Section: Introductionmentioning

confidence: 99%

Method for planning a wind–solar–battery hybrid power plant with optimal generation‐demand matching

Khalid¹,

AlMuhaini²,

Aguilera³

et al. 2018

IET Renewable Power Generation

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This study aims to propose a methodology for a hybrid wind-solar power plant with the optimal contribution of renewable energy resources supported by battery energy storage technology. The motivating factor behind the hybrid solarwind power system design is the fact that both solar and wind power exhibit complementary power profiles. Advantageous combination of wind and solar with optimal ratio will lead to clear benefits for hybrid wind-solar power plants such as smoothing of intermittent power, higher reliability, and availability. However, the potential challenges for its integration into electricity grids cannot be neglected. A potential solution is to utilise one of the energy storage technologies, though all of them are still very expensive for such applications, especially at large scale. Therefore, optimal capacity calculations for energy storage system are also vital to realise full benefits. Currently, battery energy storage technology is considered as one of the most promising choices for renewable power applications. This research targets at battery storage technology and proposes a generic methodology for optimal capacity calculations for the proposed hybrid wind-solar power system.

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“…Therefore, there is a tradeoff between system reliability and costs, which can only be addressed by the development of efficient capacity estimation techniques for the BESS. Numerous research works have been carried out in this regard for optimal sizing of storage systems along with the generation units such as wind and solar PV plants [19][20][21][22][23]. The complementary nature of these resources is utilized for determining the optimal capacity of a BESS.…”

mentioning

confidence: 99%

Generation of Hybrid Energy System (Solar-Wind) Supported with Battery Energy Storage

Srivastava¹

2022

IJRASET

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The utilization of renewable energy is significantly important for the world because global energy consumption is increasing, while conventional energy sources are no longer sufficient to meet the energy demand, triggering energy crises. In recent years, the increasing prices of fossil fuels and concerns about the environmental consequences of greenhouse gas emissions have renewed the interest in the development of alternative energy resources. Renewable energy is now considered a more desirable source of fuel than nuclear power due to the absence of risk and disasters [1]. Considering that the major component of greenhouse gases is carbon dioxide, there is a global concern about reducing carbon emissions. In this regard, different policies could be applied to reducing carbon emissions, such as enhancing renewable energy deployment and encouraging technological innovations. There are various of renewable energy sources such as Solar, biomass, wind, hydrogen, fuel cell, nanocomposite, and supercapacitor. Each of the energy sources are suitable for specific geographical locations and can suits from region to region. However, variation in solar radiation and wind speed caused by climate and weather conditions restricts the stable operation of renewable energy systems, therefore, causing the output to fluctuate. A hybrid renewable energy generation system can be highly efficient by combining multiple renewable energy sources and is regarded as a promising solution to overcome from this issue. Hybrid solar systems are the systems combining two renewable sources of energy, like solar and wind. Then, energy is generated through solar on sunny days and when there is limited sunshine but there is wind, energy can be generated through it. The study aims to focus on generation of hybrid solar-wind power plant with the optimal contribution of renewable energy resources supported by battery energy storage technology. The motivating factor behind the hybrid solar–wind power system design is the fact that both solar and wind power exhibit complementary power profiles. Advantageous combination of solar and wind with optimal ratio will lead to clear benefits for hybrid solar-wind power plants such as smoothing of intermittent power, higher reliability, and availability. However, the potential challenges for its integration into power grids cannot be neglected. A potential solution is to utilise one of the energy storage technologies, though all of them are still very expensive for such applications, especially at large scale. Therefore, optimal capacity calculations for energy storage system are also vital to realise full benefits. Currently, battery energy storage technology is considered as one of the most promising choices for renewable power applications. However, solar-wind power technology are most suitable for off-grid services, serving the remote are without having to build or extend expensive and complicated grid infrastructure. Therefor standalone system using renewable energy sources have become a preferred option. Hence hybrid energy systems are an ideal solution since they can offer substantial improvements in performance and cost reduction and can be tailored to varying end user requirements

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Joint optimisation of generation and storage in the presence of wind

Cited by 8 publications

References 11 publications

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Method for planning a wind–solar–battery hybrid power plant with optimal generation‐demand matching

Generation of Hybrid Energy System (Solar-Wind) Supported with Battery Energy Storage

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