A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids

Shokrzadeh, Shahab; Jozani, Mohammad Jafari; Bibeau, Eric; Molinski, Tom

doi:10.1016/j.energy.2015.05.140

Cited by 27 publications

(10 citation statements)

References 25 publications

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“…The characteristics of the reactive power change of renewable energy power stations have a great influence on the planning 1 and capacity configuration of energy storage power stations. [2][3][4] Different from the traditional thermal power and nuclear power stations, the output power of PV power generations is random and uncontrollable. 5 Deeply mining the historical data is one of the intuition and effective ways to extract the characteristics of the system, 6 and the scientific establishment of data samples is the premise of statistical analysis related to the accuracy and validity of the system.…”

Section: Discussionmentioning

confidence: 99%

“…The planning and designing stage of energy storage power stations mainly relies on the large historical operation data of renewable energy power stations, which are used in combination with them. The characteristics of the reactive power change of renewable energy power stations have a great influence on the planning and capacity configuration of energy storage power stations . Different from the traditional thermal power and nuclear power stations, the output power of PV power generations is random and uncontrollable .…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Han

Liu

et al. 2017

Int J Energy Res

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Summary The acquisition granularity (time feature quantity) and sampling span (spatial feature quantity) of the data are the feature factors to analyze the active power of renewable energy power stations. According to the time and space characteristics of photovoltaic (PV) power stations, the acquisition granularity and sampling span calibration methods of PV output power based on data mining technology are proposed this paper. The initial range of the acquisition granularity is determined by analyzing the maximum‐order difference components of the PV output power. Through deeply mining the continuous change state of the PV output power, an acquisition granularity calibration method of the PV output power based on the multiobjective optimization model is proposed from time characteristics. The particle swarm optimization algorithm is used to solve the model to obtain the optimal the acquisition granularity of the PV power station. Through the analysis of the sample information entropy change trend of the PV output power, a sampling span calibration method of the PV output power based on the information entropy theory is proposed from space characteristics. The sensitivity analysis of the acquisition granularity and sampling span of the data to the capacity of energy storage systems is realized by the smooth control of the PV output power using first‐order low filters. The simulation tests of the annual history operating data at a PV power station with the installed capacity of 40 MW in China verify the validity of the provided methods. The simulation results show when the acquisition granularity takes 60 seconds and the sampling span takes 33 days, it can satisfy the accuracy of the required data of energy storage systems to realize the smooth control of the PV output power.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Han

Liu

et al. 2017

Int J Energy Res

View full text Add to dashboard Cite

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“…Modern day energy grids are more efficient, produce more energy, use more distributed energy generating units and put less emphasis on fossil fuels. This approach means that maintaining high power quality indices becomes harder due to a much higher number of generating stations which experience greater output variation than traditional power plants [1]. However, it is possible to make renewable energy generating stations more stable by adding energy storage.…”

Section: Introductionmentioning

confidence: 99%

“…With the ever-increasing demand of energy consumption and numerous environmental concerns it is of increasing significance to modernize the energy grid [1]. Modern day energy grids are more efficient, produce more energy, use more distributed energy generating units and put less emphasis on fossil fuels.…”

Section: Introductionmentioning

confidence: 99%

“…It is impossible to make a 24 h forecast based on statistical data alone. There are cases where stochastic methods were applied but they had to be combined with weather data for making a 24 h forecast [1,9]. Analyzing the required storage capacity using statistical methods is convenient because it does not require the determining of numerous parameters and it is always possible to get an accurate confidence level using a histogram of power generation variation which is useful especially when dealing with the customer.…”

Section: Introductionmentioning

confidence: 99%

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Statistical analysis and dimensioning of a wind farm energy storage system

Waśkowicz

2017

Archives of Electrical Engineering

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Abstract:The growth in renewable power generation and more strict local regulations regarding power quality indices will make it necessary to use energy storage systems with renewable power plants in the near future. The capacity of storage systems can be determined using different methods most of which can be divided into either deterministic or stochastic. Deterministic methods are often complicated with numerous parameters and complex models for long term prediction often incorporating meteorological data. Stochastic methods use statistics for ESS (Energy Storage System) sizing, which is somewhat intuitive for dealing with the random element of wind speed variation. The proposed method in this paper performs stabilization of output power at one minute intervals to reduce the negative influence of the wind farm on the power grid in order to meet local regulations. This paper shows the process of sizing the ESS for two selected wind farms, based on their levels of variation in generated power and also, for each, how the negative influences on the power grid in the form of voltage variation and a shortterm flicker factor are decreased.

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Dynamic optimization of solar‐wind hybrid system connected to electrical battery or hydrogen as an energy storage system

Shams

Ahmadi

2021

Int J Energy Res

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Summary Nowadays, due to the pollution arising from the consumption of fossil fuels, destructive effects of pollutants on humans and the environment, and the reduction of reserves of these energy resources, the tendency to utilize renewable energy to meet energy needs has increased. In this investigation, first, a grid‐connected photovoltaic‐wind‐battery (PWB) hybrid system for an educational building is designed and optimized by implementing a genetic algorithm in MATLAB. The main goal is to minimize the annual total cost (ATC) and find the optimal sizes. The balance between the high costs of renewable electricity generation and the setting up a renewable system is achieved by applying the renewable energy fraction (fRE). In the second part, a photovoltaic‐wind‐fuel cell (PWF) hybrid system is designed and optimized in the same approach. A novel techno‐economic‐environmental comparison is performed to select the battery bank or hydrogen system as energy storage. Considering the environmental penalties, the effect of the environmental cost on the optimal sizes of components and annual total costs is investigated. Owing to the low tariff of the grid electricity as well as the high cost of the renewable systems, results confirms that the use of renewable systems is not cost‐effective. Also, if the authorities and householders intend to implement a storage system to store electricity, battery bank is less expensive than fuel cell system. To apply scenarios to increase renewable energy penetration, a comprehensive sensitivity analysis on the ATCs and optimal sizes is conducted technically and economically. Highlights Designing, optimizing, and comparing of PWB and PWF systems are investigated GA in MATLAB is implemented to determine optimal sizes and minimum ATCs A comprehensive sensitivity analysis is performed on the ATCs and optimal sizes The installing of the hybrid systems with the current tariffs is not cost effective Superiority of the battery bank over the hydrogen system in residential storage

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A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids

Cited by 27 publications

References 25 publications

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Statistical analysis and dimensioning of a wind farm energy storage system

Dynamic optimization of solar‐wind hybrid system connected to electrical battery or hydrogen as an energy storage system

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