Future prediction of solar power and wind power resources of using climate models for 2050

Abstract This paper assessed the potential impacts of trends detected in rainfall, temperature and wind speed on hydro and wind power resources in Burundi. Two climatic stations located at two contrasting regions, namely Rwegura catchment and northern Imbo plain, were considered. Rainfall, temperature and wind speed observed data were considered for the period 1950–2014 and future projection data from seven Regional Climate Models (RCMs) for the period 2021–2050 were used. The interannual variability analysis was made using standardized variables. Trends and rupture were respectively detected through Mann–Kendall and Pettitt non-parametric tests. Mann–Whitney and Kolmogorov–Smirnov tests were considered as subseries comparison tests. The results showed a downward trend of rainfall while temperature and wind speed revealed upward trends for the period 1950–2014. All models projected increases in temperature and wind speed compared to the baseline period 1981–2010. Five models forecasted an increase in rainfall at northern Imbo plain station while four models projected a decrease in rainfall at Rwegura station. November was forecasted by the ensemble mean model to slightly increase in rainfall for both stations. Therefore, the country of Burundi may benefit more if it plans to invest in wind power.

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Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

Energies ◽

10.3390/en14092539 ◽

2021 ◽

Vol 14 (9) ◽

pp. 2539

Author(s):

Zhengjie Li ◽

Zhisheng Zhang

Keyword(s):

Wind Power ◽

Demand Response ◽

Solar Power ◽

Load Forecasting ◽

Energy System ◽

Optimal Scheduling ◽

Scheduling Scheme ◽

Load Power ◽

Integrated Energy System ◽

Operation Cost

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

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Comparative Exploration of Mini Vertical Axis Breeze-Driven Generator

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.670-671.964 ◽

2014 ◽

Vol 670-671 ◽

pp. 964-967

Author(s):

Shu Hua Bai ◽

Hai Dong Yang

Keyword(s):

Natural Gas ◽

Wind Turbine ◽

Wind Power ◽

North Africa ◽

Large Scale ◽

Solar Power ◽

Vertical Axis ◽

Green Energy ◽

Energy Crisis ◽

Fossil Energy

Nowadays, energy crisis is becoming increasingly serious. Coal, petroleum, natural gas and other fossil energy tend to be exhausted due to the crazy exploration. In recent decades, several long lasting local wars broke out in large scale in Mideast and North Africa because of the fighting for the limited petroleum. The reusable green energy in our life like enormous wind power, solar power, etc is to become the essential energy. This article is to conduct a comparative exploration of mini wind turbine, with the purpose of finding a good way to effectively deal with the energy crisis.

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Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

Sustainability ◽

10.3390/su13126681 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6681

Author(s):

Simian Pang ◽

Zixuan Zheng ◽

Fan Luo ◽

Xianyong Xiao ◽

Lanlan Xu

Keyword(s):

Power Generation ◽

Renewable Energy ◽

Wind Power ◽

Solar Power ◽

Prediction Method ◽

Random Fluctuation ◽

Concentrating Solar Power ◽

Short Term ◽

Hybrid Forecasting ◽

Energy Cluster

Forecasting of large-scale renewable energy clusters composed of wind power generation, photovoltaic and concentrating solar power (CSP) generation encounters complex uncertainties due to spatial scale dispersion and time scale random fluctuation. In response to this, a short-term forecasting method is proposed to improve the hybrid forecasting accuracy of multiple generation types in the same region. It is formed through training the long short-term memory (LSTM) network using spatial panel data. Historical power data and meteorological data for CSP plant, wind farm and photovoltaic (PV) plant are included in the dataset. Based on the data set, the correlation between these three types of power generation is proved by Pearson coefficient, and the feasibility of improving the forecasting ability through the hybrid renewable energy clusters is analyzed. Moreover, cases study indicates that the uncertainty of renewable energy cluster power tends to weaken due to partial controllability of CSP generation. Compared with the traditional prediction method, the hybrid prediction method has better prediction accuracy in the real case of renewable energy cluster in Northwest China.

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Distribution System Fault Recovery with Undispatchable Distributed Generations

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.529.455 ◽

2014 ◽

Vol 529 ◽

pp. 455-459

Author(s):

Nan Xu ◽

Shan Shan Li ◽

Hao Ming Liu

Keyword(s):

Wind Power ◽

Distribution System ◽

Distribution Systems ◽

Solar Power ◽

Equivalent Model ◽

Point Estimate ◽

Fault Recovery ◽

Recovery Method ◽

Multi Objective ◽

The Impact

Considering the probabilistic of the wind power and the solar power, a fault recovery method for distribution systems with the wind power and the solar power is presented in this paper. For the wind power, a simplified steady-state equivalent model of an asynchronous wind generator is added into the Jacobian matrix to consider the impact of the wind power on systems. For the solar power, its output is considered as an injected power which is related with solar irradiance. Three-point estimate is employed to solve the probabilistic power flow of distribution systems with the wind power and the solar power. The restoration is described as a multi-objective problem with the mean of the system loss and the number of switch operations. Fast elitist non-dominated sorting partheno-genetic algorithm is used to solve this multi-objective problem. IEEE 33-bus system is used as an example and the results show that the models and algorithms in this paper are efficient.

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MLR institute of technology campus energy monitoring and controlling system with interconnection of grid and solar power

International Journal of Engineering & Technology ◽

10.14419/ijet.v9i2.30537 ◽

2020 ◽

Vol 9 (2) ◽

pp. 424

Author(s):

M. Sreenivasa reddy ◽

A. Shubhangi Rao ◽

Ch. Sai Prakash

Keyword(s):

Power Factor ◽

Solar Power ◽

Low Voltage ◽

Short Circuit ◽

Open Circuit ◽

Power Generating Unit ◽

Power Resources ◽

Controlling System ◽

Institute Of Technology ◽

Campus Energy

This paper mainly deals with energy consumption and monitoring of each block after carefully observing where the losses occur and how to minimize these losses and how to reduce unit consumption of each block and units consumed by capacitor bank. Base loads and Peak loads can be observed and operated in such a way as to reduce unit consumption.MLR college has 315KVA power from the grid as well as 260KW solar power generating unit where 40 percent of the power from the grid is saved. Proper planning for operating the underground bore motors used for Hostels, Mess and College buildings also saves some amount of units consumed by these motors. Further if power factor is maintained 0.99 instead of 0.2 or 0.3 some amount of units consumed can be saved. Further if maximum demand is prevented from reaching beyond the transformer rating then some amount of units consumed can be saved. Installing copper earth pits of suitable numbers for each block and balancing the loads in each phase can also reduce the losses.Synchronizing panel is to be connected to the existing 4 generators of rating 200KVA,180KVA and two numbers of 125 KVA to utilize the power resources properly.The common electrical problems like short circuit, open circuit, over voltage, low voltage, frequent power cuts, low power factor, high electricity bills damage in the meters etc. The above electrical problems are identified, rectified and frequently monitored through modern technologies like IOT.

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Impacts of Electric Road Systems on the German and Swedish Electricity Systems—An Energy System Model Comparison

Frontiers in Energy Research ◽

10.3389/fenrg.2021.631200 ◽

2021 ◽

Vol 9 ◽

Author(s):

Johanna Olovsson ◽

Maria Taljegard ◽

Michael Von Bonin ◽

Norman Gerhardt ◽

Filip Johnsson

Keyword(s):

Wind Power ◽

Peak Power ◽

Large Scale ◽

Solar Power ◽

Energy System ◽

Transport Sector ◽

Electricity System ◽

Road Systems ◽

The Impact ◽

Electricity Systems

This study analyses the impacts of electrification of the transport sector, involving both static charging and electric road systems (ERS), on the Swedish and German electricity systems. The impact on the electricity system of large-scale ERS is investigated by comparing the results from two model packages: 1) a modeling package that consists of an electricity system investment model (ELIN) and electricity system dispatch model (EPOD); and 2) an energy system investment and dispatch model (SCOPE). The same set of scenarios are run for both model packages and the results for ERS are compared. The modeling results show that the additional electricity load arising from large-scale implementation of ERS is mainly, depending on model and scenario, met by investments in wind power in Sweden (40–100%) and in both wind (20–75%) and solar power (40–100%) in Germany. This study also concludes that ERS increase the peak power demand (i.e., the net load) in the electricity system. Therefore, when using ERS, there is a need for additional investments in peak power units and storage technologies to meet this new load. A smart integration of other electricity loads than ERS, such as optimization of static charging at the home location of passenger cars, can facilitate efficient use of renewable electricity also with an electricity system including ERS. A comparison between the results from the different models shows that assumptions and methodological choices dictate which types of investments are made (e.g., wind, solar and thermal power plants) to cover the additional demand for electricity arising from the use of ERS. Nonetheless, both modeling packages yield increases in investments in solar power (Germany) and in wind power (Sweden) in all the scenarios, to cover the new electricity demand for ERS.

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Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

Frontiers in Chemical Engineering ◽

10.3389/fceng.2021.665415 ◽

2021 ◽

Vol 3 ◽

Author(s):

Hanin Alkabbani ◽

Ali Ahmadian ◽

Qinqin Zhu ◽

Ali Elkamel

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Wind Power ◽

Solar Power ◽

Electricity Sector ◽

Support Vector ◽

Renewable Power ◽

Metaheuristic Methods ◽

Metaheuristic Techniques ◽

Power Forecasting

The global trend toward a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy various demands of the market. Successful and steady integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables' stochastic behaviors. In a few reported literature studies, machine learning- (ML-) based forecasters have been widely utilized for wind power and solar power forecasting with promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind power and solar power ML forecasters, namely artificial neural networks (ANNs), recurrent neural networks (RNNs), support vector machines (SVMs), and extreme learning machines (ELMs). In addition, special attention is paid to metaheuristics accompanied by these ML models. Detailed comparisons of the different ML methodologies and the metaheuristic techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on the ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.

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Wind and Solar Micro-Grid Including Energy Storare System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.838 ◽

2013 ◽

Vol 694-697 ◽

pp. 838-841 ◽

Cited By ~ 1

Author(s):

Li Xia Lv ◽

Yue Xia Jin

Keyword(s):

Energy Storage ◽

Wind Power ◽

Power Quality ◽

Solar Power ◽

Storage System ◽

Energy Storage System ◽

Battery Storage ◽

Source Current ◽

Quality Stability ◽

Micro Grid

In order to solve the problems which are caused by wind power and solar power on-grid, such as power quality, stability, reliability and so on, this paper proposes wind and solar micro-grid including energy storage system(ESS). The micro-grid uses two buses: AC bus and DC bus. Micro-source current is connected to the grid with the same inverter, which can reduce the micro-grid control difficulties. The energy storage system connected to the grid through the inverter could directly absorb energy from the grid. The DC side of the energy storage system using bidirectional DC / DC converter, which can keep the DC side of the battery storage stable.

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