Techno-economic and environmental assessment of utilizing campus building rooftops for solar PV power generation

2021 ◽  
pp. 1-13
Author(s):  
Bhola Paudel ◽  
Niraj Regmi ◽  
Parlad Phuyal ◽  
Deependra Neupane ◽  
M. Imtiaz Hussain ◽  
...  
Keyword(s):  
Power Generation ◽  
Environmental Assessment ◽  
Solar Pv ◽  
Pv Power Generation ◽  
Campus Building

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

Solar PV Power Generation Forecasting and O&M Management Applications: A Review

2019 ◽  
Author(s):  
Chen Zhang ◽  
Tao Yang ◽  
Wei Gao ◽  
Yong Wang
Keyword(s):  
Power Generation ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Data Sets ◽  
Complex Data ◽  
Paper Machine ◽  
Solar Pv ◽  
Construction Costs ◽  
Complex Data Sets ◽  
Pv Power Generation

Abstract The growing resource shortage and environmental concerns have forced mankind to develop and utilize renewable energy sources. The penetration of solar photovoltaic (PV) power in the electricity market has been increasing over the past few decades due to its low construction costs, zero pollution nature, and enormous support from governments. However, the intermittency and randomness of PV power also cause huge grid fluctuations which limit its integration in the system. An accurate forecasting of solar PV power generation and optimization of operation and maintenance (O&M) management are essential for further development of the solar PV farms. A great number of related researches have been done in recent years. A review of PV power generation forecasting techniques together with their brief applications on the optimization of O&M management is presented in this paper. Machine learning methods are thought to be the most suitable at the present stage because of their ease of implementation and their capability in processing non-linear, complex data sets. Typical forecasting accuracy measures are summarized and further applications of PV power forecasting on the O&M management are also presented.

scholarly journals Climate change impacts on solar power generation and its spatial variability in Europe based on CMIP6

2021 ◽  
Vol 12 (4) ◽  
pp. 1099-1113
Author(s):  
Xinyuan Hou ◽  
Martin Wild ◽  
Doris Folini ◽  
Stelios Kazadzis ◽  
Jan Wohland
Keyword(s):  
Climate Change ◽  
Power Generation ◽  
Spatial Variability ◽  
Cloud Cover ◽  
Climate Change Impacts ◽  
Solar Pv ◽  
Clear Sky ◽  
Generation Structure ◽  
Recent Climate ◽  
Pv Power Generation

Abstract. Solar photovoltaics (PV) plays an essential role in decarbonizing the European energy system. However, climate change affects surface solar radiation and will therefore directly influence future PV power generation. We use scenarios from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) for a mitigation (SSP1-2.6) and a fossil-fuel-dependent (SSP5-8.5) pathway in order to quantify climate risk for solar PV in Europe as simulated by the Global Solar Energy Estimator (GSEE). We find that PV potential increases by around 5 % in the mitigation scenario, suggesting a positive feedback loop between climate change mitigation and PV potential. While increased clear-sky radiation and reduced cloud cover go hand in hand in SSP1-2.6, the effect of a decrease in clear-sky radiation is outweighed by a decrease in cloud cover in SSP5-8.5, resulting in an increase in all-sky radiation. Moreover, we find that the seasonal cycle of PV generation changes in most places, as generation grows more strongly in winter than in summer (SSP1-2.6) or increases in summer and declines in winter (SSP5-8.5). We further analyze climate change impacts on the spatial variability of PV power generation. Similar to the effects anticipated for wind energy, we report an increase in the spatial correlations of daily PV production with large inter-model agreement yet relatively small amplitude, implying that PV power balancing between different regions in continental Europe will become more difficult in the future. Thus, based on the most recent climate simulations, this research supports the notion that climate change will only marginally impact renewable energy potential, while changes in the spatiotemporal generation structure are to be expected and should be included in power system design.

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