Profitability Assessment of Residential Photovoltaic Battery Systems in Japan Using Electric Power Big Data

Residential photovoltaic (PV) battery systems are key technology in the design of low-carbon and resilient energy systems; however, limited research has assessed their profitability. This study aims to evaluate the economic performance of PV battery systems for end-users. The evaluation takes geological, technological, and socio-economic factors into consideration, thereby making the evaluation more comprehensive. We used PV power generation data and power consumption data of more than 40,000 all-electric houses in Japan. We performed scenario analyses with a sensitivity analysis. The results showed that residential PV battery systems were highly profitable when their storage battery operation modes were appropriately utilized at the end of the purchase period for solar power generation in Japan’s feed-in-tariff (FIT) scheme. The profitability, however, varied across regions. The results also indicated that the PV self-consumption rate was more than 50% when charging the battery with surplus power. The results of the sensitivity analysis suggested that the unit prices of grid electricity and the purchasing price of surplus power after the FIT scheme had a significant effect on the profitability of residential PV battery systems.

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Photovoltaic-battery systems as irradiance sensors: first results of a prototype study

10.5194/ems2021-392 ◽

2021 ◽

Author(s):

James Barry ◽

Anna Herman-Czezuch ◽

Daniel Fischer ◽

Stefanie Meilinger ◽

Rone Yousif ◽

...

Keyword(s):

Power Generation ◽

Energy Storage ◽

Radiative Transfer ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Storage Systems ◽

Energy Storage Systems ◽

Household Energy ◽

Battery Systems ◽

Pv Power Generation

In view of the rapid growth of solar power installations worldwide, accurate forecasts of photovoltaic (PV) power generation are becoming increasingly indispensable for the overall stability of the electricity grid. In the context of household energy storage systems, PV power forecasts contribute towards intelligent energy management and control of PV-battery systems, in particular so that self-sufficiency and battery lifetime are maximised. Typical battery control algorithms require day-ahead forecasts of PV power generation, and in most cases a combination of statistical methods and numerical weather prediction (NWP) models are employed. The latter are however often inaccurate, both due to deficiencies in model physics as well as an insufficient description of irradiance variability.A promising approach to improving irradiance forecasts is to use the measured PV data themselves. In this work a novel algorithm was employed in order to infer global horizontal irradiance from measured PV data of household energy storage systems, with the goal of better characterising global horizontal irradiance (GHI) and ultimately improving irradiance and power forecasts. The inversion methods developed as part of the BMWi-funded MetPVNet project were applied to five PV-battery systems in different locations across Germany, in a pilot project sponsored by the local government of North Rhine-Westphalia (MWIDE NRW). High resolution measurements of PV power and current were used together with two different PV models in order to extract the plane-of-array irradiance. These data were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde&#160;et&#160;al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions. The transposition of tilted to horizontal irradiance was performed with a new lookup table based on 3D radiative transfer simulations in MYSTIC.The PV-battery systems were all equipped with irradiance sensors to provide independent measurements of both global tilted irradiance (GTI) and GHI, in order to validate the proposed inversion and transposition methods. Comparisons were also made with the irradiance predictions of the ICON-D2 weather model, the irradiance and cloud optical properties from satellite retrievals and the aerosol optical depth from the relevant AERONET stations. This work can provide the basis for future investigations using a larger number of PV-battery systems to evaluate the improvements to irradiance forecasts by the assimilation of inferred irradiance into a NWP model. In addition, the results could be used to improve the intelligent control of the storage systems in the field.ReferencesEmde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev., 9, 1647&#8211;1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/.

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Uncertainty and global sensitivity analysis of levelized cost of energy in wind power generation

Energy Conversion and Management ◽

10.1016/j.enconman.2020.113781 ◽

2021 ◽

Vol 229 ◽

pp. 113781

Author(s):

Mi Dong ◽

Ya Li ◽

Dongran Song ◽

Jian Yang ◽

Mei Su ◽

...

Keyword(s):

Power Generation ◽

Sensitivity Analysis ◽

Wind Power ◽

Global Sensitivity Analysis ◽

Wind Power Generation ◽

Global Sensitivity ◽

Levelized Cost ◽

Cost Of Energy

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Novel Technique to Construct Technique for PV Power Generation with fuzzy logic

2020 IEEE International Conference for Innovation in Technology (INOCON) ◽

10.1109/inocon50539.2020.9298343 ◽

2020 ◽

Author(s):

Shalini ◽

Imran Khan

Keyword(s):

Power Generation ◽

Fuzzy Logic ◽

Novel Technique ◽

Pv Power Generation

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Life Cycle Energy Consumption and Carbon Dioxide Emissions of Agricultural Residue Feedstock for Bioenergy

Applied Sciences ◽

10.3390/app11052009 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2009

Author(s):

Valerii Havrysh ◽

Antonina Kalinichenko ◽

Anna Brzozowska ◽

Jan Stebila

Keyword(s):

Carbon Dioxide ◽

Power Generation ◽

Life Cycle ◽

Carbon Dioxide Emissions ◽

Crop Production ◽

Agricultural Residues ◽

Environmental Indicators ◽

Low Carbon ◽

Low Carbon Economy ◽

Energy Inputs

The depletion of fossil fuels and climate change concerns are drivers for the development and expansion of bioenergy. Promoting biomass is vital to move civilization toward a low-carbon economy. To meet European Union targets, it is required to increase the use of agricultural residues (including straw) for power generation. Using agricultural residues without accounting for their energy consumed and carbon dioxide emissions distorts the energy and environmental balance, and their analysis is the purpose of this study. In this paper, a life cycle analysis method is applied. The allocation of carbon dioxide emissions and energy inputs in the crop production by allocating between a product (grain) and a byproduct (straw) is modeled. Selected crop yield and the residue-to-crop ratio impact on the above indicators are investigated. We reveal that straw formation can consume between 30% and 70% of the total energy inputs and, therefore, emits relative carbon dioxide emissions. For cereal crops, this energy can be up to 40% of the lower heating value of straw. Energy and environmental indicators of a straw return-to-field technology and straw power generation systems are examined.

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

Electronics ◽

10.3390/electronics9101717 ◽

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.

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Modernization of CHP Cycle in Sugar Complex

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.281.578 ◽

2013 ◽

Vol 281 ◽

pp. 578-581

Author(s):

Sanjay S. Bhagwat ◽

S.D. Pohekar ◽

A.M. Wankhade

Keyword(s):

Power Generation ◽

High Pressure ◽

Electric Power ◽

Sugar Cane ◽

Combined Heat And Power ◽

Power Ratio ◽

Heat Power ◽

Sugar Factory ◽

Power Cycle ◽

Surplus Power

Keywords: CHP, Bagasse, Heat Power Ratio, TCD Abstract: A huge potential for power generation from waste fuels exists within the sugar cane industry. Newly developed advanced high pressure boiler technology and utilizing modified combined heat and power cycle opens the way to fully exploit this potential, yielding more kWh’s of electric power per tonne of cane. This paper deals feasibility of bagasse based modified CHP cycle for 2500TCD sugar factory for surplus power generation.

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A Study on the Improvement of Efficiency by Detection Solar Module Faults in Deteriorated Photovoltaic Power Plants

Applied Sciences ◽

10.3390/app11020727 ◽

2021 ◽

Vol 11 (2) ◽

pp. 727 ◽

Cited By ~ 1

Author(s):

Myeong-Hwan Hwang ◽

Young-Gon Kim ◽

Hae-Sol Lee ◽

Young-Dae Kim ◽

Hyun-Rok Cha

Keyword(s):

Power Generation ◽

Power Plants ◽

Detection System ◽

Failure Detection ◽

Solar Module ◽

Thermal Images ◽

Power Stations ◽

Photovoltaic Power ◽

Manufacturing Technologies ◽

Pv Power Generation

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants.

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