The Potential Role of Power-to-Gas Technology Connected to Photovoltaic Power Plants in the Visegrad Countries—A Case Study

With the spread of the use of renewable sources of energy, weather-dependent solar energy is also coming more and more to the fore. The quantity of generated electric power changes proportionally to the intensity of solar radiation. Thus, a cloudy day, for example, greatly reduces the amount of electricity produced from this energy source. In the countries of the European Union solar power plants are obligated to prepare power generation forecasts broken down to 15- or 60-min intervals. The interest of the regionally responsible transmission system operators is to be provided with forecasts with the least possible deviation from the actual figures. This paper examines the Visegrad countries’ intraday photovoltaic forecasts and their deviations from real power generation based on the photovoltaic power capacity monitored by the transmission system operators in each country. The novelty of this study lies in the fact that, in the context of monitored PV capacities in the Visegrad countries, it examines the regulation capacities needed for keeping the forecasts. After comparing the needs for positive and negative regulation, the author made deductions regarding storage possibilities complementing electrochemical regulation, based on the balance. The paper sought answers concerning the technologies required for the balancing of PV power plants in the examined countries. It was established that, as a result of photovoltaic power capacity regulation, among the four Visegrad countries, only the Hungarian transmission system operator has negative required power regulation, which could be utilized in power-to-gas plants. This power could be used to produce approximately 2.1 million Nm3 biomethane with a 98% methane content, which could be used to improve approximately 4 million Nm3 biogas of poor quality by enriching it (minimum 60% methane content), so that it can be utilized. The above process could enhance the viability of 4–6 low-methane agricultural biogas plants in Hungary.

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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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Large-scale penetration impact of photovoltaic power generation system on utility power plants

2009 Transmission & Distribution Conference & Exposition: Asia and Pacific ◽

10.1109/td-asia.2009.5356994 ◽

2009 ◽

Cited By ~ 1

Author(s):

T. Kato ◽

Y. Suzuoki

Keyword(s):

Power Generation ◽

Power Plants ◽

Large Scale ◽

Generation System ◽

Photovoltaic Power ◽

Power Generation System

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Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

E3S Web of Conferences ◽

10.1051/e3sconf/202018501052 ◽

2020 ◽

Vol 185 ◽

pp. 01052

Author(s):

Runjie Shen ◽

Ruimin Xing ◽

Yiying Wang ◽

Danqiong Hua ◽

Ming Ma

Keyword(s):

Power Generation ◽

Time Series ◽

Random Forest ◽

Prediction Model ◽

Power Plants ◽

Time Series Prediction ◽

Cloud Amount ◽

Continuous Model ◽

Short Term ◽

Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

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Forecast of photovoltaic power generation based on DBSCAN

E3S Web of Conferences ◽

10.1051/e3sconf/202123602016 ◽

2021 ◽

Vol 236 ◽

pp. 02016

Author(s):

Jiaying Zhang ◽

Yingfan Zhang

Keyword(s):

Power Generation ◽

Prediction Model ◽

Power Plants ◽

Large Scale ◽

Clustering Algorithm ◽

Renewable Energy Sources ◽

Stable Operation ◽

Power Prediction ◽

Photovoltaic Power ◽

Bidirectional Lstm

The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.

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ANALISIS SETTING RELE OGS SEBAGAI SISTEM PENGAMAN TRANSFORMATOR 3 UNTUK MENJAGA KONTINYUITAS ALIRAN DAYA DI GARDU INDUK PESANGGARAN

Jurnal SPEKTRUM ◽

10.24843/spektrum.2017.v04.i02.p19 ◽

2018 ◽

Vol 4 (2) ◽

pp. 145

Author(s):

I Wayan Alit Wigunawan ◽

I Gede Dyana Arjana ◽

Cok Gede Indra Partha

Keyword(s):

Power Generation ◽

Power Supply ◽

Power Plants ◽

Setting Time ◽

Transmission System ◽

Safety System ◽

Working Time ◽

Phase 2 ◽

Work Time ◽

Current Setting

Transformer 3 of Pesanggaran Substation obtains additional power plants with a maximum power of 60 MW. Power generation is used to supply the load of feeders in Substation of Pesanggaran and also channeled to 150 kV transmission system at Substation of Pesanggaran through Transformator 3 to Substation of Nusa Dua to assist power supply at Nusa Dua Substation. If there is a feeders interruption in the Substation of Pesanggaran which causes the load of feeders to be disconnected, then the power from the generator that is flowed to the 150 kV system increases. Thus the Transformer 3 works heavier, resulting in the current and temperature of the transformer also increased. Increased temperatures in the transformer for long periods can result in damage and reduced lifetime of the transformer. From the problems then calculation of current and working time OGS and OCR as the Transformer 3 safety system. The result of calculation of OGS setting of phase1 is 1600 A with setting work time of 2 seconds, phase 2 is 1700 A with setting working time of 1,5 seconds and stage 3 of 1800 A with setting work time of 1 second. The OGS relay characteristic is definite. For OCR at 150 kV side, the current setting is 277 A and setting time is 1.36 seconds, while OCR setting at 20 kV side is obtained by setting current of 2078.4 A and setting time of 1.18 seconds with OCR Relay characteristic used is inverse.

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Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2016.05.044 ◽

2016 ◽

Vol 62 ◽

pp. 971-987 ◽

Cited By ~ 64

Author(s):

Ana Cabrera-Tobar ◽

Eduard Bullich-Massagué ◽

Mònica Aragüés-Peñalba ◽

Oriol Gomis-Bellmunt

Keyword(s):

Power Plants ◽

Large Scale ◽

Transmission System ◽

Photovoltaic Power

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Electric Power Generation Equipment Specification and Supply in the Rapidly Changing Global Regulatory Scene

Volume 1: Turbo Expo 2002 ◽

10.1115/gt2002-30161 ◽

2002 ◽

Author(s):

Lawrence D. Willey ◽

Joel Chalfin

Keyword(s):

Power Generation ◽

Electric Power ◽

Power Plants ◽

Rapid Change ◽

Conformity Assessment ◽

Production Volume ◽

The European Union ◽

Electric Power Generation ◽

Technical Standards ◽

Power Generation Equipment

The proliferation of new codes & standards for power generation equipment procurement, and their increased frequency of revision, contributes to an atmosphere of increasingly rapid change in global trade considerations. This dynamic environment has amplified intensely with each year, to an extent that the life cycle of a given standard is in many instances appreciably less than the delivery cycles of heavy machinery. Other issues are created by the slower pace of harmonization of codes & standards in the European Union (EU), US and elsewhere. These codes & standards cover requirements that include emissions, acoustics, and safety that exert pronounced effects on the design, manufacture, and integration of power plant components. Conformity assessment partnering and the importance of other expert interpretation services are a key component to successfully meeting evolving compliance requirements. Delivering Customer Fulfillment for the Order to Remittance (OTR) phase of a project must be circled back to the Inquiry to Order (ITO) front end of the business cycle for new proposals. Another interesting arena is the relationship of advanced prime mover design balanced with the need for standardization to meet these regulatory challenges in the face of high production volume. The typical power generation project cycle, measured in terms of years, coupled with the present high demand worldwide results in orders for equipment that in many cases can’t foresee regulatory requirements 2 to 3 years into the future. Examples include projects in the EU where the Pressure Equipment Directive (PED) and Atmospheres Explosive (ATEX) Directive have mandatory compliance dates of May 2002 and June 2003 respectively. Electric power generation Original Equipment Manufacturers (OEMs) and their suppliers must plan for and price into contracts compliance with these laws years before the equipment is built and shipped. This is further complicated by the interpretation of specific requirements and the definition of the OEM conformity assessment strategy. To rectify this situation, it is recommended that steps be initiated to accelerate the worldwide harmonization of technical standards. In addition, consideration for the delivery cycles and commissioning of new power plants must be included in the regulatory process and in setting the dates for mandatory compliance with regional law.

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Correlation of power generation based on field data of two photovoltaic power plants in Xinjiang

2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) ◽

10.1109/auteee48671.2019.9033359 ◽

2019 ◽

Author(s):

Li Yecheng

Keyword(s):

Power Generation ◽

Power Plants ◽

Field Data ◽

Photovoltaic Power

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Accommodation Capacity Analysis of the Large Scale PV Power Generation Access to Regional Power Grid

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.772.630 ◽

2013 ◽

Vol 772 ◽

pp. 630-633

Author(s):

Tao Shi ◽

Hua Ling Han

Keyword(s):

Power Generation ◽

Power Plants ◽

Large Scale ◽

Peak Load ◽

Load Level ◽

Analysis Method ◽

Regional Power ◽

Photovoltaic Power ◽

Grid Planning ◽

Pv Power Generation

In this paper, the factors are analyzed,which affect the accommodation capacity of large scale PV power generation access to the grid . The analysis method mainly consider the peak load adjustment ability of the whole power system. Then a real case based on provincial grid is calculated and analyzed. The results show that: As an important technical style ,the generation and acommodation of the large scale photovoltaic power generation are influenced by the light resource, load level, adjustment ability, transmission channel. The planning of large-scale photovoltaic power plants must keep pace with the conventional power and grid planning.

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