This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.

https://www.mdpi.com/2079-9292/10/8/878/pdf

Parameter Extraction of Photovoltaic Module Using Tunicate Swarm Algorithm

Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations.

/pdf/solar-power-forecasting-using-cnn-lstm-hybrid-model-1d07uymi.pdf

Solar Power Forecasting Using CNN-LSTM Hybrid Model

One way to reduce CO2 emissions is to replace conventional energy sources with renewable ones. In order to encourage prosumers to invest in renewable energy, EU Member States are developing renewable energy subsidy programs. In Poland, in the years 2019–2020, the “My Electricity” program was implemented, co-financing was up to 50% of eligible costs (max PLN 5000, i.e., EUR 1111), and the total cost of the program was 251 million euro. During this period, around 400,000 prosumer installations were created in Poland, including over 220,000 prosumer PV Installations under the My Electricity program. The total power of the installation under the “My Electricity” program was 1.295 GWp with an average installation power of 5.72 kWp. It is estimated that the micro-installations will produce approx. 1.4 TWh of electricity annually. Depending on the replaced source of electricity (coal, gas, mix), in the next 30 years, it will help to avoid 26.2–42.7 million Mg of greenhouse gases calculated as carbon dioxide equivalents (CO2eq). The coefficient of subsidy expenditure from the “My Electricity” program was 194 EUR/kWp, and in the next 30 years, it will be 6.52 EUR/MWh. The investment in PV will save EUR 1550 million, which would have to be incurred for the purchase of CO2 emission permits.

The “My Electricity” Program as One of the Ways to Reduce CO2 Emissions in Poland

Analysis of factors affecting efficiency of inverters: Case study grid-connected PV systems in lower northern region of Thailand

The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.

https://www.mdpi.com/2227-9717/8/10/1225/pdf

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

The number of the PV systems installed in south korea has been gradually increasing. Interest of operation & management of PV System is rising up as PV System installation trends to increase. The vital function of operation & management is the monitoring system. Most monitoring systems are only available for equipment of suppliers and manufacturers. In this study, a monitoring system was implemented to support multi-domain equipment (inverter, junction box, water distribution board, environmental sensors) that are not limited to specific manufacturers. Monitoring system includes a visualization function, which makes it easy for users to check the power generation and the operation status of the equipment of PV System. In the future, this monitoring system will be utilized as an operation & maintenance foundation element in the PV system industry.

https://www.koreascience.or.kr:443/article/JAKO202028959353592.pdf

Heterogeneous Equipment Support Monitoring System for Operation and Maintenance of Solar power plant

Among the factors determining the performance of the new renewable energy system, the processing part of the untreated DC power is a very important element. In fact, the area where the loss of the new renewable energy system is greatest is also the DC transmission and distribution technology. It is critical to prevent this loss and to improve the performance of the system, and analyze the performance of the resulting through the application of the resulting algorithms. In this thesis, we are discussing ways to calibrate the error in the PV distribution system within the solar power generation system using the Bypass Line Configuration Algorithm. I would like to note that if the operation of the Bypass Line device generates electrical abnormalities in the system’s circuits, it can recover the performance of the system, thereby improving performance.

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Beob-Seong Song

Papers

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Heterogeneous Equipment Support Monitoring System for Operation and Maintenance of Solar power plant

Development and Research of Bypass Device to Improve DC Grid Performance