Model of Photovoltaic Power Plants for Performance Analysis and Production Forecast

Bizzarri, Federico; Bongiorno, Massimo; Brambilla, A.; Gruosso, Giambattista; Gajani, G. Storti

doi:10.1109/tste.2012.2219563

Cited by 95 publications

(37 citation statements)

References 21 publications

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“…In Eq. (24), T PV is given as a function of the nominal cell temperature, T noct = 317 K, nominal direct normal irradiance, DNI 0 , nominal diffuse horizontal irradiance, DHI 0 , nominal wind speed, v 0 , ambient temperature, T 0 , operating DNI and DHI (DHI = 0.165ÁDNI/ (1 À 0.165) (Duffie and Beckman, 2013)), and operating wind speed, v (Bizzarri et al, 2013). The detailed calculation procedures for the parameters are detailed in (De Soto et al, 2006).…”

Section: Csp Performance Modelmentioning

confidence: 99%

Techno-economic modeling and optimization of solar-driven high-temperature electrolysis systems

Lin

Haussener

2017

Solar Energy

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a b s t r a c tWe present a techno-economic analysis of solar-driven high-temperature electrolysis systems used for the production of hydrogen and synthesis gas. We consider different strategies for the incorporation of solar energy, distinguished by the use of differing technologies to provide solar power and heat: (i) thermal approaches (system 1) using concentrated solar technologies to provide heat and to generate electricity through thermodynamic cycles, (ii) electrical approaches (system 2) using photovoltaic technologies to provide electricity and to generate heat through electrical heaters, and (iii) hybrid approaches (system 3) utilizing concentrated solar technologies and photovoltaics to provide heat and electricity, respectively. We find that system 3 generates hydrogen at a high efficiency (g STF = 9.9%, slightly lower than the best performing system 1 with 10.6%) and at a low cost (C fuel = $4.9/kg, lowest cost of all three systems) at reference conditions, providing evidence for the competitiveness of this hybrid approach for scaled solar hydrogen generation. Sensitivity analysis indicates an optimal working temperature for system 3 of 1350 K, which balances the increased thermal receiver losses with the reduced electrolysis cell potential when increasing the temperature. Lower working pressure always favors high system efficiency and low cost. The working current densities for thermoneutral voltage were determined for various temperature and pressure combinations, and trends for efficient and cost-effective thermoneutral operation were identified. The water conversion extent was optimized to avoid mass transport limitations in the electrodes while ensuring large fuel generation rates. For synthesis gas production, a H 2 /CO molar ratio of 2 can be achieved by tuning the inlet feeding molar ratio of CO 2 / H 2 O, temperature, and pressure. This study introduces a flexible simulation framework of solar-driven high-temperature electrolysis systems allowing for the assessment of competing solar integration approaches and for the guidance of the operational conditions maximizing efficiency and minimizing cost, providing pathways for scalable solar fuel processing.

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Section: Csp Performance Modelmentioning

confidence: 99%

Techno-economic modeling and optimization of solar-driven high-temperature electrolysis systems

Lin

Haussener

2017

Solar Energy

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“…Subsequently, the modern world has undertaken different renewable energy initiatives to mitigate global warming and meet the growing electricity demand. Nowadays, many countries have produced a significant portion of their energy demands from renewable energy resources, particularly solar generation power plants [1]. Among the potential renewable energies, photovoltaics (PV) have undergone enormous growth over the last few years.…”

Section: Introductionmentioning

confidence: 99%

SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions

et al. 2017

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Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.

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“…Many studies provide forecasting methods to predict PV power production from 6 h to 24 h ahead with good accuracy [12][13][14][15][16][17][18][19][20][21]. Generally, these methods are designed for the TSO and the distribution system operators; therefore, they are often very complex and require considerable input data not always available and free.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy

et al. 2017

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Abstract:In recent years, the diffusion of electric plants based on renewable non-dispatchable sources has caused large imbalances between the power generation schedule and the actual generation in real time operations, resulting in increased costs for dispatching electric power systems. Although this type of source cannot be programmed, their production can be predicted using soft computing techniques that consider weather forecasts, reducing the imbalance costs paid to the transmission system operator (TSO). The problem is mainly that the forecasting procedures used by the TSO, distribution system operator (DSO) or large producers and they are too expensive, as they use complex algorithms and detailed meteorological data that have to be bought, this can represent an excessive charge for small-scale producers, such as prosumers. In this paper, a cheap photovoltaic (PV) production forecasting method, in terms of reduced computational effort, free-available meteorological data and implementation is discussed, and the economic results regarding the imbalance costs due to the utilization of this method are analyzed. The economic analysis is carried out considering several factors, such as the month, the day type, and the accuracy of the forecasting method. The user can utilize the implemented method to know and reduce the imbalance costs, by adopting particular load management strategies.

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Model of Photovoltaic Power Plants for Performance Analysis and Production Forecast

Cited by 95 publications

References 21 publications

Techno-economic modeling and optimization of solar-driven high-temperature electrolysis systems

Techno-economic modeling and optimization of solar-driven high-temperature electrolysis systems

SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions

Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy

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