Annual performance of Multiple MPPT based String Inverter under Partial Shadowing: Observations at Utility scale Solar PV Plants

Author(s):  
Alpesh Desai ◽  
Vatsal Shah ◽  
Indrajit Mukhopadhyay ◽  
Abhijit Ray
Keyword(s):  
Solar Pv ◽  
Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4675
Author(s):  
Ayat-allah Bouramdane ◽  
Alexis Tantet ◽  
Philippe Drobinski

In this study, we examine how Battery Storage (BES) and Thermal Storage (TES) combined with solar Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies with an increased storage duration and rental cost together with diversification would influence the Moroccan mix and to what extent the variability (i.e., adequacy risk) can be reduced; this is done using recent (2013) cost data and under various penetration scenarios. To do this, we use MERRA-2 climate reanalysis to simulate hourly demand and capacity factors (CFs) of wind, solar PV and CSP without and with increasing storage capabilities—as defined by the CSP Solar Multiple (SM) and PV Inverter Loading Ratio (ILR). We adjust these time series to observations for the four Moroccan electrical zones over the year 2018. Our objective is to maximize the renewable (RE) penetration and minimize the imbalances between RE production and consumption considering three optimization strategies. We analyze mixes along Pareto fronts using the Mean-Variance Portfolio approach—implemented in the E4CLIM model—in which we add a maximum-cost constraint to take into account the different rental costs of wind, PV and CSP. We propose a method to calculate the rental cost of storage and production technologies taking into account the constraints on storage associated with the increase of SM and ILR in the added PV-BES and CSP-TES modules, keeping the mean solar CFs fixed. We perform some load bands-reduction diagnostics to assess the reliability benefits provided by each RE technology. We find that, at low penetrations, the maximum-cost budget is not reached because a small capacity is needed. The higher the ILR for PV, the larger the share of PV in the mix compared to wind and CSP without storage is removed completely. Between PV-BES and CSP-TES, the latter is preferred as it has larger storage capacity and thus stronger impact in reducing the adequacy risk. As additional BES are installed, more than TES, PV-BES is favored. At high penetrations, optimal mixes are impacted by cost, the more so as CSP (resp., PV) with high SM (resp., ILR) are installed. Wind is preferably installed due to its high mean CF compared to cost, followed by either PV-BES or CSP/CSP-TES. Scenarios without or with medium storage capacity favor CSP/CSP-TES, while high storage duration scenarios are dominated by low-cost PV-BES. However, scenarios ignoring the storage cost and constraints provide more weight to PV-BES whatever the penetration level. We also show that significant reduction of RE variability can only be achieved through geographical diversification. Technological complementarity may only help to reduce the variance when PV and CSP are both installed without or with a small amount of storage. However, the diversification effect is slightly smaller when the SM and ILR are increased and the covariances are reduced as well since mixes become less diversified.


2018 ◽  
Author(s):  
Ibraheam Al-Aali ◽  
Vijay Modi

Soaring electricity demand from space cooling and excellent solar photovoltaics (PV) resources are creating an opportunity for the financial viability of low-emission solutions in Qatar that can compete with existing approaches. This study examines the big picture viability of combining large utility-scale PV with decentralized building-scale ice storage for cooling in Qatar. Qatar is found to have consistently high repeatable solar radiation intensity that nearly matches space cooling requirement. A means to exploit the low installed costs of PV, combined with low cost and long lifetime of ice storage (as opposed to batteries) are examined to meet space cooling loads. Space cooling is responsible for about 65% of Qatar’s annual electric load (which averaged 4.68 GW in 2016). While multiple gas prices are considered, a scenario with the current gas price of $3.33/MMBTU, a PV system of 9.7 GW capacity and an aggregate ice-storage capacity of 4.5 GWh could reduce the gas-fired power generation in Qatar by nearly 39%. Here, gas-fired generation capacity to meet current load exists and hence is not costed.


2019 ◽  
Vol 245 ◽  
pp. 16-30 ◽  
Author(s):  
Oludamilare Bode Adewuyi ◽  
Mohammed E. Lotfy ◽  
Benjamin Olabisi Akinloye ◽  
Harun Or Rashid Howlader ◽  
Tomonobu Senjyu ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrew Glick ◽  
Naseem Ali ◽  
Juliaan Bossuyt ◽  
Marc Calaf ◽  
Raúl Bayoán Cal
Keyword(s):  
Solar Pv ◽  

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Irfan Jamil ◽  
Jinquan Zhao ◽  
Li Zhang ◽  
Rehan Jamil ◽  
Syed Furqan Rafique

The installation of 3 × 50 MW (150 MW DC) large utility scale solar power plant is ground based using ventilated polycrystalline module technology with fixed tilt angle of 28° in a 750-acre land, and the site is located about 115 km northeast of Karachi, Pakistan, near the town of ThanoBula Khan, Nooriabad, Sindh. This plant will be connected to the utility distribution system through a national grid of 220 kV outgoing double-loop transmission line. The 3 × 50 MW solar PV will be one of the largest tied grid-connected power projects as the site is receiving a rich average solar radiation of 158.7 kW/h/m2/month and an annual average temperature of about of 27°C. The analysis highlights the preliminary design of the case project such as feasibility study and PV solar design aspects and is based on a simulation study of energy yield assessment which has all been illustrated. The annual energy production and energy yield assessment values of the plant are computed using the PVSYST software. The assumptions and results of energy losses, annual performance ratio (PR) 74.73%, annual capacity factor 17.7%, and annual energy production of the plant at 232,518 MWh/year are recorded accordingly. Bear in mind that reference recorded data indicates a good agreement over the performance of the proposed PV power plant.


2016 ◽  
Vol 64 ◽  
pp. 506-517 ◽  
Author(s):  
Lisa Ryan ◽  
Joseph Dillon ◽  
Sarah La Monaca ◽  
Julie Byrne ◽  
Mark O'Malley
Keyword(s):  
Solar Pv ◽  

2020 ◽  
Vol 8 (5) ◽  
pp. 1703-1714 ◽  

Installation of solar PV arrays at utility scale is gaining popularity nowadays because of the significant reduction in the cost of components as well as the global push towards clean energy. Solar PV plants along with Parabolic Trough Collector Solar thermal plants has the highest potential among the available Renewable Energy (RE) technologies existing in the world. The objective of this paper is to optimize the performance of commercial Solar PV and PTC power plant for a potential location and hence to arrive on a most feasible configuration for the site. A representative site located in the Abudhabi region of UAE considered for the study. This paper also details on the annual performance of the proposed plant along with its technical aspects. PVSYST 6.7.7 and SAM software is used to design the optimal size and its specifications of a 100MW PV grid connected system at Abu Dhabi (UAE) region. The design and arrangements of the system verified using simulation results. The annual energy generated from the designed utility-scale solar PV plant from PVSYST 6.7.7 calculated as 161198MWh/year with a performance ratio (PR) of 74.8% per year where as for PTC it has calculated as 157152MWh/year by using SAM. The STC (Standard Testing Condition) for the specification of PV modules are normalized operating conditions when testing the module. Design parameters such as module orientation, array yield, reference yield, final yield, global horizontal irradiation (GHI), and ambient temperature and loss factors evaluated. To evaluate the economic feasibility of proposed plant, the levelized cost of electricity (LCOE) is determined as $0.04404/kwh for Solar PV and as $0.01533/kwh for PTC, which is used to calculate lifecycle cost and energy production


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