Limits of Advisability for Master–Slave Configuration of DC–AC Converters in Photovoltaic Systems

Spertino, Filippo; Corona, Fabio; Leo, Paolo Di

doi:10.1109/jphotov.2012.2203793

Cited by 27 publications

(9 citation statements)

References 16 publications

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“…The considered PV production, in the simulation, is calculated based on yearly solar irradiance and the nominal installed power [37]. Yearly solar irradiance data was obtained through collected data from a third party weather service provider [38] for the specific location of the case study with the coordinates of 46.4746 • N, 11.2479 • E.…”

Section: Pv Generationmentioning

confidence: 99%

Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks

et al. 2019

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Proper planning of the installation of Battery Energy Storage Systems (BESSs) in distribution networks is needed to maximize the overall technical and economic benefits. The limited lifetime and relatively high cost of BESSs require appropriate decisions on their installation and deployment, in order to make the best investment. This paper proposes a comprehensive method to fully support the BESS location and sizing in a low-voltage (LV) network, taking into account the characteristics of the local generation and demand connected at the network nodes, and the time-variable generation and demand patterns. The proposed procedure aims to improve the overall network conditions, by considering both technical and economic aspects. An original approach is presented to consider both the planning and scheduling of BESSs in an LV system. This approach combines the properties of metaheuristics for BESS sizing and placement with a greedy algorithm to find viable BESS scheduling in a relatively short time considering a specified time horizon, and the application of decision theory concepts to obtain the final solution. The decision theory considers various scenarios with variable energy prices, the diffusion of local renewable generation, evolution of the local demand with the integration of electric vehicles, and a number of planning alternatives selected as the solutions with top-ranked objective functions of the operational schedules in the given scenarios. The proposed approach can be applied to energy communities where the local system operator only manages the portion of the electrical grid of the community and is responsible for providing secure and affordable electricity to its consumers. 417Regarding the calculation of the load profiles, it takes into account the real contractual delivery 418 powers to each load. Two nodes are considered as commercial consumers, while the other ones 419 refer to residential customers. For the profiles, due to lack of complete information from the actual 420 profiles, relevant (residential and commercial) profiles have been taken from an open-source 421 dataset (OpenEI) [36], normalized and multiplied by the nominal powers.422 3.2.2 PV generation 423 The considered PV production, in the simulation, is calculated based on yearly solar irradiance 424 and the nominal installed power [37]. Yearly solar irradiance data is obtained through collected 425 data from third party weather service provider [38] for specific location of study case with the 426 coordination of 46.4746° N, 11.2479° E.427 3.2.3 EVs relevant information 428 EVs are considered in some scenarios, through the load profile caused by their charging. 429Within this analysis, the number of EVs that determines the EV charging profile, is set to 10 in the 430 beginning of the scenarios with EV and that number increments gradually by rate of one EV per 431year. The nominal power of the EV chargers are considered to be 3.6 kW, i.e., a standard power 432 level of charging stations, and charging events occur mainly during the night...

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Section: Pv Generationmentioning

confidence: 99%

Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks

et al. 2019

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“…PV production is proportional to the capacity of PV generator (i.e., its rated or nominal power PPV,nom corresponding to Standard Test Conditions, GSTC =1 kW/m 2 and TSTC =25 °C). According to the model defined in [10], the thermal losses depend on the thermal coefficient of PV technology and vary as a function of G(t) and Ta(t). The considered losses are caused by dirt (ηdirt) and reflection on the glass of PV modules (ηrefl), low irradiance, electrical mismatch of I-V curves (ηmis), Joule effect in cables (ηcable) and MPPT (ηMPPT) [11].…”

Section: A Modeling Of Pv Generatorsmentioning

confidence: 99%

How much is the advisable self-sufficiency of aggregated prosumers with photovoltaic-wind power and storage to avoid grid upgrades?

Spertino

Ahmad

Ciocia

et al. 2017

2017 IEEE Industry Applications Society Annual Meeting

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This paper defines, with respect to the consumption, the maximum value of self-sufficiency that can be reached by users, who decide to install photovoltaic (PV) modules, wind turbines and electrochemical storage. The primary goal of the aggregated users, who become prosumers, is assumed the achievement of the best match between power profiles of loads and power profiles of generators. Such best match is obtained thanks to an appropriate procedure to design the sizes of generators and storages. In this procedure, power ratings of PV and wind generators and energy capacities of batteries are chosen to attain the highest levels of self-consumption and the lowest power exchange with the grid according to the load profile. Thus, the upgrade of transformers and lines is avoided and there are benefits for both prosumers and grid operators. The simulation results are very realistic, because the inputs, in terms of irradiances for PV modules, wind speeds for turbines and powers for loads, are accurate measurements. The return on investments is estimated according to current costs and market rules. The results can be useful to plan the future electricity mix in the Mediterranean areas.

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“… Regarding the DC-AC behavior, the losses can be divided mainly into three contributions: the no-load loss (1 st ); the linear (2 nd ) and the square loss (3 rd ) coefficients in percent of the power rating [27]. According to this classification, the GMPV rack is characterized by no-load loss of 0.4%, linear coefficient of 0.7% and square coefficient of 3%, whereas the BIPV inverter exhibits values of 0.9%, 6% (the highest contribution) and 0% for the same parameters.…”

Section: G Measurements Of the Main Pcu Operating Parametersmentioning

confidence: 99%

A power and energy procedure in operating photovoltaic systems to quantify the losses according to the causes

et al. 2015

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Recently, after high feed-in tariffs in Italy, retroactive cuts in the energy payments have generated economic concern about several grid-connected photovoltaic (PV) systems with poor performance. In this paper the proposed procedure suggests some rules for determining the sources of losses and thus minimizing poor performance in the energy production. The on-site field inspection, the identification of the irradiance sensors, as close as possible the PV system, and the assessment of energy production are three preliminary steps which do not require experimental tests. The fourth step is to test the arrays of PV modules on-site. The fifth step is to test only the PV strings or single modules belonging to arrays with poor performance (e.g., I-V mismatch). The sixth step is to use the thermo-graphic camera and the electroluminescence at the PV-module level. The seventh step is to monitor the DC racks of each inverter or the individual inverter, if equipped with only one Maximum Power Point Tracker (MPPT). Experimental results on real PV systems show the effectiveness of this procedure.

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Limits of Advisability for Master–Slave Configuration of DC–AC Converters in Photovoltaic Systems

Cited by 27 publications

References 16 publications

Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks

Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks

How much is the advisable self-sufficiency of aggregated prosumers with photovoltaic-wind power and storage to avoid grid upgrades?

A power and energy procedure in operating photovoltaic systems to quantify the losses according to the causes

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