Case study on the effects of partial solar eclipse on distributed PV systems and management areas

Sundararajan, Aditya; Olowu, Temitayo O.; Wei, Longfei; Rahman, Shafin; Sarwat, Arif I.

doi:10.1049/iet-stg.2019.0002

Cited by 19 publications

(11 citation statements)

References 51 publications

(90 reference statements)

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“…Processing (PRC): Five levels of processing exist: descriptive (statistics, exploratory visualisation, and regression between dependent and independent variables to understand events [25]), diagnostic (root-cause and correlation studies to understand why the observed events have occurred) [26,27], predictive (supervised, semi-supervised, and unsupervised regression and classification models to determine, based on the understanding from past and current events, including seasonal trends, how they will likely progress in the short-term or long-term future [28][29][30]), prescriptive (optimisation and numerical analysis to come up with robust sets of feasible actions for optimised outcomes that maximise an objective [31]), and cognitive (deep learning and decision-making to refine feasible decisions that encode human thought process, experiences, and available context to form actionable decisions [32,33]).…”

Section: Big Data Lifecycle Stagesmentioning

confidence: 99%

Adapting big data standards, maturity models to smart grid distributed generation: critical review

2020

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Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG. 3 Emerging and existing big data standards Key big data standards are described below, their relevance to attributes and lifecycle stages summarised in Table 2.

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Section: Big Data Lifecycle Stagesmentioning

confidence: 99%

Adapting big data standards, maturity models to smart grid distributed generation: critical review

2020

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“…Many metrics are currently used to evaluate PV system performance [60][61][62]. While uncorrected PR, measured using (11), is widely used by the utilities to measure the performance of a particular PV system, it is highly dependent on local weather (especially module temperature) and hence varies significantly over the course of a year [63]. Therefore, PR is not an effective metric to compare performance of two PV systems that experience different weather conditions.…”

Section: Metrics To Evaluate Pv Performancementioning

confidence: 99%

“…For the below series of equations, consider kWhAC actual to denote the observed energy (in AC side) generated by the PV system, P DC to denote the nameplate rated generation capacity (DC side) of the PV system, and kWh sun to denote the amount of solar energy received by the PV system cumulatively across its entire area. Then, the uncorrected PR is calculated by [64] PR = kWhAC actual /P DC kWh sun /1000 (11) This work also considers yield (PV systems of different sizes are not directly comparable), and PR corrected to STC (PV systems employing different PV models are not directly comparable), and capacity factor (ratio of the observed PV generation over a time period to its potential generation if it functioned at its full nameplate capacity continuously over the same time period). Given a time instance t in N total number of time instances and the observed generation (in kW) from the PV system at the time t denoted by kWAC actual (t), the yield, PR corrected to STC, and capacity factor are calculated as follows [65]:…”

Section: Metrics To Evaluate Pv Performancementioning

confidence: 99%

Hybrid data‐model method to improve generation estimation and performance assessment of grid‐tied PV: a case study

Sundararajan¹,

Sarwat²

2019

IET Renewable Power Generation

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Increased installed capacity of distributed photovoltaic (PV) systems has necessitated accurate measurement and tracking of PV performance under locality-specific conditions of irradiance, temperature, and derate factors. Existing PV generation estimation methods are strictly model based and not responsive to changes in weather and system losses. Metrics computed using these methods, therefore, do not capture the real PV behaviour well. This study proposes a hybrid data-model method (HDMM) that uses historical PV data in addition to model information to improve the accuracy of generation estimation. The generation estimated by HDMM is used to compute performance metrics-performance ratio, yield, capacity factor, energy performance index, and power performance index-for two real-world PV systems at Miami (ℳ, 1.4 MW) and Daytona (D, 1.28 MW) for 2017. The significance of these metrics is then evaluated, and a preliminary analysis of inverter efficiencies is provided. Results from this study show that when compared with the existing estimation method, HDMM performs better on an average by 75% for D and 10% for ℳ. Further, at a given point in time, system ℳ is likely to perform better than D. The study gives system installers and other stakeholders better PV system visibility, enabling aggregation and transactive energy. 2 Related work The performance of PV systems has been studied well in the literature, both at system level and module level [9, 10]. However, only system-level performance is of scope in this paper. In a prior work of the authors [11], an in-depth analysis of PV performance for a special case of the partial solar eclipse of 21 August 2017 was conducted to demonstrate how critical the problem of PV performance analysis is for operators under high penetration scenarios. A study with a similar scope was conducted in [12] for

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“…For environmental reasons, there has been a consistent increase in global installation of photovoltaic (PV) systems. This is further facilitated by the declining cost of PV modules over the years [1][2][3][4][5]. The increase is PV installations is evident as the financial investments in PV installation globally is also on the rise.…”

Section: Introductionmentioning

confidence: 99%

“…The increase is PV installations is evident as the financial investments in PV installation globally is also on the rise. The increase in levels of PV penetration have lead to several challenges which include reverse power flow, voltage control issues, power quality issues [6][7][8][9] (voltage and current harmonics, flicker, momentaries), protection coordination problems, and possible increase in system losses. [3,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration

Olowu¹,

Jafari²,

Sarwat³

2019

Adv. sci. technol. eng. syst. j.

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With increasing photovoltaic (PV) penetration on distribution feeders, voltage functions is always a challenge. To control these variations due to the intermittent nature of PV generation,many utility companies use the traditional voltage regulating devices such as ON/OFF load tap changers, voltage regulators, switched capacitor banks and reactors. The use of smart inverters (SI) has been reported to provide a more effective and economical way of voltage regulation on distribution feeder. It then becomes necessary to optimally control the operation of these traditional voltage regulating devices and the SIs. This paper presents a multi-objective technique that minimizes the voltage fluctuation (by implementing conservative voltage reduction), the overall system active power losses and the amount of reactive power injection from the capacitor banks with various constraints on the smart inverter reactive power injection and voltage regulator switching. The proposed algorithm is tested on an IEEE 34 node system with six units of 300kW (400kV A SIs) PVs integrated using real data from an existing 1.4MW PV plant located at FIU. The Pareto optimization results of the proposed algorithm show the various optimal values of the PVs power factor, the voltage regulator settings and the capacitor reactive power injection. Using one of the Pareto optimal solutions, the results show that the system bus voltage profiles, total system power losses and capacitor reactive power injection were effectively optimized.

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Case study on the effects of partial solar eclipse on distributed PV systems and management areas

Cited by 19 publications

References 51 publications

Adapting big data standards, maturity models to smart grid distributed generation: critical review

Adapting big data standards, maturity models to smart grid distributed generation: critical review

Hybrid data‐model method to improve generation estimation and performance assessment of grid‐tied PV: a case study

A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration

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