Opportunities and challenges of PV technology in power system

Jaiswal, Shiva Pujan; Shrivastava, Vivek; Palwalia, D. K.

doi:10.1016/j.matpr.2020.01.269

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…I D1 and I D2 are the reverse saturation currents of diodes D 1 and D 2 , respectively, and the calculation formula is shown in Eq. (2).…”

Section: Mathematical Model Of Photovoltaic Arraymentioning

confidence: 99%

“…In order to solve the aforementioned problems, governments of the world are calling for in-depth research into non-polluting, new, renewable energy sources to replace non-renewable sources. The development of clean energy, such as solar energy and wind energy, has received extensive attention [2]. Solar energy accounts for a significant portion of new energy development around the world, and photovoltaic power generation converts solar energy into electricity.…”

Section: Introductionmentioning

confidence: 99%

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Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

Hou¹,

Wang²

2023

Energy Engineering

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The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation's efficiency and power quality.

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“…I D1 and I D2 are the reverse saturation currents of diodes D 1 and D 2 , respectively, and the calculation formula is shown in Eq. (2).…”

Section: Mathematical Model Of Photovoltaic Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

Hou¹,

Wang²

2023

Energy Engineering

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“…It is no wonder that solar PVs are considered a preeminent source of clean energy, and their prevalence continues to rise because of their groundbreaking advances in both technology and marketability. These strides include an impressive array of cell types, PV efficiency, and electronic capabilities that elevate their prominence and secure their position as a pinnacle of modern technology [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization

Dimara,

Papaioannou,

Grigoropoulos

et al. 2023

Applied Sciences

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The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.

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“…The conventional electricity generation involves the higher inputs of the fossil fuels, resulting in the higher environmental pollution [1]. The fossil fuels are the major sources of the environmental pollution [2] as well as the key sources of the electricity production in the world. Gradually, the fossil fuels are depleting and the electricity demand is increasing [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Storage Selection Model for Grid-Connected Photovoltaics Systems

et al. 2021

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The grid-connected photovoltaics (GCPV) systems are a sustainable alternative to the conventional non-renewable electricity systems. However, GCPV systems create the many issues such as grid overloading, demand and supply variations, and power quality issues. A key way to address such issues is the integration of the effective energy storage technologies in GCPV systems. There are many storage technologies which can be connected with GCPV systems. The integration of a proper storage technology into the GCPV systems may provide a best alternative. This paper evaluates the different storage technologies for GCPV systems using the analytic hierarchy process (AHP) approach. The goal of the AHP model was the selection of the best storage alternative for GCPV systems based on the multiple criteria. The criteria included the storage parameters as well as the parameters related to the compatibility with GCPV systems. The results exhibited that the pumped hydro storage is the best alternative if all the storage and compatibility parameters are equally desirable. However, if AHP model gives the highest preference to the criteria ''integration simplicity regarding renewable energy'' and ''geographic limitations'', the several other storage technologies achieve the higher rankings. Hence, the AHP model provides a greater flexibility to evaluate the different storage technologies for GCPV systems under different circumstances.INDEX TERMS Analytic hierarchy process (AHP), electrical power grid, environmental sustainability, power flow balancing, power system planning, power system reliability, renewable energy, solar energy, PV.

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Opportunities and challenges of PV technology in power system

Cited by 12 publications

References 15 publications

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization

Multi-Criteria Storage Selection Model for Grid-Connected Photovoltaics Systems

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