Battery Storage System Sizing Using Synthetic Series Data

Abubakar, Ahmad; Gemignani, Matheus Mingatos Fernandes; Almeida, Carlos Frederico Meschini

doi:10.1109/pvsc40753.2019.8980615

“…Accounting for variations in hourly and daily consumption, yearly consumption profiles were simulated from the daily average load curves using regression models similar to those used in the prediction of solar radiation data presented in (Abubakar, Gemignani and Meschini Almeida, 2019).…”

Section: Case Studymentioning

confidence: 99%

“…The study presents an improvement to BESS sizing methodology using synthetic series data in ( (Abubakar, Gemignani and Meschini Almeida, 2019). Here, hourly synthetic series data is considered instead of daily synthetic data.…”

Section: Bess Sizingmentioning

confidence: 99%

“…Solar energy is one of, if not the most available renewable energy resource on the planet and the world has in the past few decades, been witnessing an exponential increase in solar PV deployment for residential and commercial building applications mostly due to concerns of environmental pollution, fragile fuel market and operational complexity and hazards associated with fossil fuel generation (Dulout et al, 2017;Abubakar, Gemignani and Meschini Almeida, 2019). Solar PV systems are suitable for various application are hardly restricted by the availability of solar resource and manufacturing material, geography or transmission distance (Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Methodology for Sizing and Selection of Battery Energy Storage System in Standalone Solar Pv Systems / Uma Metodologia Ótima Para O Dimensionamento E Seleção Do Sistema De Armazenamento De Energia De Bateria Em Sistemas Solares Fotovoltaicos Autônomos

Abubakar

¹

,

Almeida

²

,

Gemignani

³

2021

BJD

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0

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This paper presents a two-step cost-based method of optimally sizing and selecting BESS in standalone solar PV system applications considering predicted solar radiation data and economic performance (BESS cost analysis). The methodology is basically divided into two distinct parts; the first part is the sizing process and the second part is the selection process. In the first part, several BESS sizes suitable for a particular standalone PV system are determined using energy deficit and supply interruption outcomes of a PV system simulation with predicted hourly solar radiation series, hourly load demand and battery storage capacity as simulation parameters. In the second step, the economic performance of the determined BESS sizes is evaluated through a cost analysis process where two financial metrics; net present value (NPV) and payback period (PBP), are utilized. This step is necessary in order to ascertain the investment risks and benefits of the BESS sizes. To test its adequacy, the methodology was applied to two case studies; a residential load and a commercial load, and the results obtained for both case studies suggests that combining BESS sizing using predicted solar radiation data and BESS selection

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“…Owing to the global commitment to overcome these issues by reducing fossil fuel energy generation to the bare minimum, the renewable industry has experienced an exponential growth and development in recent years. Renewable energy sources, especially solar [6,7], have been increasingly adopted for residential, commercial, and industrial applications [8][9][10]. The 2020 first quarter (Q1 2020) report of the National Renewable Energy Laboratory (NREL) stated that at the end of 2019, the installed solar PV capacity totaled 627 GW DC , an increase of 115 GW DC from the previous year [11].…”

Section: Introductionmentioning

confidence: 99%

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

Abubakar

¹

,

Almeida

²

,

Gemignani

³

2021

Self Cite

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In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

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“…Solar energy is of, if not the most available renewable energy resource on the planet and the world has in the past few decades, been witnessing an exponential increase in solar PV deployment for residential and commercial building applications mostly due to concerns of environmental pollution, fragile fuel market and operational complexity and hazards associated with fossil fuel generation (Dulout et al, 2017;Abubakar, Gemignani and Meschini Almeida, 2019). Solar PV systems are suitable for various application are hardly restricted by the availability of solar resource and manufacturing material, geography or transmission distance (Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

An Optimal Methodology for Sizing and Selection of Battery Energy Storage System in Standalone Solar PV Systems

Abubakar

¹

,

Almeida

²

2020

Anais Do Congresso Brasileiro De Automática 2020

Self Cite

0

View full text Add to dashboard Cite

This paper presents a two-step cost-based method of optimally sizing and selecting BESS in standalone solar PV system applications considering predicted solar radiation data and economic performance (BESS cost analysis). The methodology is basically divided into two distinct parts; the first part is the sizing process and the second part is the selection process. In the first part, several BESS sizes suitable for a particular standalone PV system are determined using energy deficit and supply interruption outcomes of a PV system simulation with predicted hourly solar radiation series, hourly load demand and battery storage capacity as simulation parameters. In the second step, the economic performance of the determined BESS sizes is evaluated through a cost analysis process where two financial metrics; net present value (NPV) and payback period (PBP), are utilized. This step is necessary in order to ascertain the investment risks and benefits of the BESS sizes. To test its adequacy, the methodology was applied to two case studies; a residential load and a commercial load, and the results obtained for both case studies suggests that combining BESS sizing using predicted solar radiation data and BESS selection considering economic performance is an adequate process of incorporating BESS in standalone PV system applications.

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Battery Storage System Sizing Using Synthetic Series Data

Cited by 6 publications

References 13 publications

An Optimal Methodology for Sizing and Selection of Battery Energy Storage System in Standalone Solar Pv Systems / Uma Metodologia Ótima Para O Dimensionamento E Seleção Do Sistema De Armazenamento De Energia De Bateria Em Sistemas Solares Fotovoltaicos Autônomos

An Optimal Methodology for Sizing and Selection of Battery Energy Storage System in Standalone Solar Pv Systems / Uma Metodologia Ótima Para O Dimensionamento E Seleção Do Sistema De Armazenamento De Energia De Bateria Em Sistemas Solares Fotovoltaicos Autônomos

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

An Optimal Methodology for Sizing and Selection of Battery Energy Storage System in Standalone Solar PV Systems

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