Optimization and design of a low-cost, village-scale, photovoltaic-powered, electrodialysis reversal desalination system for rural India

Bian, David W.; Watson, Sterling M.; Wright, Natasha C.; Shah, Sahil; Buonassisi, Tonio; Ramanujan, Devarajan; Peters, Ian Marius; Winter, Amos G.

doi:10.1016/j.desal.2018.09.004

Cited by 41 publications

(50 citation statements)

References 13 publications

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“…The objectives of the system design for Chelluru were to (1) minimize the system capital cost; and (2) maximize the water recovery rate of brackish water desalination. The Chelluru system would also be an important case study to further improve on the parametric design theory developed by Bian et al [13], and to help elucidate the technical, economic and social challenges of desalination in rural India.…”

Section: The Pilot Pv-ed Systemmentioning

confidence: 99%

“…The PV-ED system in Chelluru was designed following the co-optimization methodology in [13], in which a cost-and performance-optimal PV-ED system using constant voltage and pumping power was designed using off-the-shelf components. The system design and operational parameters are listed in Table 1.…”

Section: The Pilot Pv-ed Systemmentioning

confidence: 99%

“…Based on these results, the system was originally designed to operate at a recovery of 80-90% in Chelluru and was operated at that recovery over the course of several weeks. Concentration profiles are presented in [16].…”

Section: Ed Field Operationmentioning

confidence: 99%

“…The model used to predict ED performance in batch operation has been developed and validated by Wright et al [15]. The coupling of solar PV power and water desalination by ED was analytically described by Bian et al for an optimized PV-ED system operated at a constant applied voltage and constant flow rate [13]. Interested readers may review our previous works for a more complete representation of non-linear mass transfer in the ED process as well as the energy flow between solar electricity generation, energy storage, and energy consumption by desalination.…”

Section: Model Validationmentioning

confidence: 99%

“…This substantial reduction in energy consumption contributes to a significant cost reduction in the off-grid power system. Bian et al articulated a parametric theory between local solar irradiance, ED system parameters, power systems parameters including number of batteries, and water storage, and was able to optimize the capital system cost using the off-shelf components [13]. Their preliminary analysis indicated a potential 42% cost reduction compared to conventional design methods using reasonable initial assumptions for a village-scale brackish water desalination system in rural India [13].…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Field Test Results From a Photovoltaic Electrodialysis Brackish Water Desalination System in Rural India

Wright

Amrose

et al. 2018

Volume 2B: 44th Design Automation Conference

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Brackish water desalination is crucial to meet basic drinking water needs in rural India. Solar photovoltaic powered electrodialysis (PV-ED) has been justified as a more cost-effective solution than the current dominant reverse osmosis approach for off-grid systems. This paper presents preliminary results from an ongoing field pilot of a village-scale PV-ED system in Chelluru, which is a small village in South India. System performance is compared to predictions of a PV-ED parametric model of local solar irradiance, ED system parameters, power systems parameters, water storage, and cost, validating the model over a single-batch ED operation. An ∼88% “solar-to-treated water” conversion efficiency was achieved in a typical ED batch operation, using 2.47±0.27 kWh/m3 for brackish desalination in the village. This paper also discusses the difficulties and local constraints encountered during the initial field testing and analyzes system performance in the context of local constraints and availability.

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Section: The Pilot Pv-ed Systemmentioning

confidence: 99%

Section: The Pilot Pv-ed Systemmentioning

confidence: 99%

Section: Ed Field Operationmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Preliminary Field Test Results From a Photovoltaic Electrodialysis Brackish Water Desalination System in Rural India

Wright

Amrose

et al. 2018

Volume 2B: 44th Design Automation Conference

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Sustainable Desalination: Future Scope in Indian Subcontinent

Rath¹,

Ghosh²,

Roy³

2022

Sustainable Water Treatment

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A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

Bhatti,

Khan,

Zoha

et al. 2024

Adv Energy and Sustain Res

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In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision‐making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto‐regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh−1 for singular values vs 7.09 cents kWh−1 using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid‐connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods.

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Optimization and design of a low-cost, village-scale, photovoltaic-powered, electrodialysis reversal desalination system for rural India

Cited by 41 publications

References 13 publications

Preliminary Field Test Results From a Photovoltaic Electrodialysis Brackish Water Desalination System in Rural India

Preliminary Field Test Results From a Photovoltaic Electrodialysis Brackish Water Desalination System in Rural India

Sustainable Desalination: Future Scope in Indian Subcontinent

A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

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