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Optimizing Photovoltaic Panel Quantity for Water Distribution Networks

Authors

Mirhan Ürkmez, Carsten Skovmose Kallesøe, Jan Dimon Bendtsen, John Leth
Type: Preprint
Publication Date: 2024-12-19
Citations: 0
DOI: https://doi.org/10.48550/arxiv.2412.15402

Abstract

The paper introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach follows an iterative process, starting with an initial estimate of the PV quantity, and then calculating the total cost of WDN operation. To calculate the total cost of the WDN, we sample PV power profiles that represent the future production based on a probabilistic PV production model. Simulations are conducted assuming these sampled PV profiles power the WDN, and pump flow rates are determined using a control method designed for PV-powered WDNs. Following the simulations, the overall WDN cost is calculated. Since we lack access to derivative information, we employ the derivative-free Nelder-Mead method for iteratively adjusting the PV quantity to find an approximation of the optimal value. The procedure is applied for the WDN of Randers, a Danish town. By determining an approximation of the optimal quantity of PVs, we observe a 14.5\% decrease in WDN costs compared to the scenario without PV installations, assuming a 25 year lifespan for the PV panels.

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  • arXiv (Cornell University)
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Scenario Based Cost Optimization of Water Distribution Networks Powered by Grid-Connected Photovoltaic Systems

2023-08-16
Mirhan Ürkmez , Carsten Skovmose Kallesøe , Jan Dimon Bendtsen +1 more
The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous … The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous study and is designed to reduce the economic costs associated with operating the WDN. To account for the uncertainty of the system, the problem is solved in a scenario optimization framework, where multiple scenarios are sampled from the uncertain variables related to PV power production. To accomplish this, a day-ahead PV power prediction method with a stochastic model is employed. The method is tested on a high-fidelity model of a WDN of a Danish town and the results demonstrate a substantial reduction in electrical costs through the integration of PVs, with PVs supplying 66.95% of the required energy. The study also compares the effectiveness of the stochastic optimization method with a deterministic optimization approach.
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Scenario Based Cost Optimization of Water Distribution Networks Powered by Grid-Connected Photovoltaic Systems

2023-01-01
Mirhan Ürkmez , Carsten Skovmose Kallesøe , Jan Dimon Bendtsen +1 more
The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous … The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous study and is designed to reduce the economic costs associated with operating the WDN. To account for the uncertainty of the system, the problem is solved in a scenario optimization framework, where multiple scenarios are sampled from the uncertain variables related to PV power production. To accomplish this, a day-ahead PV power prediction method with a stochastic model is employed. The method is tested on a high-fidelity model of a WDN of a Danish town and the results demonstrate a substantial reduction in electrical costs through the integration of PVs, with PVs supplying $66.95\%$ of the required energy. The study also compares the effectiveness of the stochastic optimization method with a deterministic optimization approach.
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Optimizing Photovoltaic Panel Quantity for Water Distribution Networks

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