Renewable energy-powered water desalination and treatment network under wind power and water demand uncertainty: A possibilistic chance-constrained programming

Alipoor, Fateme; Gilani, Hani; Sahebi, Hadi; Ghannadpour, Seyed Farid

doi:10.1016/j.esr.2024.101511

Energy Strategy Reviews

2024

DOI: 10.1016/j.esr.2024.101511

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Renewable energy-powered water desalination and treatment network under wind power and water demand uncertainty: A possibilistic chance-constrained programming

Fateme Alipoor,

Hani Gilani,

Hadi Sahebi

et al.

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2024

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Cited by 3 publications

References 80 publications

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Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

Gilani,

Sahebi

2024

Energy Conversion and Management: X

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Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

Gilani,

Sahebi

2024

Energy Conversion and Management: X

View full text Add to dashboard Cite

Studies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithm

Barutcu,

Sharma,

Çelik

et al. 2024

IET Energy Syst Integration

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In this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance‐constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance‐constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.

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A Novel Tourist Trip Design Problem with Stochastic Travel Times and Partial Charging for Battery Electric Vehicles

Kedkaew,

Nakkiew,

Jewpanya

et al. 2024

Mathematics

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This study proposes a novel mathematical model for the Multi-Day Tourist Trip Design Problem with Stochastic Travel Time and Partial Charging for Battery Electric Vehicle (MD-TTDP-STT-PCBEV). To the best of our knowledge, no prior study has fully incorporated the use of BEVs into TTDP models. Given the limited driving range of BEVs, the model requires decisions regarding the locations and policy for recharging the vehicle’s battery. The problem also incorporates real-world uncertainty by considering travel time as a random variable subjected to normal distribution. The model is formulated using chance-constraint programming, aiming to find optimal tourist routes for BEVs that maximize tourist satisfaction. Numerical experiments were conducted to compare solutions between stochastic and deterministic environments. Computational experiments using the LINGO optimization solver demonstrated that the total rating scores obtained from the stochastic model with chance-constraint programming were generally lower than those from the deterministic model due to travel time uncertainties. These results highlight the importance of incorporating real-world uncertainty and variability to achieve more accurate and reliable planning.

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Renewable energy-powered water desalination and treatment network under wind power and water demand uncertainty: A possibilistic chance-constrained programming

Cited by 3 publications

References 80 publications

Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

Studies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithm

A Novel Tourist Trip Design Problem with Stochastic Travel Times and Partial Charging for Battery Electric Vehicles

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