Infrastructure Planning and Operational Scheduling for Power Generating Systems: An Energy-Water Nexus Approach

Allen, R. Cory; Nie, Yaling; Avraamidou, Styliani; Pistikopoulos, Efstratios N.

doi:10.1016/b978-0-12-818597-1.50037-0

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…The third and final strategy utilizes a full space approach in which the problems are solved simultaneously via a larger mathematical program. [20][21][22][23]25,26 This larger mathematical program can be restructured into a bi-level optimization problem, where the optimal decisions of the planning problems (upper level-leader) provide constraints for the detailed operational scheduling problems (lower level-follower). [27][28][29] Full space approaches to the integration of planning and scheduling problems are quite popular due to their ease of implementation and feasibility guarantees; however, computationally they are very difficult to solve due to the number of variables and constraints required to accurately model the problem.…”

Section: Integration Of Planning and Scheduling Problemsmentioning

confidence: 99%

“…This iterative procedure continues until convergence is reached. The third and final strategy utilizes a full space approach in which the problems are solved simultaneously via a larger mathematical program 20‐23,25,26 . This larger mathematical program can be restructured into a bi‐level optimization problem, where the optimal decisions of the planning problems (upper level—leader) provide constraints for the detailed operational scheduling problems (lower level—follower) 27‐29 …”

Section: Introductionmentioning

confidence: 99%

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A multi‐period integrated planning and scheduling approach for developing energy systems

Allen

Baratsas

Kakodkar

et al. 2022

Optim Control Appl Methods

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In this work, we present a generic multi‐period integrated infrastructure planning and operational scheduling approach for developing energy systems. The methodology is applicable to energy systems with multiple resources, locations, processing pathways, and planning periods in which infrastructure decisions can be carried out. The framework incorporates a graph based approach to mode based scheduling, in which the mode transitions are mapped to a directed graph. We illustrate the applicability and effectiveness of the overall framework through the use of a case study examining the long‐term development of a multi‐site energy‐intensive hydrogen based energy system in Texas. In the case study, we find that developing the energy system over the course of its operational life as opposed to the beginning of its operational life reduces its capital and operational cost by over 20%.

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Section: Integration Of Planning and Scheduling Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi‐period integrated planning and scheduling approach for developing energy systems

Allen

Baratsas

Kakodkar

et al. 2022

Optim Control Appl Methods

Self Cite

View full text Add to dashboard Cite

show abstract

“…To generate sustainable solutions for RO systems it is vital to consider the system implications to resources other than water, including energy and food, referred to as food-energy-water nexus (FEWN) [49][50][51][52] when optimizing the system. Namany et al optimized the FEWN for various food security scenarios realizing that the utilization of RO reduces the environmental impact of solutions [53].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

2022

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An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H2Oaks RO desalination plant in South-Central Texas. The developed ANN is then transformed into a mixed-integer linear programming formulation for the purpose of minimizing energy consumption while maximizing water utilization. Trade-offs between the two competing objectives are visualized in a Pareto front, where indirect savings can be uncovered by comparing energy consumption for an array of water recoveries and feed flows.

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The Food-Energy-Water Nexus in Sustainable Energy Systems Solutions

Martino

Allen²,

Pistikopoulos³

2022

Handbook of Smart Energy Systems

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Infrastructure Planning and Operational Scheduling for Power Generating Systems: An Energy-Water Nexus Approach

Cited by 12 publications

References 11 publications

A multi‐period integrated planning and scheduling approach for developing energy systems

A multi‐period integrated planning and scheduling approach for developing energy systems

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

The Food-Energy-Water Nexus in Sustainable Energy Systems Solutions

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