scholarly journals Simulation-Optimization Approach for the Logistics Network Design of Biomass Co-Firing with Coal at Power Plants

2018 ◽  
Vol 10 (11) ◽  
pp. 4299 ◽  
Author(s):  
Maria Aranguren ◽  
Krystel Castillo-Villar ◽  
Mario Aboytes-Ojeda ◽  
Marcio Giacomoni
Keyword(s):  
Simulation Model ◽  
Power Plants ◽  
Agricultural Land ◽  
Programming Model ◽  
Ghg Emissions ◽  
Mixed Integer ◽  
Weather Data ◽  
United States Department ◽  
Optimization Approach ◽  
Biomass Supply

This work proposes a hybrid scheme that combines a simulation model and a mathematical programming model for designing logistic networks for co-firing biomass, specifically switchgrass, in conventional coal-fired power plants. The advantages of co-firing biomass include: (1) the creation of green jobs; (2) the efficient use of current power plant infrastructure; (3) fostering the penetration of renewable energy into power networks; and, (4) the reduction of greenhouse gas (GHG) emissions. The novelty of this work lies in the inclusion of (1) the inherent variability of biomass supply at the parcel level, and (2) the effects of climate change on future biomass supply when designing a feedstock logistic network. The design optimization is conducted at the farm/parcel level (most, if not all, previous works have used county level average data) and integrates the crop growth predictions employing United States Department of Agriculture’s (USDA’s) Agricultural Land Management with Numerical Assessment Criteria (ALMANAC) simulation model; the output of the simulations is input into the mixed integer linear programming (MILP) hub-and-spoke model to minimize the overall cost of the logistic network. Specifically, the MILP-based model selects the parcels and depot locations as well as biomass transportation flows by taking into consideration different types of soil, land cover characteristics, and predicted yields, which account for both historical and forecasted weather data. The hybrid methodology was tested by solving realistic situations, which considered varying weather conditions. The gross results indicate that the optimized logistic network enabled meeting a 20% biomass co-firing rate demand, which reduced 1,158,867 Mg per year in GHG emissions by co-firing with biomass.

Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anton Ochoa Bique ◽  
Leonardo K. K. Maia ◽  
Ignacio E. Grossmann ◽  
Edwin Zondervan
Keyword(s):  
Stochastic Optimization ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Cost Minimization ◽  
Water Electrolysis ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Strong Impact ◽  
Hydrogen Supply

Abstract A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

The PHEV Charging Infrastructure Planning (PCIP) Problem

2010 ◽  
Vol 11 (2) ◽  
Author(s):  
Yogesh Dashora ◽  
John W Barnes ◽  
Rekha S Pillai ◽  
Todd E Combs ◽  
Michael Hilliard ◽  
...  
Keyword(s):  
Programming Model ◽  
National Laboratory ◽  
Ghg Emissions ◽  
Geographic Location ◽  
Mixed Integer ◽  
Infrastructure Planning ◽  
Charging Infrastructure ◽  
Oak Ridge ◽  
Charging Station ◽  
First Time

Increasing debates over a gasoline independent future and the reduction of greenhouse gas (GHG) emissions has led to a surge in plug-in hybrid electric vehicles (PHEVs) being developed around the world. The majority of PHEV related research has been directed at improving engine and battery operations, studying future PHEV impacts on the grid, and projecting future PHEV charging infrastructure requirements. Due to the limited all-electric range of PHEVs, a daytime PHEV charging infrastructure will be required for most PHEV daily usage. In this paper, for the first time, we present a mixed integer mathematical programming model to solve the PHEV charging infrastructure planning (PCIP) problem for organizations with thousands of people working within a defined geographic location and parking lots well suited to charging station installations. Our case study, based on the Oak Ridge National Laboratory (ORNL) campus, produced encouraging results, indicates the viability of the modeling approach and substantiates the importance of considering both employee convenience and appropriate grid connections in the PCIP problem.

Design Optimization of Power Plants by Considering Multiple Partial Load Operation Points

2007 ◽  
Author(s):  
Marc Ju¨des ◽  
George Tsatsaronis
Keyword(s):  
Design Optimization ◽  
Power Plants ◽  
Optimization Procedure ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Optimization Task ◽  
Operation Conditions ◽  
Partial Load ◽  
Conventional Optimization

The design optimization of complex energy conversion systems requires the consideration of typical operation conditions. Due to the complex optimization task, conventional optimization methods normally take into account only one operation point that is, in the majority of cases, the full load case. To guarantee good operation at partial loads additional operation conditions have to be taken into account during the optimization procedure. The optimization task described in this article considers altogether four different operation points of a cogeneration plant. Modelling requirements, such as the equations that describe the partial load behavior of single components, are described as well as the problems that occur, when nonlinear and nonconvex equations are used. For the solution of the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, the solver LaGO is used, which requires that the optimization problem is formulated in GAMS. The results of the conventional optimization approach are compared to the results of the new method. It is shown, that without consideration of different operation points, a flexible operation of the plant may be impossible.

scholarly journals Risk-Averse Co-Decision for Lower-Carbon Product Family Configuration and Resilient Supplier Selection

2021 ◽  
Vol 14 (1) ◽  
pp. 384
Author(s):  
Dengzhuo Liu ◽  
Zhongkai Li ◽  
Chao He ◽  
Shuai Wang
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Programming Model ◽  
Ghg Emissions ◽  
Product Family ◽  
Mixed Integer ◽  
Risk Averse ◽  
Worst Case ◽  
Risk Neutral ◽  
Disruption Risks

Due to global pandemics, political unrest and natural disasters, the stability of the supply chain is facing the challenge of more uncertain events. Although many scholars have conducted research on improving the resilience of the supply chain, the research on integrating product family configuration and supplier selection (PCSS) under disruption risks is limited. In this paper, the centralized supply chain network, which contains only one major manufacturer and several suppliers, is considered, and one resilience strategy (i.e., the fortified supplier) is used to enhance the resilience level of the selected supply base. Then, an improved stochastic bi-objective mixed integer programming model is proposed to support co-decision for PCSS under disruption risks. Furthermore, considering the above risk-neutral model as a benchmark, a risk-averse mixed integer program with Conditional Value-at-Risk (CVaR) is formulated to achieve maximum potential worst-case profit and minimum expected total greenhouse gases (GHG) emissions. Then, NSGA-II is applied to solve the proposed stochastic bi-objective mixed integer programming model. Taking the electronic dictionary as a case study, the risk-neutral solutions and risk-averse solutions that optimize, respectively, average and worst-case objectives of co-decision are also compared under two different ranges of disruption probability. The sensitivity analysis on the confidence level indicates that fortifying suppliers and controlling market share in co-decision for PCSS can effectively reduce the risk of low-profit/high-cost while minimizing the expected GHG emissions. Meanwhile, the effects of low-probability risk are more likely to be ignored in the risk-neutral solution, and it is necessary to adopt a risk-averse solution to reduce potential worst-case losses.

scholarly journals Location and optimal sizing of photovoltaic sources in an isolated mini-grid

TecnoLógicas ◽  
2019 ◽  
Vol 22 (44) ◽  
pp. 61-80 ◽  
Author(s):  
Juliana Jiménez ◽  
John E. Cardona ◽  
Sandra X. Carvajal
Keyword(s):  
Electric Power ◽  
Power Plants ◽  
Programming Model ◽  
Irradiation Time ◽  
Optimal Solution ◽  
Solar Irradiation ◽  
Mixed Integer ◽  
Voltage Profile ◽  
Power Losses ◽  
Photovoltaic Generators

This article introduces a new mixed integer linear programming model that guarantees the optimal solution to the location and sizing problem of distributed photovoltaic generators in an isolated mini-grid. The solar radiation curves of each node in the mini-grids were considered, and the main objective was to minimize electric power losses in the operation of the system. The model is non-linear in nature because some restrictions are not linear. However, this article proposes the use of linearization techniques to obtain a linear model with a global optimal solution, which can be achieved through commercial solvers; CPLEX in this case. The proposed model was tested in an isolated 14-bus mini-grid, based on real data of topology, demand and generation adapted to a balanced operation. This model shows, as a result, the optimal location of photovoltaic generators and their optimal capacity produced by the maximum active power delivered at the maximum solar irradiation time of the region. It is also evident that the hybrid operation between small hydroelectric power plants and photovoltaic generation improves the network voltage profile and the electric power losses without the use power storage systems.

scholarly journals Application of Two-Phase Fuzzy Optimization Approach to Multiproduct Multistage Integrated Production Planning with Linguistic Preference under Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Shan Lu ◽  
Hongye Su ◽  
Lian Xiao ◽  
Li Zhu
Keyword(s):  
Production Planning ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Service Level ◽  
Mixed Integer ◽  
Planning Problem ◽  
Optimization Approach ◽  
Two Phase ◽  
Steel Rolling ◽  
Solution Approach

This paper tackles the challenges for a production planning problem with linguistic preference on the objectives in an uncertain multiproduct multistage manufacturing environment. The uncertain sources are modelled by fuzzy sets and involve those induced by both the epistemic factors of process and external factors from customers and suppliers. A fuzzy multiobjective mixed integer programming model with different objective priorities is proposed to address the problem which attempts to simultaneously minimize the relevant operations cost and maximize the average safety stock holding level and the average service level. The epistemic and external uncertainty is simultaneously considered and formulated as flexible constraints. By defining the priority levels, a two-phase fuzzy optimization approach is used to manage the preference extent and convert the original model into an auxiliary crisp one. Then a novel interactive solution approach is proposed to solve this problem. An industrial case originating from a steel rolling plant is applied to implement the proposed approach. The numerical results demonstrate the efficiency and feasibility to handle the linguistic preference and provide a compromised solution in an uncertain environment.

scholarly journals An Optimization Approach to the Intermodal Transportation Network in Fruit Cold Chain, Considering Cost, Quality Degradation and Carbon Dioxide Footprint

2018 ◽  
Vol 25 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Qianli Ma ◽  
Wenyuan Wang ◽  
Yun Peng ◽  
Xiangqun Song
Keyword(s):  
Carbon Dioxide ◽  
Programming Model ◽  
Cost Minimization ◽  
Network Planning ◽  
Cold Chain ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Intermodal Transportation ◽  
Quality Degradation ◽  
Trade Offs

AbstractThis model optimizes port hinterland intermodal refrigerated container flows, considering both cost and quality degradation, which is distinctive from the previous literature content in a way that it quantifies the influence of carbon dioxide (CO2) emission in different setting temperature on intermodal network planning. The primary contribution of this paper is that the model is beneficial not only to shippers and customers for the novel service design, but also offer, for policy-makers of the government, insights to develop inland transport infrastructures in consideration of intermodal transportation. The majority of models of multimodal system have been established with an objective of cost minimization for normal commodities. As the food quality is possible to be influenced by varying duration time required for the storage and transportation, and transportation accompanied with refrigeration producing more CO2emission, this paper aims to address cost minimization and quality degradation minimization within the constraint of CO2footprint. To achieve this aim, we put the quality degradation model in a mixed-integer linear programming model used for intermodal network planning for cold chain. The example of Dalian Port and Yingkou Port offer insight into trade-offs between transportation temperature and transport mode considering CO2footprint. Furthermore, the model can offer a useful reference for other regions with the demand for different imported food, which requires an uninterrupted cold chain during the transportation and storage.

scholarly journals Phosphorus Imbalances in the Chesapeake Bay Watershed: Can Forestland and Manure Processing Facilities Be the Answers?

2011 ◽  
Vol 40 (1) ◽  
pp. 116-132 ◽  
Author(s):  
Serkan Catma ◽  
Alan Collins
Keyword(s):  
Chesapeake Bay ◽  
Agricultural Land ◽  
Programming Model ◽  
Poultry Manure ◽  
Land Application ◽  
Mixed Integer ◽  
Cost Of Transport ◽  
Manure Application ◽  
Chesapeake Bay Watershed ◽  
The Cost

A mixed-integer linear programming model was formulated to minimize the cost of transport and processing of excess manure in the Chesapeake Bay watershed. The results showed that primarily poultry manure was moved out of surplus counties for land application or processing. In the base model, annual cost was more than $350 million, with the bulk of the cost arising from construction of energy facilities for poultry manure. Forestland application of poultry manure had the lowest average cost, and more forestland than agricultural land was used for manure application. The lowest cost scenario was $127 million annually when constraints were removed to expand manure application on agricultural land and allow unlimited construction of composting facilities. Such a low-cost solution could not realistically be implemented without further development of markets for compost.

scholarly journals A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

2019 ◽  
Vol 11 (18) ◽  
pp. 4865 ◽  
Author(s):  
Al-Aboosi ◽  
El-Halwagi
Keyword(s):  
Water Management ◽  
Wastewater Treatment ◽  
Solar Energy ◽  
Shale Gas ◽  
Programming Model ◽  
Gas Production ◽  
Mixed Integer ◽  
Management Systems ◽  
Optimization Approach ◽  
Operational Conditions

The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact.

scholarly journals A low complexity heuristic to solve a learning objects recommendation problem

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Samuel Henrique Falci ◽  
Fabiano Azevedo Dorça ◽  
Alessandro Vivas Andrade ◽  
Daniel Henrique Mourão Falci
Keyword(s):  
Programming Model ◽  
Learning Experience ◽  
Linear Optimization ◽  
Computational Cost ◽  
Low Complexity ◽  
Learning Objects ◽  
Individual Characteristics ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Discussion Forums

Abstract The recommendation of learning objects in virtual learning environments has become the focus of research to improve online learning experience. Several approaches have been presented in an attempt to model the individual characteristics of the students and offer learning objects that best suit their particularities. Most of them, though, are impractical in real-world scenarios due to the high computational cost as a huge number of repositories offering learning objects such as Youtube, Wikipedia, Stackoverflow, Github, discussion forums, social networks and many others are available and each has a large amount of learning objects that can be retrieved. In this work, we propose a low complexity heuristic to solve this problem, comparing it to a classical mixed-integer linear programming model and classical genetic algorithm in varying dataset sizes that contain from 2000 to 1360000 learning objects. Performance and optimality were analyzed. The results showed that the proposed technique was only slightly suboptimal, while its computational cost was considerably smaller than the one presented by the linear optimization approach.

Sign in / Sign up

Export Citation Format

Share Document