Linear Programming Optimization Research Articles

FlexDATE: Flexible and Disturbance-Aware Traffic Engineering With Reinforcement Learning in Software-Defined Networks

Traffic Engineering (TE) is an important network operation that routes/reroutes flows based on network topology and traffic demands to optimize network performance. Recently, new emerging applications pose challenges to TE with dynamic network conditions, where frequent routing updates are required to maintain good network performance with Software-Defined Networking (SDN). However, flow rerouting operations could lead to considerable Quality of Service (QoS) degradation and service disruption, which is often neglected by existing TE solutions. In this paper, we apply a new QoS metric named network disturbance to measure the negative impact of flow rerouting operations performed by TE. To achieve near-optimal load balancing performance and mitigate network disturbance together in dynamic network scenarios, we propose a flexible and disturbance-aware TE solution called that combines Reinforcement Learning (RL) and Linear Programming (LP). Specifically, leverages RL to intelligently identify flexible numbers of critical flows for each traffic matrix and reroutes these critical flows based on LP optimization to improve network performance with low disturbance. Empowered by a customized actor-critic architecture coupled with Graph Neural Networks (GNNs), can generalize well to unseen traffic scenarios and remain resilient to single link failures. Extensive simulations are conducted on five real-world network topologies to evaluate with real and synthetic traffic traces. The results show that can achieve the performance target (i.e., 90% of optimal performance) in 99% of network scenarios and effectively mitigate the average and maximum network disturbance by up to 9.1% and 38.6%, respectively, compared to state-of-the-art TE solutions.

The Operation Strategy of the MIDC Systems for Optimizing Renewable Energy Integration of Jeju Power System

The power system on Jeju Island currently faces challenges due to an imbalance between power supply and demand owing to increasing renewable energy (RE) generation. Consequently, the multi-infeed high-voltage direct-current (MIDC) systems are expected to play an important role in reducing RE curtailment and increasing RE penetration. However, despite the balance between power supply and demand, transmission line (TL) overloads on Jeju Island, which has a low permissible rating, may lead to RE curtailment. We propose an operational strategy for the MIDC systems to mitigate TL overloading and RE curtailment. This strategy was aimed at managing the possible congestion of TLs, maintaining a power supply–demand balance, and increasing the penetration level of RE. In this paper, we present a scheme for calculating the operating points of the MIDC systems and allocating RE curtailment using optimization with security constraints to mitigate TL overloading. The proposed optimization model simplifies solving the mathematical equations involved by employing a sensitivity factor called the power transfer distribution factor (PTDF), using which linear programming (LP) optimization can be performed. To verify the feasibility of the operational strategy, case studies are conducted using an actual Jeju Island power system with a power system analysis tool—Power System Simulator for Engineering (PSS/E). The optimization problem was solved using MATLAB.

Optimal control of a grid-connected photovoltaic agricultural water pumping system

Optimization of water pumping systems has been studied using various techniques which include classical, mathematical, and heuristics. Few studies have explored use of optimal controllers in agricultural water pumping applications. Some studies also ignore the interconnection between the water demand and energy used. Introduction of renewable energy sources such as photovoltaic (PV) necessitates different geographical studies as the intensity of the renewable energy varies widely with location. In this paper, an optimal controller for a batteryless grid-connected photovoltaic system to power water supply system for irrigation purposes was developed. The aim was to minimize the operational cost of grid energy by maximizing utilization of photovoltaic power and minimizing the utilization of grid power. A case study was done at a farm in Kajiado (−1.6033257^{circ } latitude and 36.7863352^{circ } longitude). The farm photovoltaic, grid power, water pumps (underground and booster pump), and storage tanks were modelled into a binary linear programming optimization problem and solved using intlinprog solver on MATLAB. Energy demand data was collected using a three-phase power logger, while water demand data was collected using onsite water meter and stopwatch timer. Photovoltaic power produced was estimated using Photovoltaic Geographical Information System (PVGIS). Simulation results obtained show that the use of an optimal controller results in reduced cost of energy by 44.4%. Better utilization of renewable photovoltaic energy by 24% was observed, and 3.6% more water was pumped.

Last mile delivery Optimisation model for drone-enabled Vehicle Routing Problem

In light of e-commerce's exponential growth trajectory over recent years, it has never been more critical to evaluate the last-mile delivery segment for its efficiency and cost-effectiveness. Autonomous vehicles (drones) offer considerable promise as potential solutions with ongoing investigations into emerging technologies in this context. To address problems related to last mile-delivery in logistics operations, the practicality of adopting a hybrid truck-drone delivery system is examined through this study. The researchers utilized Mixed-integer linear programming (MILP)and Gurobi optimization solvers both for optimizing performance as well as facilitating execution. While testing a drone dataset of a well-known logistics company as part of their research using an optimization model, the findings suggested that were remarkable competitive advantages including significant gains in reduction of timing. Nevertheless, there are several constraints like maintenance, recharging, difficult weather conditions & traffic congestion-necessitating focused innovative AI-based approaches. In spite of these impediments, a hybrid truck-drone’s potential applicability can remarkably boost the efficiency of last-mile delivery operations.

Active BIM system for optimized multi-project ready-mix-concrete delivery

PurposeReady-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.Design/methodology/approachThe presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.FindingsThe system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.Originality/valueThe suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

A multi-period multiple parts mixed integer linear programming model for AM adoption in the spare parts supply Chain

ABSTRACT This research proposes a multi-period multiple parts mixed-integer linear programming optimization model for the trade-off analysis of spare parts supply through computer numerical control (CNC) manufacturing and additive manufacturing (AM). The multiple spare parts have different characteristics such as volume, shape size, and geometry complexity. The model focuses on minimizing lead times and thus reducing downtime costs. Scenario analyses are developed for some parameters to assess the robustness of the model. The analysis shows that the mix between AM-based spare parts and CNC-based spare parts is sensitive to changes in demand. For the given data, the findings demonstrate that AM is cost-effective with spare parts having high geometry complexity while CNC-based manufacturing is economically feasible for spare parts with low geometry complexity and large sizes. The proposed model can support decision-makers in selecting the optimal manufacturing method for multiple spare parts having different characteristics and attributes. The paper concludes with limitations and future directions.

Plan Assessment Metrics for Dose Painting in Stereotactic Radiosurgery

As radiation therapy treatment precision increases with advancements in imaging and radiation delivery, dose painting treatment becomes increasingly feasible, where targets receive a nonuniform radiation dose. The high precision of stereotactic radiosurgery (SRS) makes it a good candidate for dose painting treatments, but no suitable metrics to assess dose painting SRS plans exist. Existing dose painting assessment metrics weigh target overdose and underdose equally but are unsuited for SRS plans, which typically avoid target underdose more. Current SRS metrics also prioritize reducing healthy tissue dose through selectivity and dose fall-off, and these metrics assume single prescriptions. We propose a set of metrics for dose painting SRS that would meet clinical needs and are calculated with nonuniform dose painting prescriptions. Sample dose painting SRS prescriptions are first created from Gamma Knife SRS cases, apparent diffusion coefficient magnetic resonance images, and various image-to-prescription functions. Treatment plans are found through semi-infinite linear programming optimization and using clinically determined isocenters, then assessed with existing and proposed metrics. Modified versions of SRS metrics are proposed, including coverage, selectivity, conformity, efficiency, and gradient indices. Quality factor, a current dose painting metric, is applied both without changes and with modifications. A new metric, integral dose ratio, is proposed as a measure of target overdose. The merits of existing and modified metrics are demonstrated and discussed. A modified conformity index using mean or minimum prescription dose would be suitable for dose painting SRS with integral or maximum boost methods, respectively. Either modified efficiency index is a suitable replacement for the existing gradient index. The proposed modified SRS metrics are appropriate measures of plan quality for dose painting SRS plans and have the advantage of giving equal values as the original SRS metrics when applied to single-prescription plans.

A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study.

In the context of the COVID-19 pandemic, utilizing the potential of online markets to sell products has become increasingly important for creating competitive advantages and ensuring the growth and survival of businesses. The pandemic has disrupted traditional business practices, and with social distancing measures in place, consumers have turned to online channels to meet their needs. As a result, businesses that have adapted quickly to online markets have been able to maintain their customer base and revenue streams. Thus, considering the potential of online markets is of utmost importance in the current pandemic situation. In this regard, the present research aims to provide a practical framework for creating a green and sustainable closed-loop supply chain network (SCLSCN), including the integration of online markets, to assist managers in making decisions that support economic, environmental, and social goals. Accordingly, a multi-objective mixed integer linear programming (MOMILP) optimization model was designed under uncertain demand and disruption caused by natural disasters in Iran's home appliance industry. The study also considered changes in the capacities of online and marketplace sales channels, revealing a significant reduction in costs at each stage. The results show that the increase in demand has a direct impact on the production level, warehousing, and transportation costs, leading to social impacts on the model. However, the current system cannot handle an increase in demand of more than 20%, requiring managers to make decisions to increase production capacity or build new factories. Thus, the study highlights the importance of considering online markets as a means to adapt to disruptions caused by the pandemic and maintain a competitive edge.

OPTIMIZATION OF WATER RESOURCES UTILIZATION DISTRIBUTION FOR AGRICULTURAL BENEFITS IN THE CLAMPIS IRRIGATION AREA SAMPANG DISTRICT

Utilization of water resources and management is an important factor and has a decisive role in order to increase production per plant in general and crop production in particular. In order to maintain self-sufficiency in food, it is necessary to make efforts to continuously increase the intensity of potential food crops. These efforts include optimizing the proper distribution of water utilization. Factors to determine the need for irrigation water include land preparation, consumptive use, percolation and seepage, replacement of water layers, and effective rainfall. While the method used in this study is optimization using a linear program using the Solver facility in Microsoft Excel with the aim of optimizing the availability of irrigation water so as to produce maximum profits and distribution of irrigation utilization that is more effective and efficient. From the optimization results using the Solver and Microsoft Excel tools, it can be seen that the maximum area of ​​land that can be planted with the available inflow at the intake for MTI (Rice I 2086 Ha/Tobacco II 667.54 Ha), MT-II (Rice I 667, 54 Ha/Tobacco I 1391 Ha/PolowijoI 567.35 Ha/Rice II 128.11 Ha), MT III (Polowijo I 323.65 Ha/Rice II 348.89 Ha/Tobacco II 1261.46 Ha/Polowijo II 825, 00) and provides an analysis of the maximum profit of agricultural products of Rp. 178,183,800,000.00,-. Keywords: Linear Program Optimization; Utilization of Water Resources; Agricultural Benefits; Klampis Irrigation.

Reliability-Constrained Optimal Scheduling of Interconnected Microgrids

This paper proposes a Mixed-Integer Linear Programming (MILP) optimization model for the scheduling problem of the interconnected microgrid system. The proposed model is capable of efficiently minimizing the microgrids' total operating costs and improving the entire system's reliability, as it is constrained based on enhancing the interconnected microgrids' reliability. The Expected Energy Not Supplied (EENS) is considered in order to ensure minimizing the interconnected microgrids' power deficiency. Furthermore, the proposed model has the capability to solve the optimization problem considering the islanded operation of the interconnected microgrids, i.e. when disturbances occur on the upstream grid. Numerical simulations on a test system containing three interconnected microgrids are performed to evaluate the effectiveness of the model and the results demonstrate the merits and features of the reliability-constrained optimal scheduling model in minimizing the interconnected microgrids' total operating costs and enhancing the interconnected system reliability.

Multi-Period Optimisation of District-Scale Building Integrated Photovoltaic Deployment

The deployment of solar energy technologies such as building integrated photovoltaics (BIPVs) is one of many ways to mitigate present greenhouse gas emissions and abate GHG emissions in future urban development. The widespread deployment of BIPVs is therefore ideal, but deployment must be met with an understanding of the life cycle carbon associated with the technologies manufacturing and their availability as a finite resource. In this paper we describe deployment schedules over the next several decades for BIPVs in a redeveloping urban quarter in Zürich, Switzerland, known as Altstetten. Altstetten has near term goals for carbon neutral buildings that must be met through energy retrofits, higher building standards, and renewable energy. In solving how best to deploy BIPV systems to reduce the life cycle emissions of the buildings in Altstetten, we develop a multi period deployment scheme from a Mixed Integer Linear Programming optimization model that determines the optimal PV deployment within a larger urban multi-energy system. This approach intermingles spatially resolved optimisations with temporally resolved projections of how to deploy the BIPV systems. We analyse results of several optimal solutions sets to investigate the role urban morphology and BIPV embodied emissions play in BIPV utility. We find that different planning strategies (e.g. reduce cost before carbon emissions) play a role in the quantity and rate at which BIPV could be expected to be deployed and that a strategy of minimizing carbon before cost results in less required BIPV deployment than if cost is of the prime concern.

Extended exergy accounting theory to design waste-to-energy management system under uncertainty

This paper seeks to study the waste-to-energy facility location planning problems under uncertainty from an integrated perspective. The extended exergy accounting method is first used to develop a multi-criteria decision analysis framework that unique in unifying multiple criteria of material, energy, social, economic and environmental aspects into homogeneous exergy terms, thus employing multiple criteria to obtain an integrated decision-making result.Robust optimization and mixed-integer linear programming mathematical techniques are applied to formulate the problem as a two-stage robust waste-to-energy facilities location model based on this framework to minimize the exergy expenditure, and determine the technology type, location and capacity size of waste-to-energy facilities over a large urban region. The uncertainty of municipal solid waste supply quantity in each waste generation site is considered.The proposed model was demonstrated using a case study for household food waste treatment in Shanghai, and two treatment scenarios are proposed. Under the same uncertainty, the deployment strategy in scenario II feeds annual 2,655,140 GJ more equivalent primary exergy into the urban ecosystem than the scenario I, indicating that the only deployed on-site biogas plant to disposal household food waste performs better than the centralized incineration and on-site biogas plant combined deployment.A further sensitivity analysis reveals several valuable insights into municipal solid waste management. If the uncertainty is not considered, it may result in a municipal solid waste treatment capacity gap of up to 11.5% and a waste-to-energy system breakdown. Introducing the uncertain budget and range can help the decision-maker adjust the planning scheme flexibly, but the results become more conservative as their values increase. In this case study, it is optimal for decision makers to choose uncertain range mˆi=12.5% and uncertain budget Γ=30.

Significance of Linear Programming for Optimization

Linear programming (LP) is a powerful mathematical technique that has revolutionized optimization problems across various fields. This abstract highlights the significance of linear programming in optimization and its profound impact on decision-making, resource allocation, and efficiency enhancement. The primary objective of linear programming is to maximize or minimize a linear objective function, subject to a set of linear constraints. LP provides a systematic framework for modeling and solving complex optimization problems, enabling decision-makers to make informed choices and allocate resources optimally. The significance of linear programming lies in its ability to address a wide range of real-world challenges efficiently and effectively. In the realm of operations research, linear programming has found extensive applications in supply chain management, production planning, scheduling, and logistics optimization. By formulating decision problems into mathematical models, LP allows organizations to streamline their processes, minimize costs, optimize resource utilization, and maximize overall productivity. Linear programming has also made significant contributions in the field of economics. It has enabled economists to analyze and optimize economic systems, such as determining the optimal production levels for different goods, optimizing resource allocation in a market, and solving complex pricing problems. LP provides a quantitative framework for economic decision-making, facilitating the identification of optimal solutions and supporting policy development. Furthermore, linear programming plays a crucial role in the field of transportation and network optimization. It aids in solving problems related to route optimization, facility location, network design, and capacity planning. By efficiently allocating resources and optimizing transportation networks, LP helps reduce costs, improve service levels, and enhance overall efficiency in the transportation sector. In summary, the significance of linear programming for optimization is evident in its wide-ranging applications and its ability to tackle complex decision problems in various domains. By employing mathematical modeling and optimization techniques, LP allows decision-makers to make informed choices, allocate resources optimally, and achieve desired objectives. The ongoing advancements in LP algorithms and computing technologies further enhance its applicability and make it an indispensable tool for optimization in today's dynamic and competitive world.

Inter-firm exchanges, distributed renewable energy generation, and battery energy storage system integration via microgrids for energy symbiosis

Policymakers and entrepreneurs are aware that reducing energy waste and underutilization are mandatory to actually foster the green transition. Nevertheless, small–medium enterprises usually meet technical and overwhelming financial constraints. They are unable to make profits, become less energy-sensitive, and cut down on their emissions simultaneously. Industrial districts are a source of both wealth and GHG (greenhouse gas) emissions. Eco-industrial parks (EIPs) supply a suitable strategy to ease symbiotic exchanges among various organizations. Surplus electricity from larger, energy-autonomous companies will be a new input for more vulnerable ones. This type of district is challenging, and it can provide an unexplored opportunity to cooperate, invest in renewable energy sources, and form alliances. To better exploit underutilized energy in industrial districts, it is essential to explore energy symbiosis (ES), i.e., an energy-based perspective of industrial symbiosis. This study presents an original mixed-integer linear programming (MILP) optimization model that aims to identify possible inter-firm exchanges and introduce microgrid-based support for distributed renewable-energy generators (DREGs) and battery energy storage systems (BESS) over a one-year simulation period. The model simultaneously targets economic and ecological objectives. The paper compares two case studies, one with battery support and one without. The optimization model was tested using a case study and found to improve energy efficiency (with a 43.46% saving in energy costs) and reduce greenhouse gas emissions (with an 84.59% reduction in GHG) by facilitating symbiotic exchanges among SMEs in industrial districts. The inclusion of BESS support further enhanced the model's ability to utilize green and recovered energy. These findings have implications for policymakers, entrepreneurs, and SMEs seeking to transition to more sustainable energy practices. Future work could explore the applicability of the MILP optimization model in other contexts and the potential for scaling up the model to larger industrial districts.

Model predictive control of solar-coupled innovative heat pump: a comparison of economic and environmental optimizationsin Latvia.

Background: Heating and cooling in buildings represents a significant amount of the energy demand in the EU, but the market penetration of renewable solutions is still marginal. The SunHorizon project aims at proving the viability and benefits of innovative coupling between heat pumps and various advanced solar panels. Methods: This study focuses on the optimal operation strategies of a technological package located in Latvia, and composed of hybrid photovoltaic thermal (PVT) panels, a gas driven heat pump and a hot water storage tank. In this work, a model predictive control is developed, based on mixed integer linear programming (MILP) optimization. This model uses innovative elements compared to traditional model predictive control (MPC), with environmental indicators for the Latvian electricity grid accounting for imports, co-simulation with TRNSYS using the transmission control protocol (TCP) and modelling of long-term storage for long and short-term decisions. The usual minimization of costs is compared to two new optimization approaches, which aims to minimize greenhouse gas (GHG) emissions and maximizing renewable use and self-consumption. Results and conclusions: The results of the optimization of costs and GHG emissions show that gains can be found within the variations in time series related to the electricity grid, but the overall operation strategies remain similar. Optimization of renewable share and self-consumption is another path for control strategy, but with less economic and environmental performance.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.

A stochastic optimization approach for optimal Tail Assignment with knowledge-based predictive maintenance

AbstractRecent developments in the field of data analysis offer enormous potential to derive predictions of future aircraft maintenance necessities from observable aircraft attributes. This procedure is called Predictive Maintenance. The Predictive Maintenance generates considerable business benefits compared to alternatives like reactive or preventive maintenance for various reasons. Nevertheless, recognizing a maintenance necessity is only the first step, since the execution of the maintenance action has to fit into the airlines operational schedule. An essential step to realize financial benefits is to integrate Predictive Maintenance into the airlines optimization processes. To contribute to this task, we expand a well-known Tail Assignment model for the assignment of a potentially heterogeneous fleet of aircraft to a schedule of flights by a number of part failure scenarios. This results in a stochastic mixed integer linear optimization program, for which an optimization algorithm with solution guarantees is developed. This optimization algorithm is based on Benders Decomposition, which is concretized for the Tail Assignment problem and optimized for this task. Using this algorithm, a large proportion of the recovery costs for almost all instances tested is saved. The algorithm solves all instances in a reasonable amount of time. The algorithm uses a state-of-the-art mixed-integer linear solver, implementing a decomposition-based solution procedure for stochastic programs, called the L-shaped method. To demonstrate the potential of the approach, we benchmark our results using four strategies, motivated by commonly used preventive, reactive and passive approaches. Our approach leads to considerable cost savings when compared to each of the four benchmark approaches. The algorithms are tested on a set of instances with up to 80 flights based on a pre-pandemic schedule of a larger German airline. To the best of our knowledge, this article presents the first attempt to implement an exact optimization approach integrating malfunction predictions of the form presented into a Tail Assignment model.

The economic impact of location on a solar farm co-located with energy storage

Deploying energy storage (ES), alongside renewable generation, can help to decarbonise electricity grids. A key aspect of deploying these is choosing a suitable location, which is both geographically feasible and economical. Previous studies identify locations with suitable geographies; here we focus on the economic impact of location. We explore how the maximised profits, determined using a mixed integer linear programming (MILP) optimisation model, of a solar farm co-located with ES vary in different regions around Great Britain (GB) as a case study. We perform a cost–benefit analysis from the point of view of a distribution-connected solar farm owner. Real solar generation data is used, along with a weather model, to accurately represent forecast and actual output. For solar farms without ES profits are higher in locations with greater solar irradiance. However for sites with ES we find greater profit variation, primarily due to different distribution charges. For the majority of GB, ES does not add sufficient value to offset its high upfront costs and is not worth adding to solar sites. Additionally, it is found to be uneconomical to add ES to most existing solar farms, despite many studies highlighting the grid benefits this would bring. We recommend that distribution network operator and market pricing better reflects the value which ES can bring to the electricity system economical to add to solar sites. To encourage increased co-location distribution operators should offer greater a differential between non-intermittent generation and intermittent generation payments, in particular at times of high system demand.

Thermo-lp: A computational tool to evaluate reaction thermodynamics for synthesizing Mn+1AXn (MAX) phases based on linear programming optimization method

Thermo-lp (Thermodynamics-Linear Programing) is a computational program written in C and Python for evaluating the thermodynamic formability of MAX phases at the finite temperature, including configurational, electronic, and vibrational entropies. The program uses the phonon density of states (PDOS) and electron density of states (EDOS) as the main inputs to obtain the Gibbs free energy in a temperature range from 0 K to 2000 K for each crystal structure in the self-contained structural and thermodynamic data pool. Thermo-lp offers a highly reliable evaluation of the overall thermodynamic stability of a target MAX phase with respect to many competing impurities using linear programing optimization algorithm. Thermo-lp program is also capable to simultaneously compose and optimize the synthetic pathways for the target MAX phase due to the implementation of constrained linear programing procedure. The capabilities of Thermo-lp program are demonstrated using the quaternary Cr2TiAlC2 o-MAX compound as the typical example by successfully predicting the thermodynamically most feasible synthetic route, and the most likely impurities at 1724 K for the annealing temperature. Program summaryProgram Title: Thermo-lpCPC Library link to program files:https://doi.org/10.17632/ykdpvcr8by.1Developer's repository link:https://gitlab.com/FxhLn/thermo-lp.gitLicensing provisions: MITProgramming language: C and PythonNature of problem: Evaluating the thermodynamic formability of a MAX phase and finding the most competing impurities associated with a specific synthetic pathway among many possible decomposition reactions requires an efficient and reliable searching algorithm within a large structural data pool.Solution method: Using phonon density of states and electron density of states as the main input data to calculate the finite-temperature corrections to Gibbs free energy. Utilizing the advanced linear programing optimization procedure to evaluate the overall thermodynamic formability of MAX phase with respect to competing impurities based on a self-built thermodynamic data pool.

Cost minimization oriented energy management of PV-assisted refueling and recharging stations for FC-ultracapacitor hybrid trams

Fuel cell (FC) and ultracapacitor hybrid trams offer a sustainable and efficient transportation option, with the ability to store and use renewable energy for propulsion. However, their widespread adoption is limited by the availability of reliable and cost-effective refueling and recharging infrastructure. In this study, the use of mixed-integer linear programming (MILP) optimization for the energy management of photovoltaic (PV)-assisted refueling and recharging stations for fuel cell ultracapacitor hybrid trams is investigated. The electrolyzers and recharging units can either be supplied by the grid or by PV, depending on the availability of renewable energy.The study's main contribution is the consideration of the tram-station and internal tram transactions in a single optimization-based energy management structure and the all-in-one renewable energy assisted refueling and recharging station concept, which considers both the hydrogen and electricity supply requirements. The proposed structure was tested using the data of the Istanbul Kabataş-Bağcılar tram line, and the results showed a 46.48% reduction in operating cost and a significant decrease in carbon dioxide emissions and power drawn from the grid. The study provides valuable information for rail system operators and state authorities and opens up possibilities for future studies in the integration of hydrogen fuel cell vehicles into refueling stations.