General Algebraic Modeling System Research Articles

Cost minimization of membrane-based separation systems for H2 recovery: A comparison of two optimization approaches

This paper addresses the optimization of membrane-based processes, with a specific focus on H2 recovery in hydrocarbon processing plants. A comparative analysis of two optimization approaches is conducted for designing H2 recovery systems for a multicomponent gas mixture containing H2/N2/CO2/CO. The main objective is to achieve the desired H2 recovery and product purity levels while minimizing costs. The paper compares two approaches for optimization: the first uses a simulation-based optimization technique with Aspen Custom Modeler/ASPEN Plus, while the second employs a simultaneous optimization method using GAMS (General Algebraic Modeling System). The methods are thoroughly compared, evaluating their strengths and weaknesses through qualitative and numerical assessments. To this end, a reference case was selected from the existing literature. The results obtained indicate that both proposed approaches are suitable for optimizing the design of a two-stage membrane configuration, as evidenced by the similarity of the optimal solutions obtained from both approaches. Subsequently, based on the analysis of numerical values for the optimal design problem for the investigated two-stage membrane configuration, a hybrid strategy for the optimal synthesis and design of multi-stage separation processes is proposed.The study fills a gap in the literature by providing a comprehensive analysis of the advantages and disadvantages of optimization approaches in the context of technology of separation of gases. This research provides valuable insights into the field and offers guidance for selecting appropriate approaches to improve the efficiency and cost-effectiveness of gas separation processes in real-world applications.

An Integrated Approach for Ammunition Depot Location Selection and Ammunition Distribution Network Design Based on P-Median and Vehicle Routing Problems

During the war, the safe provision of the required ammunition at the desired place and time can affect the fate of the war and even the country. Determining the number and locations of ammunition depots needed in the war is as important as planning their distribution. Ammunition supply has always been a force multiplier for military units. Although the timely delivery of ammunition to the unit is not sufficient for success on its own, it is an important factor. These two issues are directly related to each other. This study presents a two-stage methodology to solve the problems in question. In the first stage, it was decided which of the 3 different types of depots, whose establishment locations were known with the p-median model, would be opened, and the unit-depot assignments were determined. In the second stage, the distribution network design for the distribution of ammunition from the ammunition depot to the deployed artillery units was realized with the Capacity-Constrained Vehicle Routing Problem (CCVRP) model. This methodology was tested for a fictitious situation where the geographical location and numerical information of the depots and artillery batteries were generated generically. By solving the mathematical model in the General Algebraic Modeling System (GAMS) program, it was determined which depot should distribute how many vehicles to which artillery batteries, and the results were presented visually.

Enhancing DC distribution network efficiency through optimal power coordination in lithium-ion batteries: A sparse nonlinear optimization approach

This work deals with the problem regarding the optimal operation of lithium-ion batteries to improve the economic, technical, and environmental indices of standalone and grid-connected direct current (DC) distribution networks.To this effect, a general nonlinear programming model has been developed, which considers three objective functions: (i) the daily energy purchasing costs at the terminals of the substation, considering the maintenance costs of renewable generation (photovoltaic) and energy storage technologies (batteries); (ii) the daily energy losses associated with energy transport; and (iii) the CO2 emissions per day of operation, associated with conventional generators. This is done by integrating all the operations and technical constraints of the DC network and the devices it comprises. The sparse nonlinear optimization (SNOPT) solution method is employed, comparing its results against those of three recently reported metaheuristic optimization methodologies: the continuous genetic algorithm and the parallel versions of the particle swarm optimizer and the vortex search algorithm. The performance of the proposed methodology is validated on standalone and grid-connected networks, considering the technical and economic conditions of two regions of Colombia (rural and urban territories) and evaluating its effectiveness in terms of solution quality, standard deviation, and processing times. The results show that the SNOPT tool of the General Algebraic Modeling System (GAMS) achieves the global optimum in all test scenarios, exhibiting a standard deviation of zero and requiring less than three seconds on average to generate a solution for an entire day of operation.

Optimization of superstructure network in the CCS/CCSU system for CO2 reduction from exhaust gas industry and gas field in Indonesia as archipelago state

Industrial exhaust gases and gas fields are two significant sources of carbon dioxide (CO2) emissions that contribute to the rising levels of CO2 in the atmosphere. Among the various emission reduction systems, the CCSU (Carbon Capture, Storage, and Utilization) system has garnered extensive attention and research. This research aims to obtain the superstructure network sequentially in the CCSU system using GAMS (General Algebraic Modeling System). A mathematical approach was developed to optimize the amount of CO2 stored and utilized by varying the time difference (dt) between the source and sink from 0 to 10 years. After calculating the economic potential (EP), it was found that the Carbon Capture and Storage (CCS) system for both sources has a negative impact. In contrast, the CCSU system enhances the economic potential (EP) by generating a positive value. This is possible as the captured CO2 can be sold to the utilization sink, thereby creating a revenue stream. The EP for CO2 reduction from gas fields is greater than that from the industry, 21.68 × 106 USD compared to 12.50 × 106 USD at dt min10 years. The CCSU system, when utilizing CO2 sources from gas fields, is more profitable compared to using industrial emissions.

Optimizing Renewable Energy Integration for Sustainable Fuel Production: A Techno-Economic Assessment of Dimethyl Ether Synthesis via a Hybrid Microgrid-Hydrogen System

This study offers an in-depth analysis and optimization of a microgrid system powered by renewable sources, designed for the efficient production of hydrogen and dimethyl ether—key elements in the transition toward sustainable fuel alternatives. The system architecture incorporates solar photovoltaic modules, advanced battery storage solutions, and electrolytic hydrogen production units, with a targeted reduction in greenhouse gas emissions and the enhancement of overall energy efficiency. A rigorous economic analysis was conducted utilizing the HYSYS V12 software platform and encompassing capital and operational expenditures alongside profit projections to evaluate the system’s economic viability. Furthermore, thermal optimization was achieved through heat integration strategies, employing a cascade analysis methodology and optimization via the General Algebraic Modeling System (GAMS), yielding an 83% decrease in annual utility expenditures. Comparative analysis revealed that the energy requirement of the optimized system was over 50% lower than that of traditional fossil fuel-based reforming processes. A comprehensive assessment of CO2 emissions demonstrated a significant reduction, with the integration of thermal management solutions facilitating a 99.24% decrease in emissions. The outcomes of this study provide critical insights into the engineering of sustainable, low-carbon energy systems, emphasizing the role of renewable energy technologies in advancing fuel science.

Optimal sizing and placement of battery energy storage system for maximum variable renewable energy penetration considering demand response flexibility: A case in Lombok power system, Indonesia

Indonesia is a tropical climate country with considerable renewable electrical energy source prospects, including photovoltaic (PV) and wind energies. Nevertheless, several variable renewable energy sources (VREs) have exhibited uncertain attributes and substantial reliance on natural conditions, leading to unstable load-related power supply risks. Hence, integrating battery energy storage systems (BESSs) with VRE generators is a dependable approach to bolster renewable energy generator applications on a large-scale grid while providing load demand flexibility. This study determined adequate sizing and placement of the BESS to achieve maximum VRE generator penetration while considering the demand response flexibility. Key indicators, including technical minimum load and system ramp capacity, were identified to achieve maximum penetration of thermal generators. This study also combined a unit commitment procedure with direct current optimal power flow (DC-OPF) as a novel approach to determine the maximum VRE penetration level. An optimization problem model concerning mixed integer linear programming (MILP) was subsequently employed in this study using the CPLEX solver in the general algebraic modeling system (GAMS). The study is based on the IEEE RTS-24 system modified and a real-life case study of the Lombok energy system in Indonesia. Results from the simulated Lombok power system highlighted that optimal sizing and placement of the BESS could lower system costs by 37.66%, 33.63%, and 22.26% compared to the current system conditions during the weekday, weekend, and the lowest day scenarios, respectively. The VRE penetrations of the generators were also higher than the current system conditions by 83%, 51%, and 39% during the weekday, weekend, and the lowest day scenarios, respectively.

Smart home energy management and active power loss analysis of a residential community

As the demand for energy continues its upward trajectory, judiciously managing the global energy resources has become increasingly imperative. One effective strategy to accomplish this efficiency is by strategically scheduling intelligent household appliances, a process that considers dynamic pricing schemes and the availability of renewable energy sources. This paper presents a comprehensive study that delves into optimizing smart home appliance utilization, considering electric vehicles and a modest rooftop photovoltaic installation. The study seeks to ascertain their potential impact on both energy consumption and cost mitigation. The proposed scheduling approach is centered on the optimal scheduling of smart appliances, harnessing the power of real-time pricing schemes to curtail the overall energy expenditure. The scheduling conundrum is meticulously modelled using General Algebraic Modeling System software and subsequently tackled by the CPLEX solver, leveraging mixed-integer linear programming techniques. Results reveal a remarkable achievement, with the potential for up to an 80.6 % reduction in the overall cost compared to the base case under various scenarios considering different approaches. To validate the integrity of the study, outcomes are juxtaposed with those of the base paper, and the superiority of the proposed method is evident from the results. Furthermore, the paper considers the ramifications of optimized scheduling on the voltage profile and overall system losses nearly 7 % less compared to the base case within the context of the IEEE 33-bus system. Thus, the presented work successfully analyzes the effect of household energy scheduling on the community level and scales up to a microgrid level.

Multi-distributed resource coordination based on GAMS (general algebraic modeling system)

To achieve the economic operation of distributed energy resources in the grid, a GAMS-based multi-distributed resource optimization and control method is proposed. Firstly, considering the coexistence of electricity load and heat load in the grid system, a virtual power plant structure involving heat generation from distributed energy is established. Secondly, the General Algebraic Modeling System (GAMS) is utilized to create a detailed mathematical model for the distributed resources within the virtual grid. Subsequently, considering the influence of various uncertain factors, the CLPEX solver is employed for optimization and solution. Research results indicate that during the operation of the virtual power plant, the introduction of system heat load significantly enhances the expected profit and improves the economic efficiency of the operation.

Crop water requirement and irrigation scheduling under climate change scenario, and optimal cropland allocation in lower kulfo catchment

Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, and the allocation of crop land also not optimal that signifcantly challenges to crop productivity.Therefore, this study was conducted to evaluate the effects of climate change on future crop water requirements, and irrigation scheduling, and to allocate cropland optimally. Bias of projected precipitation and temperature were corrected by utilizing Climate Model data with the hydrologic modeling tool (CMhyd). Alongside, crop water requirements and irrigation scheduling were assessed using Crop Water Assessment Tool. After estimating crop water requirement, crop land were allocated optimally using General Algebraic Modeling System programming with non-negativity constraints (scenario 1), and non-negativity constraints based on farmers adaptation (scenario 2). Average reference evapotranspiration from 2030 to 2050 and 2060 to 2080 was increased by 11.9 %, and 16.2 %, respectively compared with the reference period (2010–2022). The total seasonal crop water requirements were 4,529 mm, 4866.7 mm, and 5272.2 mm under 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios, respectively. The meean irrigation interval in 2010–2022, 2030 to 2050, and 2060 to 2080 climate change scenarios were 8 days, 7 days, and 5 days, respectively. This irrigation interval was decreased by 14 % (2030–2050), and 34 % (2060–2080) compared with the reference period. In 2030 to 2050 and 2026 to 2080 climate change scenarios, the required irrigation water at the inlet of main canal increased by 6.8 %, and 18 %, respectively. The optimal allocated area for tomato (60.4 %), maize (20.8 %), and watermelon (18.8 %) in scenario 1 with net benefit of 1.47*108 Ethiopian Birr. The allocated areas in scenario 2 were (48 %) for maize, (31.6 %) for tomato, and (20.4 %) for watermelon with 1.34*108 Ethiopian Birr net benefit it was reduced by 19.1 % compared with the net benefit in scenario 1. Fruit crops alone may not suffice for local food needs and to address this, small farmers should grow maize, tomato, and watermelon. This research aids policymakers in encouraging climate-resilient agriculture and improving small-scale farmers' awareness through conducting workshops and training.

A highly efficient, low-carbon CCHP system and its comprehensive optimization for an integrated medical and nursing complex

As the global population ages, the demands imposed by medical treatment and care for the elderly are becoming increasingly pressing. To reduce energy consumption and systemic defects in the process of integrating medical treatment and care for the elderly, the authors of this study designed a highly efficient, flexible and low-carbon combined cooling, heating, and power (CCHP) system for the integration of medical and nursing complexes by using a multi-level model of optimization. The complex was modeled in DeST software to obtain the cooling and heating loads, and the general algebraic modeling system software and mixed-integer linear programming (MILP) were used for the collaborative optimization of the choice of equipment, capacity allocation, and operational strategy. The results showed that the proposed system could reduce the total consumption of electric power by 40.95 % when a semiconductor wall was installed on floors 8–12 of the complex for elderly care. The energy management strategy of the system was also optimized by using the vehicle-to-building subsystem in a novel wind–solar–storage and heat pump-based combined cooling, heating, and power system. The rates of reduction in emissions of CO2 and NOx, and the rate of reduction in primary energy consumption of the Wind–Solar–Storage and Heat Pump (WSSH)–CCHP system were 96.5 %, 99.1 %, and 95.6 %, respectively. Finally, the heat pump-assisted liquid-gap membrane distillation (HP-LGMD) subsystem were optimized in MATLAB. The resulting gained output ratio, flux, thermal efficiency of the system, and energy consumed for water production were 0.0269, 4.22 kg/m2, 17.07 %, and 46.88 %, respectively. The proposed system is highly energy efficient, is capable of economical scheduling, and can use energy cascading, which provides guidance on sustainable development of environment and energy.

Economic impact of government health expenditure: An application of the computable general equilibrium model to the Iran.

Considering the increase in health expenses and the government's role in health financing, this study investigated the economic impact of increases in the share of the health sector in the government budget while taxes remain unchanged and government spending is fixed. The economic model used in this study was a macroeconomic Computable General Equilibrium (CGE) model. This model was calibrated using a 2011 Social Accounting Matrix (SAM) Of Iran. The CGE model was solved with non-linear programming using the General Algebraic Modeling System package, version 2.50. The effect of this simulation on the government budget deficit, the production of different sectors of the economy, and the employment rate was investigated. Based on our fundings the elasticity of substitution in the agricultural and industrial sectors is higher than in the health and service sector. Also, the biggest decrease in production occurred in the industry, agriculture, and service sectors, respectively. With the doubling of the share of government spending in the health sector, the employment rate of this sector has increased by 40.9%, but the highest decrease in the ignition rate is related to the service sectors (-2.7%), agriculture (-0.23%), and industry (-0.14%). Increasing the share of government spending in the health sector in comparison with other sectors of the economy, provided that government spending is maintained in general, leads to a decrease in production and economic welfare. It seems that the Iranian government should seek to increase the sources of health financing and the share of government expenditures in the health sector with other ways in order to improve the health level of the society and have a positive effect on other economic sectors.

Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and IGDT-based approach

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.

A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location

This work focuses on a hybrid preference-based electric vehicle charging station location problem, which considers multiple optimization preferences of distribution network operators, charge station owners, and electric vehicle users. The problem is formulated by an uncertain mixed-integer programming model. Due to the multi-fold uncertainty of the charging process, the uncertain model parameters are expressed as Type-2 fuzzy variables (T2-FVs). The critical value-based type reduction method is adopted to handle the high computational complexity. The proposed uncertain model is converted to its equivalent deterministic chance-constrained programming model. The deterministic counterpart is solved by General Algebraic Modeling System (GAMS). At last, numerical simulations are performed to demonstrate the proposed location strategy as well as some sensitivity analyses. The results indicate that for any given parameters, the equivalent deterministic model follows the general form of mixed-integer programming one that can be easily solved by GAMS. The proposed methodology can effectively handle the multi-fold uncertainty of the charging process. Compared with crisp models, the proposed location strategy can provide more robust location decisions for electric vehicle charging stations (EVCSs). In addition, we also found that different interest groups have conflicting preferences for the locations of EVCSs, so considering multiple hybrid optimization preferences is necessary.

A novel approach for optimizing the natural gas liquefaction process

The conversion of natural gas into liquefied natural gas (LNG) requires substantial energy consumption. This study proposes a deterministic mathematical model to find the optimal operation conditions for an LNG plant to minimize the energy consumption for turbomachinery and the total thermal conductance of the heat exchangers. General Algebraic Modeling System (GAMS) linked to a Dynamic Link Library was used to calculate the thermodynamic properties of the working fluids. A derivative-based optimization algorithm is used. Results indicate that the novel optimization approach allows the satisfactory management of the model nonlinearities associated, for example, with the bilinear terms involved in the energy balances and the mathematical functions used to calculate the thermodynamic properties. A preprocessing phase for initializing process variables is developed to facilitate model convergence. In comparison to an optimal design reported in the literature, which was obtained by integrating a well-established evolutionary optimization approach with the Aspen HYSYS simulator, the results indicated that the net electrical power could be reduced by up to 10% when the derivative-based optimization algorithm is used. The proposed deterministic approach, consisting of a mathematical model, an initialization phase, and an optimization algorithm, can help process engineers overcome the challenges associated with LNG process optimization.

Modelling smart energy consumption with hybrid demand management in off-grid electrical system considering techno-economic indices

This study proposes day-ahead power scheduling for electrical systems in off-grid mode, emphasizing consumer involvement. Bi-Demand Side Management (DSM) approaches like strategic conversion and demand shifting are proposed for consumer involvement. Multiple objectives are modelled to voltage profile improvement and reduce the operation energy cost. The non-dominated solutions of the voltage of buses and operation energy cost are generated by enhanced epsilon-constraint technique, simultaneously. The General Algebraic Modeling System (GAMS) software is proposed for solving optimization problems. A combination of decision-making methods like weight sum and fuzzy procedures are implemented for finding optimal solution non-dominated solutions. The proposed method’s effectiveness is confirmed through numerical simulations carried out on several case studies that utilize the 33-bus electrical system. The findings illustrate the substantial effectiveness of demand-side participation in improving power dispatch and the optimal rate of multiple objectives. By using DSM, operation cost is reduced by 21.58% and the voltage index is improved by 13.36% than the lack of implementing DSM.

Evaluation of Efficient CCUS System Design from Chemical Industry Emission in Indonesia

Carbon dioxide (CO2) emissions from industry significantly contribute to increasing CO2 in the atmosphere as the main cause of the Green House Gas (GHG) effect and climate change. CO2 emissions cause the need for evaluation in finding emission reduction systems. The CCUS (Carbon Capture Utilization and Storage) system is one of the most studied emission reduction systems. This study aims to obtain an intuitive and quantitative CCUS network design framework using GAMS (General Algebraic Modeling System) software with a mathematical approach. Several sources of CO2 emissions and potential absorbers are scattered in several regions in Indonesia. A mathematical approach was developed to optimize the amount of CO2 stored and utilized by varying the minimum time difference (dt min) between source and sink from 0, 3, 5, 8, to 10 years. The economic potential of the source-sink pair decreases with the change in dt with an average of 6.50 x 106 USD. Based on the potential economic value, the CCUS system with industrial CO2 emission sources has a positive value that can be applied in Indonesia.

Exploring optimal pathways of the high-CO2 content natural gas source to chemicals and fuels using superstructure multi-objective optimization

This study aims to develop a mathematical formulation of superstructure multi-objective optimization to find optimal pathways of high-CO2 natural gas and PV-hydrogen to chemicals and fuels integrated with CO2 sequestration. A mixed-integer nonlinear programming optimization model is used to compete along 56 pathways in the superstructure optimization network. The model was developed with the general algebraic modeling system and computed with the standard branch and bound (SBB) solver to maximize annual net profit and minimize annual net CO2 emissions. The result shows the selected products in 2020 are liquefied natural gas, methanol, dimethyl ether, formic acid, and acetic acid, with annual net profits and net CO2 emissions of 38.17 million $/year and −8.47 million tons of CO2/year, respectively. In the projection analysis, the annual net profit growth results at a compounding annual growth rate of 7.85% per year between 2020 and 2060, while annual net CO2 emissions decrease until −16.37 million tons of CO2/year in 2060. In 2030, blue acetic acid will be selected via all separation processes. For 2050, the synfuel via partial membrane separation is selected. The other products, formic acid, methanol, and acetic acid via membrane separation (full and partial), will always be selected between 2020 and 2060. Therefore, this could be a future strategy for monetizing high–CO2–content natural gas sources with low-carbon process development.

Integrating hydro and wind resources for effective congestion management in a hybrid electricity market

Congestion management in deregulated electricity systems threatens system security and reliability. Integration of renewable energy sources, unpredictable wind power, load demand, and the requirement for quickly deployable reserves make congestion management more difficult. While deciding the dispatch of the generating units, many factors have to be considered for the economic, secure, and reliable operation of the power system. This study presents a congestion management strategy that has been developed specifically for hybrid power systems. Within a pool and bilateral power supply market structure, it introduces a generator rescheduling-based congestion management technique. Ensuring safe bilateral transactions between these companies is crucial. Renewable sources affect congestion management dynamics, according to the research. The optimization issue includes operational limitations for scenarios with and without renewable source units. Monte Carlo simulation (MCS) was used to sample hourly wind speed from the Weibull PDF wind model. GAMS CONOPT solver used the model. General Algebraic Modeling System (GAMS)-MATLAB interface imported the model into MATLAB to extract the response. The best combination of renewable sources is 2 hydro + 1 wind unit as savings of congestion cost are $1403.6/h and $1855.18/h in case 1 (3-line congestion) and case 2 (2-line congestion), respectively. Results were accomplished by updating the IEEE-24 bus reliability test system with hydro and wind generators.

Polyethylene gasification for sustainable plastic waste management with respect to energy, exergy, and environmental considerations: A non-linear programming optimization

Plastic waste poses a significant threat to the environment and plays a substantial role in contributing to climate change. One possible solution for addressing the issue of plastic waste is through plastic waste gasification, which involves converting plastic waste into a valuable gas that can be utilized as an energy source. Numerous reports have investigated the optimization procedures for converting plastic waste into syngas. Nevertheless, all of these reports have concentrated on improving the performance of the gasification process by adjusting processing variables, and none have examined how to optimize plastic waste gasification performance considering material characteristics. To improve the efficiency of polyethylene gasification, this research was undertaken with a focus on energy, exergy, and environmental factors in relation to the material conditions. Various regression models were created to forecast the energy, exergy, and environmental aspects of polyethylene gasification based on material characteristics. These models were then optimized using a general algebraic modeling system. The results indicate that the ideal conditions consist of 84.40 % carbon content, 15 % hydrogen content, and no oxygen or nitrogen content. The optimal criteria include an energy efficiency of 65.9 %, an exergy efficiency of 81.5 %, and an emission level of 28.0 kg/MWh.

Optimization of Multi-Component Gas Mixture in Pipeline Transmission Network using General Algebraic Modeling System

Optimization of Multi-Component Gas Mixture in Pipeline Transmission Network using General Algebraic Modeling System