Search Results (48)

Optimal scheduling of integrated PV/wind energy systems (IESs) is a complex task that requires innovative approaches to address uncertainty and improve efficiency. This paper proposes a novel multi-objective optimization framework for IES operation, incorporating demand response (DR), a comprehensive set of components, and innovative techniques to reduce computational complexity. The proposed framework minimizes total losses, cost, and emissions while meeting energy demands, offering significant advantages in terms of sustainability and cost reduction. The optimization model is implemented using steady-state energy analysis and non-dominated sorting genetic algorithm-III (NSGA-III) heuristic optimization, while uncertainty analysis and scenario reduction techniques enhance computational efficiency. To further reduce the computational burden, the proposed framework incorporates a novel clustering strategy that effectively reduces the number of scenarios from 1000 to 30. This innovation significantly improves the computational efficiency of the proposed framework, making it more practical for real-world applications. The effectiveness of the proposed approach is validated against multi-objective seagull optimization algorithm (MOSOA)- and general algebraic modeling system (GAMS)-based methods, demonstrating its superior performance in various scenarios. The improved management system, enabled by the proposed algorithms, facilitates informed operational decisions, enhancing the system’s installed capacity and overall flexibility. This optimization framework paves the way for more efficient and sustainable operation of integrated PV/wind energy systems. Reducing gas and heat network losses, considering both electric and thermal load response, simultaneously utilizing electricity, gas, and heat storage devices, and introducing a new clustering strategy to reduce scenarios are the specific innovations that are mentioned in this paper. Full article

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed. Full article

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. Full article

Teaching and research laboratories generate wastes of various compositions and volumes, ranging from diluted aqueous solutions to concentrated ones, which, due to milder self-regulation waste-management policies, are carelessly discarded, with little attention given to the consequences for the environment and human health. In this sense, the current study proposes the application of the supercritical water oxidation (SCWO) process for the treatment of complex refractory wastewater generated in research and teaching laboratories of universities. The SCWO, which uses water in conditions above its critical point (T > 647.1 K, p > 22.1 MPa), is regarded as an environmentally neutral process, uniquely adequate for the degradation of highly toxic and bio-refractory organic compounds. Initially, the wastewater samples were characterized via headspace gas chromatography coupled with mass spectrometry. Then, using a continuous tubular reactor, the selected operational parameters were optimized by a Taguchi L9 experimental design, aiming to maximize the total organic carbon reduction. Under optimized conditions—that is, temperature of 823.15 K, feed flow rate of 10 mL min⁻¹, oxidizing ratio of 1.5 (50% excess over the oxygen stoichiometric ratio), and sample concentration of 30%—TOC, COD, and BOD reductions of 99.9%. 91.5% and 99.2% were achieved, respectively. During the treatment process, only CO₂, methane, and hydrogen were identified in the gaseous phase. Furthermore, the developed methodology was applied for the treatment of wastewater samples generated in another research laboratory and a TOC reduction of 99.5% was achieved, reinforcing the process’s robustness. A thermodynamic analysis of SCWO treatment of laboratory wastewater under isothermal conditions was performed, using the Gibbs energy minimization methodology with the aid of the GAMS^® 23.9.5. (General Algebraic Modeling System) software and the CONOPT 4 solver. Therefore, the results showed that SCWO could be efficiently applied for the treatment of wastewater generated by different teaching and research laboratories without the production of harmful gases and the addition of hazardous chemicals. Full article

The maximum clique problem is a problem that takes many forms in optimization and related graph theory problems, and also has many applications. Because of its NP-completeness (nondeterministic polynomial time), the question arises of its solvability for larger instances. Instead of the traditional approaches based on the use of approximate or stochastic heuristic methods, we focus here on the use of integer programming models in the GAMS (General Algebraic Modelling System) environment, which is based on exact methods and sophisticated deterministic heuristics incorporated in it. We propose modifications of integer models, derive their time complexities and show their direct use in GAMS. GAMS makes it possible to find optimal solutions to the maximum clique problem for instances with hundreds of vertices and thousands of edges within minutes at most. For extremely large instances, good approximations of the optimum are given in a reasonable amount of time. A great advantage of this approach over all the mentioned algorithms is that even if GAMS does not find the best known solution within the chosen time limit, it displays its value at the end of the calculation as a reachable bound. Full article

In this paper, a new design strategy is developed for the Wireless Charging Electric Transit Bus (WCETB). The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. In particular, an improved version of the Whale Optimization Algorithm (IWOA) is adopted for the WCETB system in the road map of Wakefield City, located in the United Kingdom. The main challenge in the WCETB is to select the power transmitter and battery size efficiently from an economical point of view. For this purpose, both factors are considered in the objective function to achieve the benefits of WCETBs from an energy perspective. Two analytical economic design optimization models are developed in this work. The first model is the real- environment model, which considers a WCETB system operating under typical traffic conditions characterized by vehicle interactions and inherent uncertainties. In this scenario, vehicle speeds vary with time, and specific traffic routes may encounter congestion. The second model concentrates on a WCETB system operating in a traffic-free environment with minimal vehicle interactions and uncertainties. The IWOA is implemented for the WCETB to operate in the real environment. Under traffic-free environment conditions, we utilize mathematical procedures and General Algebraic Modeling System (GAMS) software to solve the optimization problem. This approach not only allows us to comprehensively analyze the WCETB system’s behavior but also examine the interactions among different components of the objective function and constraints. Finally, a comprehensive numerical analysis under various conditions, including changes in the number of buses and increases in the length of routes, is conducted. Full article

Renewable energy technologies and resources, particularly solar photovoltaic systems, provide cost-effective and environmentally friendly solutions for meeting the demand for electricity. The design of such systems is a critical task, as it has a significant impact on the overall cost of the system. In this paper, a mixed-integer linear programming-based model is proposed for designing an integrated photovoltaic-hydrogen renewable energy system to minimize total life costs for one of Saudi Arabia’s most important fields, a greenhouse farm. The aim of the proposed system is to determine the number of photovoltaic (PV) modules, the amount of hydrogen accumulated over time, and the number of hydrogen tanks. In addition, binary decision variables are used to describe either-or decisions on hydrogen tank charging and discharging. To solve the developed model, an exact approach embedded in the general algebraic modeling System (GAMS) software was utilized. The model was validated using a farm consisting of 20 greenhouses, a worker-housing area, and a water desalination station with hourly energy demand. The findings revealed that 1094 PV panels and 1554 hydrogen storage tanks are required to meet the farm’s load demand. In addition, the results indicated that the annual energy cost is $228,234, with a levelized cost of energy (LCOE) of 0.12 $/kWh. On the other hand, the proposed model reduced the carbon dioxide emissions to 882 tons per year. These findings demonstrated the viability of integrating an electrolyzer, fuel cell, and hydrogen tank storage with a renewable energy system; nevertheless, the cost of energy produced remains high due to the high capital cost. Moreover, the findings indicated that hydrogen technology can be used as an energy storage solution when the production of renewable energy systems is variable, as well as in other applications, such as the industrial, residential, and transportation sectors. Furthermore, the results revealed the feasibility of employing renewable energy as a source of energy for agricultural operations. Full article

This article deals with the solution of a mixed-integer nonlinear programming (MINLP) problem related to the efficient reallocation of battery energy storage systems (BESS) in monopolar direct current (DC) grids through a master–slave optimization approach. The master stage solves the integer nature of the MINLP model, which is related to the nodes where the BESS will be located. In this stage, the discrete version of the vortex search algorithm is implemented. To determine the objective function value, a recursive convex approximation is implemented to solve the nonlinear component of the MINLP model (multi-period optimal power flow problem) in the slave stage. Two objective functions are considered performance indicators regarding the efficient reallocation of BESS in monopolar DC systems. The first objective function corresponds to the expected costs of the annual energy losses, and the second is associated with the annual expected energy generation costs. Numerical results for the DC version of the IEEE 33 bus grid confirm the effectiveness and robustness of the proposed master–slave optimization approach in comparison with the solution of the exact MINLP model in the General Algebraic Modeling System (GAMS) software. The proposed master–slave optimizer was programmed in the MATLAB software. The recursive convex solution of the multi-period optimal power flow problem was implemented in the convex discipline tool (CVX) with the SDPT3 and SEDUMI solvers. The numerical reductions achieved with respect to the benchmark case in terms of energy loss costs and energy purchasing costs were $7.2091\%$ and $3.2105\%$, which surpassed the results reached by the GAMS software, with reductions of about $6.0316\%$ and $1.5736\%$. Full article

This paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consideration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO₂ emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids. Full article

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased. Full article

This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer nonlinear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master–slave optimization approach is proposed, which combines the salp swarm algorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master–slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master–slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies. Full article

There have been many studies performed on the optimal design and expansion of a natural gas transmission network; however, very few works have addressed the problem of the reinforcement of an existing natural gas transmission network with limited application. This study is focused on the reinforcement of an existing natural gas transmission network with the aim to minimize investment cost. The compressor stations have been assumed operational in either direction. A mathematical model was developed for the problem, which is non-convex mixed integer nonlinear programming (MINLP) in nature; therefore, a convex relaxation was formulated to solve the problem easily in General Algebraic Modeling System (GAMS, GAMS Development Corp., Fairfax, VA, USA) using DICOPT and CONOPT solvers. The model was applied to a small transmission network for validation and the results proved its efficiency. Full article

The northwestern highlands of Ethiopia are characterized by severe land degradation and apparently low agricultural productivity. This situation is continuously threatening the livelihoods of smallholder farmers who mainly sustain their living from the cultivation of annual crops. In recent years, however, smallholder farmers have started converting their croplands to plantations of Eucalyptus, a non-native tree species to Africa, for its rewarding economic contributions. In this study, we aggregated data from 388 smallholder Eucalyptus growers located in three agroecology zones (onwards called farm typologies). We measured the economic impact of Eucalyptus succession on croplands using a farm household model which is provided in the GAMS (General Algebraic Modeling System) platform. The results of the model varied between farm typologies and showed that households’ gross margins increased with a corresponding increase in the conversion of croplands. Results also showed that gross margins from plantations of Eucalyptus were higher than that of cultivation of food crops. Furthermore, evaluation of farm portfolios indicated a higher benefit-cost ratio (BCR) for the plantation of Eucalyptus. We concluded that the conversion of croplands in the study area is an incentive-driven process in a dynamic farming system, which strongly demands bringing policy-emanated livelihood alternatives. With this arena, the expansion of Eucalyptus is recommended for lands of terrain features, high marginality and low suitability for the cultivation of food crops and setting aside fertile arable lands. We generalized that an increase in Eucalyptus plantation pays off given the implementation of proper land resource management and the apparent impacts of Eucalyptus on biodiversity and cultural landscape is managed with sustainability perspectives. However, it demands collaborative policy efforts that can especially meet socioeconomic, environmental and public interests. Full article

Inadequate gas supply is partly responsible for the energy shortfall experienced in some energy-poor nations. Favorable market conditions would boost investment in the gas supply sector; hence, we propose a blockchain-based fair, transparent, and secure gas trading scheme that facilitates peer-to-peer trading of gas. The scheme is developed using an Ethereum-based smart contract that receives offers from gas suppliers and bid(s) from the thermal plant operator. Giving priority to the cheapest offers, the smart contract determines the winning suppliers. This paper also proposes an economic dispatch model for gas-deficient plants. Conventional economic dispatch seeks to satisfy electric load demand whilst minimizing the total gas cost of generating units. Implicit in its formulation is the assumption that gas supply to generating units is sufficient to satisfy available demand. In energy poor nations, this is hardly the case as there is often inadequate gas supply and conventional economic dispatch is of little practical value. The proposed economic dispatch model’s objective function maximizes the quantity of available gas and determines the optimal power output of each generating unit. The mathematical formulation is verified using data from the Egbin thermal station which is the largest thermal station in Nigeria and is solved using the General Algebraic Modeling System (GAMS). Obtained results indicate the viability of the novel approach as it results in a net power gain of 35 MW. On the other hand, the smart contract proved effective in accurately selecting winning suppliers and making payment. Full article

The penetration of renewable energy sources has been intensified during the last decade to tackle the climate crisis by providing clean energy. Among various renewable energy technologies, wind turbines and photovoltaic systems have received increasing attention from investors. Generally, electronic power converters are used to control renewable generations. The present study discusses the power management of smart distribution networks enriched with wind and photovoltaic units. The model aims to minimize the expected network operating cost of the system formulated as an objective function regarding AC optimal power flow constraints. In addition, stochastic programming based on unscented transformation is adopted to model the probable behavior of loads, renewable generations, and energy market prices. The model employs a linear approximation model to burden the complexity of the problem and achieve the optimum solution. The problem is tested to a 33-bus system using the General Algebraic Modeling System (GAMS). The obtained results confirm the proposed model’s potential in reducing energy costs, power losses, and voltage deviations compared to conventional power flow studies. In the proposed scheme compared to network load distribution studies, the active and reactive power losses, network energy costs, and voltage deviations are improved by about 40.7%, 33%, 36%, and 74.7%, respectively. Full article