Minimum Operating Cost Research Articles

Growth and challenges in biomedical scientific publishing: the phenomenon of mega-journals

Abstract The landscape of academic scientific publishing has seen relentless growth, with the number of articles published and indexed on Web of Science exceeding three million in 2023 alone. This increase in both the number of publications and authors has significantly impacted scientific research, enhancing our collective knowledge. In response to the surge in authorship and the shift towards open-access publishing-where authors bear the cost of publication-information has become more accessible to researchers, educators, and policymakers, fueling a lucrative global industry with annual revenues estimated at 30 billion euros. However, the system faces challenges, such as slow peer review times, potential biases towards significant findings, and issues with reviewer recruitment and compensation. Mega-journals like PLOS One and Scientific Reports have emerged in the early 2000s and 2010s, respectively, focusing on methodological rigor over novelty, publishing high volumes of articles. In 2022, mega-journals published a quarter of all biomedical literature, reflecting a shift towards specific publication scopes and capturing larger market shares. These journals charge authors publication fees in return for open access, benefiting from minimal operational costs as they do not produce in-house materials or print editions. Recent concerns include vulnerabilities to poor scientific conduct and potential biases in publication practices. Some sectorial mega-journals offer faster, often perceived as less rigorous, peer review processes. Notably, the International Journal of Environmental Research and Public Health was removed by Clarivate from the Web of Science Core Collection database, with potential wide ranging impact, especially for authors who have published in these journals (e.g., loss of the impact factor, less visibility) explored in the following presentation.

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Reliable cost-efficient integration of pumped hydro storage in islanded hybrid microgrid for optimum decarbonization

The employment of renewable energy-based resources is growing rapidly, necessitating the use of energy storage technologies to effectively manage their intermittent power generation. This study proposes an optimal planning for various distributed generators in a microgrid system. The objective is to size and operate a reliable hybrid islanded microgrid with minimum total system operational cost. To determine the optimal energy management and size of each unit, the problem is formulated applying Particle Swarm Optimization methodology utilizing sets of historical data such as wind speed, solar irradiation, and load demand on an hourly basis. With the proposed methodology, the capacities of photovoltaic, wind turbine, pumped hydro storage, and fuel cell are optimized as 1300 kW, 1125 kW, 400 kW, and 1590 kW, respectively. The obtained results concluded that optimum size reduces 14.85 % and 22.6 % in the fuel cell consumption in summer and fall respectively. Furthermore, the amount of CO2 emission is reduced by 10.6 ton in those mentioned seasons. The results obtained indicate that the proposed system having cost of energy (COE) of 0.2961 ($/kWh), and an excess energy of 196.208 MWh. An effectiveness analysis highlights the significant impact of incorporating pumped hydro storage, increasing the reliability index by 50.9 %.

Hybrid technique for leveraging unit commitment in smart grids: minimizing operating costs and carbon dioxide emissions

Hybrid technique for leveraging unit commitment in smart grids: minimizing operating costs and carbon dioxide emissions

Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub

The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. And transitioning to renewable energy sources is essential for achieving sustainability and combating climate change. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, considering the minimization of operational running costs, and the maximization of profit. In this paper, two optimization scenarios were studied to compare the results. In the first scenario, the minimization of carbon emissions was achieved. In the second scenario, the minimization of running costs and the maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then, the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly ones via the Kalman filter rather than by applying the classic polynomial interpolation methods.

A hybrid Improved Salp Swarm Algorithm and Harris Hawk Optimizer for energy planning in microgrids with minimum operating cost

ABSTRACT Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable energy supplies. This paper proposes a novel hybrid metaheuristic algorithm for optimal energy planning in microgrids using an Improved Salp Swarm Algorithm with Harris Hawk Foraging (ISSAHF). This technique is based on an improved multi-leader Salp Swarm Algorithm with an elite leader following strategy combined with Harris Hawks foraging. A simulation study is conducted on a low-voltage microgrid in off-grid and grid-connected modes. The optimization algorithm resulted in a daily average cost of 28.3370€ in off-grid mode compared to 19.2676€ in grid-connected one. Furthermore, the statistical study shows that the proposed algorithm outperforms well-established metaheuristic techniques regarding search capability and robustness. It yields mean optimal cost of 623.5248€ in off-grid and 404.7475€ for the grid-connected one, compared to other optimization techniques that vary from 667.2141€ to 959.5747€ in off-grid mode, and from 424.5841€ to 813.932€ in grid-connected mode. For robustness, the proposed technique performs well with a standard deviation of 20.765€ compared to the best (17.024€) and the worst (47.2423€) cases in off-grid mode, while in grid-connected mode, it is 28.8771€ compared to the best (21.6316€) and the worst (45.3774€) values.

Detection of Chemical Contaminants in Water for Irrigation Systems: A Systematic Review

Water pollution is a detrimental issue that occurs when there are bad changes in water quality parameters. It directly disrupts water usage and poses a danger to society, environment, economy, and agriculture. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. Improper water management pollutes rivers and lakes in Malaysia such as Klang River, Semenyih River, Kim Kim River, Slim River, and others. Various techniques are introduced to detect contaminants in water such as electronic sensors, biosensor approaches, laboratory analysis, and optical techniques. The functionality tool varies depending on the specific contaminants or parameters that are targeted, and the resources allocated. Several common contaminant types are found in polluted water including heavy metals, organic and inorganic chemicals, industrial pollutants, suspended solids, and others. The objective of the review is to study various non-optical and optical contaminant detection methods in water to identify the strengths and weaknesses of the methods. In this review, water pollution problems mainly due to the agricultural sector in several countries are discussed. Besides, conventional, and modern methods are compared in terms of parameters, complexity, and reliability. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Recent contaminant detection methods in water are also reviewed to study any loopholes in the latest methods. We found that the spectroscopy method based on light propagation theory is suitable and one of the promising methods for chemical contaminants detection for water quality analysis. This method offers fast analysis time, high sensitivity detection, non-invasive analysis, provides reliable data, and others. Undoubtedly, some contaminants in water can be challenging to be identified rapidly and confidently even though there are numerous tools and techniques available for water quality analysis. Thus, the review is important to compare previous methods and to improve current chemical contaminants detection analysis in terms of reliability with a minimum operating system and cost effectiveness.

Research on distributed optimal scheduling method based on consensus

Abstract With access to large-scale distributed power sources, the physical structure of the power grid system tends to be distributed. The application of traditional centralized optimal scheduling methods will lead to a surge in communication overhead and computational complexity, which will affect the performance and efficiency of the solution. In this paper, the minimum operating cost of the system is taken as the goal, and the distributed direct current optimal power flow (DC-OPF) optimization calculation of the photovoltaic-storage power generation system is considered to approximately solve the economic dispatch problem. The DC optimal power flow model with energy storage charging penalty is constructed. Then, the distributed optimization method based on the consensus alternating direction multiplier method (ADMM) is adopted. By decoupling the objective function and constraints of the whole system problem, the fully distributed DC-OPF calculation is realized by introducing the global consensus variable to deal with the boundary buses. Finally, the improved IEEE 30-bus example is used to test the method, and the simulation results verify the convergence and accuracy of the method.

A Game Theory Based Scheduling Approach for Charging Coordination of Multiple Electric Vehicles Aggregators in Smart Cities

In this paper, a game theory-based coordination approach is presented to find the minimum operation cost of multiple electric vehicle (EV) aggregators that are connected to the upstream network. In order to provide a comprehensive study various EVs aggregators including residential, commercial, official, university, and industry parking lots have been considered and the proposed game theory is utilized to coordinate the charging power from/to different microgrids and/or upstream network. In addition of operation cost, a reliability index, service charge and power loss are included in the objective function. The proposed approach is applied on an electricity network with 7 connected microgrids and different scenarios have been investigated. The simulation results show the overall operation cost in the coalition operation mode is globally minimized in comparison with the non-coalition operation mode. In addition, the reliability index, service charge and power loss force the aggregators to buy their necessary power from the nearby microgrids. The average network loss in the coalition and non-coalition modes are 2.88% and 4.28%, respectively. Moreover, the average reliability cost in coalition and non-coalition modes are $2.65 and $4.25, respectively.

Implementation of a multistage predictive energy management strategy considering electric vehicles using a novel hybrid optimization technique

In this paper, a novel artificial intelligence (AI)-based energy management strategy across three levels is designed for isolated microgrids. During the initial phase, AI is not employed. A precise and rapid-response microcontroller, namely the FPGA, is utilized. The performance of the FPGA is experimentally investigated under various operation conditions. In the second phase, AI plays a crucial role in enhancing both the reliability and economic effectiveness of the system. The multi-objective snake optimization (SO) algorithm is employed to attain high technical and economic performance within predefined constraints. Three objective functions are involved in this phase, focusing on the minimization of operational costs, loss of power supply probability (LPSP), and electrical energy losses in the dummy load. In the third level, a novel control strategy-based coordinated model predictive control is presented as part of the proposed AI-embedded energy management strategy. A hybrid optimization algorithm combining the multilayer feed-forward neural networks (MFFNN) and SO algorithms is proposed to predict the output power of backup sources. The microgrid under consideration is supported by a hybrid backup system comprising battery energy storage systems (BESS), electric vehicle (EV) batteries, and fuel cells (FCs). The effectiveness of this hybrid algorithm is evaluated through comparison with many other algorithms, aiming to assess its performance in accurately predicting the output power of backup sources. The results show the feasibility of using AI to achieve efficient operation and energy management in microgrids. The proposed MFFNN-SO algorithm achieves the best performance, as the normalized root mean square error (NRMSE) for FCs, BESS, and EV is about 0.1296 %, 0.0094 %, and 0.1304 %, respectively. The SO algorithm achieves a notable 6.3361% reduction in operating costs, resulting in a final operational cost of 166.4811 $.

Short-term optimal scheduling of wind-photovoltaic-hydropower-thermal-pumped hydro storage coupled system based on a novel multi-objective priority stratification method

In the new power system with high proportion of uncertain renewable energy sources (RES), there is a defect of RES consumption at the expense of other power sources' operational efficiency. This paper proposes a short-term optimal scheduling model of wind-photovoltaic-hydropower-thermal-pumped hydro storage (WPHTPHS) coupled system, which realizes the multiple optimization objectives involving minimizing operation cost of thermal power units, maximizing clean energy power generation, minimizing net load fluctuation and thermal power regulation. First, to overcome the dimension disaster problem in the solution space of high-dimensional random variables, a method for pre-solving integer state variables is proposed. Then, a novel multi-objective solution strategy of priority stratification-coupled feedback combined with improved plant growth simulation algorithm is designed. Finally, the effectiveness and superiority of the proposed model and solution method are demonstrated by case studies, and the numerical results show that the number of startups and shutdowns, standard deviation of output and operating cost of thermal power units are reduced by 90.9 %, 65.34 %, and 14.01 % respectively, compared with traditional wind-photovoltaic-thermal strategy. This study contributes to resolving the relationship between conflicting objectives and highlighting the potential advantages of WPHTPHS coupled system to maximize overall performance from economic and stability perspectives.

Spike load smoothing strategy considering behavioral decision under vehicle-road-network convergence

To reduce the impact of large-scale electric vehicles on traffic and electric load peaks, we propose a "vehicle-road-network" integration strategy that takes into account behavioral decisions. To reduce the impact of large-scale electric vehicles on traffic and electric load, we propose a peak load suppression strategy that takes into account behavioral decisions under "vehicle-road-network" integration. Firstly, a "vehicle-road-network" fusion model is constructed based on the large-scale access of EVs; then, a load model is built considering the large-scale access of EVs; finally, based on the third-generation foreground theory, the behavioral decision of EV users is analyzed based on the operation state of the fusion system, and a decision model is built by the foreground value to minimize the operation cost. The spike load smoothing strategy scheme is established with minimum operation cost. By using the fusion of Sioux Falls urban road network system and IEEE33 node grid system as the simulation example, the simulation results show that the proposed peak load smoothing strategy can coordinate the distribution of charging load and relieve traffic pressure during peak load hours, and reduce the travel and charging costs of EV users.

Flexible resource allocation optimization model considering global K-means load clustering and renewable-energy consumption

Abstract Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy transformation. The investment construction cost and operation cost of various flexible resources are different, and the adjustment ability is different in different timescales. Therefore, the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources. In this paper, the global K-means load clustering model is proposed and the 365-day net load is reduced to eight typical daily net loads by clustering. Secondly, a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed. The flexible resources involved include the flexible transformation of thermal power, hydropower, pumped storage, energy storage, and demand response. The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal. The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy. As the abandonment rate of renewable energy decreases, the optimal capacity of pumped storage, electrochemical energy storage, and hydropower units increases. When the power-abandonment rate of renewable energy is 5%, the optimal allocation capacity of thermal power flexibility transformation, pumped storage, electrochemical energy storage, hydropower unit, and adjustable load in Province A is 5313, 17 090, 5830, 72 113, and 4250 MW, respectively. Under the condition that the renewable-energy abandonment rate is 0, 5%, and 10% respectively, the configured capacity of pumped storage is 20 000, 17 090, and 14 847 MW, respectively.

Optimized energy management for interconnected networked microgrids: A hybrid NEGCN-PFOA approach with demand response and marginal pricing

This manuscript proposes a hybrid approach for operating cost reduction in networked microgrids. The proposed hybrid system is the combined performance of both the Neighbour Enhanced Graph Convolutional Networks (NEGCN) and the Piranha Foraging Optimization Algorithm (PFOA). Commonly it is named as NEGCN-PFOA technique. The primary goal of the proposed strategy is operating cost minimization in networked micro grids. The NEGCN is used for the demand prediction in networked microgrids and the PFOA is used for the operating cost optimization of networked microgrids. Moreover, the result of the system critic behaviour is researched, where the critic is a function of the control range. By then, the proposed method has been included in the working MATLAB platform, and its execution is calculated using the existing procedures. Compared to all current approaches, the proposed technique yields better outcomes like Deep Reinforcement Learning, Long Short-Term Memory and Deep Learning Artificial Neural Network. The proposed method is useful in reducing the operating cost in the networked micro grids. From the outcome, it is concluded that the proposed technique based costs is less contrasted with existing methods.

Optimal configuration of hydrogen storage capacity of hybrid microgrid considering peak regulation and frequency modulation requirements

This study proposes an innovative hydrogen storage capacity optimization configuration method that considers multiple demand factors, addressing the issue that traditional methods for optimizing hydrogen storage capacity in hybrid microgrids cannot simultaneously balance economic efficiency and grid connection quality. This method breaks through the traditional optimization framework and adopts a double-layer optimization model, combining the peak shaving operation cost of the hybrid microgrid with the interactive power deviation and power fluctuation penalty terms of the interconnection line during the frequency regulation stage, achieving dual optimization of economy and grid connection quality. Through precise mathematical modeling and efficient optimization algorithms, we have successfully obtained the minimum operating cost corresponding to the minimum power fluctuation, the minimum interconnection line interactive power deviation penalty term, and the optimal hydrogen storage capacity configuration scheme. Identify two typical days through cluster analysis. Option 1 (considering peak shaving, frequency regulation, and hydrogen electrolysis) has the lowest net load and a total operating cost of 3.0331 million yuan, which is better than Option 2 (3.0973 million yuan) and Option 3 (5.0398 million yuan). This plan effectively reduces wind and solar power waste, shortens the operating time of thermal power units, and demonstrates the rationality and economy of optimizing hydrogen storage capacity configuration.

Operational optimization of demand management strategy for hybrid power system based on battery capacity fading characteristics

In the hybrid power system (HPS), taking battery capacity fading characteristics and demand management strategy (DMS) into consideration, an operational optimization method is developed in this work. From the energy utilization and economic perspectives, the minimum total renewable energy curtailment rate (TCR), minimum total outsourced electricity (TOE) from the grid and minimum total operating cost (TOC) are respectively taken as the optimization objectives. The corresponding mathematical models of the proposed method are established, which feature mixed integer nonlinear programming (MINLP) ones. A case study of a solar-wind-biomass-battery HPS is adopted to verify and illustrate the application and effectiveness of this proposed method. Results show that the optimal operating schemes and the DMSs of the HPS based on battery capacity fading characteristics can be determined by minimizing the TCR, TOE and TOC. The target values of TOE and TOC of the HPS with adopting DMS are 15,995.3 kWh and 18,112.1 $, which are about 70 % these of the HPS without DMS. Furthermore, the relationships between battery capacity fading characteristics and DMS are comprehensively investigated in this operational optimization of the HPS. Finally, the effects of different battery types, outsourced electricity price changes on the operation of the HPS are analyzed and discussed. This work provides a valuable method for the operational optimization of the HPS.

Optimized removal of silica during manure treatment by electrocoagulation-flotation (EC-F) in view of fouling prevention of reverse osmosis membranes

Reverse osmosis (RO) membrane fouling threatens the performance of manure treatment. In this regard, silica is considered one of the most challenging inorganic foulants. Removing silica fouling from RO membranes is difficult, ultimately leading to performance deterioration, including reduced permeability and premature membrane failure. This study examines the removal of silica during manure treatment using a novel tubular electrocoagulation-flotation (EC-F) reactor with an Al electrode. The EC-F was positioned between an ultrafiltration and a RO membrane filtration system. The operational parameters—current density, electrolysis residence time, and initial pH—were optimized for maximum silica removal efficiency and minimum operational cost. At the optimum conditions (pH of 8.2, current density of 160 A/m2 and electrolysis residence time of 23 s), 88 % of silica (initial concentration of 104 mg/L) was removed. Subsequent membrane fouling assessment revealed that EC-F pretreatment decreased the fouling potential by 28 % (from 7.11 × 1016 to 5.10 × 1016 Pa·s/m2·t), indicating efficient foulant removal. SEM-EDX analysis of the membrane confirmed the lack of scale formation when using EC-F pretreated manure as a feed stream. This study demonstrates that the use of innovative EC-F pretreatment significantly reduces the operational expenditure (1.75 €/m3) compared to prior EC-F research for silica removal from manure, thereby reducing the fouling of RO membranes.

Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

The integrated energy system with electric vehicle charging station via vehicle-to-grid aims to offer a proactive solution for low-carbon development of both energy and transportation sectors. However, achieving optimal energy efficiency with minimal operational costs in such a complex system is challenging due to the high randomness of electric vehicle travel patterns. This work proposes a reinforcement learning-based energy management framework to optimize the coordinated scheduling of integrated energy and vehicle-to-grid. The problem is modeled as a Markov Decision Process and optimized using the bi-level soft actor-critic algorithm, where the upper-level agent supervises the scheduling of the integrated energy system, and lower-level agent sets the electric vehicles’ charging and discharging strategies based on upper-level decision. Numerical simulations demonstrated that by adopting a bi-level reinforcement learning approach, the proposed algorithm effectively enhances energy exchange between integrated energy and electric vehicle charging station, reducing operational costs by 8 % compared to other multi-agent algorithms. Furthermore, when compared to the Mixed-Integer Linear Programming model, the proposed algorithm achieves similar optimality with significantly less computation time, maintaining a margin of error of approximately 4 %. Additionally, integrating electric vehicles as mobile energy storage within this framework can lead to a further 10 % reduction in operating costs.

Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor

Abstract A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) is applied to address these two problems: increasing conversion and reducing operating cost (as the first optimization problem, P1) and increasing productivity and reducing operating cost (as the second optimization problem, P2). ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. The multi-objective optimization of multi-objective Bonobo optimisers (MOBO-I, MOBO-II and MOBO-III) are utilised to solve the optimization problem. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the findings of the study, the MOBO-II for Problems 1 and 2 was the most effective MOO strategy. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto Front (PF) in terms of homogeneity. The minimum operating cost, the maximum conversion and productivity obtained by MOBO-II are Mil. RM/year 114.3, 31.45 %, Mil. RM/year 545.3, respectively.

Coordinated optimization of configuration and operation of a photovoltaic integrated building cooling system with electricity and ice storages under source-load uncertainties

Solar photovoltaic integration into buildings provides an effective and attractive method to decrease fossil energy consumption and reduce greenhouse gases emission. This study proposes a flexible photovoltaic integrated building cooling system with electricity and ice storages to address the challenges posed by the instability of solar energy and the integration and coordination of multiple components. Uncertainties in solar irradiance, ambient temperature, and cooling load are characterized using a scenario analysis method that combines Latin Hypercube Sampling and the K-Means++ algorithm. Then, aiming to achieve the minimal total cost, including investment, operational, and CO2 emission costs, a two-layer stochastic optimization model is developed to simultaneously determine the optimal capacities and operational strategies for each device. Genetic algorithm and mixed integer linear programming are, respectively, used to solve the two-layer optimization problem. The comparisons between optimum schemes with and without CO2 emission cost demonstrates that when the base chiller’s capacities are 1179 kW and 1081 kW in cases 1 and 2 respectively, it can independently meet 72.37 % and 61.27 % of the cooling load demand throughout the cooling season. In addition, the optimal capacity of photovoltaics, considering CO2 emission, increases by 109.7 %, and its penetration rate reaches 18.3 %, which is 9.7 % higher than the optimal scheme not considering CO2 emission. However, the annualized investment cost increases by 52.3 %. The developed methods provide effective tools for the system design and operation management of solar energy integrated building cooling system.