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This paper presents an optimal operation algorithm for a grid-connected microgrid that incorporates renewable energy sources (RESs), plug-in electric vehicle (PEV) charging/discharging stations, and local loads. The aim of this work is to not only propose a microgrid operating algorithm but also implement it within the microgrid energy management system. The primary objective of the proposed algorithm is to reduce the overall operational cost of the microgrid while minimizing the PEV charging bills simultaneously. To this end, we propose an adaptive PEV power charging/discharging and power exchange (grid exporting/importing) scheduling strategy that accounts for the uncertainty of RES power generation, loads, and electric pricing. To address the dynamic arrival of PEVs, we propose an online optimization scheme using an adaptive rolling horizon framework. The size of the rolling window is adjusted in each time step to adapt to the dynamic nature of the PEV charging period. Additionally, we propose the design of a dynamic pricing model for PEV charging and discharging to achieve power system balance within the microgrid, thereby optimizing operating costs and minimizing PEV charging bills further. Through simulations, we demonstrate the effectiveness of the proposed strategies, which are expected to benefit both the microgrid operators and PEV owners.

The change of energy structure brings new challenges to the operation and planning of the power system, which requires the support of refined production simulation and evaluation techniques. Firstly, a scenario set generation method based on clustering coupling is proposed to generate typical operating scenarios of the power grid. Secondly, this paper proposes a power system operating cost evaluation index, considering line power flow constraints, energy storage constraints, system reserve cost, carbon emission cost, and cost per kWh. Furthermore, the calculation method of the system operation cost under the uncertainty scenario is given. In addition, the operation mode of wind and solar load shedding participating in reserve capacity scheduling is adopted, and the validity of the model is verified by the IEEE-30 node system as an example.

This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system. The multi-objective optimization dispatch problem is formulated to simultaneously minimize the operating cost, pollutant emission level as well as the power loss of conversion devices. While satisfying the system load and technical constraints, ensure high penetration of renewable energy and optimal scheduling of charging/discharging of battery storage system based on a fuzzy logic approach. The weighted sum method is adopted to obtain Pareto optimal solutions, then a fuzzy set theory is employed to find the best compromise solution. Ant lion optimizer method is considered to solve the formulated problem. To prove the efficacy and robustness of the proposed algorithm, a comparison of the performance of ant lion optimizer algorithm with other known heuristic optimization techniques has been investigated. The results obtained show that the proposed algorithm outperforms the other heuristic techniques in solving the multi-objective optimization dispatch problem. They also reveal that a better compromise between the considered contradictory objective functions is achieved when priority is given to the generation of the internal microgrid’s sources with an equivalent contribution rate of 68.45% of generated power from both fuel cell and micro-turbine, whereas the contribution rate of external grid is limited to 11.72%.

Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid. [ABSTRACT FROM AUTHOR]

This study develops and presents a methodology for determining the optimal geographic distribution and size of asphalt concrete plants in Karbala, Iraq, in order to minimize the cost of asphalt concrete produced. The purpose of this study is to discuss these points. The methodology can identify potential locations for asphalt concrete plants within a study area, considering the plants' operation and capital costs and the costs of transporting raw materials to the plants and asphalt concrete to demand centers. Matrix Laboratory (MATLAB) software have been used to program the methodology. This methodology has been applied to Karbala using actual data. The methodology's output identifies the optimal locations and sizes of asphalt concrete plants in Karbala. There are two optimal locations for asphalt concrete plants B and D; their total size is 80 tons per hour and 800 tons per hour, respectively. This is adequate to supply Karbala's asphalt concrete demand to 2032. The methodology effectively determines the optimal geographic locations and sizes of asphalt concrete plants in the study area. [ABSTRACT FROM AUTHOR]

The ability of A-share listed companies to adhere to the digital economy and achieve long-term corporate benefits amidst an uncertain external environment through financial digital transformation remains a crucial concern for entrepreneurs and scholars. The objective of this study was to scrutinize the effect of financial digital transformation on financial performance among 2566 of China's A-share listed companies in the hopes of providing informative recommendations for businesses that are currently undertaking or planning to undertake financial digital transformation. To investigate the mechanism by which financial performance among A-share listed companies was influenced by digital transformation, a panel data regression model was utilized. The findings suggest that, first and foremost, financial digital transformation significantly boosts corporate financial performance for A-share listed companies, and this enhancement is sustainable over time. For every 1% growth in financial digital transformation, corporate financial performance improves by 1.1%. Corporate financial performance is projected to improve by 29.8% during the next three financial years. Secondly, information symmetry and operational expenses function as intermediaries in the financial digital transformation process that affects firm financial performance. For every 1% increase in financial digital transformation, the information symmetry level of firms grows by 15.1%, while the operational cost rate declines by 0.8%. Thirdly, information disclosure and operating costs play a chained intermediary role. Every 1% increase in the level of information symmetry reduces the operating cost rate by 2.3%. Fourthly, in comparison to enterprises in eastern provinces, businesses located in central and western provinces are better positioned to improve their financial performance by undertaking a financial digital transformation. [ABSTRACT FROM AUTHOR]

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are one of the most promising applications that are reshaping the future urban mobility market and benefitting the urban environment. Analyzing the adoption of EVs helps both vehicle sales market management and urban transportation-related environmental cost estimation. Previous studies have shown that EV adoption is mostly affected by the economic environment and users' psychological factors; however, both factors vary among specific urban transportation networks. This paper thus proposes network-related vehicle operating cost functions and a logit-based choice model, which considers both the economic environment and users' psychological factors at a network level. The model can thus estimate the vehicle adoption for specific networks. Numerical experiments and sensitivity analyses were conducted to illustrate the proposed method and provide practical insights in estimating EV adoption, respectively. The results suggest that EV adoption greatly varies among different cities. [ABSTRACT FROM AUTHOR]

Abstract Due to rapid socioeconomic growth and current environmental concerns, reducing global greenhouse gas emissions is a key step toward sustainable development. In recent years, some researchers have begun to adopt new grid management technologies such as virtual power plants (VPP) that allow them to improve energy management and cost and reduce gas emissions. The integration of distributed energy resources (DER), such as solar photovoltaic and wind power, combined with micro‐turbines and energy storage systems and with the support of VPP intelligence, will contribute immensely to improving micro‐grids (MGs) energy performance and reducing gas emissions. In this paper, an expert multi‐objective feasibility enhanced particle swarm optimization algorithm is adopted for the optimal scheduling of energy management system to reduce the total operating cost and net emission simultaneously. The proposed algorithm is tested on an Algerian reel building‐MG integrated VPP with DERs and combined cooling, heat, and power generation. Finally, different cases are simulated for a 24‐h time and discussed to demonstrate the validity of the proposed VPP energy system and the effectiveness of the adopted algorithm to reduce gas emissions and the total operating cost.

In this present article, a systematical research and analysis is newly introduced on traditional investment estimation of nuclear power plants, where 2/3 of the first core nuclear fuel cost is incorporated into construction cost on account of historical factors, which is considered unreasonable and inappropriate. In view of facts, the cost should be distinguished into operating cost according to “Two-Stage Treatment Scheme”. It can be manifested that decreasing construction cost as well as increasing economic benefit will be reached by including 2/3 of the first core nuclear fuel cost into operating cost.

Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid.

Metrological traceability is essential to ensure the reliability of calibration tests. Calibration certificates usually include information on only one upper-level reference standard. As metrological traceability is multi-layered, generally there is no method available for end users to instantly confirm the traceability from the reference standard to a primary standard. This study focuses on the Ethereum blockchain, which has both tamper resistance and high availability, as a digital data management method. To improve the transparency and reliability of calibration tests, a smart contract that traces back to the primary standard is proposed. Consequently, it is confirmed that end users can instantly obtain traceability information. In addition, the execution of smart contracts requires transaction fees. Here, the calculation of the transaction fees is organized, and the traceability management system is discussed from a cost-effective perspective in the field of metrology. [ABSTRACT FROM AUTHOR]

Fruit branch pruning is still done manually, resulting in high labor costs, ergonomic hazards, and low efficiency and productivity. Furthermore, there are some limitations with imported agricultural machines because of their high prices and inappropriate local conditions. Hence, the present study aims to manufacture a local device for fruit branch pruning based on validating the correct stem-cutting position using a branch guide. The manufactured device comprises a 60 W electric motor, a formed base, a teethed pruning disc, a telescopic tube made up of the picker-cutting mechanism of the device, and a 12-Volt rechargeable dry battery. The device was made from local and light materials to allow a telescopic tube to reach the high position of the citrus fruit branches and consider ergonomic aspects. Field trials were executed on sour oranges trees to assess the pruning device's performance as a function of change in peripheral speeds of cutting disc (9.81, 13.74, 17.66, and 21.59 m/s), cutting disc teeth numbers (60, 80, and 100 teeth), different groups of branches diameters {A (from 5 mm to less than 10 mm), B (10 mm to less than 20 mm), C (from 20 mm to less than 30 mm) and D (from 30 mm to less than 40 mm)}. The manufactured device was assessed in terms of device productivity, pruning efficiency, pruning damage, power requirements, specific energy, and operating cost. The results indicated that the highest device productivity and pruning efficiency were 780, 218, 100 and 65 branch/h; and 96.0, 94.0, 92.0 and 90.5%, while the lowest pruning branch damage were 4.0, 6.0, 8.0 and 9.5%, specific energy were 0.0447, 0.1645, 0.4050 and 0.700 Wh per branch, for the groups of branches diameters A, B, C, and D respectively. The current investigation recommended that the optimal operational conditions were using the manufactured device for pruning citrus trees at 17.66 m/s pruning disc speed with a pruning disc teeth number of 60 teeth. [ABSTRACT FROM AUTHOR]

This paper presents a solution for planning of reactive power using a novel brainstorm optimization method with flexible alternating current transmission system (FACTS) devices. For shunt compensation static VAR compensator (SVC) and for series compensation thyristor-controlled series compensator (TCSC) are being used in this proposed work which is adapted into IEEE-14, IEEE-30 and IEEE-57 test bus system. The location for TCSC and SVC has been chosen by performing power flow analysis. In this paper brainstorm optimization algorithm (BSOA) is proposed to evaluate the optimal value of reactive VAR generation of generators, tap setting of transformers, and size of SVC and TCSC to reduce the active power loss as well as operating cost in the transmission network. The outcomes proposed by BSOA approach is obtained and compared with some other techniques like krill heard algorithm (KHA), symbiotic organisms search (SOS), particle swarm optimization (PSO), biogeography-based optimization (BBO) methods. [ABSTRACT FROM AUTHOR]

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 $. [ABSTRACT FROM AUTHOR]

The widespread use of drones in various fields has initiated a discussion on their cost-effectiveness and economic impact. This article analyzes in detail a methodological evaluation framework for the levelized cost of drone services for law enforcement purposes. Based on the data availability, we compared two vehicles: Phantom 4 Pro and Thunder-B. Moreover, we calculated their levelized costs per surveillance time and trip distance. Our approach helps users calculate the real costs of their vehicles’ services and produce equations for rapid estimations. We observed economies of scale for time and distance and showed differentiations per aircraft capacity. Furthermore, using the produced equations, we formulated a case study and compared the costs in a 4 km area constantly monitored by the two types of drones to support the best vehicle selection. We found that the Phantom 4 Pro costs less than the Thunder-B drone, for example. Thus, we demonstrate how, by applying this methodology beforehand, decision makers can select the most appropriate vehicle for their needs based on cost. Cost research estimations will improve UAV use and will help policymakers include UAV technology in crime prevention programs, especially when more data are available.

Background: Economic evaluations in dentistry provide policymakers with information to facilitate efficient resource allocation. Dentistry involves multiple activities for one treatment outcome. This original research aims to calculate direct treatment and indirect costs for dental services provided at tertiary care referral dental center. Materials and Methods: The present original study reporting is done based on using the Consolidated Health Economic Evaluation Reporting Standards statement in dentistry, the basic dental services provided are divided into direct (revenue-generating) and indirect cost centers (nonrevenue generating) using activity-based costing (ABC) method. The total cost generated is calculated by aggregating the sum of direct and indirect cost incurred and unit cost is calculated by dividing total cost obtained by the number of units. Data are analyzed using Microsoft Excel worksheet. Results: Cost calculated using ABC method differed significantly from the tariff method. The major cost components varied are human resources (848,000 INR), capital costs (3,008,500 INR), and material costs (200,000 INR). Conclusion: At a tertiary care dental hospital level, we must provide patient's perspective care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions. By taking correct economic managerial decisions using ABC and budgeting the resources to dental services, the community can approach with true patient costs at an acceptable level of quality and at the least possible cost.

The frequency, intensity and duration of extreme weather events have seriously affected human life and production. The electric power sector is the foundation of economic activity as well as a core participant in the adaptation and mitigation of climate change. Therefore, in the context of climate change, it is crucial for the stable operation of the national economy for power sector to cope with different climate change risks and improve their adaptability to climate change. In this paper, listed power companies in China are selected as samples to analyse the impact of climate change risk on the financial performance of the power system's supply and transmission-distribution sides. The empirical results show a significant positive correlation between climate change risks and the financial performance of listed electric power companies. The rainfall index and drought index positively impact the financial performance of listed electric power companies. The cryogenic freezing index has a negative impact on the financial performance of listed electric power companies., which is further analysed and proved that a cryogenic freezing disaster will cause the regional breakdown of the power system. The operating cost ratio and the proportion of clean energy supply play a mediating effect on the correlation between the comprehensive climate risk index and the return on equity of companies. To increase electric power companies' ability to adapt to climate change, climate change risks should be integrated into the risk management framework, and the company's financial performance can be improved by optimizing the energy mix and constructing safe lines. The government can promote the transformation of electric power companies by launching green financial tools.

Many aspects of the agricultural industry such a field crop planting and harvesting and chemical application in fruit crops have been employing mechanization and automation solutions for decades. However, the de-handing operation in banana postharvest operations is usually performed manually. Mechanical or automated de-handing is a potential long-term solution to address labor shortages and the associated high costs. Bananas are mainly grown in developing countries located in tropical and subtropical regions, where the development of agricultural mechanization and automation solutions started only recently and is progressing relatively slowly. In addition, large-scale banana orchards are mainly distributed in hilly and mountainous areas, though there are also some small-scale banana plantations in plain areas. The complex environment of banana orchards and the aging farming population are other important factors that make it difficult to realize mechanized operation of banana de-handing. In recent years, researchers have proposed advanced techniques that may facilitate the development of mechanical de-handing systems. However, the successful adoption of mechanical de-handing technology still faces many challenges. This paper systematically reviews the existing research on de-handing technologies and component mechanisms. A comprehensive evaluation is carried out from the perspectives of feasibility of the mechanism design, stability of the model simulation and reliability of the prototype systems developed. The future challenges and opportunities for designing and practically adopting mechanical de-handing equipment are also summarized and discussed. [ABSTRACT FROM AUTHOR]

In a typical power system network, transmission losses are considered as one of the important parameters for economic operation. To concern this problem, researchers have proposed many techniques to minimize the transmission losses based on the cost benefit analysis which is associated with the optimal placement of the reactive power sources. In the present work, a novel technique, namely oppositional crow search algorithm (CSA), is proposed for Var planning by utilizing the fuzzy logic technique to determine capacitor placement positions. In this approach, fuzzy membership value is calculated based on the loss sensitivity factor of each bus of the test networks. Then, shunt capacitors placement positions are assigned to buses having the higher membership values. Once the capacitor placement positions are evaluated, the CSA and oppositional CSA are executed to obtain the optimal setting of transformer tap positions, reactive power generation of the generators, and magnitude of shunt capacitors placed at the weak nodes. The proposed method is performed on standard IEEE 30 and IEEE 57 bus networks, and the obtained results are compared with several other established methods for Var planning. From the obtained results, it is found that the proposed method shows better performance when compared to other techniques suggested in the literature in terms of reduced active power loss and system operating cost. [ABSTRACT FROM AUTHOR]

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In this paper, a solution is provided for solving optimal reactive power dispatch (ORPD) problem by using flexible alternating current transmission (FACTS) devices. The TLBO method is applied on IEEE 14-, 30- and 57- bus test system with optimal positioning of thyristor-controlled series compensator (TCSC) and static var compensator (SVC). The location for TCSC and SVC has been chosen by performing power flow analysis. The ORPD problem is formulated to minimize both active power loss and operating cost. The main objective of this work is to dispatch optimal reactive power considering various loading conditions. The performance of the proposed method is tested under increased reactive loading and simultaneous increased of both active and reactive loading conditions. The proposed method's performance is evaluated under various operating conditions. The obtained results are compared with some of the recent promising techniques such as KHA, BBO and PSO. The simulation results show the efficacy of the proposed method in achieving the better performance of the system in terms of minimum power loss, minimum operating cost and better convergence rate. [ABSTRACT FROM AUTHOR]

This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system. The multi-objective optimization dispatch problem is formulated to simultaneously minimize the operating cost, pollutant emission level as well as the power loss of conversion devices. While satisfying the system load and technical constraints, ensure high penetration of renewable energy and optimal scheduling of charging/discharging of battery storage system based on a fuzzy logic approach. The weighted sum method is adopted to obtain Pareto optimal solutions, then a fuzzy set theory is employed to find the best compromise solution. Ant lion optimizer method is considered to solve the formulated problem. To prove the efficacy and robustness of the proposed algorithm, acomparison of the performance ofant lion optimizer algorithm with other known heuristic optimization techniques has been investigated. The results obtained show that the proposed algorithm outperforms the other heuristic techniques in solving the multi-objective optimization dispatch problem. They also reveal that a better compromise between the considered contradictory objective functions is achieved when priority is given to the generation of the internal microgrid's sources with an equivalent contribution rate of 68.45% of generated power from both fuel cell and micro-turbine, whereas the contribution rate of external grid is limited to 11.72%. [ABSTRACT FROM AUTHOR]

Abstract Wastewater generated from different sources affects the health of living organisms and the natural environment due to the availability of different pollutants. Electrocoagulation (EC) is a good technology implemented for wastewater treatment before discharging to an environment as effluents. The electrocoagulation process is an effective method to the remove the color, chemical oxygen demand (COD), turbidity, and consumption of less energy from wastewater by considering different operating parameters. In this study, the major operating parameters for the electrocoagulation process such as pH (3–7.50), electric current (0.03–0.09 A), electrolytic concentration (1–3 g/L), the distance between electrodes (1–2 cm), electrolysis time (20–60 min) and combination of electrodes (Fe–Fe and Al–Al) were studied. The maximum removal of color–94.40%, COD–97.02%, and turbidity–90.91% with required energy consumption –36kWhr/m3 was obtained at the electric current–0.09 A, electrolyte concentration–3 g/L, pH–7, electrode combination–Fe–Fe, and distance between electrodes–3 cm, respectively. The studied parameters were affected the removal % color, % COD, % turbidity, and also the consumption of energy depending on the desired setup of fixed values of the parameter. Consumption of energy and electrode dissolution is related to the cost of operating in electrocoagulation in addition to the cost of labor and the small amount of sludge produced for disposal.

The present study provides technical and economic comparisons of electrocoagulation (EC), chemical coagulation (CC), integrated EC-CC, and combinations of these techniques with ultrafiltration (UF) and nanofiltration (NF) systems based on baker's yeast wastewater treatment. Key parameters such as initial pH, coagulant dosage, and stirring speed were optimized for both EC and CC methods to achieve higher removal efficiencies at lower operating costs. Evaluating EC and CC indicates EC obtained better performance by removing 77.64% and 97.49% of COD and turbidity, respectively, but higher operating costs for treating baker's yeast wastewater. CC with operating costs of 0.737$/m3 and 1.427 $/m3 using lime and polyaluminum chloride (PACl), respectively, is more cost-effective than EC with total operating costs of 3.526 $/m3. This study was the first to compare the capital costs of EC and CC, and it was revealed that CC still required less investment than EC. The combination of EC and CC methods was studied for the first time on baker's yeast wastewater. The results showed that combining CC and EC improves COD removal efficiency by 11.2% and 16.1% when compared to single EC and single CC processes, respectively. Two types of hollow fiber membranes made of polypropylene (PP) and polyvinylidene fluoride (PVDF) for the UF process and a polyamide spiral membrane for the NF system were employed in membrane filtration integrated systems. The combination of the EC-CC system with the UF and NF systems resulted in the highest COD removal efficiency, which was around 99.7% for the EC-CC(PACl)-PVDF-NF system.

The analysis and optimization of the operational and maintenance costs of water management systems is one of the key issues of their exploitation. This article presents a general model, supported by specially designed software, able to process the analysis of exploitation costs of multistate renewable systems. The proposed model allows for the consideration of costs related to preventive inspections and repairs and additional reliability associated costs, such as costs of the system in a state of deteriorated reliability and financial losses related to reduced serviceability of the system or a lack of rendering of services. By means of a multistate approach to the reliability analysis, the model allows for the determination of the level corresponding to the appropriate reliability state that, if exceeded, should result in undertaking the repair of the system. In this study, the MATLAB 9.13 (R2022b) environment was used for simulation and estimation of the costs of system maintenance and repairs according to the proposed model. The article presents the results of the optimization of exploitation and repair costs of water management systems, allowing the estimation of the optimal period between regular inspections while maintaining the safe operation of the system. The model and software proposed can be of assistance in supporting the decision process of maintenance planning for water management systems.

In recent years, microgrids (MGs) have been developed to improve the overall management of the power network. This paper examines how a smart MG's generation and demand sides are managed to improve the MG's performance in order to minimize operating costs and emissions. A binary orientation search algorithm (BOSA)-based optimal demand side management (DSM) program using the load-shifting technique has been proposed, resulting in significant electricity cost savings. The proposed optimal DSM-based energy management strategy considers the MG's economic and environmental indices to be the key objective functions. Single-objective particle swarm optimization (SOPSO) and multi-objective particle swarm optimization (MOPSO) were adopted in order to optimize MG performance in the presence of renewable energy resources (RERs) with a randomized natural behavior. A PSO algorithm was adopted due to the nonlinearity and complexity of the proposed problem. In addition, fuzzy-based mechanisms and a nonlinear sorting system were used to discover the optimal compromise given the collection of Pareto-front space solutions. To test the proposed method in a more realistic setting, the stochastic behavior of renewable units was also factored in. The simulation findings indicate that the proposed BOSA algorithm-based DSM had the lowest peak demand (88.4 kWh) compared to unscheduled demand (105 kWh); additionally, the operating costs were reduced by 23%, from 660 USD to 508 USD, and the emissions decreased from 840 kg to 725 kg, saving 13.7%. [ABSTRACT FROM AUTHOR]

Due to rapid socioeconomic growth and current environmental concerns, reducing global greenhouse gas emissions is a key step toward sustainable development. In recent years, some researchers have begun to adopt new grid management technologies such as virtual power plants (VPP) that allow them to improve energy management and cost and reduce gas emissions. The integration of distributed energy resources (DER), such as solar photovoltaic and wind power, combined with micro‐turbines and energy storage systems and with the support of VPP intelligence, will contribute immensely to improving micro‐grids (MGs) energy performance and reducing gas emissions. In this paper, an expert multi‐objective feasibility enhanced particle swarm optimization algorithm is adopted for the optimal scheduling of energy management system to reduce the total operating cost and net emission simultaneously. The proposed algorithm is tested on an Algerian reel building‐MG integrated VPP with DERs and combined cooling, heat, and power generation. Finally, different cases are simulated for a 24‐h time and discussed to demonstrate the validity of the proposed VPP energy system and the effectiveness of the adopted algorithm to reduce gas emissions and the total operating cost. [ABSTRACT FROM AUTHOR]

The study was conducted to evaluate the comparison of the three technologies, electrodialysis (ED), nanofiltration (NF) and reverse osmosis (RO) in nitrate removal from groundwater on the basis of performances, capital expenditure (Capex) and operating cost (Opex). The comparison was estimated for an industrial plant having a capacity of 2,200 m³/d (100 m³/h) corresponding to a water consumption for 50,000 capita following the Moroccan considerations in rural medium (consumption for domestic use of 50 L/d and per capita). The first part of the study was carried out for the comparison of the performances in terms of nitrate removal and recovery rate of the three membrane-based technologies in nitrate removal from Moroccan groundwater. This study confirms the performances of ED, NF and RO in nitrate removal and shows that these performances are comparable. The second-part is dedicated to estimating and calculating Capex, Opex and the specific energy consumption for the three technologies. The cost assessment shows that ED appears to be slightly cost effective for nitrate removal from groundwater than RO and NF. [ABSTRACT FROM AUTHOR]

An oppositional based Harris Hawks optimization technique enthralled is suggested and implemented to reactive power optimization planning in power systems in this research. As transmission loss minimization is a fundamental criterion for secured power system operation, Var planning has become increasingly important for improved coordination in modern power systems. The Oppositional based Harris Hawk Optimizer (OHHO) algorithm, which is implemented on the IEEE 57 bus system, is proposed in this paper as an enhanced meta-heuristic nature inspired approach. The suggested algorithm is based on the Harris Hawk Optimizer (HHO) algorithm, which is a speculative algorithm with no intrinsic dependent variables. To get improved estimation for the predominant approach, the search space is subsequently altered by combining HHO with the Oppositional Based Learning (OBL) technique. In the present study, the OHHO is proposed for reducing transmission losses, operating costs, and improving voltage profile at buses. The impact of the optimizers' update technique on the objective functions is examined. The research focuses on issues such as reactive power provided by generator buses, shunt capacitors, and transformer tap position changes. The simulation outcomes gained on typical test systems demonstrate that the proposed OHHO outperforms HHO, and other optimization techniques recently published in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

Abstract This paper formulates and solves a techno-economic planning problem of reactive power (VAR) in power transmission systems under loadings. The objective of the proposed research work is to minimize the combination of installation cost of reactive power sources, power losses and operational cost while satisfying technical constraints. Initially, the positions for the placement of reactive power sources are determined technically. Different cost components such as VAR generation cost, line charging cost etc. are then added in the total operating cost in a most economical way. Finally, the optimal parameter setting subjected to reactive power planning (RPP) is obtained by taking advantages of hybrid soft computing techniques. For the justification of the efficiency and efficacy of the proposed approach the entire work is simulated on two inter-regional transmission networks. To validate the robustness and ease of the soft computing techniques in RPP the responses of benchmark functions and statistical proof are provided simultaneously.

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques. The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Electric vehicles (EVs) are one of the most promising applications that are reshaping the future urban mobility market and benefitting the urban environment. Analyzing the adoption of EVs helps both vehicle sales market management and urban transportation-related environmental cost estimation. Previous studies have shown that EV adoption is mostly affected by the economic environment and users’ psychological factors; however, both factors vary among specific urban transportation networks. This paper thus proposes network-related vehicle operating cost functions and a logit-based choice model, which considers both the economic environment and users’ psychological factors at a network level. The model can thus estimate the vehicle adoption for specific networks. Numerical experiments and sensitivity analyses were conducted to illustrate the proposed method and provide practical insights in estimating EV adoption, respectively. The results suggest that EV adoption greatly varies among different cities.

The Regional Integrated Energy System (RIES) has brought new modes of development, utilization, conversion, storage of energy. The introduction of Soft Open Point (SOP) and the application of Power to Gas (P2G) technology will greatly deepen the coupling of the electricity-gas integrated energy system, improve the flexibility and safety of the operation of the power system, and bring a deal of benefits to the power system. On this background, an optimal dispatch model of RIES combined cold, heat, gas and electricity with SOP is proposed. Firstly, RIES architecture with SOP and P2G is designed and its mathematical model also is built. Secondly, on the basis of considering the optimal scheduling of combined cold, heat, gas and electricity, the optimal scheduling model for RIES was established. After that, the original model is transformed into a mixed-integer second-order cone programming model by using linearization and second-order cone relaxation techniques, and the CPLEX solver is invoked to solve the optimization problem. Finally, the modified IEEE 33-bus system is used to analyze the benefits of SOP, P2G technology and lithium bromide absorption chillers in reducing system network loss and cost, as well as improving the system's ability to absorb wind and solar and operating safety. [ABSTRACT FROM AUTHOR]

For some engineering systems where there is a finite break between two consecutive missions, maintenance activities can only be performed during the break, and the budget is also limited. Due to limited resources, it is necessary to select a subset of the maintenance activities to be performed, with the aim of meeting the next mission requirement. This is termed as selective maintenance. We propose a selective maintenance model for a multi‐state system in which both maintenance task assignment and operating cost are considered. The algorithm of maintenance task assignment is proposed inspired by the 0–1 knapsack problem to help fulfill the maintenance optimization. The correctness and computationally effectiveness of this algorithm are validated. With the degradation of each component modeled as a homogenous Markov process, the operating cost of the system is modeled based on the Markov reward process and is treated as part of the total cost. Additionally, the randomness of the operating time is considered and the necessity of this consideration is illustrated. The proposed model is applied to a coal transportation system to demonstrate its effectiveness, and the effects of ignoring maintenance task assignment and operating cost are analyzed. The results show that it is essential to take the maintenance task assignment into consideration, especially when the time constraint plays a major limiting role, as failure to do so may lead to poor system optimization. Furthermore, not considering the operating cost will underestimate the requirement of the resources, more so when a higher reliability is required. [ABSTRACT FROM AUTHOR]

A novel solar assisted heat pump (SAHP) system for in-bin grain drying was proposed to solve the problems of poor uniformity and long drying time of grain in depot with higher initial water content in high grain piles. The effects of different operational parameters on the drying performance were numerically and experimentally investigated. The experimental results indicated that the thermal efficiency of the solar collector and the COP of the heat pump reached 63% and 5.03 under given conditions. The average heating capacity of the SAHP system was 130.2 kW, consistent with the simulation results under a typical sunny day in winter. The average water content of 3,760 tons of grain dropped from 12.9% to 12.5% after 42 h of drying. The specific moisture extraction rate of the proposed system and the exergy efficiency of the grain bin were 1.934 kg/kWh and 40.27%, respectively. The operating cost of SAHP grain drying decreased from 5.57 $/t to 1.43 $/t compared with the grain drying machine for the same drying weight. The daily drying capacity increased from 166 t/d to 334 t/d compared with the mechanical ventilation drying for the same water content reduction rate. This investigation provided guidance for expanding future researches on the SAHP system applied for in-bin grain drying. • A novel in-bin grain drying system using solar collectors coupled with air source heat pump is proposed. • The drying performance of the proposed system is numerically and experimentally investigated. • The average moisture content of 3,760 tons of corn dropped from 12.94% to 12.5% after 42 h of drying. • The specific moisture extraction rate of the SAHP grain drying system was 1.934 kg/kWh. • The payback period of the SAHP grain drying system was 0.33 years. [ABSTRACT FROM AUTHOR]

This paper proposes to hybridize the Whale Optimizer (WOA) and Particle Swarm Optimization (PSO) algorithms to achieve optimal integration of Distributed Generator (DG) based photovoltaic sources in the search for optimal allocation. Optimal integration reduces the Active Power Loss (APL) index and enhances the Total Voltage Variation (TVV) index and the Total Operating Cost (TOC) index. The proposed hybrid PSO-WOA algorithm is validated on standard IEEE 33-bus and 69-bus Electrical Distribution Systems (EDS). Numerical and graphical comparative studies were conducted to benchmark the hybrid approach results against those obtained using other powerful algorithms and techniques existing in the literature. Active and reactive branch currents are calculated and plotted to show how the DG affects current flows in the EDS. To make the results more realistic, linear load level variation is considered, with the loads varying from 60% to 120% of the rated loading level. [ABSTRACT FROM AUTHOR]