The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior so... [more]

The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversio... [more]

In this study, the performance of a current sensor based on giant magnetostrictive materials (GMM) and fiber Bragg grating (FBG) has been improved by optimizing the spectral characteristics of gratings. By analyzing the influence of FBG on the current sensor characteristics, three key parameters (gate region length, refractive index modulation depth, and toe cutting system) are selected for optimization. The optimal grating parameters are determined to improve the linearity and sensitivity of sensor output. Experimental tests reveal that after grating optimization, the current sensor shows excellent performance parameters, including a linearity of 0.9942, sensitivity of 249.75 mV/A, and good stability in the temperature range of 0−60 °C. This research can provide a reference for improving the grating design and performance of existing GMM-FBG current sensors.

Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP’s optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by model... [more]

In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning solution of EVCSL, and dynamic weight adjustment strategy and integration into the two-population genetic mode are proposed to improve the optimization quality for PSO. Simulation results show that the proposed improvement strategies can increase the optimization quality for PSO effectively so that a more appreciative planning solution of EVCSL can be obtained.

The rooftop photovoltaic (PV) system that uses a power optimization device at the module level (MLPE) has been theoretically proven to have an advantage over other types in case of reducing the effect of partial shading. Unfortunately, there is still a lack of studies about the energy production of such a system in real working conditions with the impact of partial shading conditions (PSC). In this study, we evaluated the electrical energy production of the PV systems which use two typical configurations of power optimization at the PV panel level, a DC optimizer and a microinverter, using their real datasets working under PSC. Firstly, we compared the energy utilization ratio of the monthly energy production of these systems to the reference ones generated from PVWatt software to evaluate the effect of PSC on energy production. Secondly, we conducted a linear decline model to estimate the annual degradation rate of PV systems during a 6-year period to evaluate the effect of PSC on the... [more]

Hybrid concentrated solar thermal power (CSP) and photovoltaic (PV) plants are gaining relevance because they combine their advantages: easy installation and low cost of PV plus dispatchability of CSP. This paper presents results of a techno-economic modelling of this hybrid approach for sites in Jordan, Tunisia and Algeria. Local boundary conditions such as meteorology, cost and electricity demand have been considered to determine the best configurations for these three sites. Different CSP technologies with thermal energy storage have been selected. Hybridization with natural gas has also been included. The optimization is done towards minimizing the LCOE while covering the electrical demand 24/7. Results are presented for different CO2 emissions ranges, as the use of fossil fuel has a strong impact on the LCOE and for environmental reasons, it may be preferred to be kept to a minimum. For most of the cases analyzed, the fraction of energy from PV that leads to minimum LCOE is lower... [more]

The leakage inductance of the transformer in a dual active bridge (DAB) dc−dc converter directly impacts the ac current waveforms and the power factor; thus, it can be considered a design requirement for the transformer. In the existing literature, a choice is made to either ensure soft switching in nominal power or to minimize the RMS current of the transformer. The inductance is typically obtained using optimization procedures. Implementing these optimizations is time-consuming, which can be avoided if a closed-form equation is derived for the optimum leakage inductance. In this paper, analytical formulas are derived to estimate the desired leakage inductance such that the highest RMS value of the current in the operation region of a DAB is kept to its minimum value. The accuracy and sensitivity of the analytical solutions are evaluated. It is shown that in a large design domain, the solution for the YY-connected MFT has a less than 3% error compared to the results obtained from an o... [more]

Solid oxide fuel cell (SOFC) performance degradation analysis and optimization studies are important prerequisites for its commercialization. Reviewing and summarizing SOFC performance degradation studies can help researchers identify research gaps and increase investment in weak areas. In this study, to help researchers purposely improve system performance, degradation mechanism analysis, degradation performance prediction, and degradation performance optimization studies are sorted out. In the review, it is found that the degradation mechanism analysis studies can help to improve the system structure. Degradation mechanism analysis studies can be performed at the stack level and system level, respectively. Degradation performance prediction can help to take measures to mitigate degradation in advance. The main tools of prediction study can be divided into model-based, data-based, electrochemical impedance spectroscopy-based, and image-based approaches. Degradation performance optimiz... [more]

The description of the functionality of a smart grid’s architectural concept, analyzing different Smart Grid (SG) scenarios without disrupting the smooth operation of the individual processes, is a major challenge. The field of smart energy grids has been increasing in complexity since there are many stakeholder entities with diverse roles. Electric Vehicles (EVs) can transform the stress on the energy grid into an opportunity to act as a flexible asset. Smart charging through an external control system can have benefits for the energy sector, both in grid management and environmental terms. A suitable model for analyzing and visualizing smart grid use cases in a technology-neutral manner is required. This paper presents a flexible architecture for the potential implementation of electromobility as a distributed storage asset for the grid’s capacity optimization by applying the Use Case and Smart Grid Architecture Model (SGAM) methodologies. The use case scenario of booking a charge se... [more]

Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are the two most significant concerns for data transmission. Sensor nodes are powered by their own battery capacity, allowing them to perform critical tasks and interact with other nodes. The quantity of electricity saved from each sensor together in a WSN has been strongly linked to the network’s longevity. Clustering conserves the most power in wireless transmission, but the absence of a mechanism for selecting the most suitable cluster head (CH) node increases the complexity of data collection and the power usage of the sensor nodes. Additionally, the disparity in energy consumption can lead to the premature demise of nodes, reducing the network’s lifetime. Metaheuristics are used to solve non-deterministic polynomial (NP) lossy clustering problems. The primary purpose of this research is to enhance the energy efficiency and network endurance of WSNs. To address this issue, this work proposes a sol... [more]

Reliability is an important issue in electricity distribution systems, with strict regulatory policies and investments needed to improve it. This paper presents a mixed integer linear programming (MILP) model for clustering electrical customers, maximizing system reliability and minimizing outage costs. However, the evaluation of reliability and its corresponding nonlinear function represent a significant challenge, making the use of mathematical programming models difficult. The proposed heuristic procedure overcomes this challenge by using a linear formulation of reliability indicators and incorporating them into the MILP model for clustering electrical customers. The model is mainly defined on a density-based heuristic that constrains the set of possible medians, thus dealing with the combinatorial complexity associated with the problem of empowered p-medians. The proposed model proved to be effective in improving the reliability of real electrical distribution systems and reducing... [more]

Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I−V characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP’s dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capa... [more]

This paper is concerned with the optimal design of axial probes, commonly used in the Non-Destructive Testing (NDT) of tube boiling in steam generators. The goal is to improve the low-frequency Foucault-current imaging of these deposits by designing a novel probe. The approach uses a combination of an inverse problem solver with global optimization to find the optimal probe characteristics by minimizing a function of merit defined using image processing techniques. The evaluation of the function of merit is computationally intensive and a surrogate optimization approach is used, incorporating a multi-particle search algorithm. The proposed design is validated through numerical experiments and aims to improve the accuracy and efficiency of identifying deposits in steam generator tubes.

Microalgal biomass is a promising alternative and renewable substrate for bioenergy production. The main problem for its commercial application is to obtain and keep a high level of production by providing microalgae with appropriate conditions for growth. The aim of this study was to determine optimal culture conditions such as temperature, photoperiod, and pH. The amount of biomass by gravimetry, optical density by spectrophotometry, and productivity were analyzed. Suitable values of cultivation parameters allowed for the increased growth and biomass productivity of Arthrospira platensis (4.24 g·L−1), Chlamydomonas reinchardtii (1.19 g·L−1), Chlorella vulgaris (2.37 g·L−1), and Dunaliella salina (4.50 g·L−1) and optical density for Ch. reinchardtii and C. vulgaris. These species had maximum biomass productivity of 0.72, 0.12, 0.36, and 0.77 g·L−1·d−1, respectively. Productivity was determined by cultivation temperature and for Ch. reinchardtii also by pH.

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective Grey Wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination Algorithm (MFPA), Multi-objective Lichtenberg Algorithm (MOLA), and... [more]

The antlion optimizer (ALO) algorithm is used in this article for maximum power point tracking (MPPT) of a solar array. The solar array consists of a single module, while there are 20 cells in the module. The voltage and current ratings of each cell are 2 V and 2.5 A, making a 100 W array in ideal condition. However, the voltage and current characteristics of the PV cell are unable to achieve maximum power. Therefore, the ALO was used for MPPT. The results of the ALO are compared with the traditional metaheuristic approaches, perturb and observe (P&O) and flower pollination (FP) algorithms. Comparison of the ALO with the stated algorithms is conducted for two cases: when solar irradiance is 1000 W/m2 and when it drops to 200 W/m2 at first then reaches 1000 W/m2. The change of irradiance is performed to simulate the partial shading condition. The simulation results depict that maximum power for the first case using the ALO reaches 91.3 W in just 0.05 s, while the P&O and PFA reach 90 W... [more]

The need for a low-cost interface between the grid and small (<250 kW) renewable distributed energy resources (DERs) is growing in importance as the number of small DERs continues to grow. In this study, a system architecture was proposed to investigate paths to an affordable interconnection for small renewable DERs.Then, a low-cost communication interface between a smart meter and smart inverter was installed using a commercially available bridge device. The interface device was selected based on an assessment concluding that it would be able to support the emerging advanced metering infrastructure (AMI) network. Next, messages were passed across the experimental end-to-end communication interface to test their speed and reliability. Success was based on whether the key functions defined in the standard IEEE 2030.5 were executed or not, which include set points, disconnect/reconnect, and Volt-VAr optimization. The results of the testing provided detailed insights into the benefits... [more]

The cyclic injection of CO2, referred to as the huff-n-puff (HnP) method, is an attractive option to improve oil recovery from unconventional reservoirs. This study evaluates the optimization of the CO2 HnP method and provides insight into the aspects of CO2 sequestration for unconventional reservoirs. Furthermore, this study also examines the impact of nanopore confinement, fluid composition, injection solvent, diffusivity parameters, and fracture properties on the long-term recovery factor. The results from over 500 independent simulations showed that the optimal recovery is obtained for the puff-to-huff ratio of around 2.73 with a soak period of fewer than 2.7 days. After numerous HnP cycles, an optimized CO2 HnP process resulted in about 970-to-1067-ton CO2 storage per fracture and over 32% recovery, compared to 22% recovery for natural depletion over the 30 years. The optimized CO2 HnP process also showed higher effectiveness compared to the N2 HnP scenario. Additionally, for rese... [more]

University buildings have high energy requirements due to their size, numerous users, and activities, which considerably contribute to environmental contamination. Implementing energy-saving solutions in these structures has a favorable influence on the economics and the conservation of energy resources. A higher education building’s energy behavior can be simulated using software to identify the optimal strategies that result in energy savings. In this research, Autodesk Revit, Autodesk Insight, and Green Building Studio are among the programs utilized to examine the energy efficiency of the university building in four European cities. Following the development of several energy-saving scenarios for the building, the offered solutions are evaluated based on their annual energy consumption, energy costs, and CO2 emissions. Finally, multicriteria analysis techniques such as the AHP and PROMETHEE are applied to choose the best scenario for each instance. The study’s findings indicate th... [more]

In this work, an induced pre-saturation tower (IPST) for oil−water separation was built on a semi-industrial scale, based on experimental results obtained on a laboratory scale prototype. The main strategy for generating these criteria was to increase the efficiency of the bench scale prototype, which is limited by conditions of low levels of automation and control, with the use of a biosurfactant as an auxiliary collector. The validation of the developed criteria allowed the construction of an IPST with three stages, all fed with previously saturated effluents. The IPST was built in stainless steel, with multistage centrifugal pumps and adapted to generate microbubbles without the use of saturation tanks or compressors. The most relevant operational parameters were selected using a fractional factorial design, while a central composite rotatable design (CCRD) followed by the application of the desirability function allowed to optimize the conditions for partial and global variables, t... [more]

This paper presents a multi-objective design optimization of a power transformer to find the optimal geometry of its core and the low- and high-voltage windings, representing the minimum power losses and the minimum core and copper weights. The optimal design is important because it allows manufacturers to build more efficient and economical transformers. The approach employs a manufacturer’s design methodology, which is based on the usage of the laws of physics and leads to an analytical transformer model with the advantage of requiring a low amount of computing time. Afterward, the multi-objective design optimization is defined along with its constraints, and they are solved using the Non-Sorting Genetic Algorithm III (NSGA-III), which finds a set of optimal solutions. Once an optimal solution is selected from the Pareto front, it is necessary to fine-tune it with the 3D Finite Element Analysis (FEA). To avoid the large computing times needed to carry out the 3D Finite Element (FE) m... [more]

This work uses design optimization of a power electronics converter to achieve the best levelized cost of energy in a PV application. The methodology uses detailed models of power electronics’ active and passive components to determine the cost and performances of the solid-state energy conversion and connect them to the system-level vision. The deterministic algorithm used for converter sizing allows taking into account a large number of variables and constraints. Methodology, models, and some illustrations of the results are provided in this paper. A sensitivity analysis was also conducted on the cost model.

Increasing sales of conventional fuel-based vehicles are leading to an increase in carbon emissions, which are dangerous to the environment. To reduce these, conventional fuel-based vehicles must be replaced with alternative fuel vehicles such as hydrogen-fueled. Hydrogen can fuel vehicles with near-zero greenhouse gas emissions. However, to increase the penetration of such alternative fuel vehicles, there needs to be adequate infrastructure, specifically, refueling infrastructure, in place. This paper presents a comprehensive review of the different optimization strategies and methods used in the location of hydrogen refueling stations. The findings of the review in this paper show that there are various methods which can be used to optimally locate refueling stations, the most popular being the p-median and flow-capture location models. It is also evident from the review that there are limited studies that consider location strategies of hydrogen refueling stations within a rural set... [more]

The operating parameters of the active direct methanol fuel cell (DMFC) are essential factors that affect cell performance. However, it is challenging to maintain the optimal maximum output power density due to the system’s complexity, the operating conditions variation, and the correlations between those parameters. This paper proposes an adaptive joint optimization method for fuel cell operating parameters. The methods include the adaptive numerical simulation of the operation parameters and the optimization for fuel cell performance. Based on orthogonal tests, a BP neural network is used to build a performance evaluation model that can quantify the influence of the operating parameters on fuel cell performance. The optimal combination of operating parameters for the fuel cell is obtained by a whale optimization algorithm (WOA) through the evaluation model. The experimental results show that the evaluation model could respond accurately and adaptively to the cell operating conditions... [more]