This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.
In Renewable Energy (RE) integrated DC Microgrid (MG), the intermittency of power variation from RE sources can lead to power and voltage imbalances in the DC network and have an impact on the MG's operation in terms of reliability, power quality, and stability. In such case, a battery energy storage (BES) technology is widely used for mitigating power variation from the RE sources to get better voltage regulation and power balance in DC network. In this study, a BES based coordinated power management control strategy (PMCS) is proposed for the MG system to get effective utilization of RE sources while maintaining the MG's reliability and stability. For safe and effective utilization of BES, a battery management system (BMS) with inclusion of advanced BES control strategy is implemented. The BES control system with optimized FOPI controllers using hybrid (atom search optimization and particle swarm optimization (ASO-PSO)) optimization technique is proposed to get improved overall performance in terms of control response and voltage regulation in DC network under the random change in load profile and uncertain conditions of RE sources in real time.
In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.
The research included corn starch (CS) films using sorbitol (S), glycerol (G), and their combination (SG) as plasticizers at 30, 45, and 60 wt %, with a traditional solution casting technique. The introduction of plasticizer to CS film-forming solutions led to solving the fragility and brittleness of CS films. The increased concentration of plasticizers contributed to an improvement in film thickness, weight, and humidity. Conversely, plasticized films reduced their density and water absorption, with increasing plasticizer concentrations. The increase in the amount of the plasticizer from 30 to 60% showed a lower impact on the moisture content and water absorption of S-plasticized films. The S30-plasticized films also showed outstanding mechanical properties with 13.62 MPa and 495.97 MPa, for tensile stress and tensile modulus, respectively. Glycerol and-sorbitol/glycerol plasticizer (G and SG) films showed higher moisture content and water absorption relative to S-plasticized films. This study has shown that the amount and type of plasticizers significantly affect the appearances, physical, morphological, and mechanical properties of the corn starch biopolymer plastic.
This study presents a nature-inspired, and metaheuristic-based Marine predator algorithm (MPA) for solving the optimal power flow (OPF) problem. The significant insight of MPA is the widespread foraging strategy called the Levy walk and Brownian movements in ocean predators, including the optimal encounter rate policy in biological interaction among predators and prey which make the method to solve the real-world engineering problems of OPF. The OPF problem has been extensively used in power system operation, planning, and management over a long time. In this work, the MPA is analyzed to solve the single-objective OPF problem considering the fuel cost, real and reactive power loss, voltage deviation, and voltage stability enhancement index as objective functions. The proposed method is tested on IEEE 30-bus test system and the obtained results by the proposed method are compared with recent literature studies. The acquired results demonstrate that the proposed method is quite competitive among the nature-inspired optimization techniques reported in the literature.
Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.
Sustainable materials, such as vegetable oils, have become an effective alternative for liquid dielectrics in power transformers. However, currently available vegetable oils for transformer application are extracted from edible products with a negative impact on food supply. So, it is proposed in this study to develop cottonseed oil (CSO) as an electrical insulating material and cooling medium in transformers. This development is performed in two stages. The first stage is to treat CSO with tertiary butylhydroquinone (TBHQ) antioxidants in order to enhance its oxidation stability. The second and most important stage is to use the promising graphene oxide (GO) nanosheets to enhance the dielectric and thermal properties of such oil through synthesizing GO-based CSO nanofluids. Sodium dodecyl sulfate (SDS) surfactant was used as surfactant for GO nanosheets. The nanofluid synthesis process followed the two-step method. Proper characterization of GO nanosheets and prepared nanofluids was performed using various techniques to validate the structure of GO nanosheets and their stability into the prepared nanofluids. The considered weight percentages of GO nanosheets into CSO are 0.01, 0.02, 0.03 and 0.05. Dielectric and thermal properties were comprehensively evaluated. Through these evaluations, the proper weight percentage of GO nanosheets was adopted and the corresponding physical mechanisms were discussed.
This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.