This paper introduces a novel hybrid meta-heuristic approach based gradient method combined with Genetic Algorithm (GA) for solving the Unit Commitment problem (UCP). The Unit commitment in power system is a scheduling operation of a set of production units to ensure better operational scheduling based on expected consumption data over a period of time. The UCP was formulated as a constrained nonlinear optimization problem designed to reduce the production cost during a definite period while satisfying the desired constraints. Therefore, various meta-heuristic and deterministic techniques has introduced to solve the UPC. However, there is no optimal approach to deal with UCP. Furthermore, the accuracy and performance of the proposed approach were applied to IEEE 14 bus test system and compared to other optimization methods. The proposed approach provides an accurate and robust results and statistically significant in solving UCP.
In our days, it becomes more and more obviously the importance of sale forces both in the activity of producers or providers and for customers or potential clients.\nThe core significance of the force sales is related to anything involved in the direct two ways relationships producers/providers ↔ consumers. BUT, an aspect not to be neglected is represented by the psychological feature: for the clients the dependence on fast food, in running, seemingly satiated, action that does not last long and, on the other side, the producers/providers which need “to see” in minds of customers and to anticipate their needs (the first level of the Maslow Hierarchy of Needs).\nThis approach states that the psychological feature, P\', must be considered a separate element of the Marketing, influencing and supporting the other ones (especially, Products and Promotion). All these efforts are directed towards knowing better the customer, to understand his needs, his aspirations, to fulfill them properly and, on the other hand, to bring this customer to producer/provider, in order to obtain the desired goods or services and the firm to get profit for its sustainability in the market.
Purpose: Compare two different implant/ abutment internal taper (16� and 11.5�), of three different external design implants to verify the difference on stress values and implant/abutment integrity, after Finite Element Analyses (FEA) and Fatigue test. Materials & Methods: Fifty-four Morse taper implants were divided into 6 groups (n=9), depending on abutment/implant connection design and implant external design. FEA and Fatigue test (N) were performed. All tested groups were modeled for FEA, simulating a polyacetal base with the installed implant was placed in an angled device of 40�. The Fatigue test was performed at the same conditions described above, and under cyclic loading tests, with a frequency of 15 Hz, according to the specifications of ISO 14801. Results: FEA demonstrated more homogeneous stress distribution on 16� Morse taper implant. Statistically differences were observed, favoring 16� Morse taper implant, when comparing to 11.5� implant, independently of the external implant design. Conclusion: According to this study, the implant/abutment internal taper influenced on stress values and implant/abutment integrity, favoring the 16� Morse taper implant.
Purpose: The purpose of this study was to evaluate and compare the accuracy of two intra-oral scanners and conventional impression methods for the fabrication of working casts. Methods: Conventional impressions of a reference cast were obtained. Digital impressions were obtained with two intra-oral scanners: Cerec Omnicam (CO) and 3Shape Trios (ST). The obtained digital stereolithographic casts were printed on Zenith D 3D printer. The reference cast and fabricated casts were scanned with a bench top scanner and saved in STL format. All STL records were analyzed in specific software: complete arch (CA), partial arch (PA) and prepared teeth area (PT). One-way and two-way analyses of variance were performed to compare the accuracy, followed by the Tukey test. Results: No significant intergroup differences in trueness and precision were observed for the two intra-oral scanners. 3D printed casts had the lowest trueness when complete arch was analyzed and differed statistically from the stone cast. For complete arch precision, stone cast presented better results, however statistically different only from the CO. Conclusions: The two intraoral scanner systems had similar accuracy. Stone casts had higher trueness than 3D printed casts for CA. For CA precision, 3D printed cast presented similar results to the stone cast.
Optimal Power Flow (OPF) is being used to dispatch available generation in such a way that minimizes a particular objective function. The need for new transmission and/or generation capacity, improving load following, the potential benefits of grid-integrated storage technologies is being included decreasing, providing spinning reserve, correcting frequency, voltage, and power factors, as well as the indirect environmental advantages gained through facilitating an increased penetration of RERs. Differential search algorithm (DSA) is being stated as a recent literature described application of state-of-the-art evolutionary algorithm (EA). Success history based parameter adaptation technique of differential evolution (SHADE) is being employed for the optimization problem. Optimal scheduling contains Equality and inequality constraints and Real time optimal power flow (RT-OPF) model. Indirect soft linking approaches is being required the construction of new dedicated sectoral models and more challengingly handling the interface between the two models in order to arrive at consistent results. The direct integration methodologies for ESOMs that improve the technical representation directly increase the optimality of solution.
The development of IES technology resolves issues of energy safety, improve social efficiency, and renewable energy sources. There are many potential benefits associated with IES technologies. It is being known now as an important strategic research direction in the field of international energy. Renewable energy sources play an important role in the modern energy system. Rapid adoption of distributed generation sources and micro grids powered by renewable sources like photovoltaic, wind, tidal, fuel cells is being reported as one of the major results of increasing global demand for energy all over the world. Two types of sizing methods viz. iterative method and artificial intelligence method are being used. Modern techniques, based on single artificial intelligence (AI) algorithms, are more popular than classical algorithms owing to their capabilities in solving complex optimization problems. Optimal sizing of a stand-alone integrated renewable energy system (IRES) which may be comprised of micro hydro power (MHP), biogas, biomass, solar, wind energy etc. is an important research problem. It is modelled as a demand response (DR) strategy based on energy consumption scheduling of appliances. Multi-objective optimization with stochastic modelling becomes necessary to correctly identify the trade-offs between cost and reliability. Other technical issues are system stability, voltage at point of common coupling (PCC) and system integrating distributed generation. Integrating hybrid distributed generation in a distribution network requires an advanced controller, which is critical for ensuring high quality and stability of voltage at PCC and frequency of the power system. For controlling voltage at PCC and for controlling system frequency and making system stable, controller is used for Proportional-Integral (PI) & Proportional-Integral Derivative (PID) controllers are being considered for investigation. Pareto-based approaches are more efficient way to obtain good solutions close to global optimum. Stand-alone Hybrid Renewable Energy Systems (SA-HRES) contains more than one objective function and many constraints. Reliability assessment helps in assessment by power system operators, planners, designers and engineers. Reliability assessment optimizes the power outage cost. A Non-linear robust control technique using improved particle swarm optimization (IPSO) control is reported to have been implemented as a master control technique for the control of interlinking converter (IC) between AC and DC micro grids.
