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Energy storage systems (ESSs) play a significant role in performance improvement of future electric traction systems. This paper investigates an ESS based on supercapacitors for trams as a reliable technical solution with considerable energy saving potential. Operating the ESS onboard a tram brings the following benefits: reduction of peak power demands, decrease of power consumption, improvement of performance, and efficiency. Whereas the commonly used ESS power management (PM) has been dealing only with PM without vehicle position knowledge, the main emphasis of the presented research is put on position-based Takagi–Sugeno fuzzy (T-S fuzzy) PM for human-driven trams with an ESS. The tram position, located by, e.g., a Global Positioning System module, is used to predict future energy demands and power flows. This information is used in the preparation of the supercapacitor energy level and its power flow control. The optimal ESS power flow with respect to the tram position is a very complex problem. Therefore, soft computing techniques are used in this paper. The main part of the proposed ESS PM is the T-S fuzzy controller. The T-S fuzzy reduces problem dimensionality using expert knowledge represented by the rules. The output membership functions were optimized by differential evolution. The designed ESS PM was verified on real traffic data. [ABSTRACT FROM PUBLISHER]

This paper proposes an application of an evolutionary game dynamics called the escort dynamics (ED) for the decentralized load management of plug-in electric vehicles (PEV). Different from earlier contributions, in the present approach, PEVs work together in a fair scheme in order to provide several ancillary services to the grid: Load shifting, active power balancing, and partial supply of reactive power demand on each phase of the distribution transformer. Meanwhile, batteries are guaranteed to be fully charged according to the constraints imposed by the owners. In the proposed formulation, chargers can be either three phase or single phase; however, in this paper, only three-phase chargers are considered. The key concepts behind ED, especially for escort functions, are provided at the beginning of this paper. Based on these concepts, the assumptions and analogies followed for the construction of the proposed approach are explained in detail, especially for the proposed definition of escort functions. A multipopulation scenario is proposed for the interaction of several PEVs using local ED routines. This interaction among populations follows another well-known evolutionary game dynamics called the best reply dynamics. Performance is evaluated using real data measured from a distribution transformer from the SOREA utility grid company in the region of Savoie, France. [ABSTRACT FROM AUTHOR]

Power electronic converters are often used in parallel both for efficiency and reliability improvement. Numerous approaches have been documented, but so far, to the best knowledge of the authors, there is no general proof demonstrating if a given configuration would lead to maximum overall efficiency throughout the whole load range. Finding a general answer is complicated by the large number of topologies and load sharing strategies that are used. This paper endeavors to find a generic solution to the question, Which current sharing technique yields best results in terms of overall efficiency?, by using a game theoretic approach and a generalized form of efficiency load current characteristic. The authors start from the assumption that each converter in parallel can be modeled as an agent endowed with intelligence, able to choose a strategy in order to achieve a target. The method is applied to dc/dc converters in general and as an example to the buck converter. Nevertheless, the results can be generalized to other converter types (in the case of ac, additional dimensions will appear, such as reactive power, phase, or frequency) or to other energy conversion or transmission devices. [ABSTRACT FROM PUBLISHER]

This paper proposes an improved hybrid method to compute the parameters of the two-diode model of photovoltaic (PV) module. Unlike previous methods, it attains the speed of the analytical approach by utilizing only datasheet information. Furthermore, its accuracy is not compromised as it does not require simplifications in its computation. Four parameters are determined analytically, while the remaining three are optimized by using an evolutionary algorithm, i.e., the differential evolution. The speed is improved because the parameters are optimized only once, i.e., at standard test condition, while the values at other conditions are computed analytically. Furthermore, a procedure to guide the initial conditions of the Newton–Raphson iteration is introduced. For validation, the algorithm is compared to other established computational methods for mono-, polycrystalline, and thin film modules. When evaluated against the experimental data, the mean absolute error is improved by one order of magnitude, while the speed is increased by approximately threefold. The standard deviation of the decision parameters over 100 independent runs is less than 0.1—which suggests that the optimization process is very consistent. Due to its speed and accuracy, the method is envisaged to be useful as a computational engine in PV simulator. [ABSTRACT FROM PUBLISHER]

Due to the limits on market requirements, material conditions, and production situations in manufacturing process, conventional optimization approaches are difficult to obtain optimal economical and technical indices with physical constraints. To optimize several conflicting objects such as production rate, economic benefits, and gas emission, a hybrid-model-based intelligent optimization method that consists of an improved genetic algorithm and derived deep learning is put forward in this paper. Integration of the hybrid model has made modeling and optimizing an indivisible whole, in which the fitness of the genetic algorithm comes from deep neural networks by weighted sum of the output variables that correspond to the input solutions. The recurrent neural network (RNN) with disposition-gated recurrent unit (dGRU) is applied to capture the dynamics of blast furnace by training the model over datasets recorded in the production scene. Meanwhile, the self-adaptive population genetic algorithm (SAPGA) with a varied population size depending on the fitness distribution is used to locate the optimal solutions under current working conditions. The hybrid intelligent optimization model, validated by both numerical tests and practical data, has been running in an ironmaking plant for one year. It has proved to be successful in meeting industry demands by optimizing multiproduction indices simultaneously. [ABSTRACT FROM AUTHOR]

In order to obtain a good optimization method for the electrical transformer design with optimal selection of parameters, performance evaluation of three evolutionary algorithms (EAs), namely, genetic algorithm (GA), differential evolution algorithm, and nondominated sorting GA (NSGA-II), is carried out. The aim of this paper is to optimize parameters of transformer design (core thicknesses, primary-turn number, secondary-turn number, primary conductor area, and secondary conductor area) for minimization of total power losses (no-load losses and load losses) in three-phase transformer topology while maintaining high efficiency and low cost. The method used for this optimization scheme combines the finite-element method (FEM) and EAs to provide an accurate selection of parameters together with the optimized magnetic flux density and decreased loss. Experimental results show that NSGA-II+FEM model successfully provides a global feasible solution by minimizing total loss and related cost while improving the efficiency of three-phase transformer, rendering it suitable for application in the design environment of industrial transformers. [ABSTRACT FROM AUTHOR]

An application of an evolutionary game dynamics called mixed strategist dynamics (MSD), for the decentralized load scheduling of plug-in electric vehicles (PEVs), is proposed in this paper. Following an analogy with the maximum entropy principle (MEP) for tuning parameters of discrete probability distributions, entropy of the total load distribution and the local load distributions are considered as objectives of the scheduling approach, and a tradeoff among them is defined by the electric vehicle owners’ convenience. While entropy maximization for the local load distributions contributes to preserve the batteries’ states of health, entropy maximization for the total load distribution reduces the undesirable peak effects over the transformer loading. The problem is formulated such that final states of charge are assured depending on time constraints defined by the owners. Furthermore, mixed strategies in the MSD are defined such that they represent the vertices of the convex set of feasible load profiles which results from the constraints imposed by owners and chargers. The synergy of several PEVs is modeled as an application of the MSD in a multipopulation scenario, where the interaction among populations follows another evolutionary game dynamics called best reply (BR) dynamics. The performance of the proposed approach is tested on real data measured on a distribution transformer from the SOREA utility grid company in the region of Savoie, France. [ABSTRACT FROM PUBLISHER]

In this paper, economic- and network-driven distribution generation (DG) placement planning problem is investigated from the local distribution company’s (DISCO’s) viewpoint considering reliability level and power loss of the electrical distribution network. Herein, the feeder’s failure rate is modeled respective to active component of the current flowing through the feeder applying several mathematical functions. In addition, the customers’ load type is modeled and load-model-based power flow is applied instead of conventional power flow. Moreover, in order to achieve realistic results, economic factors such as inflation and interest rates and technical factors including yearly load growth and hourly and daily variations of the load are considered in the planning problem. Furthermore, several analyses are done to calculate amounts of the detriment and error due to unreal modeling of the customers’ load type and the feeder’s failure rate in the DG placement planning problem. It is proved that the unreal modelings can markedly affect the results of the problem and even the optimal locations of the DGs. [ABSTRACT FROM PUBLISHER]

Using aggregated heterogeneous thermostatically controlled loads (TCLs) to provide ancillary services in a smart grid is a promising technique. This paper proposes an improved population model with lockout time considered to describe the aggregate dynamics. Based on that, a distributed model predictive control (DMPC) scheme is investigated to control a population of TCLs for the regulation service. Full derivations of the system model and the DMPC scheme are provided. A flexibility penalty is developed and added to the objective function to preserve the regulation flexibility. Simulation results, tested by real data, reveal the feasibility and effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The optimization of electrical machines and energy-harvesting devices has been one of the hot topics of research in recent years. In this context, advanced computational intelligence algorithms are suitable design tools, since they are able to optimize complex systems with a large number of constraints and free design parameters. This paper presents the social network optimization (SNO): a new population-based algorithm developed to guarantee an effective and faster exploration of the solution domain with respect to traditional optimization techniques. The proposed technique will be described and validated with a benchmark case study. Finally, the optimization problem of a tubular permanent magnet linear generator will be formulated in the context of vehicular energy-harvesting systems. [ABSTRACT FROM PUBLISHER]

This paper presents an evolutionary optimal fuzzy system with information fusion of heterogeneous distributed computing and polar-space dynamic model for online motion control of Swedish redundant robots. The intelligent fuzzy system incorporated with the parallel metaheuristic Bacteria Foraging Optimization (BFO)-Artificial Immune System (AIS), called FS-PBFOAIS and its field-programmable gate array (FPGA) realization to optimal polar-space online motion control of four-wheeled redundant mobile robots. This hybrid paradigm gains the benefits of Taguchi quality method, BFO, AIS, distributed processing, and FPGA technique. Experimental results are conducted to present effective optimization and high accuracy of the proposed FPGA-based FS-PBFOAIS tracking controller. Finally, the comparative works are provided to demonstrate the superiority of the FPGA-based FS-PBFOAIS polar-space redundant controller over other conventional control methods. [ABSTRACT FROM AUTHOR]