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Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%. [ABSTRACT FROM AUTHOR]

This article systematically compares two axial flux permanent magnet machines designed for a university student racing car application: a double-rotor single-stator yokeless and segmented armature (YASA) structure, and a single-stator single-rotor configuration. Both machines are optimized for minimum loss and active weight using three-dimensional finite element analysis and the highest performing candidate designs are compared in more detail. The studies indicate that the benefits offered by the YASA configuration over the single-stator single-rotor machine are achieved only for specific designs that are heavier. For the design space with lower mass, albeit with increased losses, the Pareto front designs overlap. In this envelope, the YASA configuration demonstrates higher efficiencies at higher speeds, whilst the single-stator single-rotor is more efficient in high torque duty cycles. This shows the performance of the two machines is very similar and the choice is application specific. To validate the finite element analysis used in the optimization, a prototype was built and tested. Results showed good alignment between simulation and experimental data. [ABSTRACT FROM AUTHOR]

Outer-rotor switched reluctance machines (SRMs) have drawn much attention as promising candidates for in-wheel direct-drive motors of future electric vehicles. This article presents a systematic performance comparison of three outer-rotor SRM topologies for in-wheel traction applications in terms of the specific torque, electromagnetic efficiency, torque ripple, radial force, and mechanical aspects. A generalized design optimization framework for SRMs is proposed to enable the fast evaluation of large numbers of designs generated from the differential evolution by incorporating an analytical current profile estimation into the transient finite element analysis. The relationship between the saliency ratio and converter volt-ampere rating is also discussed. The calculations are then benchmarked with the experimental results from an existing prototype. The effectiveness of the performance prediction method and the proposed optimization approach is validated. [ABSTRACT FROM AUTHOR]

Optimizing the design of electric machines is a vital step in ensuring the economical use of active materials. The three-dimensional (3-D) flux paths in axial flux permanent magnet (AFPM) machines necessitate the use of computationally expensive 3-D electromagnetic analysis. Furthermore, a large number of design evaluations is required to find the optimum, causing the total computation time to be excessively long. In view of this, a two-level surrogate assisted algorithm capable of handling such expensive optimization problems is introduced, which substantially reduces the number of finite element analysis (FEA) evaluations to less than 200 while conventional algorithms require thousands of designs to be analyzed. The proposed algorithm is employed to optimally design an AFPM machine within a specified envelope, and to identify the limits of cost and efficiency. In order to obtain these limits, the variables' ranges are assigned to be as wide as possible, resulting in a vast design space, the study of which was enabled by the developed special algorithm. Additionally, optimized designs with different rotor polarities are systematically compared in order to form the basis for a set of generalized design rules. [ABSTRACT FROM AUTHOR]

A two-level surrogate-assisted optimization algorithm is proposed for electric machine design using 3-D finite-element analysis (FEA). The algorithm achieves the optima with much fewer FEA evaluations than conventional methods. It is composed of interior and exterior levels. The exploration is performed mainly in the interior level, which evaluates hundreds of designs employing affordable kriging models. Then, the most promising designs are evaluated in the exterior loop with expensive 3-D FEA models. The sample pool is constructed in a self-adjustable and dynamic way. A hybrid stopping criterion is used to avoid unnecessary expensive function evaluations. [ABSTRACT FROM AUTHOR]

Switched reluctance (SR) machines are attractive because they present relatively high efficiency and torque density in spite of lacking permanent magnets. This paper focuses on a two-objective optimization of an external rotor SR motor with a stator that has concentrated coils and a rotor with magnetically isolated modules. The objectives are minimum loss and mass, and 11 independent dimensionless geometric variables are considered as inputs that affect them. A combined design of experiments (DOE) and differential evolution (DE) approach is proposed. The DOE methodology is used to reduce the search space by eliminating from consideration input variable values, leading to poor-performing designs. Following this initial DOE study, an optimization study based on DE is run over the reduced search space, which leads to significant savings in computation time. Furthermore, a directed graph-based method for comparing different designs on the Pareto front to rank the best compromise designs is proposed. [ABSTRACT FROM AUTHOR]

We introduce a district level multiple buildings energy and power simulation framework. At the building level, the proposed simulation framework, SmartBuilds, leverages EnergyPlus as the core simulation engine for building energy assessment, thereby benefiting from the capabilities of a widely accepted and used state-of-the-art modeling tool. Building models may include batteries for energy storage and renewable energy generators: PV arrays and wind turbines. At the district level, one of the main goals is to provide a versatile platform for the simulation and optimization of the interaction between the electric grid and buildings as well as of the interaction between buildings. Because buildings can participate in intelligent social-like activities through which energy tokens/budgets can be exchanged, the tools provide collective optimization opportunities beyond the usual techniques applied at building level. This paper introduces SmartBuilds’ architecture and demonstrates its usefulness in scenarios where multiple buildings are simulated concurrently to assess and seek operating schedules for battery energy storage systems that minimize the overall energy consumption at the district level. [ABSTRACT FROM AUTHOR]

A novel automated design optimization procedure based on the application of an ultrafast computationally efficient finite-element analysis (CE-FEA) for current-regulated synchronous reluctance machines supplied from power electronic converters is proposed. The CE-FEA uses only a minimum number of magnetostatic solutions in order to comprehensively evaluate performance, including ripple torque and core losses. The optimization algorithm is based on differential evolution, and uses as independent variables the torque angle and ratios for a generic rotor topology with four flux barriers. Two problems, one with two and the other with three objectives, are studied and results are compared. Global performance indices and objectives incorporate the effect of average torque output, losses, torque ripple, and power factor at fixed cost. It is shown that through optimal studies with more than 5000 candidate designs, high output power, high efficiency, and low torque ripple can be achieved, while the relatively low power factor remains an inherent limitation of synchronous reluctance technology. Simulations are validated versus tests from a 10-hp 1800-r/min prototype. [ABSTRACT FROM AUTHOR]

This paper describes a systematic method for optimally designing with multiple objectives and differential evolution (DE) algorithms, current regulated electronically controlled synchronous reluctance (SynRel) machines. A large-scale study with thousands of designs calculated with a ultrafast computationally efficient electromagnetic finite-element analysis (CE-FEA) establishes the performance limitations of conventional radially laminated technology with multiple flux barriers. The potential advantages of employing permanent-magnet (PM)-assisted technology with additional ferrites in the rotor are quantified in terms of substantially improved power factor, specific power, and efficiency. Numerical and experimental results for a 10-hp, 1800-r/min typical rating are included. [ABSTRACT FROM PUBLISHER]

A large-scale unitary study for a generic topology of surface-mounted permanent-magnet brushless motors operated from sine-wave current-regulated drives is described. A parametric motor model employing eight independent variables is introduced. A wide range of power ratings, from 1 kW to 1 MW, and a large spread of physical dimensions, with a stator inner (air-gap) diameter as low as 50 mm and as high as 500 mm, were considered. A sensitivity analysis based on the response surface method was performed, and a differential evolution algorithm was applied to find the optimal solutions for different power ratings. A computationally efficient finite-element analysis ultrafast technique was employed in order to calculate the performance of approximately 30 000 candidate machine designs within a reasonable time. The study provides rules for scaling the machine main design parameters with the power rating in order to achieve minimum active material cost per point of efficiency. [ABSTRACT FROM PUBLISHER]

This paper systematically covers the significant developments of the last decade, including surrogate modeling of electrical machines and direct and stochastic search algorithms for both single- and multi-objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing response surface (RS) and differential evolution (DE) algorithms on a permanent-magnet-synchronous-motor design with five independent variables and a strong nonlinear multiobjective Pareto front and on a function with eleven independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of candidate designs and DE performs better when more candidates are considered. [ABSTRACT FROM PUBLISHER]