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This article proposes a two-stage optimization strategy for solving the voltage-var optimization (VVO) problem considering stochastic penetration of plug-in electric vehicles (PEVs) to unbalanced, three-phase power distribution networks (PDN). The optimization strategy considers the prospect of forced active power curtailment at a minimized level, bidirectional PEVs activities. It also includes simple tap operations of shunt capacitor banks, online tap changing transformers, and voltage regulators to achieve optimal economic gain while satisfying the VVO operational constraints. The first stage aims for the optimal decomposition of the PDN into well-defined, cross-checked smaller partitions via a proposed community-based detection particle swarm optimization algorithm. The second stage incorporates a mixed-integer linear programming formulation to solve the VVO problem per partition level. This is done considering the nonlinearity and nonconvexity of the electrical system via applied approximation. This reduces the complexity and computational burdens that usually arise in solving the problem on a larger scale. The proposed two-stage strategy was tested on the modified IEEE 123 bus system with various case scenarios. Economical operation of the PDN is achieved during peak demand hours while maintaining a safe operation within the VVO problem. [ABSTRACT FROM AUTHOR]

The increasing deployment of electric vehicles (EVs) across the United States has introduced many new opportunities and challenges with regards to energy management and control. In smart and connected communities (SCCs), where advanced communication infrastructures are in place, optimal coordination of EVs can significantly impact EV owners, power systems, and charging station owners. This paper develops two scheduling frameworks (static and dynamic) for optimal coordination of a fleet of cooperative EVs in a community with many charging stations and potentially different types of chargers (e.g., level 1, level 2, and DC fast). The scheduling problems are formulated as mixed-integer multi-objective optimization models and then multi-objective solution methods are utilized to find the optimal solution for each of the two scheduling frameworks. Numerical experiments simulated based on the State of Florida verify the usefulness of smart charging for better energy management and satisfying key players’ objectives and constraints. [ABSTRACT FROM AUTHOR]

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the hierarchical distributed model predictive control (HDMPC) to solve the energy management problem in the multitime frame and multilayer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized supervisory management (SM) layer and another short time-horizon during high-power variability for a distributed coordination management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically and experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets. [ABSTRACT FROM AUTHOR]

In this paper, we develop formulation of a multi-objective optimization problem (MOOP) to optimally size a battery unit (BU) ultracapacitor (UC) hybrid energy storage system (HESS) for plug-in electric vehicle (EV). In this application, the objectives were to minimize cost, weight, volume of the HESS simultaneously maximizing the remaining cycle life of the BU at the end of the driving cycle. The MOOP is solved by the non-dominated sorting genetic algorithm type 2 algorithm. Detailed mathematical models for the BU and UC are given. The thermal effect on performance and sizing are also included in the formulation. The power demand by the EV powertrain is shared amongst the BU and HESS by two methods: First is by using wavelet transformation, while the second is by using power split ratio. The ratio of the power (i.e., power split) handled by each storage unit was determined by the optimizer. A sensitivity analysis was conducted for the power splitting ratio verification. The problem was solved for using the urban dynamometer driving schedule and the highway fuel economy test driving profiles. This has resulted in sizing of an HESS with lower cost, volume, and weight than those existing in literature. Finally, the effect of changing the motor type on the MOOP result was investigated. [ABSTRACT FROM AUTHOR]

This paper proposes a multiagent-based energy market for multimicrogrid systems using game-theoretic and hierarchical optimization approaches. The proposed method is tailored to achieve the optimal operation of smart microgrids in distribution systems. Because of rapid load variations in distribution systems, it is necessary to develop fast optimization algorithms which minimize the power mismatch in and among microgrids. In this paper, a three-level market framework is proposed. The first level comprises a game-theoretic double-auction mechanism for the day-ahead market while the next two levels are optimal rescheduling and intermicrogrid reverse auction model for the hour-ahead and real-time markets, respectively. Using the hierarchical optimization algorithm in a multiagent-based area, it is anticipated to not only minimize the optimization solution time, but also reduce the dependency on the network in grid-connected mode or load shedding in islanded mode. Using this approach, load demand response capabilities along with rescheduling of Distributed Energy Storage Systems and distributed generations could be utilized in all market levels, which will lead to optimal operation of multimicrogrid systems. Agents are developed in DIgSILENT PowerFactory and dynamic data exchange is activated for communication among agents communicating through a data distribution service which utilizes the real-time publish-subscribe communication protocol. The developed framework is applied to the modified 37-bus IEEE distribution test feeder system to validate the effectiveness of this market structure. [ABSTRACT FROM AUTHOR]

Adaptive protection techniques used for a microgrid rely on a stable communication link to and from protective devices at the point of common coupling to adjust the settings within these corresponding devices for either a grid-connected or islanded mode of operation. However, during communication outages or in the event of a cyberattack, relays’ settings are not changed. Thus, adaptive protection schemes are rendered unsuccessful. Due to their fast response, supercapacitors, which are present in the microgrid to feed pulsed loads, could also be utilized to enhance the resiliency of adaptive protection schemes against communication outages. Proper sizing of the supercapacitors is therefore important in order to maintain a stable system operation and the cost of the protection scheme. This paper presents a two-level optimization scheme for minimizing the supercapacitor size along with optimizing its controllers’ parameters. The latter will lead to a reduction of the supercapacitor fault current contribution and an increase in that of other ac resources in the microgrid in the extreme case of having a fault occurring simultaneously with a pulsed load. It was also shown that the size of the supercapacitor can be reduced if the pulsed load is temporarily disconnected during the transient fault period. Simulation results showed that the resulting supercapacitor size and the optimized controller parameters were feeding enough fault currents for several types of faults in different locations and minimizing the cost of the protection scheme. Experimental results on a hardware microgrid setup validated the simulation results. [ABSTRACT FROM AUTHOR]

The anticipated increase in electric vehicles (EVs) adoption necessitates the need for electrified transportation infrastructure to charge these vehicles. Although the EV parking garage can represent a good investment opportunity, it brings more challenges to the distribution system operator. Therefore, the allocation and sizing of the parking garage should be carefully planned. The planning process should take into consideration the economic aspects of the investor, as well as the technical aspects of the distribution system. In this paper, a bilayer Pareto multiobjective optimization problem is formulated to optimally allocate and size an EV parking garage. The optimization formulation tries to maximize the profits of the EV parking garage investor, as well as minimize the losses and voltage deviations for the distribution system operator. Dealing with these contradicting objectives simultaneously will results in a set of Pareto solutions. The results showed the different tradeoffs that might be induced while dealing with these contradictory objectives. A decision-making criterion based on statistics was used to decide on the optimal location and size of the parking garage. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location was also performed. [ABSTRACT FROM PUBLISHER]

The proper magnetic design is one of the most difficult and critical phases in developing an inductive power transfer system, especially for electric vehicle charging. In this communication, a fast and an efficient improved Tabu search algorithm coupled with 2-D finite-element analysis (FEA) was used to determine the optimum design parameters of power pads. A two-objective optimization problem was investigated considering the magnetic coupling, misalignment (horizontal, vertical, and rotational), as well as the cost. The double-D power pad structure was considered as a case of study, and the most effective design parameters were obtained. The 2-D FEA was utilized to calculate the coupling factor at different misalignment conditions. For verification purposes, the system coupling performance and cost are analyzed using the optimum design parameters and compared with three other designs presented in the literature. The proposed optimized design shows the best coupling performance with moderate cost. The results proved the validity and advantages of the proposed design methodology. [ABSTRACT FROM AUTHOR]

A kriging-assisted light beam search (LBS) method is proposed to solve multi-objective inverse problems. To reduce the computational burden and increase the convergence speed, a kriging model is introduced into the evolutionary procedure of the LBS method. To guarantee the accuracy of the final Pareto solutions, a dynamic detecting strategy is used in the LBS method. To reflect the preference of a decision maker (DM) in decision making, a boundary control mechanism is proposed to assure that all the obtained Pareto solutions are well-distributed within the preference of the DM. To testify the accuracy of the proposed method, a typical test function, a benchmark inverse problem, TEAM Workshop Problem 22, and a linear antenna array design are solved. The numerical results demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a cloud-based and hybrid wireless mesh communication framework for bilevel, nested, distributed optimization of networked clusters of microgrids. The proposed optimization framework implements a diffusion-based, fully distributed algorithm on local wireless network and a quasi-distributed approach on wide-area internet-based cloud. The lower level of the bilevel optimization implements a distributed optimal economic dispatch solution for intramicrogrid among distributed energy resources, and the upper level implements a global optimal dispatch for intermicrogrid energy exchange. To demonstrate industrial applicability of the proposed framework, the IEC 61850 interoperability protocol is adopted to achieve a certain delay performance so that the distributed optimization convergence is guaranteed. First, hardware-based prototype intelligent electronic devices are developed using embedded systems. Then, the bilevel nested optimization algorithm is implemented for integration of networked microgrids. Finally, experimental results are demonstrated from a real-time smart grid testbed featuring realistic microgrids. The results demonstrate that the proposed framework meets communication requirements for distributed optimization of networked microgrids. [ABSTRACT FROM PUBLISHER]

The smart grid is seen as a power system with real-time communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper, a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service Ethernet network with appropriate quality-of-service profiles. The developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida, and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles. [ABSTRACT FROM PUBLISHER]

This paper proposes a multiagent-based optimal microgrid control scheme using a fully distributed diffusion strategy. A two-level cooperative optimization multiagent system is adapted for distributed energy resources economic dispatch. The lower level implements an adaptive droop scheme based on online no-load frequency adjustments. The upper level implements distributed communication using diffusion between neighboring agents for optimal microgrid management. The proposed control scheme enables peer-to-peer communication among the agents without the necessity of a centralized controller, and simultaneously performs resource optimization while regulating the system frequency. The results are compared with centralized and consensus-based optimization algorithms. We have concluded that the proposed algorithm is superior over consensus algorithms in terms of convergence speed and utilizes reduced communication infrastructure compared to centralized controllers. Simulation demonstrations were conducted along with experimental results from a hardware-based microgrid using an industrial multiagent framework. The simulation and experimental results show that the proposed method and the agent framework can be deployed in real-world microgrids and offer superior decision making on optimal microgrid control. [ABSTRACT FROM PUBLISHER]

A good magnetic design of a wireless electric vehicle (EV) charging system requires accurate characterization of the equivalent circuit parameters. This is while considering the effect of the magnetic core and the conductive shielding layers. In the literature, such an analysis is done using 3-D finite element analysis, which makes the design and optimization process time-consuming and computationally expensive. In this paper, an integrated analytical model for the characterization of a circular wireless power transfer EV pads is utilized. This analytical model provides a valuable tool to optimize the design process. This model is utilized when evaluating the objective function for each individual in the multiobjective Pareto Optimality optimization algorithm. The objective is optimizing the pads with three decision variables and four constraints. [ABSTRACT FROM AUTHOR]

This paper presents an electromagnetic-artificial intelligence design optimization approach of a wireless power transfer link (WPTL). Electromagnetic solutions from a 3-DFE model of the WPTL were utilized to minimize the field signature around each design while maintaining high efficiency. The reduced field signatures around the WPTL would enable the achievement of designs that are in accordance with international electromagnetic compatibility (EMC) standards such as international commission on non-ionizing radiation protection guidelines with respect to public exposure levels of EM signatures below 27 \mu \textT . As the optimization process involved utilizing a large number of fitness function evaluations, numerous 3-DFE solutions were required increasing the computational burden. Artificial neural networks were developed, trained, and used to produce the required equivalent 3-DFE solutions to evaluate the fitness function. As a result, this process significantly reduced the computational time by nearly 90%. The genetic algorithm-based optimization process yielded the desired results of electromagnetically compatible WPTL designs at the early development stage satisfying EMC standards. [ABSTRACT FROM AUTHOR]

A model for electrical machines useful for radiated electromagnetic field studies in a multi-source environment is proposed. The various aspects of electromagnetic signature are considered. This model was created from a representative rectangular prism carrying a set of unbalanced currents in its branches. The geometry and the currents of the equivalent model were calculated based upon a genetic algorithm-based particle swarm optimization process taking into consideration the actual size and the operating conditions of the drive system being studied. The electric field was chosen as the objective function, which is the main element of the optimization. The simulated results show acceptable accuracy and excellent simulation time as compared to the full 3D FE model of the actual machine. Various types of signature studies of the model were conducted. This included stationary and time analysis in addition to the effect of rotation of the machine. For verification, we utilized two machines in a connected system to study and compare the results with their actual model. The results show satisfactory accuracy. The practical implication of this effort is in the fact that, with this equivalent model, we can evaluate radiation and stray effects for EMC evaluation at the design stage. [ABSTRACT FROM AUTHOR]

This paper presents a technique for noise and vibration reduction in low-speed sensorless control algorithm of surface mounted permanent magnet synchronous machines (SM-PMSM). The signal processing involved in high-frequency-based sensorless control algorithms has a significant effect on the vibration and noise accompanying such algorithms. Therefore, a wavelet filtering approach is proposed. This wavelet filter is capable of extracting the position information even at low values of injection voltage amplitude where conventional filters fail to extract the information. This leads to a reduction in the machine's noise and vibration. Furthermore, an optimization using hardware-in-the-loop method based on genetic algorithms was implemented in real time to obtain the optimum value of the injected voltages amplitude and frequency causing the least possible vibration with acceptable position estimation accuracy. Consequently, a significant reduction in vibration and noise was achieved. The proposed techniques were implemented on a 2-hp SM-PMSM motor both experimentally and through simulations. Comparisons between numerical and test results confirm the validity of the proposed techniques. [ABSTRACT FROM PUBLISHER]