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To improve the torque performance limited by the low carrier ratio operation in motor drive system, a master–slave motor drive system based on dual stator winding structure is researched in this article. A high-frequency harmonic current is injected through the auxiliary windings to compensate the torque ripple and dynamic issue caused by the low carrier ratio. The basic operation principle in master–slave motor drive system is presented and then the turn ratio, mutual inductance, and carrier ratio in both windings are studied to achieve the optimization for the torque performance and power loss. The control method aiming at depression on toque ripple and load disturbance is proposed and gets merged with the vector control strategy. The loss analysis considering the injected high-frequency current is presented to compare with the three-phase system in high carrier ratio operation. It is found that a 30% total loss reduction can be achieved under the same torque ripple. Both simulation and experiments show the effectiveness of proposed master–slave windings system and the control strategy. [ABSTRACT FROM AUTHOR]

Previous robust transmission expansion planning (RTEP) studies have rarely considered the important question of how large the uncertainty set should be, and hence, how the uncertainty budget can be determined objectively. This study addresses this issue by proposing a novel adaptive optimization method that optimizes the value of the uncertainty budget to minimize the size of the uncertainty set while considering the underlying risk for wind power generation (WPG) fluctuations residing outside of the proposed uncertainty set. As such, the proposed method ensures a good tradeoff between the robustness and costs of RTEP solutions. In addition, the proposed method optimizes investment strategies under forecasted WPG scenarios, while providing a security guarantee under extreme WPG scenarios. The variable-limit integral terms introduced by the proposed method are addressed in the solution process by applying the piecewise linearization approximation method combined with the quadratic Newton-Gregory interpolating polynomial technique, which allows the solution process to be cast as a mixed integer linear programming problem. The good performance and effectiveness of the proposed adaptive uncertainty set optimization method are verified by numerical results. [ABSTRACT FROM AUTHOR]

Energy storage units (such as battery or ultracapacitor) can help to reduce the power fluctuation in the impedance source network converter applications such as the photovoltaic (PV) and fuel cells systems. Due to the unknown dynamic characteristics of the energy stored impedance network, the unexpected oscillation and inrush currents in dynamic process might threaten the storage device safety. In this article, an energy stored quasi-Z-source converter with the battery is modeled and analyzed including the transient capacitor current. The impact of the network internal resonance on the battery current control loop is mathematically explored. The resulted conflict between the zero steady-state error and bandwidth requirement is revealed. That yields the sacrifice and limitation of the closed-loop dynamic performance in the conventional methods. An active damping technique is then proposed to ensure the extended bandwidth and better dynamic performances under different state of charge (SOC) occasions. The theoretical analysis and performance are both verified by the simulations and experiments. [ABSTRACT FROM AUTHOR]

Devices and the passive components in the single-phase impedance source network converter are sensitive to the double-line-frequency (DLF) pulsation. Previous research works have studied the power decoupling and the DLF equivalent model for the power pulsation suppression method. This paper explored the DLF transfer routes in the impedance source network. A comprehensive stress derivation of the passive components and switching devices are obtained for the suppression method enhancement and optimization. For the DLF passive suppression method, the explanation for the passive component volume issue is provided from the stress aspect. The component value solution space and corresponding design guidance are presented. For the DLF active suppression method (ASM), a buck-type dc-side ASM suppression method is employed and its optimization is demonstrated with the assistant circuit stress analysis. The total capacitor and inductor values get reduced by 77% and 25%, respectively. The corresponding pulsewidth modulation method implementation and the common-mode leakage current demonstration are also presented. The effectiveness of the theoretical analysis and optimization methods is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

Converters with impedance source networks combine their one-stage energy conversion with the function as buck and boost converters. Due to this fact, they are very attractive to the dc–ac applications. Many literatures have focused on the stress analysis and control enhancement of the impedance network in the dc side. For a better system design and control, the synthesis and interaction principle between the ac and dc side of the converters is required. This paper first presents small-signal modeling for the bidirectional quasi-Z-source converter by the circuit-averaging technique. Compared with previous modeling based on the shoot-through duty ratio, the modulation ratio is included for the ac-side analysis. It is revealed that the oscillation in the modulation ratio could be triggered by the ac current controller, due to the magnitude margin issue caused by the impedance network, and then instability in the impedance network can be observed, including the system input current. Based on the theoretical analysis, a design guideline for the ac current controller is provided. Experimental validation for both the model and analysis is demonstrated and the results are in good agreement with the theoretical analysis. [ABSTRACT FROM AUTHOR]

To improve the performance and integration of the power train of electric vehicles power, a dual three-phase permanent magnet synchronous machine (PMSM) drive is investigated to achieve hybrid energy storage system power management. Two sets of motor windings are connected with ultracapacitors and a battery, respectively, through the inverters. First, the operation modes of the integrated system are studied by modeling and vector diagram analysis. A new control scheme is then established to enhance the regulation for unbalanced winding power distribution. The approach to suppress the motor harmonics current under the unbalanced power operation is also discussed. Finally, a frequency dividing coordinated control is performed for good dynamic response and seamless transient process. Both simulation and experiment results verify the effectiveness of the proposed system and the control strategy. [ABSTRACT FROM PUBLISHER]

For maximizing the power transfer in an inductive power transfer system, the operating frequency of the system is normally designed to be the same as the resonant frequency of the secondary power pick-up. But the load deviation reflected from secondary side to primary side will result in the frequency mismatch issue. Therefore, the contactless load detection technique is significant to ensure the power transfer efficiency and capacity. The emerging technique taking advantages of the amplitude decay rate in a transient process is promising. The robustness and practicability can get enhanced and the complicated steady circuit model is avoided. However, the accuracy for the small resistance application is still a challenge even with sophisticated compensation algorithms. This paper classified the system detectable region through the root locus method. The transient process in different subregions is excavated mathematically. It is revealed that the variation of the system from the second order to fourth order is the key issue for the detection error in the small load resistance application. The analysis brings out a novel automatic contactless load detection method to extend the detection range. Both simulation and experimental results verified the effectiveness of the proposed theory and detection method. [ABSTRACT FROM AUTHOR]

The impedance-source network converter, utilizing a unique LC network and previously forbidden shoot-through states, provides the ability to buck and boost the input voltage in a single stage. However, the inrush shoot-through current (STC) in startup or transient process might cause undesired current stresses on converter devices. This paper focuses on the STC sensing for an effective inrush current limitation by the combined current sensing technique in the quasi-Z-source inverter. STC and phase currents for the inverter control strategy are obtained simultaneously. No extra hardware is needed and the effects of current sensor bandwidth and duty cycle on the sensing accuracy are analyzed mathematically. The voltage spike gets avoided by integrating the stray inductance into the impedance network. Finally, an STC control loop based on proposed method are embedded in the field-oriented control strategy. The inrush STC and the device current stress in the transient process get suppressed. Simulation and experimental results from a quasi-Z-source inverter validate the feasibility of the proposed methods. [ABSTRACT FROM PUBLISHER]

An ultracapacitor–battery hybrid energy storage system (HESS) for an electric vehicle (EV) based on a bidirectional quasi-Z-source inverter (qZSI) is proposed in this paper. The buck/boost characteristic in the qZSI enables the HESS incorporated into the traction inverter system. The battery converter can be eliminated and the rated voltages for the battery package and UCs are reduced. The steady power distribution principle is explained under various operation modes. An enhanced frequency dividing coordinated control strategy is established to optimize the dynamic power regulation and the battery current stress in short timescale. Both the simulation and experimental results in time and frequency domain validate the performance of the proposed HESS. [ABSTRACT FROM PUBLISHER]

This paper proposes an ultracapacitor (UC)-battery hybrid energy storage system (HESS) for electric vehicle based on asymmetric bidirectional Z-source topology. Compared with the conventional two-stage design, the HESS can be incorporated into the traction inverter system, leading to better performance and lower cost. The UC energy can be effectively utilized due to the buck/boost characteristic in the Z-source converter; meanwhile, the battery converter gets eliminated in this case. The assumption about the symmetry in the Z-source topology impendence network states for the conventional analysis no longer applies to the proposed HESS configuration. The asymmetric characteristic related with the uneven power distribution of UCs and battery is mathematically excavated in detail. The frequency dividing coordinated control is proposed to exploit the advantages of UCs and battery. The battery peak current estimation is then investigated. Finally, the steady performance and transient response in both traction and regenerative modes are verified by simulation and experimental results. [ABSTRACT FROM PUBLISHER]

The magnetic permeability of three kinds of inclusions (Carbonyl iron, Ferroferric oxide, NiFe-alloy) filled polymer (Polydimethylsiloxane) is investigated at room temperature by using a Buck circuit for the dynamic test and vibrating sample magnetometer (VSM) for the static test. Unlike the traditional measurement method using an impedance analyzer, the self-built circuit can generate a maximum test current of ampere amplitude over high frequencies to simulate the transformer's operation status. The present work considers the effects of inclusion content (from 0 VOL.% to 33 VOL.%) and testing frequencies(100 kHz-1MHz). The experimental results of permeability are compared with the theoretical predictions in the previous works. Three main conclusions can be drawn: First, the magnetic permeability increases with the inclusion addition; Second, as the testing frequency increases, the permeability of the composite material maintains constant, showing no evident dependence on the testing frequency; Third, type of magnetic inclusion will gently affect the permeability of the composite material. This work systematically investigated the permeability of polymer-based magnetic materials, and the results are helpful for the soft-core transformer application of magnetoactive elastomers. • Permeability of magnetic inclusions filled polymer exhibits neglectable variation to the test frequencies (100 kHz-1MHz). • CIP filled polymer is a potential composite material to be used for soft-core transformer applications. • Skin effect must be taken into consideration in high frequency applications. [ABSTRACT FROM AUTHOR]

In this paper, the interaction between the energy storage (ES) power distribution and system efficiency enhancement is researched based on the energy stored quasi-Z-source inverter. The corresponding current counteraction, stress reduction, power loss profile, and efficiency enhancement around the embedded energy storage units are studied in details. Firstly, the current counteraction effect on the device current is presented with the embedded ES source. The corresponding reduction in the device current stress is revealed. Then, the detailed device power loss expressions with current redistribution in the impedance network are explored mathematically. A quasi-inverted-trapezoidal power loss profile is found with the embedded source power distribution. To further increase the overall system efficiency, an optimal energy efficiency tracking strategy is proposed for the ES-qZSI (energy-stored quasi-Z-source inverter) based on the power distribution control. Both the simulation and the experiment verified that the power loss is reduced by over 40% through the proposed efficiency enhancement method. The device current and loss analysis for the embedding of energy storage can also be extended to the operating range optimization in other ES systems. [ABSTRACT FROM AUTHOR]

In this paper, the interaction between the energy storage (ES) power distribution and system efficiency enhancement is researched based on the energy stored quasi-Z-source inverter. The corresponding current counteraction, stress reduction, power loss profile, and efficiency enhancement around the embedded energy storage units are studied in details. Firstly, the current counteraction effect on the device current is presented with the embedded ES source. The corresponding reduction in the device current stress is revealed. Then, the detailed device power loss expressions with current redistribution in the impedance network are explored mathematically. A quasi-inverted-trapezoidal power loss profile is found with the embedded source power distribution. To further increase the overall system efficiency, an optimal energy efficiency tracking strategy is proposed for the ES-qZSI (energy-stored quasi-Z-source inverter) based on the power distribution control. Both the simulation and the experiment verified that the power loss is reduced by over 40% through the proposed efficiency enhancement method. The device current and loss analysis for the embedding of energy storage can also be extended to the operating range optimization in other ES systems. [ABSTRACT FROM AUTHOR]

Recent trends in the increasing penetration of renewable energy generation and the increasingly massive integration of electric vehicles (EVs) into multiple-microgrid (MMG) systems pose tremendous challenges to the effective energy management of these systems. However, the solutions of current energy management methods have the disadvantage of excessively limited robustness against uncertain fluctuations in renewable energy generation, and existing hierarchical EV scheduling methods are solved via an iterative process that is prohibitively time-consuming for real-time practical applications. This work addresses the abovementioned issues by constructing an improved data-driven polyhedral uncertainty set based on modified self-organizing feature map neural networks that perform data clustering. In addition, an iteration-free hierarchical framework is proposed for conducting the tri-layer energy management of MMGs with the massive integration of EVs. Specifically, the global positive power factor of EV aggregation is proposed to formulate a set of constraints into the upper-layer model of the tri-layer framework, which can guarantee the feasibility of the middle-and-lower layer models. The value of this factor is determined using a multi-objective optimization algorithm that makes a trade-off between the feasible range of the output of EV aggregators and the degree to which the trip demands of EVs are violated. The superiority and effectiveness of the proposed iteration-free robust hierarchical energy management method for MMGs are verified via simulations involving a practical MMG under high penetrations of renewable energy generation and massive integration of EVs. • Propose the iteration-free hierarchical scheduling method for addressing tri-layer energy management problems for MMGs with massive EVs. • The global positive power factor (GPPF) defined in the upper layer model can ensure the solution of the lower layer model is always feasible without iteration. • The GPPF value enables MG operators to flexibly select a tradeoff for the GPPF based on their operational experiences. • The novel time-series-enabled data-driven polyhedral uncertainty set can produce solutions with better robustness against uncertainties. [ABSTRACT FROM AUTHOR]

• An exergy analysis model for integrated heat and electricity energy systems. • A joint solution framework for optimization and evaluation based on exergy. • Sequential solution for reducing the complexity of the joint solution problem. • Stringent cutting planes and piecewise linearization for solving and processing. • Exergy-efficient operating mode and key operational information based on exergy. Exergy analysis provides a unified scale for evaluating the quality and quantity of energy, and is suitable and essential for scientifically and rationally guiding the optimization and evaluation of integrated energy systems with intricate multi-component configurations. The present work proposes an exergy analysis model and joint solution framework for integrated energy systems based on exergy, and presents an efficient and accurate solution method. The exergy analysis model is analogous to the energy-based model and possesses a succinct form. This model is then applied to establish an input exergy model defining the exergy of inputs, a destroyed exergy model defining the exergy lost due to the conversions, transmissions, and storages, and a benefit exergy model defining the remaining useful exergy for the loads. The joint solution framework optimizes the system from the basis of exergy conservation, and evaluates the destroyed exergy of each link and the prices of the various exergy products from an economic perspective based on the established exergy analysis model. The complexity of the framework is then reduced by establishing a sequential optimization and evaluation model. The optimization model is solved accurately and efficiently using a stringent cutting plane method to address the problem of relaxation inaccuracy, and piecewise linearization to accommodate the non-convex constraints. Numerical computations on a 97-node system demonstrate the accuracy of the proposed models in exergy-based optimization, destruction, and price evaluation, preventing substantial deviations (e.g., a 23% discrepancy in total destroyed exergy). The adopted cost allocation method based on energy quality reflects fair pricing, rectifying conventional pricing disparities (e.g., a 47.56% underestimation of the electric load using energy-based pricing). Therefore, the accuracy and efficiency of the developed exergy analysis model, solution framework, and solution method are validated. [ABSTRACT FROM AUTHOR]

A correction is presented for the article "Synthesis and Design of the AC Current Controller and Impedance Network for the Quasi-Z-Source Converter," by Zipeng Liang, Sideng Hu, Huan Yang, and Xiangning He.

The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders’ decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver’s 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

High penetrations of renewable energy sources incorporated into transmission systems pose a substantial challenge to transmission expansion planning operations due to both the strong uncertainty of these sources and their high variability. However, most current robust transmission expansion planning studies focus exclusively on the issue of uncertainty, and neglect to optimize system flexibility in an effort to cope with the high variability of these sources. The present work addresses this critical issue by proposing a flexibility-oriented robust transmission expansion planning method developed according to the unit commitment characteristics of coal-fired and gas-fired generation units that fully considers the short-term flexibility requirements of electric power systems while maintaining high robustness to the uncertainties of RES outputs in long-term transmission expansion planning problems. The proposed complex model with unit commitment constraints containing massive binary variables is solved by first reducing the number of binary variables via the application of clustering techniques to identify generator units with similar generation properties, and then decreasing the complexity of the model further by relaxing the integer variables in the unit-clustered model. Finally, we develop generalized column-and-constraint generation algorithms that can solve the clustered model and the relaxed simplified model with greatly enhanced efficiency. Comparisons of the numerical results obtained by the proposed approach with existing state-of-the-art methods when applied to a simple Garver 6-node system and a realistically-sized power system demonstrate that the proposed method produces optimal RTEP solutions that account for both the uncertainty and variability of RES outputs. Moreover, the total cost of the proposed approach is 2.22 × 1 0 6 $ less than that of the other state-of-the-art methods considered, which is of great significance in guiding practical transmission expansion planning applications. • Neglecting flexibility of units in robust transmission planning models can result in significant errors. • Existing algorithms fail to solve the flexibility-oriented planning model for large-scale systems. • Transmission planning schemes are significantly influenced by flexibility-related constraints. • The proposed generalized algorithm expedites the solution process of the model. • The relaxation technique facilitates the applicability of the flexibility-oriented model to large-scale systems. [ABSTRACT FROM AUTHOR]

This paper proposes a torque distributed control strategy for the multiphase machines in the hybrid energy storage system application. Different with the conventional energy current regulation method, the system energy management is researched by the machine torque distribution in different sets of windings. The load power is then afforded by the energy storage devices connected with the windings through the inverters. The principle of the distributed torque operation is explored with a dual three-phase permanent-magnet synchronous machine model and the asymmetrical windings operation is explained with the flux vector diagram. The windings decoupling compensation, flux observer, and parameter identification methods are presented. Based on the proposed strategy, a power frequency dividing coordinated regulation is achieved. The hybrid energy storage devices can directly respond to the average or instantaneous parts in the load torque and power fluctuation. Simulation and experiments are demonstrated with different operation cases. [ABSTRACT FROM AUTHOR]

Renewable energy sources provide an important means of reducing reliance on conventional fuels. However, some renewable energy resources such as wind and solar energies are intermittent, and their uncertainty threatens the operating security of the power grid. To solve this problem, this study proposes the use of intervals to model the power output of renewable energy resources and the power load demand, and accordingly develops an interval voltage control model, i.e. interval reactive power optimisation model. The proposed model considers the control modes of renewable energy power generators and can safeguard the security of power grids by ensuring that the voltages reside within established limits. An adaptive genetic algorithm is employed to solve the proposed model, where a newly developed interval power‐flow (IPF) calculation is used to solve the IPF equations, and penalty functions are applied to express inequality constraints. The proposed method is introduced in detail, and simulation results are presented to demonstrate its performance in comparison with a previously proposed interval voltage control method, as well as its applicability to large systems with various fluctuations of input data. The proposed approach provides robust convergence, obtains lower system power losses, and substantially reduces the computation time. [ABSTRACT FROM AUTHOR]

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the ADN operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems. • A novel characterization framework for the IFR with learning and updating modules. • A new loss function in the learning module from the physical information of the IFR. • A strategy in the updating module for the improved characterization of the IFR. • A better tradeoff between security and conservatism of the IFR characterization. • Significant potential in violation reduction under incorrect justifications. [ABSTRACT FROM AUTHOR]

To address the issue that Lagrangian dual-function-based algorithms cannot guarantee convergence and global optimality for decentralised multi-area security constrained unit commitment (M-SCUC) problems, a novel decomposition and coordination method using mixed-integer linear programming (MILP) value functions is proposed. In the proposed solution method, first, each regional system operator sets the tie-line power injections as variational parameters in its regional SCUC model, and utilises a finite algorithm to generate an MILP value function, which returns the optimal generation cost for any given interchange plan. Then, with the value functions available from all system operators, theoretically, a coordinator is able to devise a globally optimal interchange plan. After the problem is solved, considering that power exchanges may alter the financial position of each area considerably from what it would have been via scheduling independently, the authors then propose a fair savings allocation method using the value functions derived above and the Shapley value in cooperative game theory. Numerical experiments on a two-area 12-bus system and a three-area 457-bus system were carried out. The validity of the value-function-based method was verified for the decentralised M-SCUC problem. The outcome of savings allocation was compared with that of the locational marginal cost-based method. [ABSTRACT FROM AUTHOR]

To address the issue that Lagrangian dual‐function‐based algorithms cannot guarantee convergence and global optimality for decentralised multi‐area security constrained unit commitment (M‐SCUC) problems, a novel decomposition and coordination method using mixed‐integer linear programming (MILP) value functions is proposed. In the proposed solution method, first, each regional system operator sets the tie‐line power injections as variational parameters in its regional SCUC model, and utilises a finite algorithm to generate an MILP value function, which returns the optimal generation cost for any given interchange plan. Then, with the value functions available from all system operators, theoretically, a coordinator is able to devise a globally optimal interchange plan. After the problem is solved, considering that power exchanges may alter the financial position of each area considerably from what it would have been via scheduling independently, the authors then propose a fair savings allocation method using the value functions derived above and the Shapley value in cooperative game theory. Numerical experiments on a two‐area 12‐bus system and a three‐area 457‐bus system were carried out. The validity of the value‐function‐based method was verified for the decentralised M‐SCUC problem. The outcome of savings allocation was compared with that of the locational marginal cost‐based method. [ABSTRACT FROM AUTHOR]

This paper focuses on the interchange and generation scheduling problem of multi-area power systems, where decentralized decision procedure is preferred and the uncertainties of nodal injections should be considered. The problem is formulated as a multi-area robust security constrained unit commitment model, with a novel uncertainty set specified by the variance of system net load. We prove that such an uncertainty set can provide a more precise probabilistic guarantee than conventional budgeted sets. To solve the problem in decentralized manner, an upper-level robust convex optimization model is proposed to optimize the tie-line power flow and derive a generation interval for each area. Then through the coordinated uncertainty requirement decided by the generation interval, each regional system operator can solve a decoupled robust unit commitment problem independently. Besides, a modified outer approximation algorithm is developed to obtain higher-quality solution of the bilinear programming involved in the process. Simulation results on a two-area system demonstrate the effectiveness of the uncertainty set, and show the economic efficiencies of unit commitment solutions acquired from the hierarchical method. [ABSTRACT FROM AUTHOR]

The reactive power optimisation (RPO) is generally used to design an optimal profile of the voltage and reactive power of power systems in the steady state for deterministic sets of demand loads and generation values, and it is a significant procedure in voltage control. However, the input data of a power system is actually uncertain in practise, which makes RPO an uncertain non‐linear programme. To address this problem, the input data were considered as intervals, and the RPO incorporating interval uncertainties model was proposed. To solve this model, the genetic algorithm was employed as the solution algorithm, where the reliable power flow calculation was used to judge the constraints of the model. The Pareto front was established as the solution of the proposed model since it has two objective functions, i.e. the midpoint and radius of real power losses. The application of this technique to the uncertain RPO was explained in detail. Two numerical results were analysed to demonstrate the effectiveness of the proposed method, especially in comparison with the previously proposed chance‐constrained programming method. [ABSTRACT FROM AUTHOR]