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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]

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]

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]

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]