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Abstract: This paper proposes a method based on quadratic programming (QP) and augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem with piecewise quadratic cost functions and prohibited zones. The ALHN method is a continuous Hopfield neural network with its energy function based on augmented Lagrange function which can properly deal with constrained optimization problems. In the proposed method, the QP method is firstly used to determine the fuel cost curve for each unit and initialize for the ALHN method, then a heuristic search is used for repairing prohibited zone violations, and the ALHN method is finally applied for solving the problem if any violations found. The proposed method has been tested on different systems and the obtained results are compared to those from many other methods in the literature. The result comparison has indicated that the proposed method has obtained better solution quality than many other methods. Therefore, the proposed QP-ALHN method could be a favorable method for solving the ED problem with piecewise quadratic cost functions and prohibited zones. [Copyright &y& Elsevier]

The spectrum-estimation methods based on the Fourier transform suffer from the major problem of resolution. The methods were developed and are mostly applied for periodic signals under the assumption that only harmonics are present and the periodicity intervals are fixed, while periodicity intervals in the presence of interharmonics are variable and very long. A novel approach to harmonic and interharmonic analysis based on the "subspace" methods is proposed. Mm-norm and music harmonic retrieval methods are examples of high-resolution eigenstructure-based methods. Their resolution is theoretically independent of the signal-to-noise ratio (SNR). The Prony method as applied for parameter estimation of signal components was also tested in the paper. Both the high-resolution methods do not show the disadvantages of the traditional tools and allow exact estimation of the interharmonic frequencies. To investigate the methods, several experiments were carried out using simulated signals, current waveforms at the output of an industrial frequency converter, and current waveforms during out-of-step operation of a synchronous generator. For comparison, similar experiments were repeated using the fast Fourier transform (FFT). The comparison proved the superiority of the new methods. [ABSTRACT FROM AUTHOR]

The operation of a resonant-grounded network during an earth-fault condition depends on the three basic parameters: damping, detuning, and unbalance factor. These parameters are influenced by the environmental conditions (e.g. humidity, temperature, and pollution), and the network topology. Accurate values of these parameters during an earth-fault condition are required to examine the operation of the compensation system. The fault records could be used for that purpose. The recorded neutral-to-ground voltage signals have been paraineterized (using damping and detuning as parameters) according to the mathematical model of the transient process. Iteratively reweighted least squares algorithm has been used to fit the model. This algorithm is the major improvement over the classical least squares approach. It is able to filter out noise more efficiently. As a direct result, very accurate parameter identification has been achieved. This paper concludes with the practical examples. [ABSTRACT FROM AUTHOR]

Across the world, the impact of increasing electric vehicle (EV) adoption requires a better understanding. The authors hypothesize that the introduction of EV's will cause significant overloading within low voltage distribution grids. To study this, several low voltage networks were reconstructed based on the literature and modelled using DigSilent Powerfactory, taking into account the stochastic variability of household electricity consumption, EV usage, and solar irradiance. The study incorporates two distinct usage scenarios—residential loads with varying EV penetrations without and with distributed grid tied generation of electricity. The Monte-Carlo simulation took into account population demographics and showed that in urban networks, EV introduction could lead to higher cable loading percentages than allowed, and in rural networks, this could lead to voltage drops beyond the allowed limits. Distributed generation (DG) in the form of solar power could significantly offset both these overloading characteristics, as well as the active and reactive power demands of the network, by between 10–50%, depending on the topology of the network. [ABSTRACT FROM AUTHOR]

This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current. [ABSTRACT FROM AUTHOR]

• Pseudo-measurements are necessary for LV state estimation to obtain observability. • Generating and validating pseudo-measurements in LV grids is not straightforward. • Affine arithmetic is suitable to keep track of uncertainty during calculus. • Interval contraction obtains tight uncertainty intervals of unmeasured node power. • Uncertainty intervals are suitable to generate and validate pseudo-measurements. Pseudo-measurements are usually applied as substitutes for measurement data to obtain observability even in case of sparse measurement configurations. The quality of pseudo-measurements strongly affects the accuracy of state estimation results. Generating pseudo-measurements is not a straightforward task in particular for customers with highly stochastic behavior. This paper proposes a novel approach to obtain verified boundaries of pseudo-measurements, which are consistent to currently available measurement information. These boundaries allow to verify generated pseudo-measurements, whether they are compliant to current system conditions. Additionally, tight boundaries are suitable to generate pseudo-measurements on-line regardless of the customer's structure. The proposed algorithm applies Affine Arithmetic - a self-validated numerical model - to the state estimation problem and treats pseudo-measurements as main source of uncertainty. The key properties of the algorithm are rigorous, iterative, linearizing and large-scale. These properties are demonstrated by numerical simulation of two benchmark grid models of different complexity. Computational and implementation issues are addressed in detail. [ABSTRACT FROM AUTHOR]

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network. [ABSTRACT FROM AUTHOR]

This article presents the results of a novel concept of heat and cold decoupling from magnetocaloric materials (MCM) by unidirectional fluid flow. An experimental setup was developed to analyse the heat/cold decoupling and determine the optimal process parameters. Three different kinds of magnetocaloric material samples were analysed by using this setup. Comparing the measuring results to those of existing active magnetic regenerator (AMR) systems shows a very good performance and indicates a higher achievable thermal power output when using a unidirectional fluid flow. [ABSTRACT FROM AUTHOR]

• Measurement-based filter circuit identification for single-phase inverters. • Black-box modelling of filter circuit for realistic harmonic studies. • Use of frequency coupling for parameter identification. • General applicability on single-phase power electronic devices. • Impact of manufacturing tolerances of circuit elements on modelling uncertainty. This paper presents an identification methodology for filter circuits of commercially available power electronic devices based on laboratory measurements. The filter circuit is one of the main components with significant impact on the harmonic behaviour, in particular for modern power electronic devices. The generic approach does not require detailed knowledge about the circuit layout and is applicable for all single-phase power electronic devices where a voltage measurement on both sides of the filter circuit is possible. Current measurements on the circuit board, which are usually not practicable, are not required. The model parameters of the filter circuit are represented by two admittances for each considered frequency. The parameter identification is based on the analysis of the frequency couplings of power electronic devices. The proposed method is validated by simulations and exemplarily applied for a commercially available photovoltaic (PV) inverter in the laboratory. The measurement-based parameter identification can also provide more reliable results compared to the white-box simulation of a particular device, as the partly large manufacturing tolerances of the elements are inherently considered in the measurements. [ABSTRACT FROM AUTHOR]

With an increasing number of power converters using fast switching technologies, more cases of interference between devices in the frequency range from 2 to 150 kHz occur. The most widely used technique in such devices is pulse- width modulation (PWM) [e.g. in photovoltaic inverters (PVIs), active harmonic filters, battery storage systems and some types of electric vehicle chargers]. Only little is known about the behaviour of those sources, the factors influencing their emission and the interaction with other devices and the propagation in the network. To extend such knowledge, reliable and accurate models are required. In this study, a quantitative model for the prediction of voltages and currents in this frequency range is developed and verified. Since in most cases no information about internal circuits and controls of the devices is available, a measurement-based black-box approach is used. The emission around the switching frequency of single-phase voltage-source power converters is combined into a single emission source represented by a Thévenin equivalent. The general method to parameterise the model is presented and exemplarily applied for two PVIs and one battery-electric vehicle charger. Moreover, the interaction of two devices at a joint point of connection is simulated and compared with real measurements. [ABSTRACT FROM AUTHOR]

Current electricity pricing models take into account costs of generation, transmission, emissions and a fixed cost of distribution based on time of day and type of primary source. However, the authors postulate that increasing fast charging for EVs will lead to increased overloading and voltage drops observed in networks at the low voltage level. To this end, the authors propose a novel method that encourages reactive power injection to minimize voltage drops and active power injection to minimize cable loading by using a centralized, double-sided optimized algorithm. This model allows for peer-to-peer power delivery, incentivized using pricing factors, encouraging local optimization-based solutions to network overloading as well as increased islanding in the network as a means of improving grid resilience and allowing optimal EV charging and discharging. Incentivized peer-to-peer transactions are then recorded in a centralized blockchain, with instantaneous power serving as the transactional commodity for both fixed consumers and generators (houses) and mobile consumers and generators (EVs). The mathematical model, as well as the impact of such a decentralized network compared to a business-as-usual network shows the potential of decongestion of 5–10% in terms of a reduction in voltage drops and cable loading with even a low EV penetration level (<5% of available transportation) by using existing resources as V2G and voltage stability resources. • Optimized EV charging and discharging algorithm using P2P power transactions. • Instantaneous power serving as the transactional commodity. • Incorporates fixed and mobile consumers and generators. • Decongestion of 5–10% in terms of a reduction in voltage drops and cable loading. • Novel method of reducing network congestion using DERs (solar, storage and EVs). [ABSTRACT FROM AUTHOR]