Virtual load damping is a valid alternative to virtual inertia to ensure frequency support in low-inertia power systems. We show that virtual load damping can be provided by multiple small power/capacity inverter-based resources. This can be obtained by exploiting inverter-based resources installed at domestic utilities in distribution feeders, which integrate renewable energy sources. Assessing the impact of these units on frequency requires changing the paradigm used to study power grid stability, since feeders need to be simulated in detail and cannot be represented with equivalent load models anymore, as was done in the past. Time-domain analyses of modified versions of the IEEE 14-BUS and MINIWECC power system benchmarks, obtained by the addition of several feeders in different virtual load damping scenarios, are included to support our claims.

A challenge posed by the increasing penetration of inverter-based resources (IBRs) in distribution networks is how to efficiently simulate a transmission system with a large number of them, since this leads to an "explosion" in system complexity. This paper presents a two-step reduction method for IBR-dominated distribution networks. In the first step, the approach synthesizes a distribution network as a multi-port: one port at the substation connecting it to the transmission system and the others for each IBR connected to the feeder. Then, in the second step, IBRs at the connecting ports are possibly clustered, thus reducing their number. The method differs from others in the literature since it is numerical and can handle clusters of IBRs with different power ratings and set-points. Simulation results of a distribution network with IBRs show the effectiveness of the method.

Some (very) large circuits have the peculiar feature of being composed of numerous repetitions of sub-circuits identical in terms of topology and components. If a traditional simulation paradigm is adopted, such a structure poses a high computational burden. In this paper we describe a general approach based on isomorphism that greatly improves simulation efficiency by exploiting the repetitive structure of these sub-circuits. After describing the core aspects of the approach, we show its potentiality by simulating a battery energy storage system (BESS) at battery cell level in different conditions.

An adequate level of power system inertia and load damping is essential to ensure frequency stability following power imbalances. Prior to that, however, it is first necessary to be capable of estimating these parameters. In this paper, we propose an original method to estimate the global inertia and damping of a power system comprising conventional synchronous generators, as well as modern generation units based on grid forming and following converters, which can provide virtual inertia and load damping. The effectiveness of our approach is tested on a modified version of the IEEE39 power system with ambient noise.

Stability analysis of conventional power systems relies on established modelling practices and tools. Among them, a popular approach exploits the impedances at given buses computed after deriving the power flow solution of the grid typically modelled in the dq-frame. However, in modern grids, this approach is hindered by the presence of elements formulated in the abc-frame, such as inverter-based resources, which make the grid a hybrid system. This paper proposes a novel efficient and versatile tool, directly implemented at simulator level, that allows computing impedances even in these cases.

Modular multilevel converters, used in AC/DC power systems, are characterized by complex (trans-)impedances with a frequency dependence never seen before in electro-mechanical power devices. Since these impedances may severely influence the stability of the entire power system, adequate design actions are needed to ensure safety margins. A straightforward derivation of these complex (trans-)impedances requires making suitable numerical tools available to designers. In this paper, we compute these impedances with the periodic small-signal analysis, a novel numerical approach in the power system realm. After commenting on some impedances computed with this approach, we exploit them to understand how a modular multilevel converter can impact the stability of the AC or DC grids connected to it. Stability issues highlighted through periodic small-signal analysis are verified by detailed transient stability simulations.

Load forecasting plays a crucial role in the day-to-day operations of electric utilities, especially in modern power systems, where a significant share of power generation is attributable to renewable sources. Over the years, several algorithms have been developed to tackle this problem, on time scales ranging from a few hours to several months. Most recent solutions have employed machine learning techniques such as deep learning to increase the granularity of the prediction, down to the single-building level. Here, we employ a framework based on long short-term memory networks to estimate the average power consumption of a single building equipped with solar panels. We show which measurements are more important for an accurate forecast and test several prediction horizons in order to find the best trade-off between training speed and prediction accuracy. Our results reinforce the notion that long short-term memory networks can be successfully used for short-to medium-term load forecasting.

The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm to build two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. We evaluated the performance of these models and find that our approach quantitatively matches the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability in the data. Moreover, we confirm that the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. We then use these models to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input through an information-theoretic treatment of their respective transfer functions. Our simulations identify a host of cell type-specific biophysical mechanisms that define the morpho-functional phenotype to shape the cellular transfer function and place these findings in the context of a role for bursting in CA3 recurrent network synchronization dynamics.

Instability and oscillation issues originating in high-voltage direct current (HVDC) systems comprising modular multilevel converters (MMCs) are gaining increasing interest in the research community. To detect such phenomena in advance, considerable effort has been devoted recently to developing MMC models for small-signal analysis. The derivation of such models is a challenging task due to the topology and complex control structure of MMCs, which results in them having a multi-frequency response. To address this issue, scholars developed methods based on dynamic phasors and harmonic state-space modelling, which, however, require extensive pen-and-paper computations. In this paper, the periodic small-signal analysis (PAC) is adopted to determine numerically several MMCs transfer functions. Such functions can be computed between any electrical circuit node or input/output port, without the need to recast the three-phase average MMC model to derive a linear equivalent one, possibly in the DQ-frame. We show how vector fitting allows converting these functions to equivalent algebraic representations, which can be profitably used to design MMCs and study their stability following some parameter changes. To showcase this feature, we exploit one of these transfer functions to detect DC-side instability in a point-to-point HVDC system.

The dynamics of modern hybrid power systems are characterized by behaviors at different timescales, typically separated by several orders of magnitude. In this paper we apply the envelope-following method to hybrid power systems simulations, in which single-phase and three-phase representations of a power system co-occur. This simulation method is well suited for handling coexisting behaviors occurring at different timescales. Compared to the simulation approaches present in the literature, the envelope-following method does not need to continuously switch between two separate simulation engines, but rather automatically handles instantaneous variations in the dynamics of the system, such as faults or topology changes. Additionally, given the vast penetration of power electronic components in modern electricity networks, employing the envelope-following method allows using a vast array of numerical algorithms that have been developed over the years to simulate electrical and electronic circuits. The proposed approach is validated by means of power systems case studies of increasing complexity.

Modular multilevel converters (mmcs) are the basic building blocks of multi-terminal direct current systems (mtdcs). The current rapid growth of such systems makes it imperative to develop methods to analyse their dc-side stability, thereby ensuring a correct overall operation. In this paper, starting from a simplified mmc model, general conditions for dc-side stability are derived. These conditions have been validated by simulating a point-to-point high voltage direct current system and an mtdc network. mmc models of different degrees of accuracy have been adopted to show that the conditions derived in the paper hold with a good degree of approximation even when more detailed representations are considered.

In recent years, a novel approach based on multi-objective optimization has been developed to automatically tune biophysically realistic, multi-compartmental neuron models starting from electrophysiological recordings. Here, we apply this methodology to the optimization of model neurons capable of reproducing the reduced excitability observed in experiments carried out in cortical pyramidal cells in a rodent model of fetal alcohol spectrum disorder. We find that both control and ethanol-exposed model cells present an excellent match with the experiments in terms of membrane voltage dynamics, with the latter group displaying a small but significant rightward shift of their current-frequency relationship. We identify a possible interplay between model parameters and cellular morphology and suggest future improvements to better capture the features of dendritic voltage dynamics.

Synchronous spiking during cerebellar tasks has been observed across Purkinje cells: however, little is known about the intrinsic cellular mechanisms responsible for its initiation, cessation and stability. The Phase Response Curve (PRC), a simple input-output characterization of single cells, can provide insights into individual and collective properties of neurons and networks, by quantifying the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent action potentials, while a neuron is firing tonically. Recently, the PRC theory applied to cerebellar Purkinje cells revealed that these behave as phase-independent integrators at low firing rates, and switch to a phase-dependent mode at high rates. Given the implications for computation and information processing in the cerebellum and the possible role of synchrony in the communication with its post-synaptic targets, we further explored the firing rate dependency of the PRC in Purkinje cells. We isolated key factors for the experimental estimation of the PRC and developed a closed-loop approach to reliably compute the PRC across diverse firing rates in the same cell. Our results show unambiguously that the PRC of individual Purkinje cells is firing rate dependent and that it smoothly transitions from phase independent integrator to a phase dependent mode. Using computational models we show that neither channel noise nor a realistic cell morphology are responsible for the rate dependent shift in the phase response curve.

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here.

In this paper we analyze the collective behaviors of networks of Hindmarsh-Rose (HR) neurons and compare them with the behaviors of networks of piecewise-linear (PWL) approximations of the HR neurons. In all cases, the neurons are assumed to be symmetrically and diffusively coupled, with different topologies. The analysis is based on the master stability function (MSF) approach. The obtained results are verified by numerical time domain simulations of networks of 100 neurons. The synchronization properties of the PWL networks turn out to be very similar to those of the HR networks, as well as the dynamical properties of the single neurons (analyzed elsewhere).

In this paper we present a systematic approach to find piecewise-linear approximations of multivariate continuous nonlinear functions, by ensuring a good trade-off between approximation accuracy and model complexity. The proposed (suboptimal) method is based on genetic programming and takes into account the circuit constraints concerning the lower bounds for the size of each domain region (called simplex) where a given nonlinear function is approximated linearly. As a benchmark example, we approximate the well-known Hodgkin-Huxley neuron model.