Search

The analysis of the frequency response of integrated transmission-distribution networks with deep penetration of solar photovoltaic (PV) generation faces major challenges due to the complexity emerging from the dynamic models of the numerous and diverse PV units involved. This article proposes converter-based dynamic equivalent models for both distributed (distribution network-connected) and large-scale (transmission network-connected) PV units, which take into account practical issues, such as measurement and coordination delays. Different from the previous work that adopted an open-loop identification, the unknown model parameters are identified here through a novel closed-loop identification process based on the least-square minimization. This allows capturing the continuous interaction between system and PV responses, thus improving the outcome of the overall frequency response model. The proposed models are validated with the real data from the August 2018 separation event in Australia. The results demonstrate the excellent performance of the proposed models in determining the frequency response from PV in both transmission and distribution networks, hence paving the way to its adoption in the frequency stability analysis in low-carbon grids dominated by frequency-responsive renewables.

This paper deals with the derivation of dynamic equivalents of active distribution networks, hosting inverter-based generators as well as static and motor loads. Equivalents are reduced-order models for use in dynamic simulations of the transmission system. They are of the grey-box types and their parameters are identified from large-disturbance Monte-Carlo simulations accounting for model uncertainty. After presenting an overview of the identification method at a single operating point, the paper deals with the update the equivalent when the operating conditions of the distribution network change. A procedure identifies the parameters to update, hence avoiding a complete new identification. Besides illustrative examples, two sets of simulation results are reported. First, the accuracy of the equivalent is validated in a long-term voltage instability scenario. Second, a larger-scale application is presented, with numerous instances of the equivalent attached to the model of the IEEE Nordic transmission test system. This combined model is used to assess the impact on short- and long-term voltage stability of the inverter-based generators with fast and slow controls.

A reduced-order, “grey-box” model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping.

The rapid uptake of renewable energy resources and displacement of synchronous generators may pose threats to system frequency stability and resilience. Starting from the August 2018 separation event in Australia, this work models and discusses how utility-scale PV plants could contribute to frequency stability and resilience of islanded areas following separation events. In this regard, a converter-based dynamic equivalent of aggregated PV power plants is proposed which takes into account possible practical issues such as measurement and coordination delays. The unknown parameters of the proposed model are identified through a novel closed-loop identification process based on least-square minimization. Also, a simplified model is constructed to reproduce the system frequency during the event under study, thereby capturing continuous impact of PV response on the frequency. The proposed aggregated model can considerably reduce the complexity of frequency stability analysis as well as its processing time while capturing with good fidelity the frequency response from PV farms.

A reduced-order model of a distribution network hosting inverter-based generators is derived. This model aims to be used in dynamic simulations of large disturbances in the transmission system. Uncertainties affecting the parameters of the component models are addressed using Monte-Carlo simulations. For a given disturbance, the average and the standard deviation of the randomized system evolutions are extracted. The dynamic equivalent is of the “grey-box” type. Its parameters are adjusted to match as closely as possible the average system response. This involves minimizing a weighted least-square objective function, in which the weights reflect the dispersion of the randomized Monte-Carlo responses. Multiple disturbances are considered to avoid over-fitting, and a recursive procedure is used to select a small subset of them from which the parameters of the ADN equivalent are identified. Simulations results are reported on the CIGRE MV distribution network benchmark. They show a good accuracy of the equivalent.

This paper deals with the dynamic modelling of distribution systems hosting a large amount of Inverter-Based Generators (IBGs), more precisely their response to large disturbances in the transmission system. It is assumed that the individual behaviour of IBGs and loads is reasonably well captured by a parameterized model, but the values of its parameters are uncertain. Monte-Carlo simulations involving random variations of those parameters are performed. The dynamic response closest to the average is extracted as being representative. A simple procedure is described to stop the simulations as soon as sufficient information is available. Detailed simulation results are provided, relative to a distribution grid with 75 loads and 75 IBGs. The latter are represented by a generic model that reproduces the main requirements of typical grid codes.

An equivalent, i.e. a reduced-order model, of active distribution networks is derived, for use in (phasor-mode) dynamic simulations of large-disturbances. In the unreduced model, the network hosts a large number of inverter-based generators, responding to the disturbances in accordance with recent or near-future grid codes. The aggregated equivalent is of the “grey-box” type and its parameters are tuned in the least-square sense to match the dynamic responses of the unreduced system to several training disturbances. Changes in operating point are easily reflected when initializing the reduced model. Simulations are reported on a detailed 75-bus distribution system. The accuracy of the equivalent has been checked with respect to untrained disturbances and changes of the operating point.