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Your search keyword '"Rigoni, Valentin"' showing total 17 results
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17 results on '"Rigoni, Valentin"'

1. Use of fitted polynomials for the decentralized estimation of network variables in unbalanced radial LV feeders

Author

Rigoni, Valentin, Soroudi, Alireza, and Keane, Andrew

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

The lack of comprehensive monitoring equipment in low voltage (LV) residential feeders, impedes a near-term deployment of centralized schemes for the integration of domestic-scale distributed generation (DG). In this context, this paper introduces a technique that generates a set of fitted polynomials, derived from offline simulations and regression analysis, that characterise the magnitude of representative network variables (i.e. key for network operation) as a direct analytical expression of the controllable local conditions of any DG unit (i.e. active and reactive power injections). Crucially, the coefficients of these polynomials can be estimated, autonomously at the location of each DG unit, without the need for remote monitoring (i.e. using only locally available measurements). During online implementation, the method consists only of direct calculations (i.e. non-iterative), facilitating real-time operation. The accuracy of the polynomials to estimate the magnitude of the network variables is assessed under multiple scenarios on a representative radial LV feeder. Furthermore, the robustness of the method is demonstrated under the presence of new generation and electric vehicles.

Published
2020
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7. Real-Time Estimation of Support Provision Capability for Poor-Observable Distribution Networks

Author

Jafarian, Mohammad, Nouri, Alireza, Rigoni, Valentin, and Keane, Andrew

Abstract

An indispensable step towards coordinating the actions of distribution and transmission system operators (dsos-tso) is to estimate the range of flexibility that can be offered to the tso by dsos. Within this context, a data-driven probabilistic approach is proposed to evaluate the capability of a distribution network in providing active and reactive power support in real-time. To this end, in an offline phase, a linear discriminant analysis model, together with a piecewise linear model of the distribution network are trained to delineate the boundary of a region representing the adherence to distribution network operational constraints. In the implementation phase, this region comprises the feasible set of a series of optimization problems, formulated to determine the support provision capability. These optimization problems are of iterative linear programming type, which allows for real-time applicability. The evaluated support capability can be deemed as the available reserve in real-time transmission operation, which enables providing a coordinated response towards unexpected events, and facilitates the participation of distributed resources in the balancing market by granting an up-to-date estimation of available supports. This approach is tested on the IEEE 123-node system and verified through comparison with an AC optimal power flow technique.

Published
2023
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17. Representative Residential LV Feeders: A Case Study for the North West of England.

Author

Rigoni, Valentin, Ochoa, Luis F., Chicco, Gianfranco, Navarro-Espinosa, Alejandro, and Gozel, Tuba

Subjects
*ELECTRIC circuit analysis, *ELECTRIC power systems, *ELECTRICAL engineering, *PULSED power systems, *ELECTRIC power production, *ELECTRIC power transmission, *ELECTRIC power distribution
Abstract

The adoption of residential-scale low carbon technologies, such as photovoltaic panels or electric vehicles, is expected to significantly increase in the near future. Therefore, it is important for distribution network operators (DNOs) to understand the impacts that these technologies may have, particularly, on low voltage (LV) networks. The challenge, however, is that these LV networks are large in number and diverse in characteristics. In this work, four clustering algorithms (hierarchical clustering, k-medoids++, improved k-means++, and Gaussian Mixture Model—GMM), are applied to a set of 232 residential LV feeders from the North West of England to obtain representative feeders. Moreover, time-series monitoring data, presence of residential-scale generation, and detailed customer classification are considered in the analysis. Multiple validity indices are used to identify the most suitable algorithm. The improved k-means++ and GMM showed the best performances resulting in eleven representative feeders with prominent characteristics such as number and type of customers, total cable length, neutral current, and presence of generation. Crucially, the results from studies performed on these feeders can then be extrapolated to those they represent, simplifying the analyses to be carried out by DNOs. This is demonstrated with a hosting capacity assessment of photovoltaic panels in LV feeders. [ABSTRACT FROM AUTHOR]

Published
2016
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