Suchergebnisse

Distribution systems operators (DSOs) encounter the challenge of managing network losses in large geographical areas with hundreds of secondary substations and thousands of customers and with an ever-increasing presence of renewable energy sources. This situation complicates the estimation process of power loss, which is paramount to improve the network energy efficiency level in the context of the European Union energy policies. Thus, this article presents a methodology to estimate power losses in large-scale low voltage (LV) smart grids. The methodology is based on a deep-learning loss model to infer the network technical losses considering a large rollout of smart meters, a high penetration of distributed generation (DG) and unbalanced operation, among other network characteristics. The methodology has been validated in a large-scale LV distribution area in Madrid (Spain). The proposed methodology has proven to be a potential network loss estimation tool to improve the energy efficiency level in large-scale smart grids with a high penetration of distributed resources. The accuracy of the proposed methodology outperforms that of the state-of-the-art loss estimation methods, exhibiting a rapid convergence which allows for its use in real-time operations. ; This work has been partly funded by the Spanish Ministry of Economy and Competitiveness through the National Program for Research Aimed at the Challenges of Society under the project OSIRIS (RTC-2014-1556-3).

The use of conventional low voltage induction motors fed by pulse width modulated (PWM) inverters has begun to present important problems. These waveforms consist of steep-fronted pulses having very short rise times (about 100 ns in modern IGBT bridges) and high frequency repetition rates (up to 20 kHz) whose immediate consequences are additional electrical stresses in an induction motorrsquos insulation system. In this paper a frequency domain model for the analysis and characterization of the internal voltage distribution in random wound coils is presented. The model allows voltage prediction in time domain when an inverse fast Fourier transform (FFT) transformation is performed, and requires only a few frequency domain impedance measurements. This methodology will be useful for accurately predicting the voltage distribution in motor windings during the design stage, and reducing the risk of premature failure in motor insulation. Experimental and theoretical results are presented and compared and model effectiveness using different approximations is studied. ; This research has been suported by the Spanish Science and Technology Ministry under contract MAT 2002 03210, and the Madrid Regional Government under the Initiative for Regional R&D Laboratoy Network.

This paper has been presented at the 25th International Conference on Electricity Distribution (CIRED 2019). ; The so-called 20-20-20 targets committed to by the European Union drives the need for a more efficient distribution network. The energy efficiency improvement required involves a 20% reduction of the energy consumption compared to the 1990s. For such cutback, Distribution Systems Operators are encouraged to develop the best strategies to identify and reduce power losses in their networks. This task becomes challenging in Low Voltage Distribution networks due to diversity in the feeder's topology configuration, load distribution and the presence of renewable-based distributed generation. In this paper, a clustering-based methodology is proposed as an energy losses tool to support the energy efficiency decision-making process. A feeder's clustering process using the K-means algorithm is carried out upon a customised network characteristics set that was previously reduced to two coordinates by applying Principal Component Analysis. The relationship between power losses and the net energy imported under the different scenarios is obtained for each feeder class identified. The data and network used in this process correspond to the roll-out deployed at the Spanish Smart Grid Demonstration Project (OSIRIS) ; This work has been partly funded by the Spanish Ministry of Economy and Competitiveness through the National Program for Research Aimed at the Challenges of Society under the project OSIRIS (RTC-2014-1556-3).

Over the last decades, active power networks have reached great attention due to the incorporation of distributed energy resources into low voltage power systems. In this paper, a decentralized energy management strategy is proposed as an efficient way to minimize both active power losses and voltage profile deviation of an distribution power network with photovoltaic solar farms, and also at the same time, aims to improve the reliability and the security of supply. The coordinated energy management concept relies on a two-step optimization approach based on genetic algorithms (GA) and MINLP, in which a multi-objective function is used which takes into account reliability and operational technical constraints in its formulation. The suitability of the proposed methodology is tested on an existing low voltage power system, in which two aspects are considered: firstly, determining the optimal allocation of PV units and secondly, establishing the optimal reschedule of the active power of the generation units partic ipating in the energy mix and minimizing both the real power losses and voltage deviation of the entire power system. ; This work has been partly funded by the European Union seventh framework program FP7-SMARTCITIES-2013 under grant agreement 608860 IDE4L – Ideal grid for all.

Recently, the distribution networks are working close to their physical device limits. When congestion takes place, distributed switches can be controlled to change their status in order to find a new optimal network configuration that solves that congestion. In this paper, a new methodology for congestion management by means of distributed network reconfiguration is presented. Switches and controllable voltage units such as PV units were used in the optimization process. The optimization process is guided by a weighted objective function that takes into account real power losses as well as operational limits of the power system under study. The methodology is tested in an Italian real power distribution system. ; The research leading to these results has received funding from the European Union seventh framework program FP7-SMARTCITIES-2013 under grant agreement 608860 IDE4L – Ideal grid for all.

Congestion management is one of the core enablers of smart distribution systems where distributed energy resources are utilised in network control to enable cost-effective network interconnection of distributed generation (DG) and better utilisation of network assets. The primary aim of congestion management is to prevent voltage violations and network overloading. Congestion management algorithms can also be used to optimise the network state. This study proposes a hierarchical and distributed congestion management concept for future distribution networks having large-scale DG and other controllable resources in MV and LV networks. The control concept aims at operating the network at minimum costs while retaining an acceptable network state. The hierarchy consists of three levels: primary controllers operate based on local measurements, secondary control optimises the set points of the primary controllers in real-time and tertiary control utilises load and production forecasts as its inputs and realises network reconfiguration algorithm and connection to the market. Primary controllers are located at the connection point of the controllable resource, secondary controllers at primary and secondary substations and tertiary control at the control centre. Hence, the control is spatially distributed and operates in different time frames. ; The research leading to these results has received funding from the European Union seventh framework program FP7-SMARTCITIES-2013 under grant agreement 608860 IDE4L – Ideal grid for all.