The IGREENGrid Project: Increasing Hosting Capacity in Distribution Grids

Varela, J.; Hatziargyriou, Nikos D.; Puglisi, Lisandro J.; Rossi, Marco; Abart, Andreas; Bletterie, Benoît

doi:10.1109/mpe.2017.2662338

Cited by 41 publications

(21 citation statements)

References 2 publications

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“…Moreover, aggregated flexibility can be used not only for providing technical services to cope with distribution grid congestion issues [7] or to increase the grid hosting capacity [8], but also can help to improve the efficiency of electricity markets [9]. For instance, [10] presents a collection work that represents current trends in energy management systems from the aggregators point of view.…”

Section: B Aggregated Flexibility Servicesmentioning

confidence: 99%

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Olivella‐Rosell

Rul⋅lan

Lloret-Gallego

et al. 2020

IEEE Trans. Smart Grid

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The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.

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Section: B Aggregated Flexibility Servicesmentioning

confidence: 99%

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Olivella‐Rosell

Rul⋅lan

Lloret-Gallego

et al. 2020

IEEE Trans. Smart Grid

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“…The analyses presented in this paper have been performed on a set of about 7300 low voltage networks from a DSO supplying a geographical area in Austria which is dominantly rural. The data has been collected and pre-validated in the frame of the IGREENGrid project [22,23].…”

Section: Available Data Setmentioning

confidence: 99%

On the Classification of Low Voltage Feeders for Network Planning and Hosting Capacity Studies

2018

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The integration of large amounts of generation into distribution networks faces some limitations. By deploying reactive power-based voltage control concepts (e.g., volt/var control with distributed generators), the voltage rise caused by generators can be partly mitigated. As a result, the network hosting capacity can be accordingly increased, and costly network reinforcement might be avoided or postponed. This works however only for voltage-constrained feeders (opposed to current-constrained feeders). Due to the low level of monitoring in low voltage networks, it is important to be able to classify feeders according to the expected constraint in order to avoid the overloading risk. The main purpose of this paper is to investigate to which extent it is possible to predict the hosting capacity constraint (voltage or current) of low voltage feeders on the basis of a large network data set. Two machine-learning techniques have been implemented and compared: clustering (unsupervised) and classification (supervised). The results show that the general performance of the classification or clustering algorithms might be considered as rather poor at a first glance, reflecting the diversity of real low voltage feeders. However, a detailed analysis shows that the benefit of the classification is significant.

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“…The large majority of related work studies scheduling for peak power reduction. However, besides line and transformer loading, voltage constraints play a significant role in hosting capacity restriction of European distribution grids (Varela et al 2017). Therefore, our work differs from the aforementioned categories in the following points: We propose a completely distributed smart charging approach by considering the real-time conditions of the grid using an event-driven architecture to collect data from the grid.…”

Section: Contributionmentioning

confidence: 99%

Enhancing power quality in electrical distribution systems using a smart charging architecture

et al. 2018

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The electrification of the mobility sector comes with multiple challenges such as the lack of information on when, where, how long and how fast charging processes of electric vehicles will take place. In order to keep up with increasing power demand of charging processes, besides better predictions also the active control of charging processes will be necessary to minimize infrastructure costs. This work deals with a realtime mechanism for supporting the Power Quality (PQ) in electric distribution grids in terms of congestion and voltage management. In the paper, we propose a distributed smart charging approach that considers real-time conditions of the distribution grid provided by an event-driven architecture that collects data from different points in the grid. Our approach adopts the traffic light model, which allows smooth changing of the charging power to avoid drastic changes of the grid state. In order to be ready for real-world application, the algorithm is validated by a series of experiments on two setups: Pure software (co-)simulation and Power Hardware In the Loop (PHIL) where physical charging stations and electric cars are controlled in a laboratory setup.

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The IGREENGrid Project: Increasing Hosting Capacity in Distribution Grids

Cited by 41 publications

References 2 publications

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

On the Classification of Low Voltage Feeders for Network Planning and Hosting Capacity Studies

Enhancing power quality in electrical distribution systems using a smart charging architecture

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