Decentralized voltage control of clustered active distribution network by means of energy storage systems

Maryam Bahramipanah, Mario Paolone
Elsevier, 2016
Article

The paper presents a network partitioning strategy for the optimal voltage control of Active Distribution Networks (ADNs) actuated by means of a limited number of Distributed Energy Storage Systems (DESSs). The proposed partitioning uses a linear programming approach by means of the known concept of voltage sensitivities. Then, two decentralized optimal control algorithms are proposed relying, respectively, on the Thévenin equivalents and a recursive approach. These algorithms are developed using the Multi- Agent System (MAS) concept. With respect to a centralized control algorithm, the aim of the network clustering is to reduce the number of exchanged messages among the clusters when one of the two proposed decentralized control algorithms is adopted. The effectiveness of the two proposed controls is assessed with respect to the performances of the equivalent centralized control using numerical examples composed by the IEEE 13 and IEEE 123 buses distribution test feeders adapted to include stochastic generation and DESSs.

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Maryam Bahramipanah, Mario Paolone
Elsevier, 2016
Article

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https://infoscience.epfl.ch/record/218055?ln=fr

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Concepts associés (20)

Concepts associés

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Publications associées (23)

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Network clustering for voltage control in active distribution network including energy storage systems

Maryam Bahramipanah, Mostafa Nick, Mario Paolone

This paper presents a network partitioning strategy for the optimal voltage control of Active Distribution Networks (ADNs) by means of Dispersed Energy Storage Systems (DESSs). The proposed partitioning is based on the concept of voltage sensitivity coefficients and is adopted for a decentralized voltage- control strategy specifically developed for radial ADNs. The aim of the partitioning is to decompose the network into quasi- autonomous areas and to limit the information exchange only at the interfacing nodes between adjacent areas. The information exchange relies on Thévenin equivalents to represent the external grids of each cluster/area. The effectiveness of the proposed decentralized control approach is assessed with respect to an equivalent centralized control approach. Such an assessment is carried out using a numerical example referring to IEEE 123 buses distribution test feeder suitably adapted to include stochastic generation and DESSs.

IEEE2015

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Optimal Allocation of ESSs in Active Distribution Networks to achieve their Dispatchability

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This paper presents a method for the optimal siting and sizing of energy storage systems (ESSs) in active distribution networks (ADNs) to achieve their dispatchability. The problem formulation accounts for the uncertainty inherent to the stochastic nature of distributed energy sources and loads. Thanks to the operation of ESSs, the main optimization objective is to minimize the dispatch error, which accounts for the mismatch between the realization and prediction of the power profile at the ADN connecting point to the upper layer grid, while respecting the grid voltages and ampacity constraints. The proposed formulation relies on the so-called Augmented Relaxed Optimal Power Flow (AR-OPF) method: it expresses a convex full AC optimal power flow, which is proven to provide a global optimal and exact solution in the case of radial power grids. The AR-OPF is coupled with the proposed dispatching control resulting in a two-level optimization problem. In the first block, the site and size of the ESSs are decided along with the level of dispatchability that the ADN can achieve. Then, in the second block, the adequacy of the ESS allocations and the feasibility of the grid operating points are verified over operating scenarios using the Benders decomposition technique.

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A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

Mokhtar Bozorg, Mostafa Nick, Mario Paolone

In this paper we present an adaptive robust optimization framework for the day-ahead scheduling of Active Distribution Networks (ADNs) where the controlled devices are distributed Energy Storage Systems (ESSs). First, the targeted problem is formulated using a two-stage optimization approach. The first-stage decisions determine the amount of import/export energy from the external grid at each hour, as well as energy exchanges for each ESS. The second stage deals with the intra-day control, given the first stage decisions. In order to effectively consider the impacts of uncertainties, the problem is transformed into a `min-max-min' formulation. Here, we minimize the first (day-ahead scheduling) and second stage (intra-day operation) costs while uncertainties are maximally affecting the cost function of the second stage. The Benders dual cut algorithm is employed for the solution of the optimization problem. IEEE 34 bus standard network is the benchmark grid for assessing the performances and effectiveness of the developed robust optimization process.

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