Voltage Responsive Distribution Networks: Comparing Autonomous and Centralized Solutions

Cuffe, Paul; Keane, Andrew

doi:10.1109/tpwrs.2014.2360073

Cited by 24 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q(U) control is instead based on a voltage target and a droop to get a variable reactive power output depending on the voltage deviation. It is also often used, especially targeting local voltage control purposes [24], but also for coordinated [9,25] or semi-coordinated applications [26]. The development of this control in the OPF is performed adding one additional constraint equation.…”

Section: Wind Farm Control Modesmentioning

confidence: 99%

Optimal Control of Wind Farms for Coordinated TSO-DSO Reactive Power Management

et al. 2018

View full text Add to dashboard Cite

Abstract:The growing importance of renewable generation connected to distribution grids requires an increased coordination between transmission system operators (TSOs) and distribution system operators (DSOs) for reactive power management. This work proposes a practical and effective interaction method based on sequential optimizations to evaluate the reactive flexibility potential of distribution networks and to dispatch them along with traditional synchronous generators, keeping to a minimum the information exchange. A modular optimal power flow (OPF) tool featuring multi-objective optimization is developed for this purpose. The proposed method is evaluated for a model of a real German 110 kV grid with 1.6 GW of installed wind power capacity and a reduced order model of the surrounding transmission system. Simulations show the benefit of involving wind farms in reactive power support reducing losses both at distribution and transmission level. Different types of setpoints are investigated, showing the feasibility for the DSO to fulfill also individual voltage and reactive power targets over multiple connection points. Finally, some suggestions are presented to achieve a fair coordination, combining both TSO and DSO requirements.

show abstract

Section: Wind Farm Control Modesmentioning

confidence: 99%

Optimal Control of Wind Farms for Coordinated TSO-DSO Reactive Power Management

et al. 2018

View full text Add to dashboard Cite

show abstract

“…For the purpose of our analysis, we used π model of lines for positive sequence as in [12], [14]- [17], [20], [22]. Parameters of the line, active and reactive power flows change along a section.…”

Section: Mathematical Analysismentioning

confidence: 99%

Analytical Estimation of Reactive Power Capability of a Radial Distribution System

Stanković

Söder

2018

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

The control of reactive power exchange between grids of different voltage levels has always been a concern for system operators. With production moving from the transmission to the distribution level, its importance increases. This paper proposes a novel approach to estimate reactive power capability of the grid as a whole. A linearized analytical model for an estimation of available reactive power exchange at the interface between two grids has been developed. The maximum estimation error for the scenarios we tested was only 2%. The model gives the relation between important grid parameters and the supported reactive power. The conclusions drawn from the model are confirmed on typical Swedish distribution network with scattered wind power and small industry consumers. Common scenarios in development of distribution grids are applied to show relevant parameters influence. One studied scenario is replacement of overhead lines with cables. It is shown that this particular change enhances the reactive power capability of the grid which is directly seen from the analytical analysis without running any optimal power flow. The analytical model proposed in this paper gives fundamental understanding of the reactive power capability of radial distribution grids.

show abstract

“…The goal is to find maximum consumption of the reactive power from the slack bus 1 by the IEEE14 grid while minimizing the active power losses in the grid. This example illustrates the arising question in scientific community about using distribution grids and distributed generation to help with voltage control on transmission grid [27]- [29]. Here, IEEE14 is used as a distribution grid that provides support to a transmission grid (slack bus 1).…”

Section: Global and Closest Local Optimamentioning

confidence: 99%

Optimal Power Flow Based on Genetic Algorithms and Clustering Techniques

Stanković

Söder

2018

2018 Power Systems Computation Conference (PSCC)

View full text Add to dashboard Cite

Optimal power flow problems have been studied extensively for the past decades. Two approaches for solving the problem have been distinguished: mathematical programming and evolutionary algorithms. The first is fast but is not converging to a global optimum for every case. The second ones are robust but time-consuming. This paper proposes a method that combines both approaches to eliminate their flaws and take advantage of their benefits. The method uses properties of genetic algorithms to group their chromosomes around optima in the search space. The centers of these groups are identified by clustering techniques and furthermore used as initial points for gradient based search methods. At the end, the proposed method finds global optimum and its closest local optima. Continuous Newton-Raphson method is used to overcome ill-conditioned points in search space when calculating power flows. The proposed method is compared against similar methods showing considerable improvement.

show abstract

Voltage Responsive Distribution Networks: Comparing Autonomous and Centralized Solutions

Cited by 24 publications

References 30 publications

Optimal Control of Wind Farms for Coordinated TSO-DSO Reactive Power Management

Optimal Control of Wind Farms for Coordinated TSO-DSO Reactive Power Management

Analytical Estimation of Reactive Power Capability of a Radial Distribution System

Optimal Power Flow Based on Genetic Algorithms and Clustering Techniques

Contact Info

Product

Resources

About