Optimal capacity management applied to a low voltage distribution grid in a local peer-to-peer energy community

Rao, Bharath Varsh; Stefan, Mark; Brunnhofer, Thomas; Schwalbe, Roman; Karl, Roman; Kupzog, Friederich; Taljan, Gregor; Zeilinger, Franz; Stern, Peter; Kozek, Martin

doi:10.1016/j.ijepes.2021.107355

Cited by 18 publications

(5 citation statements)

References 16 publications

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“…Input: Voltage time series data of threephase feeders and customers in the station area, If measurement devices are installed at each level nodes of the radial distribution network, the connection relationship can be obtained through hierarchically solving (6). Considering the fact that the large numbers of customers supplied by stations, applying programming models requires more data, which increases computational complexity and amplifies the impact of errors on the results.…”

Section: Algorithm 2 Phase Identificationmentioning

confidence: 99%

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Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

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Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

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Section: Algorithm 2 Phase Identificationmentioning

confidence: 99%

“…It helps determine the operating status of lines, replace ageing lines in a timely manner, and monitor non‐technical losses. In addition, in the distribution network with integrated DERs, correct topology is conducive to energy management and distribution by operators to ensure the economic operation of the system [6].…”

Section: Introductionmentioning

confidence: 99%

Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

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show abstract

“…The DERs should have sufficient capacity to balance the load and maintain voltage within an acceptable range. Their variable nature makes it challenging for distribution system operators to predict and manage their capacity [13]. The mismatch between the DER capability and the load demand can result in voltage instability.…”

Section: Introductionmentioning

confidence: 99%

An Improved Analysis of Distributed Resource Access Location and Capacity on Voltage Stability of Distribution Networks in Smart Grid

Ling Liang

2024

jes

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Concerns about how Distributed Energy Resources (DERs) may affect distribution network voltage stability have been brought up by the widespread usage of DERs in the elegant Grid. A Smart Grid connects a range of distributed energy resources (DERs) to the distribution network, including electric cars, wind turbines, and solar panels. This connection can alter the working conditions of the network. These changes can lead to voltage stability problems, which can affect the overall efficiency and reliability of the Smart Grid. Thus, there is a need for an improved analysis of the effects of Distributed Resource Access Location and Capacity (DRALC) on voltage strength in distribution networks. Here, we propose an improved analysis of the impact of DRALC on voltage stability in distribution networks in the Smart Grid. Our analysis considers the location and capacity of DERs and their effect on voltage stability using a combination of analytical and simulation methods. We first develop a mathematical model to represent the distribution network, which includes the DERs as voltage sources. Next, we use the voltage stability index and load flow analysis to evaluate the voltage stability of the network under different DRALC scenarios. Finally, we validate our analysis by performing simulations on a real-world distribution network.

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“…An optimum capacity management framework generating profiles for the controllable generators and devices for clearing the electricity market in a local energy community is proposed and tested on a pilot site in Ref. [24]. The optimization of a hybrid system that contains a photovoltaic installation and generating units using biomass to supply a university campus was implemented in Ref.…”

Section: Introductionmentioning

confidence: 99%

Techno-economic assessment of university energy communities with on/off microgrid

Sima

Popescu

et al. 2022

Renewable Energy

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Optimal capacity management applied to a low voltage distribution grid in a local peer-to-peer energy community

Cited by 18 publications

References 16 publications

Improved topology identification for distribution network with relatively balanced power supplies

Improved topology identification for distribution network with relatively balanced power supplies

An Improved Analysis of Distributed Resource Access Location and Capacity on Voltage Stability of Distribution Networks in Smart Grid

Techno-economic assessment of university energy communities with on/off microgrid

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