Distribution Feeder Load Research Articles

Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates

With increasing renewable generation and demand response, the load profiles of distribution feeders become more fluctuating and uncertain, requiring real-time load estimation (RTLE) with high temporal granularity. Smart meters (SM) provide new data sources that have the potential to enable RTLE. However, it is cost prohibitive to communicate and process real-time high-resolution data from a massive number of SMs. To address the challenge, this paper proposes a novel solution to RTLE using High-Reporting-Rate SMs (HRRSMs) installed at a sparsely selected subset of customers in the feeder. The first step is to select customers for installing HRRSMs based on clustering, such that load profiles can best represent those of the others and the whole feeder. Then, a state-of-the-art Deep Learning (DL) model is trained to capture the relation between the historical load profiles of the selected customers and that of the feeder. Finally, real-time HRRSM data from the selective customers is fed to the trained model to perform RTLE with high resolution. The method is also robustified to address anomalies in real-time HRRSM data streams. The proposed method is validated on a large real-world SM dataset. Simulation results show that even with a small number of HRRSM installation, the proposed method can track feeder loads with much improved accuracy and temporal granularity compared with conventional methods based on historical data of regular SMs, providing a cost-effective solution to the monitoring of distribution feeder loads.

Accurate initialization of islanded microgrid including induction motor load using unified power-flow approach

An increased integration of distributed generators into microgrids presents a technical challenge to maintain voltage and frequency stability, especially in islanded or autonomous operation. For stability assessment of the microgrid, a proper initialization is particularly needed to get the right steady-state operating point when the widely used induction motor (IM) loads are mainly considered. Since a conventional power-flow approach is not straightforward and suitable for initializing the IM loads in the isolated microgrid network due to an unavoidable discrepancy between initial-bus scheduled and actual values of IM’s reactive powers. To eliminate this mismatch, this paper presents a precise power-flow initialization using a unified Newton–Raphson approach. The correct steady-state initializations are demonstrated using 6bus and 13bus microgrid systems. IEEE standard 33, 38, and 69bus distribution networks are employed to explore computational performances of the proposed algorithm for the case where a large number of IM loads are incorporated via distribution feeder loads.

A SAX-Based Advanced Computational Tool for Assessment of Clustered Rooftop Solar PV Impacts on LV and MV Networks in Smart Grid

Future distribution networks with increasing level of solar PV penetration will be managed using smart grid technologies capable of producing appropriate and timely response during normal and abnormal operational events. Distribution feeder loads vary throughout the day according to the trend of consumption of the customers. Solar PV outputs fluctuate in proportion to irradiance level of sun. Simultaneous occurrence of both of these variations would result in various operating conditions that may lead to unexpected events, and would require a large amount of network data to be processed and analyzed for decision making. It is envisaged that such data will be available in the future grids with the availability of smart technologies and advanced communication in residential dwellings, commercial buildings and industrial complexes. In this paper, an advanced intelligent computational tool is developed to characterize and analyze the large amount of data associated with wide variations in network behavior using SAX (Symbolic Aggregate Approximation) and pattern recognition. The proposed tool is capable of dealing with network asymmetry, load unbalance, single-phase solar PV integration and their impacts on upstream networks and will assist in making right and timely decision to mitigate adverse impacts of solar PV. The proposed tool has been tested with a practical three-phase distribution system in Australia and can provide an extensive assessment with less computational efforts and time.

Transformer and feeder load balancing using a heuristic search approach

Heuristic search algorithms based on some heuristic rules are proposed to reach the desired switching operations through which power transformer and distribution feeder load balancing can be achieved. The proposed method can be applied to changing load conditions as well as constant load conditions. The effectiveness of the approach is demonstrated by conducting feeder reconfiguration on a distribution system within the Taipei South District Office of the Taiwan Power Company. It is found that the desired switching operations can be reached in a very efficient manner by using the proposed algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Determination of critical switches in distribution system

A methodology for determining the critical switches in a distribution system is presented. The daily load curves of various types of load customers are used in a three-phase load flow program. The program calculates the hourly current loading of distribution feeders and current flows through all line switches. The hourly optimal network configurations are solved to achieve balanced loading among feeders by minimizing the cost function of switching operations. The critical switches of the distribution system are then identified by investigating the hourly optimal switching operation patterns over a 24-hour period. A distribution system comprising nine distribution feeders in the Taiwan Power Company is selected for computer simulation. Results indicate the efficiency and effectiveness of the proposed methodology for the practical operation of distribution systems. >

Forecasting distribution feeder loads: modeling and application to volt/VAr control

The kVAr and kW loads on typical residential and industrial feeders are modeled on the basis of actual data. Using a regression model, it is shown that the two loads conform. The model permits computation of the power factor and the effective amount of fixed and switched capacitance on the feeders. The load variation is decomposed into three major components, namely a weather-load component, deterministic hourly load variation, and random fluctuations, and a one-step-ahead-feeder kW load forecast model is developed. An application of the regression and the load forecast models to the volt/VAr control problem on distribution systems is demonstrated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>