Reverse Power Flow Due to Solar Photovoltaic in the Low Voltage Network

Majeed, Issah Babatunde; Nwulu, Nnamdi

doi:10.1109/access.2023.3273483

Cited by 3 publications

(2 citation statements)

References 90 publications

(147 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two models are needed to forecast each day of the week. For working days, only previous working days are considered (1). As for weekends and national holidays, the same days of the week are considered in calculating the average (2) because of different consumption patterns.…”

Section: Forecast Modelsmentioning

confidence: 99%

“…The electric power system has undergone drastic changes in the last two decades. The distribution network (DN) has, specifically, been put under huge constraints because of the increasing number of distributed energy resources, such as solar and wind power plants [1,2]. This is part of the European Green Deal programme that aims to make Europe carbon neutral and sustainable by 2050 with carbon-neutral electric energy production.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Rober,

Maruša,

Beković

2023

Energies

View full text Add to dashboard Cite

This paper presents a step-by-step approach to assess the energy flexibility potential of residential consumers to manage congestion in the distribution network. A case study is presented where a selected transformer station exhibits signs of overloading. An analysis has been performed to evaluate the magnitude of the overloading and the timing of the overload occurrence based on their historical load data. Based on the historical load data, the four most prominent consumers have been chosen for the flexibility assessment. Temperature load dependency has been evaluated for the selected consumers. The paper’s novel approach focuses on selecting individual consumers with the highest energy flexibility potential, and analysing their load patterns to address transformer overloading. To achieve this, machine learning algorithms, specifically, multiple linear regression and support vector machines, were used for load profile forecasting during the overload occurrences. Based on the forecast and measured load patterns, flexibility scenarios were created for each consumer. The generated models were evaluated and compared with the forecasting based on the average load of the past days. In the results, three potential consumers were identified who could resolve the transformer overloading problem. The machine learning models outperformed the average-based forecasting method, providing more realistic estimates of flexibility potential. The proposed approach can be applied to other overloaded transformer stations, but with a limited number of consumers.

show abstract

Section: Forecast Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%