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 7 publications

(2 citation statements)

References 90 publications

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“…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%

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

Rober,

Maruša,

Beković

2023

Energies

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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.

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Section: Forecast Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rober,

Maruša,

Beković

2023

Energies

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Analysis of power quality and additional loss in distribution network with distributed generation

Liu,

Liu

et al. 2024

Electric Power Systems Research

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Optimization planning of distributed photovoltaic integration in distribution networks using combinatorial search algorithm

Wang,

Hu,

Wang

et al. 2024

International Journal of Low-Carbon Technologies

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The current scenario sees the potential emergence of challenges such as power imbalances and energy dissipation upon the incorporation of distributed photovoltaic (PV) systems into distribution networks, impacting power quality and economic viability. To address these identified risks, this study introduces an innovative combinatorial search algorithm designed to autonomously derive optimal planning strategies for distribution networks. The process begins by establishing distinct planning models for distributed PVs and distribution network systems, followed by the application of the search algorithm to align these models and generate relevant Pareto datasets and multi-objective positioning criteria. By strategically combining and optimizing existing solutions to bolster the distribution network’s load-carrying capacity, the optimal strategy is progressively refined under the guidance of multiple objective constraints. Subsequent multiphase simulation experiments validate the efficacy of our approach in minimizing energy losses when compared to analogous methodologies. The distribution network integration planning strategies derived through our method showcase outstanding performance in reducing energy losses, conducting steady-state voltage safety assessments, optimizing installation capacity utilization rates, and enhancing economic returns, thus emphasizing the substantial potential of our approach in facilitating the seamless integration of distributed PVs into distribution networks.

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

Cited by 7 publications

References 90 publications

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

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

Analysis of power quality and additional loss in distribution network with distributed generation

Optimization planning of distributed photovoltaic integration in distribution networks using combinatorial search algorithm

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