Using Deep Learning Techniques for South African Power Distribution Networks Load Forecasting

2019 ◽  
Author(s):  
Sibonelo Motepe ◽  
Ali N. Hasan ◽  
Bhekisipho Twala ◽  
Riaan Stopforth
Keyword(s):  
Deep Learning ◽  
South African ◽  
Power Distribution ◽  
Load Forecasting ◽  
Distribution Networks ◽  
Power Distribution Networks ◽  
Learning Techniques

scholarly journals Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4643 ◽  
Author(s):  
Vasileios Evangelopoulos ◽  
Panagiotis Karafotis ◽  
Pavlos Georgilakis
Keyword(s):  
Power Distribution ◽  
Mean Squared Error ◽  
Distribution Network ◽  
Load Forecasting ◽  
Distribution Networks ◽  
Prediction Intervals ◽  
Probabilistic Forecasting ◽  
Power Distribution Networks ◽  
Spatial Error ◽  
Electric Loads

The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

scholarly journals Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2405
Author(s):  
Samar Fatima ◽  
Verner Püvi ◽  
Ammar Arshad ◽  
Mahdi Pourakbari-Kasmaei ◽  
Matti Lehtonen
Keyword(s):  
Power Distribution ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Power Distribution Networks ◽  
Network Losses ◽  
Network Costs ◽  
Cost And Benefit Analysis ◽  
Cost Parameters ◽  
The Cost ◽  
Pv Penetration

Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits.

scholarly journals Service Restoring Reconfiguration for Distribution NetworksConsidering Uncertainty in Available Information

2021 ◽  
Vol 11 (9) ◽  
pp. 4169
Author(s):  
Hirotaka Takano ◽  
Junichi Murata ◽  
Kazuki Morishita ◽  
Hiroshi Asano
Keyword(s):  
Power Distribution ◽  
Solution Method ◽  
Optimization Problems ◽  
Problem Formulation ◽  
Distribution Networks ◽  
Accurate Information ◽  
Power Distribution Networks ◽  
Combinatorial Optimization Problems ◽  
Service Restoration ◽  
Available Information

The recent growth in the penetration of photovoltaic generation systems (PVs) has brought new difficulties in the operating and planning of electric power distribution networks. This is because operators of the distribution networks normally cannot monitor or control the output of the PVs, which introduces additional uncertainty into the available information that operations must rely on. This paper focuses on the service restoration of the distribution networks, and the authors propose a problem framework and its solution method that finds the optimal restoration configuration under extensive PV installation. The service restoration problems have been formulated as combinatorial optimization problems. They do, however, require accurate information on load sections, which is impractical in distribution networks with extensively installed PVs. A combined framework of robust optimization and two-stage stochastic programming adopted in the proposed problem formulation enables us to deal with the PV-originated uncertainty using readily available information only. In addition, this problem framework can be treated by a traditional solution method with slight extensions. The validity of the authors’ proposal is verified through numerical simulations on a real-scale distribution network model and includes a discussion of their results.

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