Reactive power loadability based optimal placement of wind and solar DG in distribution network

Ramawat, Deepesh; Prajapat, Ganesh P.; Swarnkar, Nagendra Kumar

doi:10.1109/poweri.2016.8077410

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Renewable energy sources (RES) such as the photovoltaic (PV) system have played an important part in reducing environmental pollution in recent years due to their ability to reduce greenhouse effects [1]. As a kind of mature and widely used power generation method, PV power generation perfectly conforms to the strategy of sustainable development and the concept of safe power generation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

2022

Sensors

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With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

2022

Sensors

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“…Majed et al [12] addressed this issue by using a probabilistic optimisation model to find the optimal location of wind farm by minimising the annual energy losses of the system at that location. Ramawat et al [13] used reactive power loadability to acquire the optimal location of the wind generation. Particle swarm was used as an optimisation technique and the algorithm was tested on the 14-bus Kumamoto system in Japan.…”

Section: Introductionmentioning

confidence: 99%

“…Ramawat et al . [13] used reactive power loadability to acquire the optimal location of the wind generation. Particle swarm was used as an optimisation technique and the algorithm was tested on the 14‐bus Kumamoto system in Japan.…”

Section: Introductionmentioning

confidence: 99%

Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids

Al-Muhaini

Bizrah

Heydt

et al. 2019

IET Renewable Power Generation

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Power utilities have shown great interest in utilising renewable energy resources for various environmental and economic reasons. However, the modelling and integration of conventional and renewable resources into electric microgrids will increase the complexity of microgrid reliability analyses. Therefore, an accurate reliability assessment of power distribution systems is expected to become a major challenge in the future. More extensive research must be conducted to improve system reliability assessment techniques and determine the impact of increased penetration of renewable resources. In this work, the reliability of wind-based electric microgrids was evaluated using a Markov model, taking the intermittent nature of wind speed into account. The effects of different wind speed modelling techniques based on the auto-regressive moving average method, Markov model, and probability distribution function on the reliability analysis of electric microgrids were assessed. The Roy Billinton test system was used to illustrate the analysis and evaluate the different load and system indices.

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Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index

Kumar

Deng

et al. 2023

Applied Energy

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Reactive power loadability based optimal placement of wind and solar DG in distribution network

Cited by 5 publications

References 16 publications

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids

Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index

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