Optimal placement and sizing of renewable energy generation considering uncertainties using intelligent water drops algorithm

Sowmya, M; Sheela, A.; Rai, Vishnu Shankar

doi:10.1109/iciiecs.2017.8276018

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Using network analyses and nonlinear algorithms, optimal allocation of DG units has been proposed in References 27‐30 as another solution, which helps find the optimal DG installation location from protection point of view. In order to find the best location and size of DG, different analyses must be performed by changing the DG size and location for each possible fault.…”

Section: Introductionmentioning

confidence: 99%

Anoff‐linealgorithm forfuse‐reclosercoordination in distribution networks with photovoltaic resources

Dadkhah

Fani

Heydarian‐Forushani³

et al. 2020

Int Trans Electr Energ Syst

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Summary Distributed energy resources (DERs) have a significant impact on the network's short‐circuit level and fault current direction, but it is almost impossible to precisely predict the presence of DERs in the distribution network. Hence, implementation of new settings for protection system based on a specific scenario for distributed generator, in terms of location and penetration level, is not a suitable solution. Utilization of adaptive protection and changing the overall design of protection system are very expensive and in many cases inapplicable. Consequently, it is crucial to propose a new method guaranteeing proper operation of protection system for any scenario of DERs based on the existing structure of distribution networks. This article proposes a new method evaluating the precise impact of photovoltaic resources on fault current profile and coordination of protective devices. Moreover, the proposed algorithm can update the settings of protective equipment, keeping protection coordination in distribution networks with variable penetration level of photovoltaic resources. Furthermore, the proposed method preserves fuse‐recloser coordination according to fuse saving theorem for transient faults.

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Section: Introductionmentioning

confidence: 99%

Anoff‐linealgorithm forfuse‐reclosercoordination in distribution networks with photovoltaic resources

Dadkhah

Fani

Heydarian‐Forushani³

et al. 2020

Int Trans Electr Energ Syst

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Analysis of MPPT Algorithm for Solar Photovoltaic System

Logeswaran¹,

Monika²,

Sheela³

2022

2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)

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Real-time day ahead energy management for smart home using machine learning algorithm

Vasudevan

Venkatraman

Ramkumar

et al. 2021

IFS

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Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error.

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Optimal placement and sizing of renewable energy generation considering uncertainties using intelligent water drops algorithm

Cited by 3 publications

References 12 publications

Anoff‐linealgorithm forfuse‐reclosercoordination in distribution networks with photovoltaic resources

Anoff‐linealgorithm forfuse‐reclosercoordination in distribution networks with photovoltaic resources

Analysis of MPPT Algorithm for Solar Photovoltaic System

Real-time day ahead energy management for smart home using machine learning algorithm

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