Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

Ying, Hao; Dong, Lei; Liang, Jun; Liao, Xiaozhong; Wang, Lijie; Shi, Lefeng

doi:10.1016/j.renene.2020.03.169

Cited by 48 publications

(22 citation statements)

References 44 publications

(48 reference statements)

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“…Recent studies on optimum PVPF in microgrid environments highlight the benefits of complex ANN models. For the case of a PV power generation microgrid with plug-in EVs (PVEVM), the paper [17] shows that a combination of a preceding clustering process of training sets comprising of numerical weather predictions (NWP) with the use of Density Peak Optimized (DPK)-medoids as input to a generalized RNN, provides high accuracy forecasts for a 30-min window and day-ahead horizon. The proposed PVPF model can not only forecast with high accuracy on sunny days, but also exhibits high-accuracy forecasting performance under unstable weather conditions.…”

Section: State-of-the-art Review and Related Work 21 Photovoltaic (Pv) Forecastingmentioning

confidence: 99%

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

et al. 2021

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Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets.

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Section: State-of-the-art Review and Related Work 21 Photovoltaic (Pv) Forecastingmentioning

confidence: 99%

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

et al. 2021

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“…The significant expansion of electric vehicles is another application that will necessitate a close synergy with rooftop and other PV installations [4]. Again, the system's optimization in these cases needs to be supported by reliable power forecasting because of the inherent uncertainty of the availability of solar energy and the randomness of the EV charging behavior [5]. Furthermore, power forecasting of the various forms of power production is also an important tool in electricity markets and energy markets [6,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Ρουμπακιάς

Stamatelos

2022

Applied Sciences

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Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system’s protection and management. Accurate short-term prediction of a grid-connected PV park’s output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this end during the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system’s output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park’s output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis.

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“…Moreover, it is not only beneficial for system operators as PV plant managers can prevent potential penalties arising from differences between produced and predicted energy [8,9]. Therefore, PV power forecasting contributes to stabilize and optimize the operation and performance of the grid and renewable energy microgrids [10][11][12][13] by means of the comparative analysis between required and predicted energy. It also positively affects the customer by reducing the uncertainty and the generated energy cost [8,14].…”

Section: Introductionmentioning

confidence: 99%

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Santos¹,

García-Santiago²,

Camarero³

et al. 2022

Energies

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The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities.

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Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

Cited by 48 publications

References 44 publications

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

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