A 3D Design of Small Hybrid Farm for Microgrids

Tehrani, Kambiz; Simde, Diane; Fozing, Jourdain; Jamshidi, Mo

doi:10.23919/wac55640.2022.9934384

2022 World Automation Congress (WAC) 2022

DOI: 10.23919/wac55640.2022.9934384

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A 3D Design of Small Hybrid Farm for Microgrids

Kambiz Tehrani

Diane Simde

Jourdain Fozing

et al.

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2023

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Cited by 2 publications

References 33 publications

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Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Wang

Shen

et al. 2023

Electronics

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Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization ability, FL is introduced to combine data from multiple locations without sharing to collaboratively train the prediction model. Furthermore, a hybrid LSTM-BPNN prediction model is designed to improve the accuracy of predictions. LSTM is used to extract important features from the time-series data, and BPNN maps the extracted high-dimensional features to the low-dimensional space and outputs the predicted values. Experiments show that the minimum MAPE of the hybrid prediction model constructed in this paper can reach 1.2%, and the prediction effect is improved by 30% compared with the traditional model. Under the FL mode, the trained prediction model not only improves the prediction accuracy by more than 20% but also has excellent generalization ability in multiple scenarios.

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Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Wang

Shen

et al. 2023

Electronics

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A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty

2023

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A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.

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A 3D Design of Small Hybrid Farm for Microgrids

Cited by 2 publications

References 33 publications

Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty

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