Forecast-driven stochastic optimization scheduling of an energy management system for an isolated hydrogen microgrid

Dong, Weichao; Sun, Hexu; Mei, Chunxiao; Li, Zheng; Zhang, Jingxuan; Yang, Haofan

doi:10.1016/j.enconman.2022.116640

Cited by 35 publications

(8 citation statements)

References 48 publications

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“…The time the forecasting technique takes to execute is the execution time, said in seconds. Moreover, the performance of the proposed system is compared with the conventional methods such as Long Short-Term Memory Network (LSTM) [16], recurrent neural networks with Markov decision process (RNN-MDP) [17], Long Short-Term Memory With Genetic Algorithm-Adaptive Weight Particle Swarm Optimization (LSTM-GA-AWPSO) [18], Bi-Level Reinforcement Learning Proximal Policy Optimization (Bi-level RL-PPO) [19] and Bidirectional Long Short-Term Memory Network With Convolutional Neural Network (BiLSTM-CNN) [20] The RHBA-based GR-ResNet-50 energy-management model learns and trains on microgrid operating status data to optimize energy scheduling. Figure 5 shows the simulation findings for the best power from the PV, wind, fuel cell, and diesel generator power grid.…”

Section: Resultsmentioning

confidence: 99%

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Zhiqiang Wang

2024

jes

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The power grid presents several obstacles for demand forecasting and resource scheduling, such as a substantial amount of data, a growing number of factors influencing the demand profile, uncertainty in the generation profile of distributed energy from renewable sources, and a shortage of historical data. Here, we provide a unique market-oriented energy storage method based on artificial intelligence (AI) that aims to optimize operational profit in the electricity market between consumers, energy storage, and grid service providers. The approach is divided into two parts; the first is the Residual Network-50 (GR-ResNet-50) algorithm, which is used to overcome future uncertainties related to load needs and power pricing. The second uses deep learning based on the Radial Heap Basis Algorithm (RHBA) to determine the most effective charging or discharging operation considering the grid peak states, load demand, and the state of the batteries. The performance of the proposed method in this study is compared with the conventional method in terms of 0.007% of low MAPE and 20 seconds of less execution time. The research shows that the proposed strategy significantly lowers on-peak power, improves operational profit, and improves effective performance.

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Section: Resultsmentioning

confidence: 99%

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Zhiqiang Wang

2024

jes

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“…Another key strategy to tackle the outstanding challenges is the application of highly sophisticated forecasting techniques. Accurate wind energy forecasts enable grid operators to anticipate fluctuations in power supply, resulting in better system balance and energy management decisions [13,14]. This technique is widely recognized as a fundamental tactic in the process of integrating wind energy, which ensures both grid stability and effective energy management.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

Habib,

Hossain

2024

Energies

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This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications.

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“…Instead, the development of optimal models may extend to also incorporate additional aspects, such as the operational requirements of wider system boundaries and the interplay of different components, aiming to serve the purpose of overarching optimization objectives at the system level. Similar research is topical in the area of wind-based microgrids [18][19][20][21][22], where wind energy penetration is considerable and where the output of wind power forecasting models is used to inform the operation of advanced energy management systems.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems

Moustris

Zafirakis

2023

Energies

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Grid operators of islands with limited system tolerance are often challenged by the need to curtail wind energy in order to maintain system stability and security of supply. At the same time, and in the absence of storage facilities and/or other means of flexibility such as demand-side management, wind park owners face the problem of rejected wind energy production that varies considerably within the year. In the prospect of a more dynamic market operation in island grids, estimation of the anticipated wind energy curtailments may allow the evaluation of different options for wind park owners, such as short-term leasing of energy storage and/or direct, bilateral power purchase agreements with flexible demand entities. To enable such options, effective wind energy forecasting is necessary not only in terms of theoretical production, but also in terms of actual production being absorbed by the system. In this direction, the current research works on the prediction of day-ahead wind energy production in island grids, aiming to generate both theoretical (expected) and actual wind power forecasts. To that end, we use artificial neural networks for the development of different day-ahead forecasting models of hourly granularity, and we then test their performance in a large-scale non-interconnected island system, where annual wind energy curtailments for local wind parks may exceed 25% of the respective theoretical yield. Our results indicate that models developed provide a fair accuracy of day-ahead wind energy predictions, which is further elaborated by initiating a discussion on the emergence of alternative actor schemes in similar systems.

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Forecast-driven stochastic optimization scheduling of an energy management system for an isolated hydrogen microgrid

Cited by 35 publications

References 48 publications

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems

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