Distributed Multi-Energy Operation of Coupled Electricity, Heating, and Natural Gas Networks

Xu, Da; Wu, Qiuwei; Zhou, Bin; Li, Canbing; Bai, Li; Huang, Sheng

doi:10.1109/tste.2019.2961432

Cited by 289 publications

(180 citation statements)

References 34 publications

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“…As the serious concerns of environmental protection and energy crisis growing, the development of renewable energy, such as solar energy, hydro energy, wind energy, tidal energy, geothermal energy, etc., is becoming an important part of the energy portfolio worldwide [1][2][3][4][5]. However, wind power always exhibits high variability and volatility due to the fact that wind speed is influenced by synoptic weather patterns.…”

Section: A Motivationmentioning

confidence: 99%

Wind Speed Forecasting Based on Extreme Gradient Boosting

et al. 2020

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As the integration of wind power into electrical energy network increasing, accurate forecast of wind speed becomes highly important in the case of large-scale wind power connected into the grid. In order to improve the accuracy of wind speed forecast and the generalization ability of the model, Extreme Gradient Boosting (XGBoost) as an improvement from gradient boosting decision tree (GBDT) is trained and deployed in the cheaper central processing unit (CPU) devices instead of graphics processing unit (GPU) devices, thus, a wind speed forecast model based on Extreme Gradient Boosting is proposed in this paper. Firstly, the historical data is taken as a part of the input vectors for the model. Moreover, considering the monthly change of wind speed characteristics, the dataset of wind power is divided into four parts by month so that the models are constructed in different complexity by month. Finally, compared with back propagation neural networks (BPNN) and linear regression (LR) models, the experimental results show that the improved XGBoost model can promote the forecast accuracy effectively. INDEX TERMS Short-term wind speed forecasting, XGBoost, time series, historical characteristics, power grid.

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Section: A Motivationmentioning

confidence: 99%

Wind Speed Forecasting Based on Extreme Gradient Boosting

et al. 2020

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“…Distributed energy resources (DERs), such as wind energy and solar energy, have developed rapidly across the world and played an important role in the power systems [1,2]. Especially, solar energy, as a renewable energy source, is complete free, accessible and scalable [3].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach

et al. 2020

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Solar energy is the key to clean energy, which can generate large amounts of electricity for the future smart grid. Unfortunately, the randomness and intermittency of solar energy resources bring difficulties to the stable operation and management of the power systems. To reduce the negative impact of photovoltaic (PV) plants accessing on the power systems, it is great significant to predict PV power accurately. In light of this, we propose a hybrid deep learning approach based on convolutional neural network (CNN) and long-short term memory recurrent neural network (LSTM) for the PV output power forecasting. The CNN model is leveraged to discover the nonlinear features and invariant structures exhibited in the previous output power data, thereby facilitating the prediction of PV power. The LSTM is used to model the temporal changes in the latest PV data, and predict the PV power of next time step. Then, the prediction results in the two models are comprehensively considered to obtain the expected output power. The proposed approach is extensively evaluated on real PV data in Limberg, Belgium, and numerical results demonstrate that the proposed approach can provide good prediction performance in PV systems. INDEX TERMS Solar energy, deep learning, photovoltaic (PV) power forecasting, power systems.

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“…Over the last decades, a number of methods have been developed to solve the optimization problems. In general, these methods can be divide into mathematicsbased optimization methods and heuristic methods [3], [4]. Mathematics-based optimization methods including linear programming [5], non-linear programming [6], [7], mixed…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Coordinated Day-Ahead Scheduling Problem of Integrated Electricity-Natural Gas System With Microgrid

Zheng

Huang

et al. 2020

IEEE Access

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This paper presents a multi-objective optimization algorithm for coordinated day-ahead scheduling problem of integrated electricity-natural gas system with microgrid (IENGS-M). Mathematically, the day-ahead scheduling of IENGS-M is formulated as a multi-objective optimization problem considering multitudinous constraints. In order to solve the problem efficiently, we introduce an acceleration of differential evolution, Lévy search strategy and a treatment mechanism to multitudinous and complex constraints into the original Non-dominated Sorting Genetic Algorithm-III (NSGA-III). Furthermore, a decision making method based on a fuzzy function approach is used to determine a final optimal solution from the Paretooptimal solutions. Simulation studies are carried out on a modified IEEE 39-bus system and 15-node gas system to verify the effectiveness of the modified NSGA-III (MNSGA-III), in comparisons with the NSGA-II and NSGA-III. The simulation results show that the Pareto-optimal solutions obtained by MNSGA-III has better convergence performance and diversity than the NSGA-II and NSGA-III.

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Distributed Multi-Energy Operation of Coupled Electricity, Heating, and Natural Gas Networks

Cited by 289 publications

References 34 publications

Wind Speed Forecasting Based on Extreme Gradient Boosting

Wind Speed Forecasting Based on Extreme Gradient Boosting

Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach

Multi-Objective Optimization for Coordinated Day-Ahead Scheduling Problem of Integrated Electricity-Natural Gas System With Microgrid

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