scholarly journals Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 22
Author(s):  
Hanlin Dong ◽  
Zhijian Fang ◽  
Al-wesabi Ibrahim ◽  
Jie Cai
Keyword(s):  
Energy Storage ◽  
Optimization Technique ◽  
Load Forecasting ◽  
Energy System ◽  
Mixed Integer ◽  
Programming Approach ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Integrated Energy System ◽  
Optimal Coordination

This research proposes an optimization technique for an integrated energy system that includes an accurate prediction model and various energy storage forms to increase load forecast accuracy and coordinated control of various energies in the current integrated energy system. An artificial neural network is utilized to create an accurate short-term load forecasting model to effectively predict user demand. The 0–1 mixed integer linear programming approach is used to analyze the optimal control strategy for multiple energy systems with storage, cold energy, heat energy, and electricity to solve the problem of optimal coordination. Simultaneously, a precise load forecasting method and an optimal scheduling strategy for multienergy systems are proposed. The equipment scheduling plan of the integrated energy system of gas, heat, cold, and electricity is proposed after researching the operation characteristics and energy use process of the equipment in the combined power supply system. A system economic operation model is created with profit maximization in mind, while also taking into account energy coordination between energy and the power grid. The rationality of the algorithm and model is verified by analyzing the real data of a distributed energy station in Wuhan for two years.

scholarly journals Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN

2020 ◽  
Vol 194 ◽  
pp. 01029
Author(s):  
Cao Yuwei ◽  
Zeng Ming ◽  
Jiang Shigong ◽  
Yang Weihong ◽  
Shi Pengjia ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Operation ◽  
Selection Strategy ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Integrated Energy System

Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.

scholarly journals A short term load forecasting of integrated energy system based on CNN-LSTM

2020 ◽  
Vol 185 ◽  
pp. 01032
Author(s):  
Xianjun Qi ◽  
Xiwei Zheng ◽  
Qinghui Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pearson Correlation ◽  
Load Forecasting ◽  
Energy System ◽  
Composite Model ◽  
Short Term ◽  
Characteristic Quantity ◽  
Integrated Energy System ◽  
Coupling Characteristics

The accurate forecast of integrated energy loads, which has important practical significance, is the premise of the design, operation, scheduling and management of integrated energy systems. In order to make full use of the coupling characteristics of electricity, cooling and heating loads which is difficult to deal with by traditional methods, this paper proposes a new forecast model of integrated energy system loads based on the combination of convolutional neural network (CNN) and long short term memory (LSTM). Firstly, the Pearson correlation coefficients among the electricity, cooling and heating load series of the integrated energy system are calculated, and the results show that there is a strong coupling relationship between the loads of an integrated energy system. Then, the CNN-LSTM composite model is constructed, and CNN is used to extract the characteristic quantity which reflects the load coupling characteristics of the integrated energy system. Then, the characteristic quantity is converted into the time series input to LSTM, and the excellent time series processing ability of LSTM is used for load forecasting. The results show that the CNN-LSTM composite model proposed in this paper has higher prediction accuracy than the wavelet neural network model, CNN model and LSTM model.

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