Optimization of Off-grid Industrial Park Integrated Energy System Considering Production Process

2020 ◽  
Author(s):  
Yizhi Zhang ◽  
Xiaojun Wang ◽  
Yin Xu ◽  
Jinghan He ◽  
Wei Sun ◽  
...  
Keyword(s):  
Production Process ◽  
Energy System ◽  
Industrial Park ◽  
Integrated Energy System

Regional integrated energy system energy management in an industrial park considering energy stepped utilization

Energy ◽  
2020 ◽  
Vol 201 ◽  
pp. 117589 ◽  
Author(s):  
Xu Zhu ◽  
Jun Yang ◽  
Xueli Pan ◽  
Gaojunjie Li ◽  
Yingqing Rao
Keyword(s):  
Energy Management ◽  
Energy System ◽  
Industrial Park ◽  
Integrated Energy System ◽  
System Energy

scholarly journals Research on Optimization of Linkage Operation of Integrated Energy System in Industrial Parks based on Chaotic Particle Swarm Algorithm

2021 ◽  
Vol 245 ◽  
pp. 01052
Author(s):  
Yang Yang ◽  
Mengjin Hu ◽  
Mengju Wei ◽  
Yongli Wang ◽  
Minhan Zhou ◽  
...  
Keyword(s):  
Particle Swarm ◽  
Clean Energy ◽  
Energy System ◽  
Utilization Efficiency ◽  
Particle Swarm Algorithm ◽  
Energy Structure ◽  
Industrial Park ◽  
Industrial Parks ◽  
Integrated Energy System ◽  
Swarm Algorithm

Industrial parks cover a variety of production capacities and energy-consuming entities, with large load demand and complex energy-using structure, and common problems such as low energy utilization efficiency and unreasonable energy structure. The construction of an integrated energy system (IES) with a combined cooling, heating and power system as the core unit in the industrial park is of great significance for achieving reliable, efficient and clean energy use in the park. Therefore, this article is based on the integrated energy system of the industrial park, aims at the lowest total cost of park operators, and considers the constraints of grid node balance, equipment output and energy storage equipment, and constructs source-grid-load-storage linkage operation optimization model, and build a chaotic particle swarm algorithm (CPSO) to solve the model. Finally, a typical industrial park in my country is taken as an example to analyze the scientificity of the model.

scholarly journals Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1355 ◽  
Author(s):  
Linjuan Zhang ◽  
Jiaqi Shi ◽  
Lili Wang ◽  
Changqing Xu
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Energy System ◽  
Multitask Learning ◽  
Effective Characteristics ◽  
Industrial Park ◽  
Energy Forecasting ◽  
Training Stage ◽  
Integrated Energy System ◽  
Term Energy

Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by employing deep multitask learning whose structure is constructed by deep belief network (DBN) and multitask regression layer. The DBN can extract abstract and effective characteristics in an unsupervised fashion, and the multitask regression layer above the DBN is used for supervised prediction. Then, subject to condition of practical demand and model integrity, the whole energy forecasting model is introduced, including preprocessing, normalization, input properties, training stage, and evaluating indicator. Finally, the validity of the algorithm and the accuracy of the energy forecasts for an industrial-park IES system are verified through the simulations using actual operating data from load system. The positive results turn out that the deep multitask learning has great prospects for load forecast.

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