Planning Method for Power-Gas-Heat-Steam Integrated Energy System in Industrial Park Considering Annual Comprehensive Cost

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.

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

Entropy ◽

10.3390/e22121355 ◽

2020 ◽

Vol 22 (12) ◽

pp. 1355 ◽

Cited By ~ 1

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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Research on Planning and Operation of Distributed Integrated Energy System in Industrial Park

10.1109/ciced50259.2021.9556709 ◽

2021 ◽

Author(s):

Dong Kaisong ◽

Hu Dian-Gang ◽

Li Shaoyu ◽

Ma Xiping ◽

Cui Yingshuo

Keyword(s):

Energy System ◽

Industrial Park ◽

Integrated Energy System ◽

Planning And Operation

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

2020 IEEE Power & Energy Society General Meeting (PESGM) ◽

10.1109/pesgm41954.2020.9281647 ◽

2020 ◽

Author(s):

Yizhi Zhang ◽

Xiaojun Wang ◽

Yin Xu ◽

Jinghan He ◽

Wei Sun ◽

...

Keyword(s):

Production Process ◽

Energy System ◽

Industrial Park ◽

Integrated Energy System

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Multi-objective two-stage adaptive robust planning method for an integrated energy system considering load uncertainty

Energy and Buildings ◽

10.1016/j.enbuild.2021.110741 ◽

2021 ◽

Vol 235 ◽

pp. 110741

Author(s):

Rujing Yan ◽

Jiangjiang Wang ◽

Shuaikang Lu ◽

Zherui Ma ◽

Yuan Zhou ◽

...

Keyword(s):

Energy System ◽

Planning Method ◽

Two Stage ◽

Load Uncertainty ◽

Multi Objective ◽

Robust Planning ◽

Integrated Energy System

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A Dynamic Multi-Stage Planning Method for Integrated Energy Systems considering Development Stages

Frontiers in Energy Research ◽

10.3389/fenrg.2021.723702 ◽

2021 ◽

Vol 9 ◽

Author(s):

Donglou Fan ◽

Xun Dou ◽

Yang Xu ◽

Chen Wu ◽

Guiyuan Xue ◽

...

Keyword(s):

Penetration Rate ◽

System Development ◽

Clean Energy ◽

Energy System ◽

Planning Method ◽

Development Theory ◽

Development Stages ◽

Integrated Energy Systems ◽

Multi Stage ◽

Integrated Energy System

Integrated energy system (IES) planning is a long-term and rolling decision-making process. According to System Development Theory, the development-needs at different stages are different. Therefore, an IES dynamic multi-stage planning method considering different development stages is proposed. The first step of the method is putting forward a model based on the degree of system coupling, the reserve ratio, and the penetration rate of clean energy to divide dynamic development stages. Secondly, establishing a dynamic multi-stage planning model of the IES by combining the needs of different development stages through dynamic goals and constraints. Finally, the results given by the optimal configuration of critical IES equipment will be analysed in different scenarios. Following these steps, the result shows that the dynamic multi-stage planning method proposed is able to reduce the total planning cost of the system by 14% and reducing the clean energy penetration rate by 3%. Therefore, the proposed dynamic multi-stage planning scheme is effective and economical.

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