Energy station and distribution network collaborative planning of integrated energy system based on operation optimization and demand response

Summary In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative opt...

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Vydáno v:International journal of energy research Ročník 44; číslo 6; s. 4888 - 4909
Hlavní autor: Liu, Xiaoou
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chichester, UK John Wiley & Sons, Inc 01.05.2020
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ISSN:0363-907X, 1099-114X
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Abstract Summary In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative optimization are proposed considering multiagent interest balance. First, the three‐stage optimization framework and model of station and network planning of IES are proposed based on the operation scheduling of IES and the response of energy station and users as different subjects, aiming at optimization of station and network cost‐effectiveness, respectively. Second, the distribution network simplification method is proposed based on topological equivalence principle. The topological structure characteristics of distribution network is analyzed, the rapid generation strategy of single tie line network based on broken circle method is proposed, and then an encoding/decoding scheme is proposed based on particle swarm optimization algorithm. Third, considering the load characteristics and energy station demand response based on flexible comfort level of users, a dynamic spot price optimization method and operation strategy of IES are proposed. And then the station and network collaborative planning solution is presented based on the operation optimization of IES. Finally, an example is given to verify the practicability and effectiveness of the proposed method in this paper.
AbstractList Summary In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative optimization are proposed considering multiagent interest balance. First, the three‐stage optimization framework and model of station and network planning of IES are proposed based on the operation scheduling of IES and the response of energy station and users as different subjects, aiming at optimization of station and network cost‐effectiveness, respectively. Second, the distribution network simplification method is proposed based on topological equivalence principle. The topological structure characteristics of distribution network is analyzed, the rapid generation strategy of single tie line network based on broken circle method is proposed, and then an encoding/decoding scheme is proposed based on particle swarm optimization algorithm. Third, considering the load characteristics and energy station demand response based on flexible comfort level of users, a dynamic spot price optimization method and operation strategy of IES are proposed. And then the station and network collaborative planning solution is presented based on the operation optimization of IES. Finally, an example is given to verify the practicability and effectiveness of the proposed method in this paper.
In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative optimization are proposed considering multiagent interest balance. First, the three‐stage optimization framework and model of station and network planning of IES are proposed based on the operation scheduling of IES and the response of energy station and users as different subjects, aiming at optimization of station and network cost‐effectiveness, respectively. Second, the distribution network simplification method is proposed based on topological equivalence principle. The topological structure characteristics of distribution network is analyzed, the rapid generation strategy of single tie line network based on broken circle method is proposed, and then an encoding/decoding scheme is proposed based on particle swarm optimization algorithm. Third, considering the load characteristics and energy station demand response based on flexible comfort level of users, a dynamic spot price optimization method and operation strategy of IES are proposed. And then the station and network collaborative planning solution is presented based on the operation optimization of IES. Finally, an example is given to verify the practicability and effectiveness of the proposed method in this paper.
Author Liu, Xiaoou
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  surname: Liu
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  organization: China Energy Engineering Group Tianjin Electric Power Design Institute Co., Ltd
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Snippet Summary In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and...
In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network...
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SubjectTerms Algorithms
Collaboration
demand response
Distribution
Encoding-Decoding
Energy
Energy distribution
Energy management
energy station and distribution network collaborative planning
Equivalence principle
flexible comfort level of users
integrated energy system
Integrated energy systems
Multiagent systems
network rapid generation strategy
Operation scheduling
Optimization
Particle swarm optimization
Topology
Title Energy station and distribution network collaborative planning of integrated energy system based on operation optimization and demand response
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Volume 44
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