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Research Papers: Energy Systems Analysis

Modeling and Operational Optimization Based on Energy Hubs for Complex Energy Networks With Distributed Energy Resources

[+] Author and Article Information
Shixi Ma

Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: mashixi@sjtu.edu.cn

Dengji Zhou

Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: ZhouDJ@sjtu.edu.cn

Huisheng Zhang

Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: zhslm@sjtu.edu.cn

Shilie Weng

Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: slweng@sjtu.edu.cn

Tiemin Shao

PetroChina Beijing Oil &
Gas Pipeline Control Center,
Beijing 100032, China

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received February 9, 2018; final manuscript received August 13, 2018; published online September 26, 2018. Assoc. Editor: Abel Hernandez-Guerrero.

J. Energy Resour. Technol 141(2), 022002 (Sep 26, 2018) (11 pages) Paper No: JERT-18-1113; doi: 10.1115/1.4041287 History: Received February 09, 2018; Revised August 13, 2018

Energy hubs is an integrated system which is capable of transporting, transforming, and storing several types of energy. A number of hubs can be combined as a network and achieve higher efficiency by exchanging information and energy with each other. A decision-making framework for optimal integration of independent small-scale distributed energy systems and traditional large scale combined heating and power (CHP) plants is presented, and an energy supply system with renewable energy resources in Shanghai is cited as a case study. A performance simulation model of this energy network is proposed based on energy hub concept and energy flow between its elements. Furthermore, a novel optimization method named Whales optimization algorithm (WOA) is presented for 24 h operational optimization. A case study is undertaken on a seven-node energy system, including four energy hubs and three load hubs. The results of the case study show that the proposed model and optimization method can improve energy utilization efficiency and reduce system operating costs, even under a system contingency condition.

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Figures

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Fig. 1

Paradigm of a typical energy hub

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Fig. 2

Diagram of whale optimization method

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Fig. 3

Block diagram of energy hub optimal management with WOA optimizer

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Fig. 4

Schematic view of energy hubs

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Fig. 5

Schematic of the test system: electric district heating, and natural gas network

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Fig. 18

Power output at energy hub 1–4 considering an ICE contingency

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Fig. 19

Heat output at energy hub 1–4 considering an ICE contingency

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Fig. 15

Power output and heat exchange at energy hub 2

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Fig. 16

Power output and heat exchange at energy hub 3

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Fig. 17

Electricity exchange with grid before and after optimization in case 1

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Fig. 6

Solar radiation and wind speed on typical day

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Fig. 7

Cooling, heating, and electricity loads for commercial areas on a typical day

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Fig. 8

Cooling, heating, and electricity loads for industrial areas on a typical day

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Fig. 9

Cooling, heating, and electricity loads for residential areas on a typical day

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Fig. 10

Power output of energy hub 4 in the typical day

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Fig. 11

Natural gas and electricity consumption before and after optimization of energy hub 1

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Fig. 12

Natural gas and electricity consumption before and after optimization of energy hub 2

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Fig. 13

Natural gas and electricity consumption before and after optimization of energy hub 3

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Fig. 14

Power output and heat exchange at energy hub 1

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Fig. 20

Convergence characteristic of various optimization techniques

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