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Frontiers in Energy

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Front. Energy    2020, Vol. 14 Issue (2) : 241-253    https://doi.org/10.1007/s11708-020-0660-9
RESEARCH ARTICLE
Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China
Pei LI1, Guotian CAI2(), Yuntao ZHANG2, Shangjun KE3, Peng WANG2, Liping GAO2
1. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China; Energy Development Research Institute, CSG, Guangzhou 510663, China
2. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
3. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, China; Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215123, China
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Abstract

To improve the overall efficiency of the energy system, the basic structure for the energy internet of coordination and optimization of “generation-grid-load-storage” of Huangpu District, Guangzhou, China is designed, while the arrangement for the output of centralized and distributed energy module and energy storage are proposed. Taking economic benefit maximization, environmental benefit maximization and energy efficiency maximization as sub-objectives, the mathematical model of multi-objective optimal allocation and operation strategy of the energy internet is established considering supply-demand balance constraints, equipment characteristic constraints, operation mode constraints, and energy conditions constraints. The calculation results show that without considering the outsourced electricity, the balanced strategy, the economic development strategy, the environmental protection strategy, and the energy efficiency strategy are obtained by calculation, which are all superior to the traditional energy supply strategy. Moreover, considering the outsourced electricity, the proportion of outsourced electricity to total electricity is 19.8%, which is the system optimization of the energy internet under certain power demand. Compared with other strategies without outsourced electricity, the outsourced electricity strategy can have a certain emission reduction effect, but at the same time reduce the economic benefit. Furthermore, the huge difference in demand for thermal and cooling load between industrial and commercial areas results in the installed capacity of gas distributed energy stations in industrial areas being nearly twice as large as that in commercial areas. The distributed photovoltaic power generation is allocated according to the proportion of the installed roof areas of photovoltaic power generation system in residential, industrial, and commercial areas.
Keywords generation-grid-load-storage      energy internet      economy-environment-energy efficiency      multi-objective optimal allocation      mathematical model     
Corresponding Author(s): Guotian CAI   
Online First Date: 26 March 2020    Issue Date: 22 June 2020
 Cite this article:   
Pei LI,Guotian CAI,Yuntao ZHANG, et al. Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China[J]. Front. Energy, 2020, 14(2): 241-253.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-020-0660-9
https://academic.hep.com.cn/fie/EN/Y2020/V14/I2/241
Fig.1  Basic framework of the energy internet in Huangpu.
Fig.2  An example of load duration curve.
Fig.3  Collaborative operation mode of the energy internet.
Fig.4  Input-output diagram of multi-objective optimization for configuration and operation in the energy internet.
User Final energy demand
Electricity/kWh Heat/(t·h–1) Cooling/kWh
Huangpu 16966000000 1501.130 1556000000
Residential area 1296540000 0 0
Industrial area 14447160000 1455.890 0
Commercial area 1222300000 45.24 1556000000
Tab.1  Energy demand of Huangpu, Guangzhou
Fig.5  Reduced graph of prediction of typical daily electrical load in Huangpu.
Energy technology Life cycle/a Unit capacity cost/(yuan·kW–1) Operation cost/(yuan·kWh–1) Pool purchase price/(yuan·kWh–1) Efficiency/% Average use time of power-generating equipment/h
Coal power 20 4000 0.030 0.426 40 4000
Garbage power 20 25000 0.030 0.650 20 8000
NG cogeneration 20 4000 0.020 0.665 55 7000
Photovoltaic power generations 20 5000 0 0.700 15 1000
Gas distributed energy stations 20 8000 0.020 0.665 70 6000
Tab.2  Information of energy technology [28–35]
Fuel (unit) Low hear value per unit of fuel/MJ Emission factor per unit of fuel /(g − CO2? MJ−1)
Coal/t 27631 96.690
Natural gas/(104·m3) 389310 56.100
Outsourced electricity/(104·kWh) 6379000
Municipal solid waste/t 7954 33
Tab.3  Carbon dioxide emissions from energy combustion [36]
Item Parameter Value
Coal Price/(yuan·t–1) 615
Natural gas Price/(yuan·m–3) 2.490
Thermal load Price/(yuan·t–1·h–1) 250
Cooling load Price/(yuan·kWh–1) 0.600
Social discount rate Percentage/% 8
NG cogeneration HER 0.300
Gas distributed energy stations HER 2
500 kV AC channel utilization hours Time/h 4000
Outsourced electricity Price/(yuan·kWh–1) 0.397
Estimation of residential roof area Area/km2 0.550
Estimation of industrial roof area Area/km2 1.470
Estimation of commercial roof area Area/km2 0.630
Tab.4  Energy price and other parameters [37–40]
Strategy wS wAE wU
Balanced strategy 0.333 0.333 0.333
Economic development strategy 0.600 0.200 0.200
Environmental protection strategy 0.200 0.600 0.200
Energy efficiency strategy 0.200 0.200 0.600
Tab.5  Weight coefficient distribution plan
Fig.6  Equipment configuration layout of different strategies.
Strategy Economy Environment Energy use efficiency
Economic benefits/109yuan Ranking CO2 emissions/t Ranking Energy efficiency/% Ranking
Balanced strategy 33.817 3 11778744 2 52.050 2
Economic development strategy 34.327 1 11868092 3 51.860 3
Environmental protection strategy 33.497 4 11682656 1 51.540 4
Energy efficiency strategy 34.317 2 11942568 4 52.750 1
Tab.6  Comparison of four energy supply allocation strategies in terms of economy, environment, and energy use efficiency
Strategy Comprehensive performance index Strategy Comprehensive performance index
Balanced strategy –0.582* Traditional energy supply strategy –0.419*
Economic development strategy –0.290* Traditional energy supply strategy –0.200*
Environmental protection strategy 0.215* Traditional energy supply strategy 0.266*
Energy efficiency strategy –0.604* Traditional energy supply strategy –0.512*
Tab.7  Horizontal comparisons of comprehensive performance of the energy internet strategies and traditional energy supply strategies
User Strategy
Balanced strategy Economic development strategy Environmental protection strategy Energy efficiency strategy
Residential area PV: 117.900 MW PV: 134.700 MW PV: 134.700 MW PV: 99.600 MW
Industrial area PV: 315.100 MW
CCHP: 247 MW		 PV: 360 MW
CCHP: 259.400 MW		 PV: 360 MW
CCHP: 237.100 MW		 PV: 266.300 MW
CCHP: 268 MW
Commercial area PV: 135.100 MW
CCHP: 129.700 MW		 PV: 154.300 MW
CCHP: 129.700 MW		 PV: 154.300 MW
CCHP: 129.700 MW		 PV: 114.100 MW
CCHP: 129.700 MW
Tab.8  Equipment configuration of users in different strategies
Coal power/MW Garbage power/MW NG cogeneration/MW Photovoltaic power generations/MW Gas distributed energy stations/MW Outsourced electricity-base load/(103 kW·h) Outsourced electricity- peak load/(103 kW·h)
656.800 100 1149.400 480 275.300 262720 73400
Tab.9  Allocation of energy technology in Huangpu considering outsourced electricity
Economic benefits/(109yuan) CO2 emissions/t
28.614 11100000
Tab.10  Economic and environmental indicators of the energy internet system in Huangpu considering outsourcing electricity
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