- Email: [email protected]
Optimal Operation of Park-based Integrated Energy System
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ScienceDirect ScienceDirect Energy Procedia 00 (2018) 000–000 Energy Procedia 00 (2018) 000–000 ScienceDirect ScienceDirect
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia Procedia 00 152(2017) (2018)000–000 89–94 Energy www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy sysCUE2018-Applied Energyand Symposium and Low Forum 2018:cities Lowand carbon cities and Applied Energy Symposium Forum 2018: carbon tems, CUE2018, 5–7 June 2018, Shanghai, China urban energy sysurban energy systems, 5–7 June 2018, Shanghai, China tems, CUE2018, 5–7 June 2018, Shanghai, China
Optimal Operation Park-based Integrated Energy The 15th Internationalof Symposium on District Heating and Cooling Optimal Operation of Park-based Integrated Energy System Assessing the feasibility System of using the heat demand-outdoor a,b,
c
a,b
a,b
c
c
Bingqi Jiaoa,b,*,function Ke Xu , Shengyu Wua,b, Yaohua Wanga,bheat , Jing Xuc, Shiju Wang c temperature for a long-term district forecast Bingqi Jiao *, Ke Xuc, Shengyu Wu , Yaohua Wang , Jing demand Xu , Shiju Wang a
State Grid Energy Research Institue Co., Ltd., Changping District, Beijing 102209, China a Planning a Ltd.,Laboratory, c China c State Grid aEnergy and Energy Power System Research Changping District, Beijing China Statea,b,c Grid Research Institue Co., Changpingb., District, Beijing 102209, I. Andrić *, A.Tianjin Pina , P. Ferrão , J. Fournier B.Tianjin Lacarrière , O. 102209, Le Corre c b Stateand Grid Electric PowerResearch Company, Heibei District, 300010, China102209, China State Grid Energy Power System Planning Laboratory, Changping District, Beijing c a State Grid Tianjinand Electric Company, District, 300010, IN+ Center for Innovation, Technology PolicyPower Research - InstitutoHeibei Superior Técnico,Tianjin Av. Rovisco Pais China 1, 1049-001 Lisbon, Portugal b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France As energy, environmental and climate issues have become increasingly prominent, it has been widely agreed that the As energy, environmental and represented climate issues prominent, it has been widely that the development of clean energy byhave solarbecome energy,increasingly wind energy, and geothermal energy willagreed be vigorously development energy represented by solar energy, wind energy, andand geothermal willamong be vigorously promoted. As of an clean effective carrier for coordinated conversion and coordinated efficient energy operation different Abstract promoted. As an effective carrier for coordinated conversion coordinated efficient operation among different energy quality and diversified energy varieties, the integratedand energy system isand gaining more and more attention and energy support. quality and diversified varieties, integrated energysupply systemrequirements is gaining more andpark-type more attention and policy In this paper, energy according to the the heating and power of the integrated District heating networks commonly addressed in the literature as one of requirements thescheduling most effective decreasing the policy support. In thisare paper, according toa the heating andmodel power of park-type integrated energy system during the heating period, mixed-integer forsupply optimal of the thesolutions system isfor proposed. greenhouse gas chooses emissions from theofbuilding These systems require highcomprehensively investments returned through energy system during thetypes heating period,sector. aand mixed-integer model for and optimal schedulingwhich ofconsiders thearesystem proposed. The model two economic environmental goals, theisbalance be-the heat sales.The Due to the changed and building renovation policies, heat demand in supply the future could decrease, model chooses two climate types of conditions economic goals, and comprehensively considers the balance between supply and demand of the hourly loadand andenvironmental the operational characteristics of each energy device. Espeprolonging the model investment return period. tween andtakes demand the hourly load and the operational of each energy supplya device. Especially, supply the intoof account the water distribution rules ofcharacteristics the heating supply system. Finally, typical day is The cially, main scope of thistakes paper is to assessofthe the feasibility of usingrules the result heat demand – supply outdoor temperature function forday heat thetomodel into account distribution of the heating system. a typical isdemand selected verify the effectiveness thewater proposed model. The shows that, compared to Finally, its environmental operforecast. district ofeconomic Alvalade, located inproposed Lisbon (Portugal), used a case The is consisted selected verify the effectiveness of the model. The was result shows compared to its district environmental oper- of 665 ation,The thetosystem’s operation has 18.7% of cost saving while hasasa that, 3.4% of study. more energy consumption. buildings vary in both construction period and typology. Threewhile weather (low, energy medium, high) and three district ation, that the system’s economic operation has 18.7% of cost saving has ascenarios 3.4% of more consumption. renovation scenarios developed intermediate, deep). To estimate the error, obtained heat demand values were Copyright © 2018were Elsevier Ltd. All(shallow, rights reserved. Copyright © 2018from Elsevier Ltd. Allheat rights reserved. compared withand results a dynamic demand model, previously developed validated by Symposium the authors. and Forum Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility of the scientific committee of and Applied Energy Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy The 2018: resultsLow showed thatcities whenand only weather change is the considered, the margin of error could be acceptable for and someForum applications Selection and peer-review under responsibility of scientific committee of Applied Energy Symposium carbon urban energy systems, CUE2018. Symposium and Forum 2018: Low carbon cities and urban energy systems. (the 2018: error in annual demand wasurban lowerenergy than 20% for all weather scenarios considered). However, after introducing renovation Low carbon cities and systems, CUE2018. scenarios, the error value energy increased up to optimal 59.5% (depending on the weather and renovation Keywords: Integrated system; operation; heat pump; heat storage; electricscenarios boiler combination considered). The Keywords: value of slope coefficient on average withinheat the pump; range heat of 3.8% up toelectric 8% per decade, that corresponds to the Integrated energyincreased system; optimal operation; storage; boiler decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the 1. Introduction coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1. Introduction improveWith the accuracy of development heat demand estimations. the rapid and wide application of distributed generation technologies, energy monib
With the rapid development and wide application of distributed generation technologies, energy moni-
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.* Corresponding author. Tel.: +86 189 1169 8549; fax: +86 010 66603580. address: [email protected]. *E-mail Corresponding author. Tel.: +86 189 1169 8549; fax: +86 010 66603580. Keywords: Heataddress: demand;[email protected]. Forecast; Climate change E-mail
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.064
90
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2018) 000–000
2
toring and management technologies, and new energy trading methods, integrated energy systems have received extensive attention and development from governments, scholars, and research institutions in various countries in recent years [1]-[4]. The concept of an integrated energy system was first proposed in Europe. It consists of a social energy supply network and a terminal integrated energy unit system. It is a comprehensive integration of heating, cooling, hydrogen, and electricity, energy, information, and transportation support systems. The system, through the scientific scheduling among various energy sources (traditional energy/renewable energy, cold/heat/electricity/gas/hydrogen, etc.) within the system, can achieve energy coordination and efficient use, and meet the user's multiple energy use. The demand for improving the safety and reliability of social energy supply is hailed as the main form of human energy in the future [5]. The core issue of the park-based integrated energy system is to study the coordinated control and management mechanisms of various energy sources in the system, and strive to optimize the dispatch of various energy sources to meet the needs of users within the park, such as power supply, cooling/heating, etc., aiming at achieving efficient use of energy. This paper focuses on the coordinated optimization of park-based integrated energy systems. First, based on the actual operating mechanism and control strategy of the integrated energy system, considering the operation constraints of the equipment, we establish a daily optimization scheduling model of the park-type integrated energy system. Then the simulation and analysis of the system's optimization scheduling strategy are carried out. Finally, the conclusion is given. 2. Integrated energy system configuration This paper takes the northern park of the State Grid Customer Service Center as the research object. The park is the northern call center and power supply technology R&D center of the State Grid Corporation of China. In the first phase, there are 10 buildings (5 office buildings and 5 employee apartments) with a total construction area. More than 140,000 square meters. The park has both production and life uses. Energy demand includes power supply, summer space cooling, winter space heating, and year-round hot water supply. This paper focuses on power supply and winter space heating energy. The integrated energy system is connected to the municipal power grid and is connected to a 838 kW photovoltaic power generation system. The system configures three screw-type ground source heat pump (GSHP, each with a capacity of 1355 kW) units and a regenerative electric boiler system (four pressure-bearing electric boilers and a heat storage tank) to meet the heating demand. 3. System optimal operation modeling 3.1. Objective function and variables of the day-ahead optimal operation The optimal operation model of the integrated energy system has two types of economic and environmental goals: (1) The economic goal is to minimize the day-ahead operation cost of the system, which mainly includes the purchase of electricity from the external grid and financial subsidies obtained from photovoltaic power generation. The specific form of the economic objective function is as follows: 24
24
min Ft B PtTL F PV Pt PV
t 1 t 1
(1) TL
represents the power purchased from the grid In the above formula, t is the variable subscript; Pt PV B is the predicted photovoltaic power; Ft represents the time-of-use price of during the tth period, Pt PV represents the PV subsidy price. electricity purchased from the grid, and F
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2014) 000–000
91
(2) The environmental goal is to minimize the carbon emissions. Since the target of the study is the use of green energy besides electricity purchased from the outside, the scale of external electricity purchase reflects the scale of carbon emission. Therefore minimizing carbon emissions is equivalent to minimizing the amount of electricity purchased from the grid. The specific form of the environmental objective function is as follows: 24
min PtTL
(2)
t 1
The decision variables of the optimal operation model include the variables introduced in the constraint conditions, such as power balance and equipment operation, as well as some auxiliary modeling, in addition to the day-to-day power purchase and photovoltaic power generation in the objective function. The specific composition of variables and decision variables will be described in detail below. 3.2. System operation constraints The constraints for the optimal operation of the integrated energy system in the heating period include the hourly electric / thermal power balance constraints, the constraints related to operating characteristics and power distribution of each device, and the constraints of the decision variables. (1) Heat load balance constraint N HP
NB
QtB_H QtHWT_H LH t , QtHP_H ,i ,i
i 1 i 1
t 1, 2,
(3)
,T HP_H
In which, LtH is the heat load demand within the supply range of the centralized energy station. Qt ,i is B_H the heat load for the ith ground source heat pump in tth period. Qt ,i is the heat load for the ith boiler in tth B HP HWT_H is the heat load supplied by the heat storage tank for tth period. N and N repreperiod. Qt sent the number of ground source heat pump units and electric boilers, respectively. (2) Flow constraint of heat supply pump
F HP_H
N HP
N PHWP
(4)
F PHWP U tPHWP =Ft H , t 1, 2, , T ,j U tHP_H ,j
j 1 j 1
HP_H
PHWP
and F are the rated flows of the heating circulating pumps of the GSHP and the In which, F H HP_H and heat storage electric boiler system, respectively. Ft is the current flow of the system. U t ,i PHWP th are the operating modes of the i GSHP and the secondary heating circulation pump of regenerU t ,i ative electric boiler system heat exchanger in the tth period, respectively, which are all binary variables. N HP and N PHWP are the numbers of GSHP units and the secondary heating circulation pump of regenerative electric boiler system heat exchanger, respectively. (3) Heat load distribution constraint of GSHP (5) Ft H QtHP_H U tHP_H F HP_H LH , T , i 1, 2, , N HP t , t 1, 2, ,i ,i HP_H
is In which, LtH is the heat load demand within the supply range of the centralized energy station. Qt ,i B_H th th th the heat load for the i ground source heat pump in t period. Qt ,i is the heat load for the i boiler in tth B HP HWT_H is the heat load supplied by the heat storage tank for tth period. N and N repreperiod. Qt sent the number of ground source heat pump units and electric boilers, respectively. (4) Heat load distribution constraint of electric boiler (6) Ft HQtB_H U tB_H Ft PHWP LtH , t 1, 2, , T , i 1, 2, , N B ,i ,i
QtB,i QtB_HWT QtB_H ,i ,i , t 1, 2,
, T , i 1, 2,
,NB
(7)
3
92
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2018) 000–000
4
B
B_H
B_HWT
In which, Qt ,i , Qt ,i and Qt ,i respectively represent the heat generation, heat supply, and heat B_H th th storage of the i electric boiler in t period; U t ,i represents the heating mode of the ith electric boiler in tth period, which is a binary variable. The variable, when it is set to 1, indicates that the corresponding mode is active; otherwise, it indicates that the electric boiler is not in the corresponding mode. (5) Heat load distribution constraint of heat storage bank
Ft H QtHWT_H
N PHWP
i 1
H U tPHWP Ft PHWP L 1, 2, ,i t , t
(8)
,T
(6) Heat stored constraint of heat storage bank
NB
(9)
Wt HWT 1 HWT Wt HWT QtB_HWT QtHWT_H , t 1, 2, , T ,i 1 i 1
HWT
HWT
In which, is the self-heating rate of the heat storage tank; W and W refer to maximum and minimum heat amounts stored in the heat storage bank. (7) Electric load balance constraint
PtTL Pt PV LEt Pt HP_H Pt B Pt B_HWT_HWP , t 1, 2,
HWT
respectively
(10)
, 24
TL
LEt
is the link line power between the park and the upper power grid; is the In the above formula, Pt park power load forecast value of the park in addition to the power consumption of the heat supply devicPV
indicates the Photovoltaic system power generation of the integrated eneres during the tth period; Pt gy system. Additionally, there also include the maximum and minimum output constraints, electricity consumption constraints [6] and other operation mode constraints of the GSHP, boilers and heat storage bank. This model partially contains nonlinearity. With the help of specific linearization techniques [7], the day-ahead optimal operation model of the park-based integrated energy system will eventually be transformed into a mixed integer linear model with mixed integer linear features. For the economic goal and environmental goal, we can directly call the mature mixed integer linear optimization software to solve. 4. System operation study and discussion 4.1. Load demand and PV forecast in the studied typical day
Fig. 1. Typical daily load demand, PV generation and TOU during the heating period.
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2014) 000–000
The heat load, electric load and photovoltaic power generation curve of a typical sunny day during the heating period of this study are shown in Figure 1. The figure also shows the level of Time-of-Use (TOU) prices purchased from the grid. Compared to the electric load requirement, the power output of PV can only supply a small part of load. The heat load and the TOU curve both have obvious peaks and valleys, so the energy system can be optimized to make full use of TOU to meet the heat load. 4.2. Economic operation of the integrated energy system Figure 2 gives the thermal load balance and the operation of the thermal storage device when the parktype integrated energy system is operating according to economic objective. In the valley price segment, the electric boiler will preferentially store heat in the heat storage device. At this time, the heat load is mainly supplied by the ground source heat pump unit, and the number of ground source heat pump operation units is increased or decreased according to the heat load demand. During the peak electricity price period from 18:00 to 23:00, the heat load demand is small, and the heat supply is mainly ensured by the ground source heat pump. In the rest of the peak electricity price and the flat electricity price segment, the demand for heating is higher, and the heat storage device and the ground source heat pump unit will jointly participate in the supply of heat load.
Fig. 2. Economic operation of the integrated energy system during typical daily in the heating period. (a) Thermal load balance; (b) Thermal storage operation.
4.3. Comparison of system economic operation and environmental operation Table 1 summarizes the comparison of the main results when the heating time of the research object is typically run on economic and environmental goals. The result shows that, compared to its environmental operation, the system’s economic operation has 18.7% of cost saving while has a 3.4% of more energy consumption. When the system operates according to economic objective, the electric boiler in the valley electricity price periods will preferentially store heat in the heat storage device and release heat during periods of high electricity prices. When the system is operated in an environment-friendly manner, heat storage and heat release through the heat storage device will increase additional energy consumption. Therefore, the electric boiler is mainly based on the direct supply of heat. Since the ground source heat pump has cooling advantages in each price period in this example, the total amounts of heat supplied by the ground source heat pump units are the same under both types of operating targets.
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5
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Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2018) 000–000
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Table 1. main comparative results of the economic and environmental performance of the typical daily solar heating during the heating period of the research object. Project Total operation cost (CNY) Total electricity supply (kWh) In: electricity purchased from grid PV generation Total heat supply (kWh) In: heat supplied by heat pump heat supplied by electric boiler directly heat supplied by heat storage
Economic operation 70343 101304 97928 3377 100371 58442 0 41929
Environmental operation 86497 97946 94569 3377 100371 58442 32270 9659
5. Conclusion This paper chooses an actual park-based integrated energy system and studies its day-ahead optimal operation during heating period. Taking the economic and environmental goals as targets, considering various types of equipment operation constraints and load balance constraints, the corresponding dayahead optimal operation model is established. On this basis, we simulate the system operation in a typical day and compare the operation results of the integrated energy system under different goals. The results show that there is a contradiction between system operation economic and environmental performances and a compromise operation strategy is recommended if needed. Acknowledgements All the authors acknowledge the financial supports from Science and Technology Project of State Grid of Corporation of China titled with “Technology and Economic Analysis of Potential Assessment of Power System Peak Regulation for Large-scale New Energy Integrated Grid”. References [1] Wu J. Drivers and State-of-the-art of Integrated Energy Systems in Europe. Automation of Electric Power Systems 2016; 5: 1-7. [2] Jia H, Wang D, Xu X, et al. Research on Some Key Problems Related to Integrated Energy Systems. Automation of Electric Power Systems 2015; 7: 198-207. [3] Yu X, Xu X, Chen S, et al. A Brief Review to Integrated Energy System and Energy Internet. Transactions of China Electrotechnical Society 2016; 31(1): 1-13. [4] Guerrero JM, Blaabjerg F, Zhelev T, et al. Distributed generation: Toward a new energy paradigm. IEEE Trans on Electronic Magazine 2010; 4(1): 52-64. [5] Stanislav P, Bryan K, Tihomir M. Smart Grids better with integrated energy system. IEEE Electrical Power & Energy Conference 2009; 1-8. [6] Wang C, Jiao B, Guo L, et al. Robust scheduling of building energy system under uncertainty. Applied Energy 2016; 167: 366-376. [7] Floudas CA. Nonlinear and mixed-integer optimization: fundamentals and applications. Oxford University Press on Demand; 1995.
ScienceDirect ScienceDirect Energy Procedia 00 (2018) 000–000 Energy Procedia 00 (2018) 000–000 ScienceDirect ScienceDirect
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia Procedia 00 152(2017) (2018)000–000 89–94 Energy www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy sysCUE2018-Applied Energyand Symposium and Low Forum 2018:cities Lowand carbon cities and Applied Energy Symposium Forum 2018: carbon tems, CUE2018, 5–7 June 2018, Shanghai, China urban energy sysurban energy systems, 5–7 June 2018, Shanghai, China tems, CUE2018, 5–7 June 2018, Shanghai, China
Optimal Operation Park-based Integrated Energy The 15th Internationalof Symposium on District Heating and Cooling Optimal Operation of Park-based Integrated Energy System Assessing the feasibility System of using the heat demand-outdoor a,b,
c
a,b
a,b
c
c
Bingqi Jiaoa,b,*,function Ke Xu , Shengyu Wua,b, Yaohua Wanga,bheat , Jing Xuc, Shiju Wang c temperature for a long-term district forecast Bingqi Jiao *, Ke Xuc, Shengyu Wu , Yaohua Wang , Jing demand Xu , Shiju Wang a
State Grid Energy Research Institue Co., Ltd., Changping District, Beijing 102209, China a Planning a Ltd.,Laboratory, c China c State Grid aEnergy and Energy Power System Research Changping District, Beijing China Statea,b,c Grid Research Institue Co., Changpingb., District, Beijing 102209, I. Andrić *, A.Tianjin Pina , P. Ferrão , J. Fournier B.Tianjin Lacarrière , O. 102209, Le Corre c b Stateand Grid Electric PowerResearch Company, Heibei District, 300010, China102209, China State Grid Energy Power System Planning Laboratory, Changping District, Beijing c a State Grid Tianjinand Electric Company, District, 300010, IN+ Center for Innovation, Technology PolicyPower Research - InstitutoHeibei Superior Técnico,Tianjin Av. Rovisco Pais China 1, 1049-001 Lisbon, Portugal b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France As energy, environmental and climate issues have become increasingly prominent, it has been widely agreed that the As energy, environmental and represented climate issues prominent, it has been widely that the development of clean energy byhave solarbecome energy,increasingly wind energy, and geothermal energy willagreed be vigorously development energy represented by solar energy, wind energy, andand geothermal willamong be vigorously promoted. As of an clean effective carrier for coordinated conversion and coordinated efficient energy operation different Abstract promoted. As an effective carrier for coordinated conversion coordinated efficient operation among different energy quality and diversified energy varieties, the integratedand energy system isand gaining more and more attention and energy support. quality and diversified varieties, integrated energysupply systemrequirements is gaining more andpark-type more attention and policy In this paper, energy according to the the heating and power of the integrated District heating networks commonly addressed in the literature as one of requirements thescheduling most effective decreasing the policy support. In thisare paper, according toa the heating andmodel power of park-type integrated energy system during the heating period, mixed-integer forsupply optimal of the thesolutions system isfor proposed. greenhouse gas chooses emissions from theofbuilding These systems require highcomprehensively investments returned through energy system during thetypes heating period,sector. aand mixed-integer model for and optimal schedulingwhich ofconsiders thearesystem proposed. The model two economic environmental goals, theisbalance be-the heat sales.The Due to the changed and building renovation policies, heat demand in supply the future could decrease, model chooses two climate types of conditions economic goals, and comprehensively considers the balance between supply and demand of the hourly loadand andenvironmental the operational characteristics of each energy device. Espeprolonging the model investment return period. tween andtakes demand the hourly load and the operational of each energy supplya device. Especially, supply the intoof account the water distribution rules ofcharacteristics the heating supply system. Finally, typical day is The cially, main scope of thistakes paper is to assessofthe the feasibility of usingrules the result heat demand – supply outdoor temperature function forday heat thetomodel into account distribution of the heating system. a typical isdemand selected verify the effectiveness thewater proposed model. The shows that, compared to Finally, its environmental operforecast. district ofeconomic Alvalade, located inproposed Lisbon (Portugal), used a case The is consisted selected verify the effectiveness of the model. The was result shows compared to its district environmental oper- of 665 ation,The thetosystem’s operation has 18.7% of cost saving while hasasa that, 3.4% of study. more energy consumption. buildings vary in both construction period and typology. Threewhile weather (low, energy medium, high) and three district ation, that the system’s economic operation has 18.7% of cost saving has ascenarios 3.4% of more consumption. renovation scenarios developed intermediate, deep). To estimate the error, obtained heat demand values were Copyright © 2018were Elsevier Ltd. All(shallow, rights reserved. Copyright © 2018from Elsevier Ltd. Allheat rights reserved. compared withand results a dynamic demand model, previously developed validated by Symposium the authors. and Forum Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility of the scientific committee of and Applied Energy Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy The 2018: resultsLow showed thatcities whenand only weather change is the considered, the margin of error could be acceptable for and someForum applications Selection and peer-review under responsibility of scientific committee of Applied Energy Symposium carbon urban energy systems, CUE2018. Symposium and Forum 2018: Low carbon cities and urban energy systems. (the 2018: error in annual demand wasurban lowerenergy than 20% for all weather scenarios considered). However, after introducing renovation Low carbon cities and systems, CUE2018. scenarios, the error value energy increased up to optimal 59.5% (depending on the weather and renovation Keywords: Integrated system; operation; heat pump; heat storage; electricscenarios boiler combination considered). The Keywords: value of slope coefficient on average withinheat the pump; range heat of 3.8% up toelectric 8% per decade, that corresponds to the Integrated energyincreased system; optimal operation; storage; boiler decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the 1. Introduction coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1. Introduction improveWith the accuracy of development heat demand estimations. the rapid and wide application of distributed generation technologies, energy monib
With the rapid development and wide application of distributed generation technologies, energy moni-
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.* Corresponding author. Tel.: +86 189 1169 8549; fax: +86 010 66603580. address: [email protected]. *E-mail Corresponding author. Tel.: +86 189 1169 8549; fax: +86 010 66603580. Keywords: Heataddress: demand;[email protected]. Forecast; Climate change E-mail
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.064
90
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2018) 000–000
2
toring and management technologies, and new energy trading methods, integrated energy systems have received extensive attention and development from governments, scholars, and research institutions in various countries in recent years [1]-[4]. The concept of an integrated energy system was first proposed in Europe. It consists of a social energy supply network and a terminal integrated energy unit system. It is a comprehensive integration of heating, cooling, hydrogen, and electricity, energy, information, and transportation support systems. The system, through the scientific scheduling among various energy sources (traditional energy/renewable energy, cold/heat/electricity/gas/hydrogen, etc.) within the system, can achieve energy coordination and efficient use, and meet the user's multiple energy use. The demand for improving the safety and reliability of social energy supply is hailed as the main form of human energy in the future [5]. The core issue of the park-based integrated energy system is to study the coordinated control and management mechanisms of various energy sources in the system, and strive to optimize the dispatch of various energy sources to meet the needs of users within the park, such as power supply, cooling/heating, etc., aiming at achieving efficient use of energy. This paper focuses on the coordinated optimization of park-based integrated energy systems. First, based on the actual operating mechanism and control strategy of the integrated energy system, considering the operation constraints of the equipment, we establish a daily optimization scheduling model of the park-type integrated energy system. Then the simulation and analysis of the system's optimization scheduling strategy are carried out. Finally, the conclusion is given. 2. Integrated energy system configuration This paper takes the northern park of the State Grid Customer Service Center as the research object. The park is the northern call center and power supply technology R&D center of the State Grid Corporation of China. In the first phase, there are 10 buildings (5 office buildings and 5 employee apartments) with a total construction area. More than 140,000 square meters. The park has both production and life uses. Energy demand includes power supply, summer space cooling, winter space heating, and year-round hot water supply. This paper focuses on power supply and winter space heating energy. The integrated energy system is connected to the municipal power grid and is connected to a 838 kW photovoltaic power generation system. The system configures three screw-type ground source heat pump (GSHP, each with a capacity of 1355 kW) units and a regenerative electric boiler system (four pressure-bearing electric boilers and a heat storage tank) to meet the heating demand. 3. System optimal operation modeling 3.1. Objective function and variables of the day-ahead optimal operation The optimal operation model of the integrated energy system has two types of economic and environmental goals: (1) The economic goal is to minimize the day-ahead operation cost of the system, which mainly includes the purchase of electricity from the external grid and financial subsidies obtained from photovoltaic power generation. The specific form of the economic objective function is as follows: 24
24
min Ft B PtTL F PV Pt PV
t 1 t 1
(1) TL
represents the power purchased from the grid In the above formula, t is the variable subscript; Pt PV B is the predicted photovoltaic power; Ft represents the time-of-use price of during the tth period, Pt PV represents the PV subsidy price. electricity purchased from the grid, and F
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2014) 000–000
91
(2) The environmental goal is to minimize the carbon emissions. Since the target of the study is the use of green energy besides electricity purchased from the outside, the scale of external electricity purchase reflects the scale of carbon emission. Therefore minimizing carbon emissions is equivalent to minimizing the amount of electricity purchased from the grid. The specific form of the environmental objective function is as follows: 24
min PtTL
(2)
t 1
The decision variables of the optimal operation model include the variables introduced in the constraint conditions, such as power balance and equipment operation, as well as some auxiliary modeling, in addition to the day-to-day power purchase and photovoltaic power generation in the objective function. The specific composition of variables and decision variables will be described in detail below. 3.2. System operation constraints The constraints for the optimal operation of the integrated energy system in the heating period include the hourly electric / thermal power balance constraints, the constraints related to operating characteristics and power distribution of each device, and the constraints of the decision variables. (1) Heat load balance constraint N HP
NB
QtB_H QtHWT_H LH t , QtHP_H ,i ,i
i 1 i 1
t 1, 2,
(3)
,T HP_H
In which, LtH is the heat load demand within the supply range of the centralized energy station. Qt ,i is B_H the heat load for the ith ground source heat pump in tth period. Qt ,i is the heat load for the ith boiler in tth B HP HWT_H is the heat load supplied by the heat storage tank for tth period. N and N repreperiod. Qt sent the number of ground source heat pump units and electric boilers, respectively. (2) Flow constraint of heat supply pump
F HP_H
N HP
N PHWP
(4)
F PHWP U tPHWP =Ft H , t 1, 2, , T ,j U tHP_H ,j
j 1 j 1
HP_H
PHWP
and F are the rated flows of the heating circulating pumps of the GSHP and the In which, F H HP_H and heat storage electric boiler system, respectively. Ft is the current flow of the system. U t ,i PHWP th are the operating modes of the i GSHP and the secondary heating circulation pump of regenerU t ,i ative electric boiler system heat exchanger in the tth period, respectively, which are all binary variables. N HP and N PHWP are the numbers of GSHP units and the secondary heating circulation pump of regenerative electric boiler system heat exchanger, respectively. (3) Heat load distribution constraint of GSHP (5) Ft H QtHP_H U tHP_H F HP_H LH , T , i 1, 2, , N HP t , t 1, 2, ,i ,i HP_H
is In which, LtH is the heat load demand within the supply range of the centralized energy station. Qt ,i B_H th th th the heat load for the i ground source heat pump in t period. Qt ,i is the heat load for the i boiler in tth B HP HWT_H is the heat load supplied by the heat storage tank for tth period. N and N repreperiod. Qt sent the number of ground source heat pump units and electric boilers, respectively. (4) Heat load distribution constraint of electric boiler (6) Ft HQtB_H U tB_H Ft PHWP LtH , t 1, 2, , T , i 1, 2, , N B ,i ,i
QtB,i QtB_HWT QtB_H ,i ,i , t 1, 2,
, T , i 1, 2,
,NB
(7)
3
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4
B
B_H
B_HWT
In which, Qt ,i , Qt ,i and Qt ,i respectively represent the heat generation, heat supply, and heat B_H th th storage of the i electric boiler in t period; U t ,i represents the heating mode of the ith electric boiler in tth period, which is a binary variable. The variable, when it is set to 1, indicates that the corresponding mode is active; otherwise, it indicates that the electric boiler is not in the corresponding mode. (5) Heat load distribution constraint of heat storage bank
Ft H QtHWT_H
N PHWP
i 1
H U tPHWP Ft PHWP L 1, 2, ,i t , t
(8)
,T
(6) Heat stored constraint of heat storage bank
NB
(9)
Wt HWT 1 HWT Wt HWT QtB_HWT QtHWT_H , t 1, 2, , T ,i 1 i 1
HWT
HWT
In which, is the self-heating rate of the heat storage tank; W and W refer to maximum and minimum heat amounts stored in the heat storage bank. (7) Electric load balance constraint
PtTL Pt PV LEt Pt HP_H Pt B Pt B_HWT_HWP , t 1, 2,
HWT
respectively
(10)
, 24
TL
LEt
is the link line power between the park and the upper power grid; is the In the above formula, Pt park power load forecast value of the park in addition to the power consumption of the heat supply devicPV
indicates the Photovoltaic system power generation of the integrated eneres during the tth period; Pt gy system. Additionally, there also include the maximum and minimum output constraints, electricity consumption constraints [6] and other operation mode constraints of the GSHP, boilers and heat storage bank. This model partially contains nonlinearity. With the help of specific linearization techniques [7], the day-ahead optimal operation model of the park-based integrated energy system will eventually be transformed into a mixed integer linear model with mixed integer linear features. For the economic goal and environmental goal, we can directly call the mature mixed integer linear optimization software to solve. 4. System operation study and discussion 4.1. Load demand and PV forecast in the studied typical day
Fig. 1. Typical daily load demand, PV generation and TOU during the heating period.
Bingqi Jiao et al. / Energy Procedia 152 (2018) 89–94 Author name / Energy Procedia 00 (2014) 000–000
The heat load, electric load and photovoltaic power generation curve of a typical sunny day during the heating period of this study are shown in Figure 1. The figure also shows the level of Time-of-Use (TOU) prices purchased from the grid. Compared to the electric load requirement, the power output of PV can only supply a small part of load. The heat load and the TOU curve both have obvious peaks and valleys, so the energy system can be optimized to make full use of TOU to meet the heat load. 4.2. Economic operation of the integrated energy system Figure 2 gives the thermal load balance and the operation of the thermal storage device when the parktype integrated energy system is operating according to economic objective. In the valley price segment, the electric boiler will preferentially store heat in the heat storage device. At this time, the heat load is mainly supplied by the ground source heat pump unit, and the number of ground source heat pump operation units is increased or decreased according to the heat load demand. During the peak electricity price period from 18:00 to 23:00, the heat load demand is small, and the heat supply is mainly ensured by the ground source heat pump. In the rest of the peak electricity price and the flat electricity price segment, the demand for heating is higher, and the heat storage device and the ground source heat pump unit will jointly participate in the supply of heat load.
Fig. 2. Economic operation of the integrated energy system during typical daily in the heating period. (a) Thermal load balance; (b) Thermal storage operation.
4.3. Comparison of system economic operation and environmental operation Table 1 summarizes the comparison of the main results when the heating time of the research object is typically run on economic and environmental goals. The result shows that, compared to its environmental operation, the system’s economic operation has 18.7% of cost saving while has a 3.4% of more energy consumption. When the system operates according to economic objective, the electric boiler in the valley electricity price periods will preferentially store heat in the heat storage device and release heat during periods of high electricity prices. When the system is operated in an environment-friendly manner, heat storage and heat release through the heat storage device will increase additional energy consumption. Therefore, the electric boiler is mainly based on the direct supply of heat. Since the ground source heat pump has cooling advantages in each price period in this example, the total amounts of heat supplied by the ground source heat pump units are the same under both types of operating targets.
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Table 1. main comparative results of the economic and environmental performance of the typical daily solar heating during the heating period of the research object. Project Total operation cost (CNY) Total electricity supply (kWh) In: electricity purchased from grid PV generation Total heat supply (kWh) In: heat supplied by heat pump heat supplied by electric boiler directly heat supplied by heat storage
Economic operation 70343 101304 97928 3377 100371 58442 0 41929
Environmental operation 86497 97946 94569 3377 100371 58442 32270 9659
5. Conclusion This paper chooses an actual park-based integrated energy system and studies its day-ahead optimal operation during heating period. Taking the economic and environmental goals as targets, considering various types of equipment operation constraints and load balance constraints, the corresponding dayahead optimal operation model is established. On this basis, we simulate the system operation in a typical day and compare the operation results of the integrated energy system under different goals. The results show that there is a contradiction between system operation economic and environmental performances and a compromise operation strategy is recommended if needed. Acknowledgements All the authors acknowledge the financial supports from Science and Technology Project of State Grid of Corporation of China titled with “Technology and Economic Analysis of Potential Assessment of Power System Peak Regulation for Large-scale New Energy Integrated Grid”. References [1] Wu J. Drivers and State-of-the-art of Integrated Energy Systems in Europe. Automation of Electric Power Systems 2016; 5: 1-7. [2] Jia H, Wang D, Xu X, et al. Research on Some Key Problems Related to Integrated Energy Systems. Automation of Electric Power Systems 2015; 7: 198-207. [3] Yu X, Xu X, Chen S, et al. A Brief Review to Integrated Energy System and Energy Internet. Transactions of China Electrotechnical Society 2016; 31(1): 1-13. [4] Guerrero JM, Blaabjerg F, Zhelev T, et al. Distributed generation: Toward a new energy paradigm. IEEE Trans on Electronic Magazine 2010; 4(1): 52-64. [5] Stanislav P, Bryan K, Tihomir M. Smart Grids better with integrated energy system. IEEE Electrical Power & Energy Conference 2009; 1-8. [6] Wang C, Jiao B, Guo L, et al. Robust scheduling of building energy system under uncertainty. Applied Energy 2016; 167: 366-376. [7] Floudas CA. Nonlinear and mixed-integer optimization: fundamentals and applications. Oxford University Press on Demand; 1995.