- Email: [email protected]
An Optimal Scheduling Model for a Hybrid Energy Microgrid Considering Building Based Virtual Energy Storage System
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 88 (2016) 375 – 381
CUE2015-Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems
An Optimal Scheduling Model for a Hybrid Energy Microgrid Considering Building Based Virtual Energy Storage System Xiaolong Jina, Yunfei Mua*, Hongjie Jiaa, Tao Jiangb, Houhe Chenb, Rufeng Zhangb a
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China b School of Electrical Engineering, Northeast China Dianli University, Jilin 132012, China
Abstract An optimal scheduling model for a hybrid energy microgrid considering the building based virtual energy storage system (VSS) is developed in this paper. The VSS model is developed by utilizing the building thermal equilibrium equation taking the heat storage characteristics of building into consideration. Firstly, mathematical models of various energy systems and VSS in the hybrid energy microgrid are developed. Then, an optimal scheduling model is developed to minimize the operation costs of the microgrid. Numerical studies demonstrate that the proposed optimal scheduling model can provide the microgrid with an effective and economical scheduling scheme and reduce the operation costs. © Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ©2016 2015The The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the organizing committee of CUE 2015
Keywords: Microgrid; Optimal Scheduling; Virtual storage System (VSS); Low-carbon City
Nomenclature Abbreviation CHP
Combined heat and Power
VSS
Virtual Storage System
* Yunfei Mu. Tel.: +86-1582-250-9583; fax: +86-022-27892809. E-mail address: [email protected].
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of CUE 2015 doi:10.1016/j.egypro.2016.06.003
376
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
HVAC
Heating, Ventilation and Air Conditioning
Variables: t t Pgrid / Pgas
Electric/ Natural gas power purchases
t PCHP
Electric power generated by CHP
t PEC
Electric power consumed by electric chiller
Pbtt
Charging and discharging power of the battery storage system
Parameters and constants Cet / Cgas
Wholesale electricity/natural gas price
t t PHVAC / Pother
Electric power consumption of HVAC/ other devices in the building
Wbt ,min / Wbt ,max
The minimum/maximum power storage of the battery storage system
t PPV
Electric power generated by photovoltaic
t PMGL
Electric loads of the microgrid without building
k wall / k win
Heat transfer coefficient of wall/window in the building
Fwall / Fwin
Wall/Window area in the building
Tout ,t / Tin,t
Outdoor/ Indoor temperature
I t / SC
Solar radiation/Shading coefficient
Qman,t / Qea ,t
Internal heat gain caused by metabolism/ electric appliances in the building
t QAC
Cooling generated by absorption chiller
ρ, C,V
The density, specific heat capacity and volume of the air in the building
ηCHP / ηHE
Efficiency of CHP/heat recovery system
γCHP
Thermoelectric ratio of CHP
COPAC / COPEC
Coefficient of performance of absorption chiller/electric chiller
1. Introduction Renewable and distributed power generations have been recognized as solutions for safe, secure, sustainable and affordable energy production, distribution and consumption in the future low-carbon cities.
377
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
As an effective way to handle the variability and uncertainty of the renewable energies while ensure the economical energy supply, the hybrid energy microgrid is drawing more and more attentions around the world. A wide range of energy systems and energy loads including different renewable energy systems, combined heat and power (CHP) system [1], low-carbon buildings [2], electric vehicles [3] have been employed in the hybrid energy microgrid. Thus, the optimization, coordination and management of the various energy systems in the microgrid are of significant importance for the integration of renewable energy and reducing the costs of energy utilization for the microgrid. A hierarchical energy management system was designed for a community level microgrid based on energy hub model in [1]. An optimal scheduling and control model for microgrid is proposed in [4] taking several sources of uncertainty into consideration. Lu et al. [5] presented a mixed-integer linear programming algorithms (MINLP) based optimal scheduling strategy for the microgrid including buildings to minimize the daily operation costs. An optimal scheduling model for a hybrid energy microgrid considering the building based VSS under the time-sensitive electricity pricing is developed in this paper. The VSS model is developed by utilizing the building thermal equilibrium equation taking the heat storage characteristics of building into consideration. It not only can guarantee the customer comfort level, but also can contribute to operation costs reduction of the microgrid. 2. Hybrid Energy Microgrid Models The hybrid energy microgrid including building with renewable energy resources is shown in Fig. 1. The various energy systems models of the microgrid are described as follows. Electric distribution netwok
PV
Electric Power Flow Electric loads
Battery storage system
Natural Gas Flow
CHP: Combined Heat and Power HVAC: Heating, Ventilating, and Air Conditioning
Heat Flow
PV: Photovoltaic
HVAC
Electric chillers
Natural gas Other loads CHP
Heat recovery system
Absorption chiller
Building
Fig. 1. Description of the hybrid energy microgrid including building
1) Combined heat and power The power generation of the CHP is shown as Eq. (1). t t PCHP Pgas u ηCHP
(1)
2) Absorption Chiller The absorption chiller is driven by the recovered heat from the CHP. The cooling generated by the absorption chiller depends on the exhaust heat from the CHP, as shown in Eq. (2). t t QAC ηHE u γ CHP u PCHP u COPAC (2)
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Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
3) Electric Chiller The electricity consumption of electric chiller is depicted by Eq. (3). t PEC
t QEC / COPEC
(3)
4) Building based VSS The VSS model is developed by utilizing the building thermal equilibrium equation (shown in Eq. (4)). Due to the heat storage characteristics of building, the cooling consumption can be adjusted according the time-sensitive electricity pricing without disturbing the customer comfort level (taking the indoor temperature regulation deadband into consideration) at the same time. So the building can be modeled as a VSS to participate in the optimal scheduling of the hybrid energy microgrid for operation costs reduction. kwall u Fwall u Tout Tin kwin u Fwin u Tout Tin I u Fwin u SC Qindoor Qc ,building
ρ u C uV u
(4)
dTin dτ
The charging/discharging cooling power of the building based VSS is given by Eq. (5). t QVSS Qcct,building Qct ,building t c ,building
where Q is the cooling consumption of the building without indoor temperature regulation; the cooling consumption of the building with indoor temperature regulation.
(5) t c ,building
Qc
3. Formulation of Optimal Scheduling Model 3.1. Objective function The objective function depicted in Eq. (6) is to minimize the total operation costs for the microgrid. t t min f ( x, u) min ®¦ Cet Pgrid Cgas Pgas (6) ½¾ ¯ tT ¿ 3.2. Constraints 1) Electrical power balance: t t t t Pgrid PCHP PPV Pbtt PLoad t Load
t MGL
P
P
P
t HVAC
(7)
0
P
t other
(8)
2) Cooling demand balance:
't u [kwall u Fwall u Tout ,t Tin ,t k win u Fwin u Tout ,t Tin ,t t t I t u Fwin u SC Qindoor ,t QAC QEC ] U u C u V u Tin ,t 1 Tin ,t
Qindoor ,t
0
Qman ,t Qea ,t
(9)
(10)
3) Electric power purchases: t t t Pgrid ,min d Pgrid d Pgrid ,max
(11)
4) Operational constraints for the CHP, electric battery storage system and electric chiller: t t t PCHP ,min d PCHP d PCHP ,max
t bt ,min
P
dP dP t bt
t bt ,max
(12) (13)
is
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Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381 24
¦P
t bt
(14)
0
t 1
24
Wbt ,min d Winit ¦ Pbtt d Wbt ,max
(15)
t t t PEC ,min d PEC d PEC ,max
(16)
t 1
4. Case Study 4.1. Case study
Outdoor temperature
Solar radiation
0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
35 30 25 20
15 10 5 0
Power generation/consumption and internal heat gain (kW)
Outdoor temperature (͘C)
40
Solar radiation (kW/m2 )
The hybrid energy microgrid test case in Fig. 1 is utilized to verify the effectiveness of the developed optimal scheduling model. The office hours are set to be from 9:00am to 21:00 pm. The indoor temperature set-point is 22.5ºC and the indoor temperature can be regulated from 20ºC to 25ºC. The outdoor air temperature, solar radiation and electric power generation/consumption of the microgrid in a typical day are shown in Fig. 2. 220 200 180 160 140 120 100 80 60 40 20 0
PV
Electric loads
HVAC
Other
Qman
Qea
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)
1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Hour of day (h)
Fig. 2. (a) Outdoor air temperature and solar radiation in a day; (b) Power generation/consumption in a day
4.2. Simulation Results The schedules of the hourly electricity consumptions/ generations in a day are shown in Fig. 3. The schedules of hourly cooling generations in a day are shown in Fig. 4. 50
Electricity (kW)
300 200
40
100 0 -100 -200
140
60
30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 20 Charge battery storage
-300
10
-400 -500
Hour of day (h)
0
Fig. 3. Hourly electricity generation/consumption
t Pother
120
t HVAC
100
P
t PMGL
t PEC
Pbtt t CHP
P
80 60
50 40 30
20
t grid
0
Cet
Use electricity for cooling generation for the lower electricity prices
60
40
t PPV
P
Use natural gas for cooling generation for the higher electricity prices
Discharge VSS
-20
20 Charge battery storage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Electricity price ($/MWh)
Discharge battery storage
Cooling (kW)
Positive value stands for generating or purchasing electricity
400
Electricity price ($/MWh
500
t QVSS
Qindoor ,t t QAC t QEC
Cet
10
Charge VSS
-40
Hour of day (h)
0
Fig. 4. Schedule of the hourly cooling generation
The scheduling results shown that, during office hours, the microgrid tends to purchase more electric power when the electricity price is low (9:00am-13:00 pm and 19:00-21:00 pm) and purchase less electric power when the electricity price is high (14:00-18:00pm). The battery storage system is fully charged in the non-office hours (1:00-8:00 am) when the electricity price is low and it is discharged to provide electricity during office hours. The CHP operates to provide electricity (and heat for the absorption chiller)
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Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
only when the grid electricity price is high (14:00-18:00 pm). The electric chiller undertakes a large portion of cooling demand when the electricity price is not very high (9:00am-13:00 pm and 19:00-21:00 pm). And the absorption chiller serves a large portion of the building cooling demand when the electricity price is high (14:00-18:00pm). The building based VSS discharged during the office hours to participate in the optimal scheduling for operation costs reduction. The operation costs in a day including VSS are 192.4 USD and the operation costs in a day without VSS are 215.5 USD, which contributes to 12.1% cost saving for the microgrid. The schedules of the hourly cooling consumption of the building in a day are shown in Fig. 5 (a) and the hourly indoor temperatures of the building with/without VSS in a day are shown in Fig. 5 (b). As Fig. 5 shows, the indoor temperature without VSS is kept at the set-point, while the indoor temperature with VSS is adjusted in the regulation band contributing to the operation costs reduction. From Fig. 5 (a) we can find out that the cooling consumption of the building without VSS is more than the cooling consumption of the building with VSS at most of the office hours and the difference between them is the discharging/charging cooling power of the VSS. 160
Cooling consumption of building excluding VSS Cooling consumption of building including VSS
40
Indoor temperature including VSS Indoor temperature excluding VSS Outdoor temperature
100 80
35
Discharge VSS
120
Charge VSS
60 Office hours
40
Temperature (͘C)
Cooling consumption (kW)
140 Indoor temperature regulation band contributing to costs decrease including VSS
30 25 Office hours
20
20
0
15
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)
Indoor temperature set-point: 22.5º C
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)
Fig. 5. (a) Schedule of the hourly cooling consumption of the building in a day; (b) Hourly indoor temperature of the building with/without VSS in a day
5. Conclusions An optimal scheduling model for a hybrid energy microgrid considering the building based VSS is developed in this paper. The building in microgrid is modeled as a VSS to participate in the optimal scheduling for operation costs reduction. The charging/discharging cooling power of the building based VSS can be adjusted according the time-sensitive electricity pricing. Numerical studies demonstrate that the effectiveness of the proposed optimal scheduling model. Acknowledgements
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
This work was financially supported by the National High-tech R&D Program of China (863 Program with No. 2015AA050403), the project National Natural Science Foundation of China (Grant No. 51307115, 51377117, and 51277128), Science and Technology Project of Tianjin Electric Power Corporation (Forecast and Coordination Control Technique of Smart Grid Park). References [1] Xu X, Jia H, Wang D, et al. Hierarchical energy management system for multi-source multi-product microgrids. Renew Energy 2015; 78: 621-630. [2] Zhao Y, Lu Y, Yan C, et al. MPC-Based Optimal Scheduling of Grid-Connected Low Energy Buildings with Thermal Energy Storages. Energy Build 2014; 86:415–426. [3] Mu Y, Wu J, Jenkins N, et al. A Spatial-Temporal model for grid impact analysis of plug-in electric vehicles. Appl Energy 2014; 114:456-465. [4] Farzan F, Jafari M A, Masiello R, et al. Toward Optimal Day-Ahead Scheduling and Operation Control of Microgrids Under Uncertainty. IEEE Trans Smart Grid 2015; 6(2):499-507. [5] Lu Y, Wang S, Sun Y, et al. Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Appl Energy 2015; 147:49-58.
381
ScienceDirect Energy Procedia 88 (2016) 375 – 381
CUE2015-Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems
An Optimal Scheduling Model for a Hybrid Energy Microgrid Considering Building Based Virtual Energy Storage System Xiaolong Jina, Yunfei Mua*, Hongjie Jiaa, Tao Jiangb, Houhe Chenb, Rufeng Zhangb a
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China b School of Electrical Engineering, Northeast China Dianli University, Jilin 132012, China
Abstract An optimal scheduling model for a hybrid energy microgrid considering the building based virtual energy storage system (VSS) is developed in this paper. The VSS model is developed by utilizing the building thermal equilibrium equation taking the heat storage characteristics of building into consideration. Firstly, mathematical models of various energy systems and VSS in the hybrid energy microgrid are developed. Then, an optimal scheduling model is developed to minimize the operation costs of the microgrid. Numerical studies demonstrate that the proposed optimal scheduling model can provide the microgrid with an effective and economical scheduling scheme and reduce the operation costs. © Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ©2016 2015The The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the organizing committee of CUE 2015
Keywords: Microgrid; Optimal Scheduling; Virtual storage System (VSS); Low-carbon City
Nomenclature Abbreviation CHP
Combined heat and Power
VSS
Virtual Storage System
* Yunfei Mu. Tel.: +86-1582-250-9583; fax: +86-022-27892809. E-mail address: [email protected].
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of CUE 2015 doi:10.1016/j.egypro.2016.06.003
376
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
HVAC
Heating, Ventilation and Air Conditioning
Variables: t t Pgrid / Pgas
Electric/ Natural gas power purchases
t PCHP
Electric power generated by CHP
t PEC
Electric power consumed by electric chiller
Pbtt
Charging and discharging power of the battery storage system
Parameters and constants Cet / Cgas
Wholesale electricity/natural gas price
t t PHVAC / Pother
Electric power consumption of HVAC/ other devices in the building
Wbt ,min / Wbt ,max
The minimum/maximum power storage of the battery storage system
t PPV
Electric power generated by photovoltaic
t PMGL
Electric loads of the microgrid without building
k wall / k win
Heat transfer coefficient of wall/window in the building
Fwall / Fwin
Wall/Window area in the building
Tout ,t / Tin,t
Outdoor/ Indoor temperature
I t / SC
Solar radiation/Shading coefficient
Qman,t / Qea ,t
Internal heat gain caused by metabolism/ electric appliances in the building
t QAC
Cooling generated by absorption chiller
ρ, C,V
The density, specific heat capacity and volume of the air in the building
ηCHP / ηHE
Efficiency of CHP/heat recovery system
γCHP
Thermoelectric ratio of CHP
COPAC / COPEC
Coefficient of performance of absorption chiller/electric chiller
1. Introduction Renewable and distributed power generations have been recognized as solutions for safe, secure, sustainable and affordable energy production, distribution and consumption in the future low-carbon cities.
377
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
As an effective way to handle the variability and uncertainty of the renewable energies while ensure the economical energy supply, the hybrid energy microgrid is drawing more and more attentions around the world. A wide range of energy systems and energy loads including different renewable energy systems, combined heat and power (CHP) system [1], low-carbon buildings [2], electric vehicles [3] have been employed in the hybrid energy microgrid. Thus, the optimization, coordination and management of the various energy systems in the microgrid are of significant importance for the integration of renewable energy and reducing the costs of energy utilization for the microgrid. A hierarchical energy management system was designed for a community level microgrid based on energy hub model in [1]. An optimal scheduling and control model for microgrid is proposed in [4] taking several sources of uncertainty into consideration. Lu et al. [5] presented a mixed-integer linear programming algorithms (MINLP) based optimal scheduling strategy for the microgrid including buildings to minimize the daily operation costs. An optimal scheduling model for a hybrid energy microgrid considering the building based VSS under the time-sensitive electricity pricing is developed in this paper. The VSS model is developed by utilizing the building thermal equilibrium equation taking the heat storage characteristics of building into consideration. It not only can guarantee the customer comfort level, but also can contribute to operation costs reduction of the microgrid. 2. Hybrid Energy Microgrid Models The hybrid energy microgrid including building with renewable energy resources is shown in Fig. 1. The various energy systems models of the microgrid are described as follows. Electric distribution netwok
PV
Electric Power Flow Electric loads
Battery storage system
Natural Gas Flow
CHP: Combined Heat and Power HVAC: Heating, Ventilating, and Air Conditioning
Heat Flow
PV: Photovoltaic
HVAC
Electric chillers
Natural gas Other loads CHP
Heat recovery system
Absorption chiller
Building
Fig. 1. Description of the hybrid energy microgrid including building
1) Combined heat and power The power generation of the CHP is shown as Eq. (1). t t PCHP Pgas u ηCHP
(1)
2) Absorption Chiller The absorption chiller is driven by the recovered heat from the CHP. The cooling generated by the absorption chiller depends on the exhaust heat from the CHP, as shown in Eq. (2). t t QAC ηHE u γ CHP u PCHP u COPAC (2)
378
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
3) Electric Chiller The electricity consumption of electric chiller is depicted by Eq. (3). t PEC
t QEC / COPEC
(3)
4) Building based VSS The VSS model is developed by utilizing the building thermal equilibrium equation (shown in Eq. (4)). Due to the heat storage characteristics of building, the cooling consumption can be adjusted according the time-sensitive electricity pricing without disturbing the customer comfort level (taking the indoor temperature regulation deadband into consideration) at the same time. So the building can be modeled as a VSS to participate in the optimal scheduling of the hybrid energy microgrid for operation costs reduction. kwall u Fwall u Tout Tin kwin u Fwin u Tout Tin I u Fwin u SC Qindoor Qc ,building
ρ u C uV u
(4)
dTin dτ
The charging/discharging cooling power of the building based VSS is given by Eq. (5). t QVSS Qcct,building Qct ,building t c ,building
where Q is the cooling consumption of the building without indoor temperature regulation; the cooling consumption of the building with indoor temperature regulation.
(5) t c ,building
Qc
3. Formulation of Optimal Scheduling Model 3.1. Objective function The objective function depicted in Eq. (6) is to minimize the total operation costs for the microgrid. t t min f ( x, u) min ®¦ Cet Pgrid Cgas Pgas (6) ½¾ ¯ tT ¿ 3.2. Constraints 1) Electrical power balance: t t t t Pgrid PCHP PPV Pbtt PLoad t Load
t MGL
P
P
P
t HVAC
(7)
0
P
t other
(8)
2) Cooling demand balance:
't u [kwall u Fwall u Tout ,t Tin ,t k win u Fwin u Tout ,t Tin ,t t t I t u Fwin u SC Qindoor ,t QAC QEC ] U u C u V u Tin ,t 1 Tin ,t
Qindoor ,t
0
Qman ,t Qea ,t
(9)
(10)
3) Electric power purchases: t t t Pgrid ,min d Pgrid d Pgrid ,max
(11)
4) Operational constraints for the CHP, electric battery storage system and electric chiller: t t t PCHP ,min d PCHP d PCHP ,max
t bt ,min
P
dP dP t bt
t bt ,max
(12) (13)
is
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Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381 24
¦P
t bt
(14)
0
t 1
24
Wbt ,min d Winit ¦ Pbtt d Wbt ,max
(15)
t t t PEC ,min d PEC d PEC ,max
(16)
t 1
4. Case Study 4.1. Case study
Outdoor temperature
Solar radiation
0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
35 30 25 20
15 10 5 0
Power generation/consumption and internal heat gain (kW)
Outdoor temperature (͘C)
40
Solar radiation (kW/m2 )
The hybrid energy microgrid test case in Fig. 1 is utilized to verify the effectiveness of the developed optimal scheduling model. The office hours are set to be from 9:00am to 21:00 pm. The indoor temperature set-point is 22.5ºC and the indoor temperature can be regulated from 20ºC to 25ºC. The outdoor air temperature, solar radiation and electric power generation/consumption of the microgrid in a typical day are shown in Fig. 2. 220 200 180 160 140 120 100 80 60 40 20 0
PV
Electric loads
HVAC
Other
Qman
Qea
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)
1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Hour of day (h)
Fig. 2. (a) Outdoor air temperature and solar radiation in a day; (b) Power generation/consumption in a day
4.2. Simulation Results The schedules of the hourly electricity consumptions/ generations in a day are shown in Fig. 3. The schedules of hourly cooling generations in a day are shown in Fig. 4. 50
Electricity (kW)
300 200
40
100 0 -100 -200
140
60
30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 20 Charge battery storage
-300
10
-400 -500
Hour of day (h)
0
Fig. 3. Hourly electricity generation/consumption
t Pother
120
t HVAC
100
P
t PMGL
t PEC
Pbtt t CHP
P
80 60
50 40 30
20
t grid
0
Cet
Use electricity for cooling generation for the lower electricity prices
60
40
t PPV
P
Use natural gas for cooling generation for the higher electricity prices
Discharge VSS
-20
20 Charge battery storage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Electricity price ($/MWh)
Discharge battery storage
Cooling (kW)
Positive value stands for generating or purchasing electricity
400
Electricity price ($/MWh
500
t QVSS
Qindoor ,t t QAC t QEC
Cet
10
Charge VSS
-40
Hour of day (h)
0
Fig. 4. Schedule of the hourly cooling generation
The scheduling results shown that, during office hours, the microgrid tends to purchase more electric power when the electricity price is low (9:00am-13:00 pm and 19:00-21:00 pm) and purchase less electric power when the electricity price is high (14:00-18:00pm). The battery storage system is fully charged in the non-office hours (1:00-8:00 am) when the electricity price is low and it is discharged to provide electricity during office hours. The CHP operates to provide electricity (and heat for the absorption chiller)
380
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
only when the grid electricity price is high (14:00-18:00 pm). The electric chiller undertakes a large portion of cooling demand when the electricity price is not very high (9:00am-13:00 pm and 19:00-21:00 pm). And the absorption chiller serves a large portion of the building cooling demand when the electricity price is high (14:00-18:00pm). The building based VSS discharged during the office hours to participate in the optimal scheduling for operation costs reduction. The operation costs in a day including VSS are 192.4 USD and the operation costs in a day without VSS are 215.5 USD, which contributes to 12.1% cost saving for the microgrid. The schedules of the hourly cooling consumption of the building in a day are shown in Fig. 5 (a) and the hourly indoor temperatures of the building with/without VSS in a day are shown in Fig. 5 (b). As Fig. 5 shows, the indoor temperature without VSS is kept at the set-point, while the indoor temperature with VSS is adjusted in the regulation band contributing to the operation costs reduction. From Fig. 5 (a) we can find out that the cooling consumption of the building without VSS is more than the cooling consumption of the building with VSS at most of the office hours and the difference between them is the discharging/charging cooling power of the VSS. 160
Cooling consumption of building excluding VSS Cooling consumption of building including VSS
40
Indoor temperature including VSS Indoor temperature excluding VSS Outdoor temperature
100 80
35
Discharge VSS
120
Charge VSS
60 Office hours
40
Temperature (͘C)
Cooling consumption (kW)
140 Indoor temperature regulation band contributing to costs decrease including VSS
30 25 Office hours
20
20
0
15
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)
Indoor temperature set-point: 22.5º C
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)
Fig. 5. (a) Schedule of the hourly cooling consumption of the building in a day; (b) Hourly indoor temperature of the building with/without VSS in a day
5. Conclusions An optimal scheduling model for a hybrid energy microgrid considering the building based VSS is developed in this paper. The building in microgrid is modeled as a VSS to participate in the optimal scheduling for operation costs reduction. The charging/discharging cooling power of the building based VSS can be adjusted according the time-sensitive electricity pricing. Numerical studies demonstrate that the effectiveness of the proposed optimal scheduling model. Acknowledgements
Xiaolong Jin et al. / Energy Procedia 88 (2016) 375 – 381
This work was financially supported by the National High-tech R&D Program of China (863 Program with No. 2015AA050403), the project National Natural Science Foundation of China (Grant No. 51307115, 51377117, and 51277128), Science and Technology Project of Tianjin Electric Power Corporation (Forecast and Coordination Control Technique of Smart Grid Park). References [1] Xu X, Jia H, Wang D, et al. Hierarchical energy management system for multi-source multi-product microgrids. Renew Energy 2015; 78: 621-630. [2] Zhao Y, Lu Y, Yan C, et al. MPC-Based Optimal Scheduling of Grid-Connected Low Energy Buildings with Thermal Energy Storages. Energy Build 2014; 86:415–426. [3] Mu Y, Wu J, Jenkins N, et al. A Spatial-Temporal model for grid impact analysis of plug-in electric vehicles. Appl Energy 2014; 114:456-465. [4] Farzan F, Jafari M A, Masiello R, et al. Toward Optimal Day-Ahead Scheduling and Operation Control of Microgrids Under Uncertainty. IEEE Trans Smart Grid 2015; 6(2):499-507. [5] Lu Y, Wang S, Sun Y, et al. Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Appl Energy 2015; 147:49-58.
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