Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations
Abstract
:1. Introduction
1.1. DCP-IESs and Their Key Characteristics
1.2. Literature Review
1.3. Scientific Contribution of the Study
2. Existing Problems to Solve
2.1. Three Coupled Configuration Issues
2.2. Redundancy and Device Switching Problems
3. Methodology
3.1. Hypotheses
3.2. Configuration Model
3.3. Equipment Model Based on Operational Data
3.4. Estimation of System Reliability
4. Case Study
4.1. Cooling and Power Load Prediction Using Operational Data
4.2. Basic Parameter Determination
4.3. Results
4.4. Switching Logic Analysis
4.5. Reliability Analysis and Redundant Design
5. Conclusions
- In the multi-objective configuration model, the carbon emissions of DCP-IESs were converted into economic indicators through carbon pricing and were optimized for the initial investment, operational costs, and maintenance costs of the system. Renewable energy, waste heat, free-cooling, and cooling storage were all considered. Compared with traditional energy systems, the results indicated that it would only take 2.88 years for the economics of the DCP-IES to catch up to those of the TDC-ES; the carbon emissions of the DCP-IES were 39,323 tons lower than those for the TDC-ES, and the PUE was 1.389.
- Multi-energy integration led to the frequent device switching of the DCP-IES. Based on the given device switching logic, the relevant constraints for avoiding the switching faults were fed back into the configuration model to correct the model. The results indicated that the total initial investment increased by $0.24 million, and the PUE was 1.388.
- In 12 scenarios of redundant design, both the cooling availability and power availability of DCP-IESs were calculated for systems with parallel- and series-arranged devices, based on Markov processes. The results indicated that the total initial investment for the DCP-IES meeting the four “9” reliability requirement, represented by Scenario A3, was $37.41 million, and the total initial investment for the DCP-IES meeting the five “9” reliability requirement, represented by Scenario B2, was $45.21 million. As the reliability increases, the initial investment cost increases increasingly faster.
- Using the new energy configuration method, the configuration scheme of the DCP-IES could be obtained under economical, low-carbon, and reliability requirements. With the help of the new DCP-IES configuration model, DCP planners can easily obtain energy indicators during the planning stage, designers can quickly achieve low-carbon energy allocations, and operators can obtain operational strategies of system devices based on real-time load forecasting results.
Author Contributions
Funding
Conflicts of Interest
Nomenclature
Acronyms | |
DCP-IES | data-center-park-integrated energy system |
DCP | data center park |
ECT | energy conversion technology |
FC | free-cooling |
UPS | uninterrupted power supplies |
IDC | internet data center |
PUE | power usage effectiveness |
ACS | air conditioning system |
TDC-ES | traditional data center energy system |
CHP | combined heating and power |
CCP | combined cooling and power |
MILP | mixed integer linear programming |
SSP | stationary state probability |
INI | initial investment |
OC | operating cost |
CE | carbon emissions |
AC | absorption chiller |
CS | cooling storage |
PG | power grid |
PV | photovoltaic |
WP | wind power |
USD | United States Dollar ($) |
Indices | |
the output of each energy conversion technology at every hour (kW) | |
the uniform annual value of initial cost ($) | |
the annual operative cost ($) | |
the equipment maintenance cost ($) | |
the carbon trading price ($/tCO2) | |
the carbon emission factor of natural gas (tCO2/Nm3) | |
the carbon emission factor of power grid (tCO2/kWh) | |
the discount rate | |
the generating efficiency of CCP | |
the time interval (h) | |
the lower calorific value of natural gas (kJ/Nm3) | |
the price per unit capacity of device j ($/kW) | |
the service life of technology j (year) | |
the time-of-use electricity price ($/kWh) | |
the natural gas price ($/Nm3) | |
basic power cost of transformer ($) | |
the fixed cost factor of device j ($/kW) | |
the variable cost factor of equipment j ($/kWh) | |
the coefficient matrix | |
the column vector of cooling loads | |
the column vector of electrical loads | |
the state of equipment j | |
the efficiency of equipment j for producing cooling or heating | |
the minimum output of equipment j (kW) | |
the rated capacity of equipment j (kW) | |
the partial load ratio | |
the column vector of system electrical loads excluding consumption by chillers | |
the maximum of free-cooling capacity that can be carried in the environment at t (kW) | |
the wet-bulb temperature outdoors (K) | |
the gross cooling storage at time t (kWh) | |
failure rate (time/day) | |
repair rate (time/day) | |
transfer density matrix | |
the SSP vector of each state | |
the number of states | |
the availability rate of the system with n devices connected in series (%) | |
the investment payback period (year) | |
the selling price of electricity ($/kWh) | |
the selling price of cooling ($/kWh) | |
The pre-cooling time of AC (min) | |
the available capacity of cooling storage (kWh) | |
the available capacity of UPS (kWh) |
Appendix A
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Electric Power Industry | Carbon Emission Factor (tCO2/MWh) |
---|---|
China (2017) | 0.620 |
USA (2017) | 0.420 |
UK (2017) | 0.237 |
Germany (2016) | 0.560 |
France (2017) | 0.074 |
Japan (2016) | 0.544 |
Russia (2016) | 0.358 |
India (2017) | 0.723 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
---|---|---|---|---|---|---|---|---|
Energy conversion technology | CCP | Absorption chiller | Chiller | Free-cooling | Cooling storage | Power grid | PV | Wind power |
Device J | COP | ||||||
---|---|---|---|---|---|---|---|
1 | 225.03 | 1.93 | 0.014 | 20 | 0.46 | 40 | - |
2 | 99.82 | 1.77 | 0.004 | 15 | - | - | 0.9 |
3 | 74.72 | 1.77 | 0.003 | 20 | - | - | 5.8 |
4 | 16.83 | 1.40 | 0.003 | 20 | - | - | - |
5 | 114.08 $/m3 | 1.57 | 0.003 | 20 | - | - | - |
6 | 14.26 | 0.00 | 0.003 | 20 | - | - | - |
7 | 798.59 | 2.07 | 0.013 | 25 | - | - | - |
8 | 926.93 | 2.07 | 0.013 | 20 | - | - | - |
Iterations | 1 | 20 | 40 | 60 | 80 | 100 | 120 | 140 | |
---|---|---|---|---|---|---|---|---|---|
Computing time (s) | BCA | 1803 | 1743 | 1821 | 1845 | 1794 | 1827 | 1819 | 1807 |
NSGA-II | 150 | 167 | 334 | 497 | 733 | 1972 | 2145 | 3156 | |
INI 1 (million USD) | BCA | 35.09 | 35.09 | 35.09 | 35.09 | 35.09 | 35.09 | 35.09 | 35.09 |
NSGA-II | 37.69 | 36.69 | 35.86 | 36.36 | 35.19 | 35.69 | 35.36 | 35.16 | |
OC 2 (million USD) | BCA | 30.45 | 30.45 | 30.45 | 30.45 | 30.45 | 30.45 | 30.45 | 30.45 |
NSGA-II | 32.17 | 31.99 | 32.06 | 31.09 | 30.74 | 31.16 | 31.31 | 30.81 | |
CE 3 (tCO2) | BCA | 232,250 | 232,250 | 232,250 | 232,250 | 232,250 | 232,250 | 232,250 | 232,250 |
NSGA-II | 245,335 | 243,972 | 244,517 | 237,157 | 234,431 | 237,702 | 238,792 | 234,976 |
Types | CCP | AC 1 | Chiller | FC 2 | CS 3 | PG 4 | PV | WP 5 | Total |
---|---|---|---|---|---|---|---|---|---|
DCP-IES INI (million USD) | 7.37 | 2.94 | 5.18 | 0.26 | 3.81 | 9.57 | 4.88 | 1.10 | 35.09 |
DCP-IES OC (million USD) | 10.27 | 0.33 | 0.64 | 0.11 | 0.56 | 18.42 | 0.10 | 0.01 | 30.45 |
DCP-IES CE (tCO2) | 42,488 | 0 | 0 | 0 | 0 | 189,762 | 0 | 0 | 232,250 |
TDC-ES INI (million USD) | 0 | 0 | 2.99 | 0 | 0 | 8.71 | 0 | 0 | 11.71 |
TDC-ES OC (million USD) | 0 | 0 | 0.86 | 0 | 0 | 37.69 | 0 | 0 | 38.55 |
TDC-ES CE (tCO2) | 0 | 0 | 0 | 0 | 0 | 271,573 | 0 | 0 | 271,573 |
Devices | Capacity (kW) | Quantity | INI (Million USD) | OC (Million USD) |
---|---|---|---|---|
CCP | 8500 | 4 | 7.57 | 10.27 |
AC | 7624 | 4 | 2.94 | 0.33 |
Chiller | 2800USRT 1 | 7 | 5.21 | 0.67 |
FC | 2500 | 6 | 0.26 | 0.11 |
CS | 10,000 m3 | 2 | 3.82 | 0.56 |
PG | 46,276 | 1 | 9.57 | 18.42 |
PV | 6100 | - | 4.88 | 0.10 |
WP | 1186 | - | 1.10 | 0.01 |
Total | - | - | 35.34 | 30.63 |
Device | CCP | AC | Chiller | FC | CS | PG | PV | WP |
---|---|---|---|---|---|---|---|---|
Failure rate (time/day) | 0.00547945 | 0.00136986 | 0.000913240 | 0.000684790 | 0.0103903 | 0.00305800 | 0.0170452 | 0.0121137 |
Repair rate (time/day) | 2.541667 | 2.000000 | 2.000000 | 1.000000 | 1.000000 | 5.083333 | 3.2906082 | 3.4042571 |
Redundant Scenario | Available Rate of Electricity (%) | Available Rate of Cooling (%) | INI (Million USD) |
---|---|---|---|
A | 99.9399 | 99.9894 | 35.34 |
A1 | 99.9900 | 99.9897 | 37.11 |
A2 | 99.9399 | 99.9940 | 35.64 |
A3 | 99.9900 | 99.9948 | 37.41 |
B | 99.9996 | 99.9954 | 44.91 |
B1 | 99.9998 | 99.9957 | 46.67 |
B2 | 99.9996 | 99.9992 | 45.21 |
B3 | 99.9998 | 99.9993 | 46.97 |
C | 99.9989 | 99.9935 | 43.44 |
C1 | 99.9995 | 99.9935 | 45.21 |
C2 | 99.9989 | 99.9989 | 43.74 |
C3 | 99.9995 | 99.9990 | 45.51 |
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Liu, Z.; Yu, H.; Liu, R.; Wang, M.; Li, C. Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations. Energies 2020, 13, 448. https://doi.org/10.3390/en13020448
Liu Z, Yu H, Liu R, Wang M, Li C. Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations. Energies. 2020; 13(2):448. https://doi.org/10.3390/en13020448
Chicago/Turabian StyleLiu, Zhiyuan, Hang Yu, Rui Liu, Meng Wang, and Chaoen Li. 2020. "Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations" Energies 13, no. 2: 448. https://doi.org/10.3390/en13020448