Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model
Abstract
:1. Introduction
Ref. | Renewable Energy | Energy Storage Device | Operation Strategy | Equipment Model | System Optimization Model |
---|---|---|---|---|---|
[8] | Solar and wind energy | EES, TES | / | Static model | Coupled single objective optimization |
[13] | Wind energy | EES, TES | / | Static model | Coupled single objective optimization |
[14] | Solar and wind energy | EES, TES, Fuel cell | / | Dynamic model | Coupled single objective optimization |
[15] | Wind energy | EES, HES | / | Dynamic model | Coupled multi-objective optimization |
[18] | Geothermal energy | / | FEL FTL | Static model | Weak decoupling single objective optimization |
[21] | Geothermal energy | TES | FEL FTL | Static model | Weak decoupling weighted multi-objective optimization |
[22] | Geothermal energy | TES | FEL FTL | Dynamic model | Weak decoupling weighted multi-objective optimization |
[24] | Solar energy | TES | FEL FTL | Static model | Weakly decoupled multi-objective optimization |
[25] | Solar energy Geothermal energy | EES TES | FEL FTL FHL | Static model | Weakly decoupled multi-objective optimization |
[27] | Geothermal energy | TES | FEL FTL | Dynamic model | Weakly decoupled multi-objective optimization |
[30] | Solar, wind and geothermal energy | EES TES | FSF | Dynamic model | Weakly decoupled multi-objective optimization |
[31] | Solar energy | EES TES | Dynamic strategy | Dynamic model | Weakly decoupled multi-objective optimization |
[34] | Solar, wind and geothermal energy | EES, hydrogen storage | FOF | Static model | Bi-level optimization model |
[35] | Solar and wind energy | SES | FOF | Static model | Bi-level optimization model |
2. Modeling of RIESs
2.1. Basic structure of RIESs
2.2. Equipment Mathematical Model
2.2.1. Energy Conversion Device
2.2.2. Energy Storage Devices
3. Bi-Level Optimization Model
3.1. Upper-Level Optimal Configuration Model
3.1.1. Optimization Objective
3.1.2. Optimization Variables and Constraints
3.2. Lower-Level Optimal Scheduling Model
3.2.1. Optimization Objective
3.2.2. Optimization Variables and Constraints
- Equipment operating power constraints
- (1)
- Energy conversion equipment
- (2)
- Energy storage equipment
- 2.
- Energy balance constraint
3.3. Model Solving
4. Case Study
4.1. System Design Parameters
4.2. System Optimization Results and Analysis
4.2.1. Optimization Results
4.2.2. Analysis of Results
4.3. System Operation Result and Analysis
4.3.1. Calculation of Operating Load
4.3.2. Operation Results
4.3.3. Operation Result Analysis
4.4. Uncertainty Analysis of Energy Price
5. Conclusions
- Compared with System 1 without energy storage devices, energy storage devices can increase the capacity of CHP units and ABCs in System 2 and System 3 and reduce the capacity of GSHPs and gas boilers, especially the TES device. Affected by the equipment capacity, the equipment cost increase rates of System 2 and System 3 compared with System 1 are 5.7% and 17.8%, respectively. This shows that the EES device will significantly increase the equipment cost of System 3.
- The difference in equipment capacity affects not only the equipment cost but also the operation performance of the system. Under the design conditions, higher equipment operation efficiency and lower grid power consumption make the operation cost, carbon tax, and total cost of System 2 lower than that of System 1, with reductions of 2.9%, 5.5%, and 1.5%, respectively. Under the influence of TOU electricity price, the EES device can significantly reduce the operating cost of System 3, which is 5.7% lower than that of System 1.
- Under the operating conditions, the operating cost, carbon tax, and total cost of System 2 and System 3 remain lower than that of System 1, even if the energy price changes. Therefore, in the design of future RIESs, energy storage devices, especially TES devices, can be used to improve the energy efficiency of RIESs and reduce the operation cost and total cost.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Nomenclature | Greek symbols | ||
Abbreviation | η | Charging and discharging efficiency | |
A | Area | ϑ | Energy price/ carbon tax price |
ABC | Absorption chiller | α | Energy storage ratios |
Cap | Capacity | λ | Carbon dioxide emissions factor |
CCHP | Combined cooling heating and power | γ | Charging and discharging ratios |
CHP | Combined heating and power | Subscript | |
COP | Coefficient of performance | a | Ambient |
EES | Electrical energy storage | abc | Absorption chiller |
EH | Energy hub | c | Cooling |
FEL | Following electric load | ch | Charge |
FHL | Following hybrid electric-heating load | chp | Combined heating and power |
FOF | Following objective function | carbon dioxide | |
FSF | Following system flexibility | dis | Discharge |
FTL | Following thermal load | e | Electricity |
GA | Genetic algorithm | equ | Equipment |
GB | Gas boiler | ees | Electrical energy storage |
GSHP | Ground source heat pump | gas | Natural gas |
HES | Hydrogen energy storage | gb | Gas boiler |
L | Load | gshp | Ground source heat pump |
P | Power | grid | Grid power |
P-G | Power-gas | h | Heating |
PLF | Part-load ratio | inv | Initial investment |
PLR | Part-load factor | k | Device type |
PV | Photovoltaic | op | Operating |
RIES | Regional integrated energy system | pv | Photovoltaic |
S | Energy storage device status | r | Rated |
SES | Share energy storage | tes | Thermal energy storage |
t | Temperature/Time | Superscript | |
TES | Thermal energy storage | max | Maximum |
TOU | Time-of-use | min | Minimum |
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System Name | TES Device | EES Device |
---|---|---|
System 1 | ✕ | ✕ |
System 2 | ✓ | ✕ |
System 3 | ✓ | ✓ |
Items | Mathematical Models | Ref. |
---|---|---|
PV | , . | [36] |
CHP unit | , , = 0.1, = 0.4, = −0.2. | [37] |
GB | , , , , , . | [38] |
GSHP | , , , , , . | [38] |
ABC | , , , , , . | [39] |
Equipment Name | Unit Price | Equipment Name | Unit Price |
---|---|---|---|
PV | 2315 (CNY/m2) | CHP unit | 6812 (CNY/kW) |
Boiler | 790 (CNY/kW) | GSHP | 2782 (CNY/kW) |
ABC | 1436 (CNY/kW) | TES device | 358 (CNY/kW) |
EES device | 1794 (CNY/kW) |
Design Conditions | Design Dry-Bulb Temperature | Groundwater Temperature |
---|---|---|
Summer | 36 ℃ | 17 ℃ |
Winter | −1 ℃ | 11 ℃ |
Equipment Name | Symbol | Unit | Limitations |
---|---|---|---|
CHP unit | kW | [0,2000] | |
Boiler | kW | [0,1000] | |
GSHP | kW | [0,2300] | |
TES device | [0,1000] | ||
EES device | [0,1000] | ||
PV | [0,1500] |
Item | Time Period | Description | |
---|---|---|---|
Gas | 0.3275 | – | – |
Electricity | 1.224 | 20:00–23:00 | Peak hours |
0.911 | 9:00–12:00, 16:00–20:00 | High hours | |
0.68 | 8:00–9:00, 12:00–16:00 | Flat hours | |
0.306 | 0:00–8:00, 23:00–24:00 | Valley hours | |
Carbon tax | 0.3 ( | – | – |
System Name | |||||||
---|---|---|---|---|---|---|---|
System 1 | 1122 | 205 | 1780 | 520 | 0 | 0 | 1500 |
System 2 | 1265 | 0 | 1682 | 618 | 961 | 0 | 1500 |
System 3 | 1272 | 0 | 1678 | 622 | 947 | 926 | 1500 |
Operating Condition | Summer | Winter | ||||
---|---|---|---|---|---|---|
System 1 | 526,636 | 1,582,405 | 2,672,662 | 301,465 | 774,952 | 1,581,733 |
System 2 | 509,527 | 1,558,375 | 2,663,411 | 285,012 | 718,724 | 1,537,640 |
System 3 | 512,367 | 1,484,274 | 2,660,567 | 294,405 | 688,785 | 1,578,434 |
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Jin, B.; Liu, Z.; Liao, Y. Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model. Energies 2023, 16, 2629. https://doi.org/10.3390/en16062629
Jin B, Liu Z, Liao Y. Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model. Energies. 2023; 16(6):2629. https://doi.org/10.3390/en16062629
Chicago/Turabian StyleJin, Baohong, Zhichao Liu, and Yichuan Liao. 2023. "Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model" Energies 16, no. 6: 2629. https://doi.org/10.3390/en16062629