Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment
Abstract
:1. Introduction
- (1)
- To the best of our knowledge, the CVaR as risk evaluation approach has rarely been applied in local-area ESI operating strategy research. In this paper, risk assessment of local-area ESI is proposed to quantify the impact occurred by uncertainty of energy supply-side and demand-side.
- (2)
- In the modeling process, risk cost, which includes the fuel cost, maintenance cost and facility adjustment cost, is especially analyzed in the case of overestimation and underestimation as multiple sources of energy interact, and constraints of multi-energy systems network are fully taken into account.
- (3)
- By considering the advantage of the algorithm and the complexity of the ESI model, a bi-level optimization algorithm which combines particle swarm optimization (PSO) approach with interior point (IP) method is used to solve the stochastic multi-period mixed-integer model for dispatching issue.
2. Mathematical Model Formulation
2.1. Mathematical Model of Electricity Supply Equipment in Local-Area ESI
2.1.1. Micro Turbine CHP Model
2.1.2. Fuel Cell Model
2.1.3. Photovoltaic Power Generation Model
2.1.4. Wind Power Generation Model
2.2. Mathematical Model of Heat Supply Equipment in Local-Area ESI
2.2.1. Ground Source Heat Pump Model
2.2.2. Gas Boiler Model
2.3. Uncertainty Analysis in Local-Area IES
2.3.1. Uncertainty of Energy Supply Side
2.3.2. Uncertainty of Energy Demand Side
2.3.3. Reduction of Uncertainty Scenarios
3. CVaR Based Dispatching Model for Local-Area ESI
3.1. Risk Modelling via CVaR
3.2. Risk Cost of ESI
3.2.1. Overestimated Cost
Overestimated Cost for Electricity
Overestimated Cost for Heat
3.2.2. Underestimated Cost
Underestimated Cost for Electricity
Underestimated Cost for Heat
3.2.3. Total Cost
3.3. Objective and Constraint of ESI Economic Dispatch
3.3.1. Objective Function
3.3.2. Constraint Conditions
The Upper and Lower Bound of Energy Supply Equipment Output
Power Balance Constraints
Tie-Line Power Constraints
Ramping Rate Constraints
Multi-Energy Flow Constraints
System State Variables Constraints
3.4. Solving Algorithm
Upper Level Optimization Model
Lower Level Optimization Model
4. Case Study
4.1. Simulation Based on Different Confidence Level
4.2. The Influence of Multi-Energy Flow Constraints on Simulation Results
4.3. The Influence of Equipment Adjustment Cost on Simulation Results
4.4. Convergence Analysis
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Equipment Name | Minimum Power Output (kW) | Maximum Power Output (kW) | Ramping up Rate (kW/min) | Ramping down Rate (kW/min) | Maintenance Cost (Yuan/kW) |
---|---|---|---|---|---|
CHP | 5 | 130 | 5 | 10 | 0.0149 |
FC | 10 | 200 | 10 | 10 | 0.009 |
GSHP | 0 | 200 | 5 | 5 | 0.0311 |
GB | 100 | 1500 | 5 | 6 | 0.025 |
Utility grid | −600 | 600 | - | - | - |
Appendix B
Direction of Line | Length (m) | Direction of Line | Length (m) |
---|---|---|---|
12–2 | 50 | 2–10 | 50 |
2–3 | 130 | 8–9 | 100 |
3–4 | 100 | 10–9 | 100 |
4–5 | 100 | 11–10 | 100 |
3–6 | 100 | 1–11 | 50 |
7–6 | 100 | - | - |
Direction of Pipeline | Length (m) | Diameter (mm) | Direction of Pipeline | Length (m) | Diameter (mm) |
---|---|---|---|---|---|
4–3 | 50 | 100 | 1–2 | 150 | 100 |
5–3 | 100 | 100 | 7–1 | 100 | 100 |
2–4 | 150 | 100 | 6–4 | 50 | 100 |
Direction of Pipeline | Length (m) | Diameter (mm) | Direction of Pipeline | Length (m) | Diameter (mm) |
---|---|---|---|---|---|
1–2 | 100 | 200 | 3–4 | 100 | 200 |
2–3 | 300 | 200 | 3–5 | 350 | 200 |
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Name | Peak Period | Normal Period | Valley Period |
---|---|---|---|
Period | 10:00–15:00 18:00–21:00 | 7:00–10:00 15:00–18:00 21:00–23:00 | 23:00–7:00 |
Electricity purchasing price (Yuan/kWh) | 0.83 | 0.49 | 0.17 |
Electricity Selling price (Yuan/kWh) | 0.65 | 0.38 | 0.13 |
β | Electrical Power Output (kWh) | Thermal Power Output (kWh) | |||
---|---|---|---|---|---|
CHP | FC | Grid | GSHP | GB | |
0.80 | 1741.5 | 2759.4 | 2012.1 | 2287.1 | 13,642.0 |
0.85 | 2028.2 | 2527.0 | 1953.1 | 2265.9 | 13,291.0 |
0.90 | 1607.8 | 2727.7 | 2336.0 | 3000.4 | 13,103.0 |
0.95 | 1738.0 | 2091.0 | 2831.1 | 2949.0 | 12,985.0 |
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Shi, J.; Wang, Y.; Fu, R.; Zhang, J. Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment. Sustainability 2017, 9, 1655. https://doi.org/10.3390/su9091655
Shi J, Wang Y, Fu R, Zhang J. Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment. Sustainability. 2017; 9(9):1655. https://doi.org/10.3390/su9091655
Chicago/Turabian StyleShi, Jiaqi, Yingrui Wang, Ruibin Fu, and Jianhua Zhang. 2017. "Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment" Sustainability 9, no. 9: 1655. https://doi.org/10.3390/su9091655