Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector
Abstract
:1. Introduction
1.1. VPP Concept
1.2. Literature Review
1.3. Literature Gap and Research Contributions
2. Case Study
- Base case with DER.
- Base case with DER and DR.
- Base case with DER, DR, and Storage.
3. VPP Modelling
3.1. Solar PV
- η = Efficiency of a solar panel (14.9%)
- St = Solar irradiation (W/m2)
- t = Time step (h)
- N = Number of solar panels connected (no)
- A = Area occupied by solar panels (m2)
- T = Total time period (h)
- PVeo = Estimated output of solar PV, PVso = Scheduled output of solar PV, Lifetime of PV = 25 years
- PV Inverter size = 1.05*solar capacity
3.2. Battery
- = Operational price of battery in dispatch ($)
- = maintenance coefficient of battery
- = Discharging or recharging of the battery (AH)
- = Depreciation coefficient of the battery
- = Pollution coefficient of the treatment cost
- t = Timestep (h)
- T = Total time period (h)
3.3. Loads
- = Total demand on the feeder
- = Schedulable demand
- = Non-Schedulable demand
- = Emergency demand t = Timestep
3.4. Main Grid
4. Objective Function and Constraints
- = Discharging rate of the battery
- = Charging rate of the battery
- = Total capacity of the battery
- = Initial capacity of the battery
- = Maximum rating of solar PV
- NS = Number of units of solar PV installed
- = Maximum annual operation hours for PV technology
- PV Capacity ≤ 20% of Transformer Capacity (to prevent reverse power flow during low demand)
- PV Capacity ≤ 80% of Connected Load
- Battery Maximum Discharge Capacity ≤ 20% of total storage capacity (to prevent reduction of battery life)
- The initial capacity of the battery = 500 kWh.
PSO and Dispatch Strategy
- (1)
- Gbest (global best solution of each particle)
- (2)
- Target objective
- (3)
- Stopping criteria
- (1)
- Pbest (particle valid solution)
- (2)
- Particle velocity
- (3)
- Particle best known local solution
5. Results and Discussions
5.1. Grid Connected Mode
5.1.1. Base Case
5.1.2. Case with DER
5.1.3. Case with DER & DR
5.1.4. Case with DER, DR & Storage
5.2. VPP Autonomous Mode
5.2.1. Case with DER and Storage
5.2.2. Case with DER, DR & Storage
5.3. Benefits for Consumers
Influence of VPP on the Energy Charges
6. Comparison with Different Optimization Techniques
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature and Abbreviations
η | Efficiency of a solar panel (14.9%) |
St | Solar irradiation (W/m2) |
T | Time-step (s) |
N | Number of solar panels connected either in series or parallel |
A | Area occupied by solar panels (m2) |
Estimated output of solar PV | |
Total demand on the feeder(kW) | |
Schedulable demand (kW) | |
Non Schedulable demand (kW) | |
Emergency demand (kW) | |
Operational price of battery in dispatch ($) | |
Maintenance coefficient of battery | |
Discharging or recharging of the battery | |
Depreciation coefficient of the battery | |
Total Cost of VPP($) | |
Total Cost of PV($) | |
Total Cost of Batteries ($) | |
Total cost of Grid ($) | |
Total cost of EENS($) | |
Purchasing cost of electricity ($/kWh) | |
Maximum rating of solar PV[kW] | |
Energy imported from the grid (kWh) | |
Cost of not supplying demand ($/kWh) | |
EENS | Expected Energy Not Supplied (kWh) |
Ttod | Energy usage charges [$/kWh] |
MaxH | Maximum annual operation hours for technology [hour] |
NS | Number of units of solar PV installed |
Discharging rate of the battery (Amps) | |
Charging rate of the battery (Amps) | |
Total capacity of the battery (Ah) | |
Initial capacity of the battery (Ah) | |
Cost of VPP of utility imports ($/kWh) | |
Cost of VPP of utility exports ($/kWh) | |
Selling cost of electricity ($/kWh) | |
Photovoltaic | |
Time of Day | |
Mixed Integer Linear Programming | |
Electrical Transient Analyzer Program | |
Failure rate/Year | |
Distributed Generation | |
Particle Swarm Optimization | |
Distributed Energy Resource | |
Demand Side Management | |
Virtual Power Plant | |
Punjab State Power Corporation Limited | |
National Renewable Energy Laboratory | |
Hybrid Optimization Model For multiple Energy Resources | |
Supervisory Control and Data Acquisition |
Appendix A
Algorithm A1. %P&O Tracking |
function [P,V,I] = PandO(sun,v,T) Vr = zeros(size(sun)); Ir = zeros(size(sun)); for k = 1:size(sun)-1 if size(k)~= 0 Vr0 = 2.8; Vr1 = 2.8 + v; Ir0 = max60(Vr0,sun(k),T(k)); Ir1 = max60(Vr1,sun(k+1),T(k+1)); Pr0 = Ir0.*Vr0; Pr1 = Ir1.*Vr1; break; end end for m = k+1:size(sun)-1 if size(m+1)~=0 if Pr1>Pr0 if Vr1>Vr0 Vr1=Vr1+v; else Vr1=Vr1-v; end else if Vr1>Vr0 Vr1=Vr1-v; else Vr1=Vr1+v; end end Ir0=Ir1; Pr0=Pr1; Ir1=max60(Vr1,sun(m+1),T(m+1)); Pr1=Ir1.*Vr1; Vr(m+1)=Vr1; Ir(m+1)=Ir1; end end V=Vr; I=Ir; P=Vr.*Ir; |
Appendix B
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Industrial Feeder | |
---|---|
Loading Parameters | |
Average demand | 1033 kW |
Peak demand | 4288 kW |
Annual energy demand | 9,718,850 kWh |
Peak Month | November |
Total consumers | 257 |
Length | 2174 m |
Current capacity | 254 Amp |
kVA capacity on 11 kV | 4839 kVA |
Maximum demand | 290 Amp/5518 kVA |
Reliability Parameters | |
No. of Transformers | 47 |
Transformer Failure rate/year | 0.15 |
Transformer Mean time to repair | 0.5 h |
No. of sectional lines | 41 |
Sectional Lines Failure rate/year | 0.5 |
Sectional Lines Mean time to repair | 1.5 h |
No. of Substations | 1 |
Substation Failure rate/year | 0.6 |
Substation Meantime to repair | 4 h |
Period | Time | Cost/kWh |
---|---|---|
1 April 2018 to 31 May 2018 | 06:00 a.m. To 06:00 p.m. | $0.09 |
06:00 p.m. To 10:00 p.m. | ||
10:00 p.m. To 06:00 a.m. | $0.07 | |
1 June 2018 to 30 September 2018 | 06:00 a.m. To 06:00 p.m. | $0.09 |
06:00 p.m. To 10:00 p.m. | $0.12 | |
10:00 p.m. To 06:00 a.m. | $0.09 | |
1 October 2018 to 31 March 2019 | 06:00 a.m. To 06:00 p.m. | $0.09 |
06:00 p.m. To 10:00 p.m. |
Base Case | With DER | With DER & DR | With DER, DR & Storage | |
---|---|---|---|---|
PV Capacity | - | 1916 kW | 1916 kW | 1916 kW |
Battery Capacity | - | - | - | 9511 kWh |
Annual Savings | $0 | $367 | $402 | $1028 |
Optimized operational cost | $3284 | $2917 | $2882 | $2247 |
Total Electricity import | 36,413 kWh | 32,085 kWh | 32,085 kWh | 32,085 kWh |
Peak Demand | 2945 kW | 2582 kW | 2582 kW | 2250 kW |
EENS during autonomous operation for during 10 to 12 h in typical section TU | 3491 kWh | 1904 kWh | 1579 kWh | 1316 kWh |
PV output for during 10 to 12 h in section TU | - | 260 kWh | 260 kWh | 260 kWh |
DR (load shifting) for during 10 to 12 h period in section TU | - | - | 324 kWh | 324 kWh |
Battery output for during 10 to 12 h in Section TU | - | - | - | 262 kWh |
Energy Charges with TOD Tariff (On = On-Peak; Mid = Mid-Peak; Off = Off-Peak) | ||||||
---|---|---|---|---|---|---|
Charges in $ without VPP Implementation | Charges in $ with VPP Implementation | |||||
Month | on | mid | off | on | mid | Off |
January | 637.39 | 2906.07 | 137.96 | 464.95 | 1480.49 | 576.54 |
February | 578.47 | 2673.97 | 121.38 | 345.83 | 1418.59 | 524.55 |
March | 566.45 | 3050.77 | 142.78 | 312.65 | 1359.35 | 593.26 |
April | 544.19 | 3167.03 | 147.45 | 288.85 | 1468.1 | 591.64 |
May | 553.85 | 3215.48 | 151.89 | 263.8 | 1439.22 | 586.75 |
June | 696.08 | 3050.88 | 187.39 | 180.67 | 2130.61 | 78.23 |
July | 752.45 | 3265.23 | 197.4 | 183.74 | 2202.05 | 105.45 |
August | 754.69 | 3289.84 | 196.13 | 192.72 | 2316.59 | 99.35 |
September | 685.08 | 3038.58 | 186.89 | 166.66 | 2129.64 | 101.47 |
October | 545.7 | 3176.31 | 150.78 | 271.22 | 1526.44 | 586.59 |
November | 571.03 | 2760.63 | 125.46 | 336.78 | 1349.21 | 527.29 |
December | 639.79 | 2952.88 | 134.68 | 459.25 | 1589.36 | 577.26 |
Optimization Algorithm | Optimal Operational Cost |
---|---|
System without Optimization | $3284 |
Proprietary Derivative Free | $2534 |
MILP | $2390 |
Proposed PSO | $2247 |
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Sharma, H.; Mishra, S.; Dhillon, J.; Sharma, N.K.; Bajaj, M.; Tariq, R.; Rehman, A.U.; Shafiq, M.; Hamam, H. Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector. Energies 2022, 15, 752. https://doi.org/10.3390/en15030752
Sharma H, Mishra S, Dhillon J, Sharma NK, Bajaj M, Tariq R, Rehman AU, Shafiq M, Hamam H. Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector. Energies. 2022; 15(3):752. https://doi.org/10.3390/en15030752
Chicago/Turabian StyleSharma, Harpreet, Sachin Mishra, Javed Dhillon, Naveen Kumar Sharma, Mohit Bajaj, Rizwan Tariq, Ateeq Ur Rehman, Muhammad Shafiq, and Habib Hamam. 2022. "Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector" Energies 15, no. 3: 752. https://doi.org/10.3390/en15030752