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1
Seema Thakur(st107641)
Advisor: Dr. Weerakorn Ongsakul
Optimal Generation Scheduling of Cascaded Hydro-thermal and Wind Power Generation By
Particle Swarm Optimization
Master’s Thesis
Energy
2
Introduction
Contribution to Research
Benefits of the Proposed Innovative Approach
Methodology
Problem Formulation
Results & Discussion
Conclusion
Recommendation
Outlines
3
Introduction Bulk amount of electrical energy cannot be stored. So, in real time, the generation
from different power plants should match with the power demand.
Electricity generating companies and power systems have the problem of deciding how best to meet the varying demand for electricity, which has a daily and/or weekly cycle.
Generation scheduling is the process of allocating the required load demand between the available generation units such that the cost of operation is minimized, while honoring the operational constraints of the available generation resources. There have been many algorithms proposed for the scheduling.
A typical system is composed of hydro and thermal generating units. The generation scheduling done for such a system is called hydrothermal scheduling.
It helps to minimize the total fuel cost and also for the optimal utilization of the available natural renewable resources.
Considering the aspect of near future, integrating wind energy in an existing hydrothermal system seems to be an attractive option.
4
Contribution to Research
Generation scheduling is a challenging task due to its complex mathematical behavior. Thus, various optimization techniques have been used in the past but they have various drawbacks.
So far the research has been done for generation scheduling of hydro-thermal system or wind-thermal system. But no works has been done for a system having all the three generations.
In this research work, an innovative approach called Particle Swarm Optimization (PSO) is introduced for the generation scheduling of different systems incorporating all three different types of generation to optimize the generation cost.
In addition , the effectiveness of different versions of PSO is evaluated in this work.
The proposed simulation model has been tested in test systems as well as in the real power system of Nepal.
5
Cost reduction
Maximization of renewable energy use (hydro and wind)
Reduction of thermal generation : Reduction of GHG emission.
(Without any extra monitory investment )
Benefits of the Proposed Innovative Approach
6
Methodology
The approach is inspired by the social behavior of animals such as fish schooling and bird flocking.
A set of unknown variables are randomly initialized in a feasible region and the best solution is selected.
The solution tries to converge depending upon its distance from the best solution.
In each iteration within the runtime of the program, the solution converses and gives the best solution.
PSO
Figure 1: Fish schooling and Bird flocking
7
Problem Formulation
Fig: Schematic diagram of a typical power system
The objective is to minimize the total operating cost
Minimize,
Subjected to various physical limitations,
T
t
I
itii PFCTC
1 1, )(
8
Problem FormulationII. Constraints
a. Power Balance Constraints
b. Hydro power generation limits
c. Hydro plant water discharge limits
d. Reservoir water storage limits
e. Initial and final reservoir water storage volume
f. Hydro power generation constraints
g. Hydraulic continuity constraints
h. Thermal power generation limits
i. Ramp response rate limit on thermal generation units
j. Spinning reserve limits
k. Power output limits on wind energy system
9
Results & Discussion
10
GS[3] SA[16] FEP[21 ] PSO[21] PSO[22] PSO-TVIW (proposed)
SPSO-TVAC (Proposed)
709300
709400
709500
709600
709700
709800
709900
Comparison of results with previous works
Methods
Co
st (
$)
0
400
800
1200
1600
2000
1 2 3 4 5 6
Time Intervals
Pow
er
genera
tion (
MW
)
Hydro (MW) Thermal (MW)
Generation level of plants with time interval
Test 1: A Simple System with a Hydro and a Thermal Generating Unit
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Test 2: A system with Four Cascaded Hydro and Three ThermalGenerating Units
SA [2] EP [2] PSO [2] CPSO (Proposed)
PSO-TVIW (proposed)
PSO-TVAC (proposed)
SPSO-TVAC (proposed)
36,000
38,000
40,000
42,000
44,000
46,000
48,000
Comparative results on test system with different approaches
Methods
Co
st (
$)
12
BPSO PSO-TVIW PSO-TVAC SPSO-TVAC 39000
39400
39800
40200
40600
41000
With/Without wind power generation
Without wind power generation With wind power generation
Methods
Co
st (
$)
BPSO PSO-TVIW PSO-TVAC SPSO-TVAC 39000
39400
39800
40200
40600
41000
Without wind power generation
Without wind power generation
Methods
Co
st (
$)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2402468
101214
0
100
200
300
400
500
600
Forecasted Wind Velocity Test System [25]
Time Interval (hr)
Win
d ve
loci
ty (m
/s)
Win
d Po
wer
( kW
)
13
Convergence characteristics of the proposed approached of all PSO schemes without considering wind generation in the system.
0 10 20 30 40 50 60 70 80 90 1003.8
3.9
4
4.1
4.2
4.3
4.4
4.5x 10
4
Iterations
To
tal G
en
era
tion
Co
st $
CPSO
PSO-TVIWPSO-TVAC
SPSO-TVAC
Convergence characteristics of the proposed approached of all PSO schemes considering wind generation in the system.
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
200
400
600
800
1000
1200
Pth3
Pth2
Pth1
Ph4
Ph3
Ph2
Ph1
Time Interval (hr)
Pow
er G
ener
atin
(M
W)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
200
400
600
800
1000
1200Pth3
Pth2
Pth1
Ph4
Ph3
Ph2
Ph1
WindTime Interval (hr)
Pow
er G
ener
atio
n (
MW
)
Optimal generation scheduling by SPSO-TVAC for each time interval
Optimal generation scheduling by SPSO-TVAC for each time interval
15
Generation Scheduling
for
Nepali Power System
16
BPSO TVIW TVAC SPSO_TVAC 30,50031,00031,50032,00032,50033,00033,50034,00034,50035,000
Comparative results from different PSO approaches for the real system model of Nepal
Without wind power generation With wind power generation
Methods
Gen
erat
ion
Co
st (
$)
BPSO TVIW TVAC SPSO_TVAC 30,50031,00031,50032,00032,50033,00033,50034,00034,50035,000
Comparative results from different PSO approaches for the real system model of Nepal
Without wind power generation
Methods
Gen
erat
ion
Co
st (
$)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
5
10
15
20
0100200300400500600700
Forecasted Wind Velocity Kagbeni, Nepal
Time Interval (hr)
Win
d ve
loci
ty (m
/s)
Win
d Po
wer
(kW
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
5
10
15
20
0100200300400500600700
Forecasted Wind VelocityChussang, Nepal
Time interval (hr)
Win
d ve
loci
ty (m
/s)
Win
d Po
wer
(kW
)
17
Hydrothermal generation (MW) schedule by SPSO-TVAC without considering wind generation in the Nepali system
18
Figure: Hydrothermal generation (MW) schedule by SPSO-TVAC with the consideration of wind generation in the Nepali system
19
Conclusion This research work helps to reduce the GHG emission by reducing the thermal
generation in the system. (Social benefit)
Helps to optimize the generation cost.(Economical benefit)
Maximizes the utilization of renewable resources . (Environmental benefit)
The comparison with the results from the already existing techniques clearly demonstrates that the proposed method gives the best result.
The simulation results for both test model and real system model (Nepali system) illustrate that the proposed SPSO_TVAC is the most efficient approach to find the optimal solution for generation scheduling of hydrothermal system with/without penetration of wind power to the system, considering all the associated constraints.
In this proposed concept , there is no physical system modification necessary in the existing system Results, no additional constructional cost.
20
Thank You
Q & A