UNCERTAINTY TREATMENT IN ECONOMIC DISPATCH...
Transcript of UNCERTAINTY TREATMENT IN ECONOMIC DISPATCH...
UNCERTAINTY TREATMENT IN ECONOMIC DISPATCH
WITH RENEWABLE ENERGYPRESENTED BY:
KIPKEMOI KIRUI GEOFFREYSUPERVISOR: MR. P.M MUSAU
EXAMINER: PROF. M. MANG’OLIDATE: 19TH MAY, 2016
IntroductionDefinition of Terms
Uncertainty is the (changefulness) unpredictability, inaccuracy, variability. It is a state for power system components, where it is impossible to exactly describe a future outcome, or more than one possible outcome due to limited knowledge [1].
Economic Dispatch is the determination of the optimal output of a
number of electricity generating facilities to meet the system load
at the lowest possible cost subject to generation, transmission and
operational constraints [39].
Objectives
i. To study uncertainties present in a power system.
ii. To study the effect of introducing renewable energy sources on the power system uncertainties.
iii. To determine how to deal with uncertainties in a power system.
Uncertainties present in a power system
• Generation availability• Load requirements• Market forces • Fuel prices • Forces of nature such as extreme weather• Technological developments• Regulatory uncertainties. New reliability
standards (environmental policies )
Effects of uncertainties introduced by RE to power system
• Stability: is the ability of a power system to maintainsynchronism when subjected to severe disturbance. Sudden disconnection of an entire solar or wind farm at full generation, the power system will lose the production capacity. Unless the remaining power plants have enough ‘spinning reserve’ to compensate for the loss in a short time, large frequency and voltage drops will occur and possibly followed by complete loss of power. To avoid this new generator technology is being developed that can ‘ride through’ and use of reactive power compensating devices.
Conti’ of Effects of uncertainties introduced by RE to a power system
• Security: is the ability of the system to withstand disturbances without causing a breakdown of the power system. Breakdowns leads to interruption of power to consumers. This can occur due to:
1. Insufficient active power reserve leading to load shedding. 2. Grid congestion (overloaded lines) that require disconnection
of loads to avoiding cascading faults. 3. Bus bar voltages getting out of permitted ranges 4. System running into stability problems (frequncy,voltage)
Conti’ of Effects of uncertainties introduced by RE to a power system
RE improves system securityi. Increases the diversity of a power system (no
sole dependence on one source)ii. Its resources are continuously replenished on
human timescales (fossil get depleted)However, variability and the unpredictability of wind and solar power can cause a power imbalance on the grid.
Conti’ of Effects of uncertainties introduced by RE to a power system• Power Quality: Degree of deviation from the
normal sinusoidal voltage and current waveforms.
To achieve integration of RE converters are used, these converters introduce harmonics.
Conti’ of Effects of uncertainties in a power system
Effects of harmonics 1. Excessive heating of equipment decreases
their lifetime.2. Increase line losses3. Cause flickers that result in an uncomfortable
visual effect on the eyes
Conti’ of Effects of uncertainties introduced by RE to a power system
• Phase ImbalanceMajority of PV sources are connected in the form of single-phase units. Unbalanced voltage profiles among phases can shift the neutral point to an unacceptable value.Unbalanced three-phase condition can lead to instability problems and higher network losses
How to reduce uncertainty in RE• Weather forecasts (RE is a function of weather)
However, has errors upto 20%This can be further improved by on‐site monitoring
Conti’ of How to reduce uncertainty of RE
A case study was conducted for 11 sites in the U.S. using modelled and measured data [14].A comparison of the results shows confidence can be increased by incorporating data from on‐site instrumentation. On‐site monitoring reduced uncertainty from 9.2% to 5.7% as shown in figure 2.3 below
Conti’ of How to reduce uncertainty of RE
Problem Formulation
Conti’ of Problem Formulation
Constraints
Uncertainty Treatment(Generation availability)
Uncertainty treatment in wind generators
dw (absolute difference) Crwj Cpwj
<=5 1.5 3.0
<=10 2.0 4.0
<=15 3.0 6.0
<=20 4.5 6.5
>20 7.0 8.5
Uncertainty treatment in solar generators
• Solar_Fuel_Cost = (Cpvi *(solar_a)) + Cppvi * ds+ Crpvi *ds
ds(absolute difference) Cppvi Crpv
<=5 1.5 3.0
<=10 2.5 4.0
<=15 5.5 6.0
<=20 6.5 7.5
>20 8.0 8.5
Methodology
PSO method was chosen to implement (UTIED).PSO is population based optimization techniquebased on the movement and intelligence of swarms.
Conti’ of Methodology
Why PSOa. Its simple concept and coding
implementationb. Faster in convergencec. Generate high-quality solutionsd. Convergence is independent of initial
points
PSO Parameter Representation
• Swarm: All possible generation from power plant • Particle: An individual power generation (solution) • Velocity: Rate of change from one possible solution
to another in iteration• Position: Generation at every instant relative to the
global best• Dimension: Number of generating units against the
number of possible generations (possible gen from all units)
FLOW CHART
Results
Generator type Power Generated in (MW) Cost ($/Hr)
Thermal generator 1 200 550Thermal generator 2 80 252Thermal generator 3 31.488 93.4562Thermal generator 4 35 123.967Solar generator 28 70Wind generator 39 68.25Iterations taken to converge 90Power loses 13.4793Total 413.488 1157.67
Conti’ of Results
Generator type Power Generated in (MW) Cost ($/Hr)
Thermal generator 1 200 550Thermal generator 2 80 252Thermal generator 3 31.4821 93.4273Thermal generator 4 35 123.967Solar generator 28 224Wind generator 39 189.25Iterations taken to converge 156Power loses 13.4792Total 413.482 1432.64
Total cost of Generation against amount of RE available at a constant
discrepancy
Total value of RE (MW)
70 60 50 40 30 20 10 0
Total cost of power generation ($/Hr)
943.245 959.886 977.073 995.878 1018.09 1043.62 1075.52 1124.81
At a penetration level of 50% RE
Alpha(Available RE-predicted RE )
0 5 10 15 20
30% RE penetration cost of generation($/Hr)
766.243 891.257 1016.25 1141.21 1316.2
50% RE penetration cost of generation($/Hr)
735.15 915.108 1095.12 1275.15 1433.16
Graph of cost of generation against level of discrepancy(alpha)
Graph of a cost of generation against available RE at a discrepancy level of (0)
A graph of cost of generation for 30% and 50% penetration level of RE against
discrepancy
Discussion• The cost of generation is minimum when the difference
between the predicted and the available power is minimum. As the discrepancy between the predicted and the available power increases the cost also increases. This is therefore calls for precise prediction of RE power for efficient planning of operations of the power system. Increasing uncertainty increases costs incurred due to penalties and cost of reserves.
• Cost of generation reduces with increase in the amount of RE power under minimum uncertainty. This is because thermal generators are fuel dependant while for RE there is no cost of fuel, the resource is available in nature for free. The only costs incurred are maintenance and operating costs.
Conti’ of Discussion• At a penetration level of 50% of RE, and low
uncertainty levels the cost of generation is lower than that at 30% level of RE penetration. However, as uncertainty of RE increases the cost of generation with high penetration level increases as shown by figure 4.4 above. Integration of RE in power system can only be economical if uncertainty in the prediction of resources can be done accurately. Accurate forecasting of RE, minimizes costs of reserves and penalties incurred.
Conclusion
• This project has developed a model to successfully include wind and solar generators in the economic dispatch problem
• Has developed a model of treating uncertainty in generation availability of RE
Recommendation
• This project considered uncertainty on generation availability; in future it could be extended to consider other uncertainties like load requirements, fuel prices, regulatory uncertainties and uncertainties due to nature such as extreme weather conditions.
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