Slide 1
Wind Hydro Power Optimization based on Stochastic Forecasting of
Wind1By: Muhammad Sharjeel Javaid (201404920) Numan Saeed
(201404640)Course Instructor: Dr. Ali Taleb Al-Awami
Electrical Engineering Department, College of Engineering
Sciences and Applied Engineering. KING FAHD UNIVERSITY OF PETROLEUM
& MINERALS1-First progress report summary2
31-First progress report summary
Formulation
Operation StrategyProfitsW-H10555 OW8741.8 Difference1813.2
Output Power to Grid (MATLAB)HoursPrice/Unit00:00-08:0054
/MWh08:00-22:00103.84 /MWhEnergy Price TariffProfit
Comparison42-Goals for final progress reportPrimary GoalsUnderstand
AIMMSDeterministic Model in AIMMSUncertain Model in AIMMSScenario
Analysis (Monte Carlo Simulations)Robust OptimizationComparison
Secondary GoalsEffect of PenaltyThermal Generator Incorporation
in our SystemComparison
53-Understanding aimmsAIMMS Literature ReviewAIMMS The Language
ReferenceAIMMS Optimization Modeling AIMMS Function ReferenceGoogle
Groups4-Deterministic Model in AIMMS (Input data)6
4-Deterministic Model in AIMMS (RESULTS)
7
Output Power ContributionsEnergy Stored in Reservoir with Pump
OperationOff-Peak HoursPeak HoursUncertain model analysisScenario
Generation (Monte carlo simulation)Robust optimization8To be
uncertain is to be uncomfortable, but to be certain is to be
ridiculous Chinese proveb
5-uncertain model analysis (Input data)9All input parameters and
bounds remains same, except available windFor each hour standard
deviation is introduced
5-uncertain model analysis (Scenario generation results)10Also
known as Monte Carlo SimulationsTotal 150 scenarios are solved in
AIMMSProfitsMaximum22427.02 Minimum24323.74 Average23346.88
5-uncertain model analysis (Robust Optimization
results)11Available Wind parameter was declared uncertain in
AIMMSSolved the Robust Optimization by generating RO_Model in
AIMMS
6-Comparison of ROBUST Optimization and scenario analysis12
Optimization TechniqueProfitsRO19530.29 Monte Carlo23346.88
Difference3816.59 Effect of Penalty functionand how to overcome the
loss137-Penalty function effectLuckily, for the given wind
scenario, we never ran out of energy resources.However, if at any
instant, our whole system is unable to produce the required 3 MW
(preset lower limit), we are penalized.To verify penalty we reduce
the available wind in the beginning when we have 0 MWh stored in
reservoir. 147-Penalty function effectWind Scenario is changed by
changing available wind to 1 MW for first 3 hours, as shown
15
ProfitsRevenue22309.99 Penalty7920 Net Profit14389.99 8-How to
overcome the loss16
8-How to overcome the lossNow after adding Generator total
supplied power remained within the limits (i.e. 3
