1. A Dynamic Interval Goal Programming Approach to the Regulation of a Lake-River System Raimo P. Hämäläinen Juha Mäntysaari S ystems Analysis Laboratory Helsinki University of Technology Systems Analysis Laboratory Helsinki University of Technology www.sal.hut.fi

2. Päijänne-Kymijoki lake river system LAKE PÄIJÄNNE KONNIVESI RUOTSALAINEN RIVER KYMIJOKI 0 10 20 30 40 50 km Jyväskylä LAKE PYHÄJÄRVI Lahti Kotka Finland

3. 4:th largest in Finland Control: Outflow from Päijänne to the river Kymijoki Inflows: forecasted Regulation policies: Water levels at six time points Päijänne-Kymijoki lake river system

4. Need for modelling Development of feasible regulation strategies is a dynamic control problem –No intuitive solutions –Planning againts long historical inflow data –Analysis of regulation impacts –Many interest groups  multicriteria optimization in a dynamic system

5. Goals in terms of water levels Users give desired water levels at: –six different points during one year –ideal level + acceptable interval (min, max)

6. Dynamics of the lake Päijänne

7. Constraints Outflow from Päijänne: Min/max flow Fixed and hard Max change in outflow: Soft, violation penalties Water level in the lake Pyhäjärvi: Fixed rule based regulation Part of the dynamics

8. Criteria and penalty functions Criterion for goal levels: Quadratic cost for differences of goal points from regulated water levels Penalty outside the goal interval: Quadratic difference from the limits (min or max) Penalty for violation of change in outflow rate: Quadratic cost outside the maximum flow limit, otherwise zero

9. Cost function minimized = Sum of deviations from goal + penalty outside goal intervals Criteria and penalty functions

10. Model assumptions Lake dynamics Optimization against one to four year history Lower dam regulation by a given rule Regulator uses a rolling two goal optimization principle Adjustment rules

11. Generation of the optimal regulation strategy

12. Goal programming Goal (infeasible point) Problem: Find a point in the feasible set closest to the goal point/set  Weighted, Min Max, Lexicographic Aspects in regulation: –Dynamic problem –Goal interval (set) d Goal point/set cost function

13. Why goal programming ? Economic, social and environmental impacts 37 primary + 20 secondary = 57 different impacts For example: Power production, flood damages, number of destroyed loon nests Some impacts are interdependent: energy produced and the value of energy  Use of tradeoff comparison questions or criteria classification becomes difficult

14. ISMO spreadsheet application

15. Minimizes deviations from goal levels and goal intervals Satisfies flow constraints Simulates the regulator’s operating principles Preference model Set of goal levels + acceptability intervals Optimization againts history data for a selected one to four year period Modifiable parameters Flow constraints in the river steepness of the penalty function ISMO spreadsheet application

16. Use of ISMO

17. ISMO example

18. Inflow 1980-1984

19. Utopia and realistic solutions

21. Impacts Nature –Spawning areas for pike fish –Water level when ice melts –number of destroyed loon nests Social –Recreational losses –Professional fishing: Reduction of the water level during 10-Dec and 28-Feb Economic –Power production –Flood damages –Days infavourable for log floating

22. Comparison of impacts:  User evaluates and modifies goal levels

23. ISMO is implemented in MS Excel 7.0 (MS Office 95) –Solver provides optimization routines –10-20 minutes for one solution Benefits –Rapid development –Easy: data input, model modification, visualisation and printing Users accept easily –Excel is a commonly used office program Spreadsheet modelling works !

24. Generation of alternative regulation strategies Impact tables of regulation –a key info material in decision analysis interviews and conferences Sensitivity tool –individual changes for water levels and related impacts –helps representatives to better understand the restrictions of the system Added value

25. Further development Different information patterns Iterative optimization of the goal levels to produce maximum amount/value of the energy Now used to develop new regulation policies and their impacts

26. References –Marttunen, M., Järvinen, E. A., Saukkonen, J. and Hämäläinen, R. P., “Regulation of Lake Päijänne - a Learning Process Preceding Decision-Making”, Finnish Journal of Water Economy, 6:29-37, 1999. –Hämäläinen, R. P., Kettunen, E., Marttunen, M. and Ehtamo, H., “Evaluating a framework for multi-stakeholder decision support in water resources management”, Manuscript, 1999. (Downloadable from http://www.sal.hut.fi/Publications/pdf-files/mhamb.pdf) –Hämäläinen, R. P. and Mäntysaari, J., “A Dynamic Interval Goal Programming Approach the Regulation of a Lake-River System”, Manuscript, 2000. (Downloadable from http://www.sal.hut.fi/ Publications/pdf-files/mhama.pdf) –Hämäläinen, R. P., “Interactive Multiple Criteria Decision Analysis in Water Resources Planning”, Home pages of the Lake Päijänne project, 1998, www.paijanne.hut.fi