1. OASIS Basics Computer Aided Negotiations of Water Resources Disputes

2. 2 What is OASIS ? Operational Analysis and Simulation of Integrated Systems Program for modeling the operations of water resources systems

3. 3 What does OASIS do ? …….. and How? Simulates the routing of water through a system represented by: Nodes (points of interest) Demand Junction Reservoirs Arcs (convey water from node to node) Inflows (positive or negative, fixed by the user) Routing accounts for human control and physical constraints Terminal 120110 130100 140 Reservoir Junction Arc Inflow 135 Demand Reservoir

4. 4 Examples of Human Control Water is moved from node to node according to “operating criteria”. Water is allocated to demand nodes based on pre-established criteria Examples of Physical Constraints Storage Area Elevation (SAE) tables: Storage volume available at each water level in a reservoir Maximum flow: restriction on the arc flow imposed by a maximum gate opening Head - Flow relationships to describe the hydraulics of a canal or canal capacity Evapotranspiration: “Demand” applied to each node to account for evapotranspiration losses in a reservoir MASS BALANCE : Main constraint that is accounted for during each time step of the routing

5. 5 What does OASIS do ? …….. and How? Water is routed (moved) through the system by calculating: Flow through arcs (arcflows), Volume stored in each node and Volume allocated to each demand node these calculations are made for every time step during a pre-established period of simulation, using a Linear Programming solver which is called every time step

6. 6 Use of Linear Programming in OASIS OASIS automatically constructs the LP each time step, solves it, and then turns that mathematical solution into a simulated record of flows and storages The LP is internal – hidden, unless you request it Every time step, flow, storage and delivery are decision variables whose values are solved by LP.

7. 7 Routing with a linear program You specify what to do but do not have to tell OASIS how to do it Constraints: rules that OASIS must obey Goal: rule that OASIS tries to meet Written to an objective function which the solver optimizes – determines the solution which best meets the goals within the constraints Think of the solver as a “smart operator” who obeys the laws of physics (and other specified constraints)

8. 8 Decision Variables and State Variables Decision variable: A variable whose value is decided by the LP solver. OCL Rules are written with the values of the decision variables unknown. State variable: Any variable whose value is known. Some state variables represent decision variables from previous time steps that the LP solver has already solved. Other state variables represent external conditions (e.g. inflow, rainfall, date, time-step number)

9. 9 Goals Goal: a rule that OASIS tries to satisfy In any time step, OASIS might not be able to satisfy all or any of a given operating goal By their nature, operating goals are in competition with each other OASIS chooses between competing goals by weights that you assign Example: “Try to deliver 45 mgd to demand node 472”.

10. 10 Balancing Goals The modeler assigns a weight value to every operating goal in the model to construct the objective function The LP solution scores points by multiplying the weight value by the value of the associated decision variable In every time step, OASIS determines the values of the decision variables by solving the LP The solution obeys every constraint The solution is the set of decision variable values that gets the maximum number of points from the set of goals

11. 11 Weights Every goal should be assigned a weight value Positive weights encourage actions while negative weights (penalties) discourage them. Zero weight is the same as if the goal did not exist.

12. 12 More About Weights Assigning weights is usually simple—assign lowest to highest in hierarchical order Weights do not represent a quantity that you can measure in the real-world system Weights do represent the relative importance of the different priorities of the operators of the real-world system It gets easier with experience

13. 13 An analogy to money Water is like money Weights are your spending/saving priorities The lp will allocate your money based on your spending/saving priorities When money is scarce, which bills do you pay first (should you satisfy a minimum flow requirement or keep water in a lake)? When money is plentiful, where should you put the extra (should you put it on the floodplain or in a lake)?

14. 14 Example of Using Weights - 1 No benefit for moving water downstream; storage remains in node 100 and node 130 wt = 100 wt = 0 terminal wt = 0 120110 130100 wt = 200 140

15. 15 Example of Using Weights - 2 No benefit for moving water between reservoirs, but weight on terminal arc pulls from node 130 until it empties. wt = 100 wt = 0wt = 150wt = 0 terminal wt = 0 120110 130100 wt = 200 140

16. 16 Example of Using Weights - 3 No benefit for moving water between reservoirs, but weight on terminal arc pulls from node 130 until it empties, then pulls from node 100. wt = 100 wt = 0wt = 250wt = 0 terminal wt = 0 120110 130100 wt = 200 140

17. 17 Example of Using Weights - 4 Water released from node 100 gets 300 weight points, plus 100 points for being stored in node 130, so water is transferred from node 100 to node 130. wt = 100 wt = 0wt = 100 terminal wt = 100 120110 130100 wt = 200 140

18. 18 Example of Using Weights - 5 Weight on terminal arc causes the system to be drained. Moral: use weights on arcs only where necessary. wt = 100 wt = 250wt = 100 terminal wt = 100 120110 130100 wt = 200 140

19. 19 Example of Using Weights - 6 OASIS gets the same number of points whether it keeps water in the upstream reservoir or sends it to the downstream reservoir This is called alternate optima, and it should be avoided wt = 200 wt = 0 terminal wt = 0 120110 130100 wt = 200 140

20. 20 Building an OASIS model Basedata: timeseries data GUI: much of what you need to specify can be done here OCL (Operations Control Language): provides flexibility “set” the value of a variable Define new constraints and goals for the lp Create new variables, enter new data, exchange information with other programs Output: Quickview, Onevar, output.dss