From the PIP procedure to MODSSs

1. MNR L03 From the PIP procedure to MODSSs Andrea Castelletti Politecnico di Milano

2. Planning actions and management actions Planning actions:decided once forever or over a long time horizon. Management actions:decided frequently or even periodically, often on a daily basis. How are they taken? Planning actions:by means of a Project, i.e. by evaluating different alternatives (i.e. mix of planning actions) with the aim of individuating those that better satisfy the DM and/or Stakeholders’ point of views. Management actions:taken on the basis of the Regulator’s experience, i.e. somehow empirically. Does not work!!!

3. inflows inflows t t capacity capacity levels levels t t releases releases t t G D G D Planning a new reservoir Planning decision:to build the reservoir Management decision:water volume to be released in the next 24 hours ....... Deciding to build the reservoir does require deciding how it will be daily regulated, otherwise it is not possible to evaluate if and how the farmers are satisfied. The management must be always considered when either the planning requires it or it change the context in which the current managemt is performed. Planning the management

4. Release plan Planning the management IDEA: we can define the management decision for each day of the Project horizon (N years) by specifying the sequence of decisions (N*365) over that horizon. This sequence constitutes a planning decision. Is this the best solution? To reply let’s consider the management only, i.e. let’s assume the reservoir has already been built. Simplification: when the system is a periodic one, only 365 management decisions have to be defined.

5. Cabora Bassa irrigation MOZAMBIQUE Taking decisions in full rationality at+1 st ut model Decision: volume of water to release every day from the dam in order to satisfy the farmers’ demand

6. The release plan It ? catchment at+1 st+1 ut reservoir + users m0 … m364 wt+1

7. s * t The rule curve It catchment a*t+1 ut st+1 s*t+1 reservoir + users m0 … m364 wt+1

8. s s * s* t t The rule curve It ? catchment a*t+1 ut s*t+1 reservoir + users m0 … m364 wt+1

9. The rule curve Rule curve for Cabora Bassa Actual path

10. s s * * t t delay The control policy It catchment a*t+1 ut mt(st) s*t+1 reservoir + users m0 … m364 wt+1 p= {mt(•) t = 0,1,…,h}

11. forecaster â t+1 mt(st ,wt ,It ,at) mt(st,wt) delay The control policy It catchment delay at+1 at+1 mt(st) mt(st,wt,It,at) mt(st ,wt ,ât+1) st+1 ut reservoir + users m0 … m364 wt+1 delay

12. forecaster â t+1 mt(st ,wt ,It ,at) mt(st,wt) delay The control policy Why a single decision ut? It’s more rational a whole set Mt ! It catchment delay at+1 at+1 mt(st) mt(st,wt,It,at) mt(st ,wt ,ât+1) st+1 ut reservoir + users m0 … m364 Mt wt+1 delay

13. mt(st,wt) delay The control policy It catchment delay at+1 at+1 mt(st) mt(st,wt,It,at) mt(st ,wt ,at+1) st+1 ut reservoir + users m0 … m364 wt+1 delay

14. Simulation ANALYST scenario choice manag. policy performance indexes delay delay comparison & generation of policies It model of the manag. system model of the physical system catchment at+1 delay st+1 manag. policy ut reservoir + users wt+1

15. Set-valued simulation ANALYST scenario choice performance indexes DM delay delay It model of the manag. system model of the physical system catchment at+1 delay st+1 set valued manage policy manag. policy ut Mt reservoir + users wt+1

16. In a deterministic world Let’s introduce a simplification: We are dealing with deterministic inflows We know {a1,…,ah} for any time horizon {1,…,h}

17. Defining criteria and indicators 1. Reconnaissance optimization Identifying the model Defining actions (measures) * * history B*mz. = utopia 3. Designing policy p*mz. MOZAMBIQUE Single-Objective control problem Design Procedure Problem formulation 2. Conceptualisation xt+1= ft(xt, ut, at+1) at+1 ~ ft (•) utUt (xt) ut= mt(xt) p = {mt(•) t = 0,1,…,h}

18. Defining criteria and indicators 1. Reconnaissance Identifying the model 3. Designing policy Single-Objective control problem Design Procedure 2. Conceptualisation Defining actions (measures) Integrated Modelling Framework

19. hydropower Cabora Bassa Cabora Bassa Kafue ZAMBIA Kariba irrigation irrigation ZIMBABWE MOZAMBIQUE MOZAMBIQUE Taking decisions in partial rationality Many interests Many DMs Full rationality Partial rationality xt+1= ft(xt, ut, at+1)

20. Present situation BZim BMozcon BMoz BZam today (BZamopt;BZimopt)

21. The optimal solution for Mozambique BZim E D F BMozott BMoz BZam DBMoz utopia today (BZamott;BZimott) BMozcon

22. The Pareto frontier BZim E Pareto frontier D F BMozott BMoz BZam utopia today (BZamott;BZimott) BMozcon

23. BZam utopia BZim D BZam alternative today BMoz D BMoz The Pareto frontier

24. zambia at+1 ~ ft (•) mozambique utUt (xt) ut= mt(xt) zimbabwe * * p = {mt(•) t = 0,1,…,h} Multi-objective control problem Formulation Pareto frontier xt+1= ft(xt, ut, at+1)

25. In an uncertain world Considering the inflows as deterministic is an unrealitsic assumption. However, we can not simply say that future inflows are unknow Evaluation Prediction Rational decision Predicting the future requires some past characteristic of the process to keep in the future: modelling the inflow as a random process (stochastic). THE STEADY STATE PARADIGM

26. Decision-making in uncertain condition - example Knowing exactly what will happen, we would select alternative A2that returns 1500 €.

27. Risk aversion Laplace criterion provide alternative A2 as the best choice. And you, what would you select? Maybe the worst case: min

28. BZim BMoz BZam Partial rationality + Uncertain worldThe Multi-Objective Control problem Generating the whole Frontier is not always possible. In some cases, interacting with the Stakeholders is more appropriate, thus generating the Front point by point. NEGOTIATIONS.

29. BZim afflussi domanda irrigua BMoz BZam Negotiations 40 35 30 25 20 15 10 5 0 BMoz BZim BZam Just showing the value of the objectives could be not enough, in some cases showing the associated trajectories can be more useful ….

30. Pareto frontier Design Procedure 1. Reconnessaince zambia 2. Conceptualisation 3. Policy design 4. Estimating the effects mozambique 5. Evaluation 6. Comparison and negotiations reasonable alternatives zimbabwe Mitigation and compensation, no Agreement? yes Final decision Multi-objective control problem 2. Conceptualisation 3. Designing policy 4. Estimating effects 5. Evaluation 6. Comparison and negotiations Agreement?

31. Planning TwoLe/P MODSS TwoLe 5. Evaluation 6. Comparison or negotiation TwoLe/M no Mitigation and compensation reasonable alternatives Agreement? yes Management Final (political) decision Daily management 1. Reconnaissance 2. Conceptualisation 3. Designing alternatives Stakeholders 4. Estimating effects

32. TwoLe: a 2 level MODSS planning Data analyst DM stakeholders TwoLe/P models and policies TwoLe/M DM users management release decision operational control