Dam Viable Management under Uncertainty

Michel De Lara
(last modiﬁcation date: October 10, 2017)

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1 Problem data
2 Maximizing the expected payoﬀ and computing the resulting probability to satisfy the constraint
3 Maximizing the viability probability to guarantee jointly payoﬀ and summer water volume

1 Problem data
1.1 Dam dynamics
1.2 Criterion: intertemporal payoﬀ
1.3 Constraint: minimal volume during the Summer months
1.4 Water turbined strategy
1.5 Probabilistic model on water inputs and expected criterion
2 Maximizing the expected payoﬀ and computing the resulting probability to satisfy the constraint
2.1 Dynamic programming equation
2.2 Data for the numerical simulations
2.3 Scilab code for the additive stochastic dynamic programming equation
2.4 Optimal strategy when the ﬁnal “value of water” is not zero
2.5 Evaluation of the probability to satisfy the tourism constraint
3 Maximizing the viability probability to guarantee jointly payoﬀ and summer water volume
3.1 Multiplicative dynamic programming equation
3.2 Scilab code for the multiplicative stochastic dynamic programming equation
3.3 Maximal viability probability function and viability kernels
3.4 Maximal viability probability as a function of guaranteed thresholds

1 Problem data

We consider a dam manager intenting to maximize the intertemporal payoﬀ obtained by selling power produced by water releases, when the water inﬂows (rain, outﬂows from upper dams) are random. However, the manager must also respect a minimal volume during the Summer months for tourism reasons.

1.1 Dam dynamics

We model the dynamics of the water volume in a dam by

♯ S◟(t◝ +◜-1◞) = min {S , S◟(◝t◜)◞ − q◟(◝t◜)◞ + a◟(◝t◜)◞}, t ∈ 𝕋 := {t0,...,T − 1} future volume volume turbined inflow

with

time t ∈𝕋 := {t₀,…,T} is discrete (such as days), and t denotes the beginning of the period [t,t + 1[,
S(t) volume (stock) of water at the beginning of period [t,t + 1[, belonging to the discrete set $𝕏 = {0,1,2,...,S ♯}$ , made of water volumes, where $S ♯$ is the maximum dam volume,
a(t) inﬂow water volume (rain, etc.) during [t,t+1[, belonging to $𝕎 = {0,1,2,...,a ♯}$
decision-hazard: a(t) is not available at the beginning of period [t,t + 1[
q(t) turbined outﬂow volume during [t,t + 1[, decided at the beginning of period [t,t + 1[, supposed to depend on S(t) but not on a(t), belonging to the discrete set $𝕌 = {0,1,2,...,q♯}$ , where q^♯ is the maximum which can be turbined by time unit (and produce electricity),
$s(t) = [S (t) − q(t) + a(t) − S ♯] +$ the spilled volume

The dam manager is supposed to make a decision, here turbining q(t) at time t, before knowing the water input a(t). Such a case is called decision-hazard. The constraint on the water turbine q(t) is

0 ≤ q(t) ≤ S(t).

(1)

A scenario is a sequence of uncertainties:

a(⋅) := (a(t0),...,a (T − 1)).

(2)

1.2 Criterion: intertemporal payoﬀ

The manager original problem is one of payoﬀ maximization where turbining one unit of water has unitary price p(t). On the period from t₀ to T, the payoﬀs sum up to

T∑−1 p(t)q(t) + UtilFin (T, S(T )), t=t0

(3)

where

the sequence
$p(⋅) = (p (t0),...,p(T − 1 ))$ (4)

of prices is supposed to be known in advance (in other models, it could be progressively revealed to the manager),
the ﬁnal term $UtilFin (T, S(T ))$ gives value to the water volume in the dam at the horizon T.

1.3 Constraint: minimal volume during the Summer months

For “tourism” reasons, the following constraint is imposed

♭ stock S (t) ≥ S , ∀t ∈ {July,August }.

In what follows, we shall be more speciﬁc about the sense with which this constraint has to be satisﬁed, namely in probability.

1.4 Water turbined strategy

A strategy Rule : 𝕋 × 𝕏 → 𝕌 assigns a water turbined $q = Rule (t,S )$ to any state S of dam stock volume and to any decision period t ∈ 𝕋. Once given, we obtain uncertain volume trajectories $S (⋅) := (S(t0),...,S(T − 1 ),S (T))$ and turbined trajectories $q(⋅) := (q(t0),...,q(T − 1))$ produced by the “closed-loop” dynamics

S(t0) = S0 ♯ S(t + 1) = min {S ,S (t) − q(t) + a(t)} q(t) = Rule (t,S(t))

(5)

and function of the scenario a(⋅). Thus, in the end, we obtain an uncertain payoﬀ

T∑ −1 CritRule(t0,S0,a (⋅)) := p(t)q(t) + UtilFin (T, S(T )), t=t0

(6)

where $S(⋅)$ and $q(⋅)$ are given by (5).

1.5 Probabilistic model on water inputs and expected criterion

We suppose that sequences of uncertainties $(a(t0),...,a(T − 1))$ are random variables with a known probability distribution ℙ on the set $♯ T−t0 {0,...,a }$ .

We suppose that the random variables $(a(t0),...,a(T − 1))$ are independent with distribution $π0 (t),...,πa♯(t)$ on the set ${0,...,a ♯}$ :

ℙ{a(t) = 0} = π (t),...,ℙ{a (t) = a ♯} = π ♯(t). 0 a

(7)

Notice that the random variables $(a(t0),...,a(T − 1))$ are independent, but that they are not necessarily identically distributed. This allows us to account for seasonal eﬀects (more rain in autumn and winter).

To each strategy Rule, we associate the expected payoﬀ

∑T−1 𝔼 [CritRule(t0,S0,a(⋅))] = 𝔼[ p(t)q(t) + UtilFin (T, S(T ))], t=t0

(8)

where the expectation 𝔼 is taken with respect to the probability ℙ.

2 Maximizing the expected payoﬀ and computing the resulting probability to satisfy the constraint

2.1 Dynamic programming equation

The dynamic programming equation associated with the problem of maximizing the expected payoﬀ (8)

T∑− 1 max 𝔼[ p(t)q(t) + UtilFin (T, S(T ))] t=t0

(9)

final payoff ◜-------◞◟------◝ V (T,S ) = UtilFin (T, S(T )), ♯ V(t,S ) = max ♯ 𝔼a(t)[ p◟(◝t)◜q◞ +V (t + 1,m◟in-{S-,S-−◝◜-q +-a-(t)}◞ )], q∈{0,1,2,...,min{S,q }} instant. payoff future stock volume

(10)

where the expectation 𝔼 is taken with respect to the probability in (7).

2.2 Data for the numerical simulations

We know will make numerical simulations, and try diﬀerent strategies. We shall consider a daily management over one year

t0 = 1 and T = 365,

(11)

with

3 ♯ 3 ♯ 0.4 ♯ ♯ 0.5 ♯ S0 = 0 hm , S = 100 hm , q = --- × S and a = ---× S 7 7

(12)

where we say that, during one week, one can turbine at maximum 40% of the dam volume, and that during one week of full water inﬂows, an empty dam can be half-ﬁlled.

The sequence of prices is known in advance. We shall produce it by one sample from the expression

p(t) = (1 + 𝜖(t))p with p-= 66 MWh ∕hm3 × 2.7 euros∕MWh3

(13)

where $𝜖(t)$ is drawn from a sequence of i.i.d. uniform random variables in $[− 1∕2,1 ∕2]$ .

The probability of water inﬂows (from zero to the maximum $a ♯$ ) is known in advance.¹

Copy the following Scilab code into a ﬁle DamData.sce.

  // exec DamData.sce

  // ----------------------------------------------------
  // DATA
  // ----------------------------------------------------


  // State
  volume_max=100;
  volume_min=0;

  // Control
  control_max=0.4/7*volume_max;
  control_max=control_max+1;

  // Time
  tt0=1;
  horizon=365;
  TT=tt0:(horizon-1);
  bTT=tt0:(horizon);

  // Prices
  price=66*2.7;
  price=price*(1/2+0.5*(rand(TT)-1/2));

  // Uncertainties
  uncertainty_max=floor(0.5/7*volume_max);
  uncertainty=[0:uncertainty_max];

  // Probabilities
  unnormalized_proba=cumsum(ones(uncertainty))-1;
  proba1=unnormalized_proba/sum(unnormalized_proba);
  // more rain in winter
  proba182=proba1($:-1:1);
  // less rain in summer

  // simulation of independent sequences of water inflows between 1 and
  // uncertainty_max+1

  Simulations=50;

  WW=zeros(Simulations,horizon-tt0+1);

  for ss=1:Simulations do
    for tt=bTT do
      proba=(1-sin(%pi*tt/365))*proba1+sin(%pi*tt/365)*proba182;
      WW(ss,tt)=dsearch(rand(),cumsum(proba));
    end
  end

  Scenarios=WW;

  xset("window",1);xbasc();
  plot2d2(bTT,Scenarios')

Copy the following Scilab macros into the same ﬁle DamData.sce.

In the macro trajectories, the output CC is the mean payoﬀ averaged over the scenarios: by the law of large numbers, CC is an approximation of the expected payoﬀ if the number of scenarios is large enough (Monte Carlo method).

  // ----------------------------------------------------
  //  MACROS
  // ----------------------------------------------------

  // Dynamics
  function ssdot=dynamics(ss,qq,aa)
    ssdot=max(volume_min,min(volume_max,ss-qq+aa));
  endfunction

  // Instantaneous payoff function
  function c=instant_payoff(tt,ss,qq,aa)
    c=price(tt)*qq;
  endfunction


  // Final payoff function
  function c=final_payoff(tt,ss)
    c=0;
  endfunction


  // Trajectories simulations

  function [SS,QQ,CC]=trajectories(SS0,scenarios,policy)
    SS=[];
    QQ=[];
    CC=[];
    nb_simulations=size(scenarios,'r');
    for simu=1:nb_simulations do
      ss=SS0;
      qq=[];
      cc=0;
      aa=scenarios(simu,:);
      for tt=TT do
        qq=[qq,policy(tt,ss($))];
        ss=[ss,dynamics(ss($),qq($),aa(tt))];
        cc=cc+instant_payoff(tt,ss($),qq($),aa(tt));
      end
      cc=cc+final_payoff(TT($),ss($));
      SS=[SS;ss];
      QQ=[QQ;qq];
      CC=[CC;cc];
    end
    //
    disp('The payoff is '+string(mean(CC)));
  endfunction

2.3 Scilab code for the additive stochastic dynamic programming equation

Copy the following Scilab code into the ﬁle Damoptimality.sce.

  ////////////////////////////////////////////////////////////////////////
  //    STOCHASTIC ADDITIVE DYNAMIC PROGRAMMING EQUATION
  ////////////////////////////////////////////////////////////////////////

  // ----------------------------------------------------
  // DATA
  // ----------------------------------------------------

  states=[0:volume_max];
  controls=[0:control_max];

  cardinal_states=prod(size(states));
  cardinal_controls=prod(size(controls));
  cardinal_uncertainty=prod(size(uncertainty));

  state_min=min(states);
  state_max=max(states);

  // ----------------------------------------------------
  //  MACROS
  // ----------------------------------------------------

  function [FEEDBACK,VALUE]=SDP(FINAL_PAYOFF)
    VALUE=zeros(bTT'*states);
    FEEDBACK=zeros(TT'*states);

    VALUE(horizon,:)=FINAL_PAYOFF;// vector

    // backward time
    for tt=TT($:-1:1) do
      loc=zeros(cardinal_controls,cardinal_states);
      // local variable containg the values of the function to be minimized
      for jj=1:cardinal_controls do
        hh=controls(jj);
        loc(jj,:)=0;
        // the following loop computes an expectation
        for dd=1:cardinal_uncertainty do
          ww=uncertainty(dd);
          loc(jj,:)=loc(jj,:)+ ...
                    proba(dd)*(-1/%eps*bool2s(states < hh)+bool2s(states >= hh)) .* ...
                    (instant_payoff(tt,states,hh,ww)+ ...
                     VALUE(tt+1,dynamics(states,hh,ww)-state_min+1));
        end;
      end
      //
      [mmn,jjn]=min(loc,'r');
      [mmx,jjx]=max(loc,'r');
      // mm is the extremum achieved
      // jj is the index of the extremum argument
      //
      VALUE(tt,:)=mmx;
      // maximal payoff
      FEEDBACK(tt,:)=controls(jjx);
      // optimal feedback
    end
  endfunction

We ﬁrst consider that the ﬁnal “value of water” is zero:

UtilFin (T,S0 ) = 0.

(14)

  // We start with a zero value of water at the end of the year
  zero_final_payoff_vector=zeros(states);

  // ----------------------------------------------------
  //  SIMULATIONS
  // ----------------------------------------------------

  [FEEDBACK,VALUE]=SDP(zero_final_payoff_vector);

  // optimal strategy
  function uu=optimal_rule(tt,xx)
    uu=FEEDBACK(tt,xx-state_min+1);
  endfunction


  // Trajectories simulations and visualization
  SS0=0;
  [SS,HH,CC]=trajectories(SS0,Scenarios,optimal_rule);
  xset("window",10);// xbasc();
  plot2d(bTT,SS')
  xtitle('Stock volumes in a dam following an optimal strategywith a zero final value of water', ...
         '(time)','(volume)')

  // Payoff histogram
  xset("window",20);xbasc();
  histplot(100,CC)
  xtitle('Histogram of the optimal payoff with a zero final value of water')


  disp('The minimum of the optimal payoff is '+string(min(CC)));
  disp('The mean of the optimal payoff is '+string(mean(CC)));
  disp('The maximum of the optimal payoff is '+string(max(CC)));

Question 1 Picture the trajectories of the stocks corresponding to the optimal strategy. Evaluate the optimal expected payoﬀ, and compare it with the value function $V (t0,0)$ evaluated at the initial time t₀ and the initial stock $S = 0 0$ . Explain why these two quantities should be close. What do you observe for the ﬁnal stocks? Explain why.

2.4 Optimal strategy when the ﬁnal “value of water” is not zero

Till now, there was no gain in leaving water in the dam at the ultimate decision period. From now on, we consider that the “value of water” $UtilFin (T,S0)$ is given by

UtilFin (T, S ) = ---1--V (t ,S ) 0 1 + rf 0 0

(15)

where 1
1+rf is a discount factor. We shall take $rf = 0.1$ when $T = 365$ days.

  ////////////////////////////////////////////////////////////////////////
  //    VALUE OF WATER
  ////////////////////////////////////////////////////////////////////////

  final_payoff_vector=(1/(1+0.1))*VALUE(1,:);

Copy the following Scilab code into the ﬁle DamOptimality.sce.

  // ----------------------------------------------------
  //  SIMULATIONS
  // ----------------------------------------------------

  [FEEDBACK,VALUE]=SDP(final_payoff_vector);

  // optimal strategy
  function uu=optimal_rule(tt,xx)
    uu=FEEDBACK(tt,xx-state_min+1);
  endfunction


  // Trajectories simulations and visualization
  [SS,HH,CC]=trajectories(SS0,Scenarios,optimal_rule);
  xset("window",10);// xbasc();
  plot2d(bTT,SS')
  xtitle('Stock volumes in a dam following an optimal strategy with a final value of water', ...
         '(time)','(volume)')

  // Payoff histogram
  xset("window",20);xbasc();
  histplot(100,CC)
  xtitle('Histogram of the optimal payoff with a final value of water')

  disp('The minimum of the optimal payoff is '+string(min(CC)));
  disp('The mean of the optimal payoff is '+string(mean(CC)));
  disp('The maximum of the optimal payoff is '+string(max(CC)));

Figure 1: Stock volumes in a dam following an optimal strategy, with a ﬁnal value of water

Question 2 Picture the trajectories of the stocks corresponding to the optimal strategy. Evaluate the optimal expected payoﬀs for diﬀerent values of the initial stock $S0$ , and compare them with the value function $V (t0,S0 )$ evaluated at the initial time t₀ and the initial stock $S0$ . Display the histogram of the optimal payoﬀ. Compare the mean of the optimal payoﬀ with the upper and lower bounds of the distribution.

Figure 2: Histogram of the optimal payoﬀ, with a ﬁnal value of water

2.5 Evaluation of the probability to satisfy the tourism constraint

Let $S ⋆(⋅)$ denote an optimal stock trajectory.

Question 3 Evaluate the probability

{ } ℙ S⋆(t) ≥ S♭, ∀t ∈ {July, August }

(16)

that the water volume $⋆ S (t)$ remains above F% of $♯ S$ during the months of July and August, where F% varies between 0% and 100%.

Figure 3: Probability that the summer tourism constraint is satisﬁed under the optimal strategy, with a ﬁnal value of water

  // ----------------------------------------------------
  // PROBABILITY CONSTRAINT EVALUATION
  // ----------------------------------------------------
  Summer=([1:horizon] >= horizon/2 & [1:horizon] <= horizon/2+2*30*horizon/364);
  VP=[]
  for jj=0:100 do
    VP=[VP,mean(bool2s(min(SS(:,Summer),'c') >= jj/100*volume_max))];
  end

  xset("window",30);xbasc();
  plot(0:100,VP)
  xtitle('Probability that the summer tourism constraint is satisfied under the optimal strategy with a final value of water', ...
         '(guaranteed fraction of dam volume)','(probability)')

3 Maximizing the viability probability to guarantee jointly payoﬀ and summer water volume

The payoﬀ at time t ∈ 𝕋 is

t water turbined profit ∑ ◜ ◞◟ ◝ B (t) = p(s)q(s) s=t0

and

water turbined profit final stock value T∑−1 ◜--◞◟-◝ ◜-------◞◟-------◝ B(T ) = p(t)q(t) + UtilFin (T, S(T )) t=t0

We propose an alternative stochastic viability formulation to (9)-(16) under the form

( | ) || || water input scenarios along which || { | the stocksS(t) ≥ S♭, ∀t ∈ {July, August } } max ℙ | a(⋅)|| and | |( || ♭ |) the ﬁnal proﬁtB (T) ≥ B

(17)

Figure 4: Maximal viability probability as a function of guaranteed thresholds S^♭ and B^♭

3.1 Multiplicative dynamic programming equation

The dynamic programming equation associated with the problem of maximizing the viability probability (17) is

final constraint ◜--◞◟-◝ V (T,S,B ) = 1{B≥B ♭} , V (T − 1,S,B ) = max 𝔼a (T− 1)[1 ♭ q∈ {0,1,2,...,min{S,q♯}} {S≥S ,T−1∈{July,August} ×V (t + 1,min {S♯,S − q + a(T − 1)}, B + p(T − 1)q + UtilFin (T,min {S♯,S − q + a(T − 1)})], instantaneous constraint ◜--------◞◟---------◝ V(t,S,B ) = max ♯ 𝔼a (t)[1{S(t)≥S♭,t∈{July,August}} q∈ {0,1,2,...,min{S,q}}♯ ×V (t + 1,m◟in-{S-,S(t) −◝◜-q(t) +-a(t)}◞, B◟-+◝p◜(t)q◞ )], ∀t = t0,...,T − 2, future stock volume future payoff

(18)

where the expectation 𝔼 is taken with respect to the probability (7). Notice that the equation for t = T − 1 takes into account the term $UtilFin (S (T))$ in the payoﬀ.

3.2 Scilab code for the multiplicative stochastic dynamic programming equation

Copy the following Scilab code into the ﬁle DamViability.sce.

  // exec DamViability.sce

  // ----------------------------------------------------
  // DATA
  // ----------------------------------------------------

  horizon=10;

  SSmax=volume_max;
  nb_SS=SSmax+1;
  state_PP=0:(horizon*SSmax);
  nb_PP=length(state_PP);
  PPmax=max(state_PP);
  controls=0:nb_SS;
  nb_CC=length(controls);

  // For a practical work lower the number of points

  volume_max=10;
  SSmax=volume_max;
  nb_SS=SSmax+1;
  state_PP=0:(horizon*SSmax);
  nb_PP=length(state_PP);
  PPmax=max(state_PP);
  // controls=0:nb_SS;
  controls=linspace(0,SSmax+1,10);
  nb_CC=length(controls);


  uncertainties=0:1;
  nb_WW=length(uncertainties);
  proba=ones(1,nb_WW)/nb_WW;

  ////////////////////////////////////////////////////////////////////////
  //    MULTIPLICATIVE STOCHASTIC DYNAMIC PROGRAMMING EQUATION
  ////////////////////////////////////////////////////////////////////////


  // ----------------------------------------------------
  //  MACROS
  // ----------------------------------------------------

  function [FEEDBACK,VALUE]=MSDP(SS_min,PP_min)
    //  SS_min= SSmin * Summer ;
    //  PP_min=PPmin;

    VALUE=list();
    FEEDBACK=list();
    VALUE(horizon)=ones(nb_SS,1)*bool2s(state_PP >= PP_min);
    shift=[(horizon-1):(-1):1];
    for tt=shift do
      VVdot=VALUE(tt+1);
      VV=zeros(VVdot);
      for ss=1:nb_SS do
        SS=ss-1;
        if SS >= SS_min(tt) then
          for pp=1:nb_PP do
            PP=pp-1;
            locext=[];
            for cc=1:ss do
              // control constraint
              UU=cc-1;
              locint=0;
              for oo=1:nb_WW do
                ww=uncertainties(oo);
                SSdot=(min(SSmax,SS-UU+ww));// physical value
                ssdot=SSdot+1;// corresponding index
                Ppdot=(min(PPmax-1, ...
                           PP+price(tt)*UU+bool2s(tt==horizon-1)*final_payoff(tt,ssdot)));
                //physical value
                ppdot=min(round(ppdot/PPmax)+1,nb_PP);//corresponding index
                locint=locint+proba(oo)*VVdot(ssdot,ppdot);
              end;// of the expectation loop
              locext=[locext,locint];
            end;// of the control loop
            VV(ss,pp)=max(locext);
          end;// of the pp loop
        end;// of the if condition on SS
      end;// of the ss loop
      VALUE(tt)=VV;
    end;// of the time tt loop
  endfunction

3.3 Maximal viability probability function and viability kernels

Question 4 Compute the maximal viability probability. Deduce the viability kernels with conﬁdence levels 100%, 95% and 90%.

  stacksize('max');

  PPmin=0.13*PPmax;
  SSmin=0.89*SSmax;

  [FEEDBACK,VALUE]=MSDP(SSmin*Summer,PPmin)

3.4 Maximal viability probability as a function of guaranteed thresholds

Copy the following Scilab code into the ﬁle DamViability.sce.

  //precision=10;
  precision=2;

  Thresholds_EE=linspace(0.1,0.15,precision)*PPmax;
  nb_EE=length(Thresholds_EE);
  //
  Thresholds_BB=linspace(0.75,0.99,precision)*SSmax;
  nb_BB=length(Thresholds_BB);

  ViabProba=zeros(nb_BB,nb_EE);

  for bb=1:nb_BB do
    SS_min=Thresholds_BB(bb)*Summer;
    for ee=1:nb_EE do
      PP_min=Thresholds_EE(ee);
      [FEEDBACK,VALUE]=MSDP(SS_min,PP_min);
      VV=VALUE(1);
      ViabProba(bb,ee)=VV(nb_SS-2,1);
      // initial state
    end
    // of the ee loop
  end
  // of the bb loop

  save("ViabStoch.dat",ViabProba)

Question 5 Launch the above code (maybe you will have to reduce the time step, or the horizon, and adapt the code in consequence if the computation takes too much time). Visualize the maximal viability probability starting from an almost full dam. Draw iso-probability curves. Comment on what you observe.

  SP=10^6;
  SP=10;
  // scale probability
  SC=10;
  SC=2;

  xset('window',1);// xclear(); // xbasc();
  xset('colormap',jetcolormap(20));
  //    plot3d1(Thresholds_BB,SC*Thresholds_EE,SP*ViabProba);
  //    plot3d1((1:nb_BB)/nb_BB,(1:nb_EE)/nb_EE,SP*ViabProba);
  plot3d1(SC*Thresholds_BB,Thresholds_EE,SP*ViabProba);
  xtitle("Maximum viability probability","Environmental constr. (volume)", ...
         "Economic constraint (profit)")

  // ----------------------------------------------------------------

  xset('window',10);contour(Thresholds_BB,Thresholds_EE,ViabProba,[0.7:0.05:1]);
  xtitle("Maximum viability probability","Environmental constraint (volume)", ...
         "Economic constraint (profit)")

Figure 5: Iso-values for the maximal viability probability as a function of guaranteed thresholds S^♭ and B^♭

References

D. P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientiﬁc, Belmont, Massachusets, second edition, 2000. Volumes 1 and 2.

M. De Lara and L. Doyen. Sustainable Management of Natural Resources. Mathematical Models and Methods. Springer-Verlag, Berlin, 2008.

Dam Viable Management under Uncertainty

Contents

1 Problem data

1.1 Dam dynamics

1.2 Criterion: intertemporal payoﬀ

1.3 Constraint: minimal volume during the Summer months

1.4 Water turbined strategy

1.5 Probabilistic model on water inputs and expected criterion

2 Maximizing the expected payoﬀ and computing the resulting probability to satisfy the constraint

2.1 Dynamic programming equation

2.2 Data for the numerical simulations

2.3 Scilab code for the additive stochastic dynamic programming equation

2.4 Optimal strategy when the ﬁnal “value of water” is not zero

2.5 Evaluation of the probability to satisfy the tourism constraint

3 Maximizing the viability probability to guarantee jointly payoﬀ and summer water volume

3.1 Multiplicative dynamic programming equation

3.2 Scilab code for the multiplicative stochastic dynamic programming equation

3.3 Maximal viability probability function and viability kernels

3.4 Maximal viability probability as a function of guaranteed thresholds

References