Capacity expansion of power generation

Description: Models the extensive form of a deterministic multi-stage capacity expansion problem. In this model we can have multiple resources of the same type which have identical properties. The model can be further developed into a stochastic one.

Tags: ampl-only, energy, planning, mip, power-generation

Notebook author: Gyorgy Matyasfalvi <gyorgy@ampl.com>

Model author: Ahmed et al., Gyorgy Matyasfalvi

References:

1. Ahmed, S., King, A. J. and Parija G. (2003). “A Multi-Stage Stochastich Integer Programming Approach for Capacity Expansion Under Uncertainty”. In: J. of Global Optimization 26.1 (May 2003), pp. 3-24. URL: https://doi.org/10.1023/A:1023062915106

# Install dependencies
%pip install -q amplpy

# Google Colab & Kaggle integration
from amplpy import AMPL, ampl_notebook

ampl = ampl_notebook(
    modules=["coin"],  # modules to install
    license_uuid="default",  # license to use
)  # instantiate AMPL object and register magics

Use %%ampl_eval to evaluate AMPL commands

%%ampl_eval
option version;

option version 'AMPL Version 20230301 (Linux-5.15.0-1037-azure, 64-bit)\
Demo license with maintenance expiring 20240131.\
Using license file "/home/gomfy/miniconda3/envs/tutorial/lib/python3.11/site-packages/ampl_module_base/bin/ampl.lic".\
';

Use %%writeifile to create files

%%writefile cap_ef.mod

# Declare index sets
set STAGES ordered;
set RESOURCES ordered;

# Decision variables
var cap_exp {STAGES, RESOURCES} >= 0;        # Variable cost associated with increase in capacity
var cap_dec {STAGES, RESOURCES} integer; # Fixed cost associated with decision to involve a new resource

# Parameters
param var_cost {STAGES, RESOURCES} > 0;
param fix_cost {STAGES, RESOURCES} > 0;
param cap_ub {STAGES, RESOURCES} > 0;
param demand {STAGES};

# Objective function
minimize total_cost: 
    sum{t in STAGES, i in RESOURCES} (var_cost[t,i]*cap_exp[t,i] + fix_cost[t,i]*cap_dec[t,i]);

# Constraints
subject to cap_acqu_bound {t in STAGES, i in RESOURCES}:
        cap_exp[t,i] <= cap_ub[t,i]*cap_dec[t,i]; # Upper bounds on total capacity

subject to statisfy_demand {T in STAGES}:
        sum {t in STAGES, i in RESOURCES: ord(t) <= ord(T)} cap_exp[t,i] >= demand[T]; # Demand needs to be satisfied at all times

subject to one_resource:
        sum {t in STAGES} cap_dec[t,first(RESOURCES)] <= 1;

Writing cap_ef.mod

%%writefile cap_ef.dat

# Index sets
set STAGES := 2030 2040 2050;
set RESOURCES := hydro nuclear coal;

# Billions of USD
param var_cost:    hydro  nuclear  coal   :=
            2030   0.01   0.01    0.8
            2040   0.011  0.013   1.9 
            2050   0.012  0.014   2.7    ;

# Billions of USD
param fix_cost:    hydro  nuclear  coal   :=
            2030   25     10       1
            2040   28     11       2.3 
            2050   25     11.5     3.8  ;

# In GWH 
param cap_ub:      hydro  nuclear  coal   :=
            2030   300    12       20
            2040   290    15       21 
            2050   280    18       20   ;

# In GWH
param demand := 
            2030 500 
            2040 1000 
            2050 1105   ;

Writing cap_ef.dat

%%writefile cap_ef.run
# ---------------------------------------------------------------
# Solve capcity expansion problem then display decison variables
# ---------------------------------------------------------------

option solver cbc;
option solution_round 6;

model cap_ef.mod;
data cap_ef.dat;
solve;

for {t in STAGES} {
    printf "\n\n\t\t**%s**\n", t;
    printf {i in RESOURCES}: "%s: %d\t", i, cap_dec[t,i];
    printf "\n";
}
printf "\n";

Writing cap_ef.run

Use %%ampl_eval to run the script cap_ef.run

%%ampl_eval
commands cap_ef.run;

cbc 2.10.7: cbc 2.10.7: optimal solution; objective 640.67
212 simplex iterations
212 barrier iterations
4 branching nodes


		**2030**
hydro: 1	nuclear: 14	coal: 2	


		**2040**
hydro: 0	nuclear: 34	coal: 0	


		**2050**
hydro: 0	nuclear: 5	coal: 0