DEFINITION OF SCARCITY-BASED WATER PRICING POLICIES
THROUGH HYDRO-ECONOMIC STOCHASTIC PROGRAMMING
HECTOR
1
1,3
MACIAN-SORRIBES ,
4- FROM MROC VALUES TO SCARCITY-BASED PRICING POLICIES
SCARCITY: Demand > Supply
 IF scarcity exists, THEN too low price
 IF price is OK, THEN Demand = Supply
 Stochastic
programming
model building
 Forward optimization run
 Retrieve MROC values at
the desired nodes
HOWEVER:
 What price must water have?
 How can it be calculated?
SCARCITY-BASED
WATER PRICING








Opportunity
costs
Externalities
Considers the value of water
Encourages users to save water
Puts water on its most valuable uses
Achieves efficiency in water management
Only valid if marginal values are charged
Marginal value calculus is challenging
Requires intensive monitoring operations
Equity problems if users cannot pay water
2- THE MARGINAL RESOURCE OPPORTUNITY COST (MROC)
 System physical features
 Desired pricing policy features :
o Spatial dimension (basinwide, district, etc.)
o Temporal dimension (multiannual, monthly, etc.)
o Dependent variables (storages, inflows, etc.)
Raw MROC values
calculus
Benefits
BMC
MROCL,t = λL,t
MROCL,t = BMC - BBC
The model












2 reservoirs (Arenos and Sichar)
2 sub-basins: upper and middle basin
4 agricultural demands
1 minimum environmental flow
2 streams with seepage losses
Current rule: zone-based management
DSS building
Build-up of
scarcity-based
step pricing
policies
 Apply prices by:
 Direct implementation
 Transformation into curtailments
Objective: maximize benefits
Regular hydro-economic SDP approach
91-point discrete storages grid
16-point discrete inflows grid
Lag-1 Markov chain employed
Built using the GAMS software
N
A
O
N
NL
E
O
LB
LU
C
EN
A
MIJ AR ES
VEO
SEC
O
S
VEO
MO
NT
AN
Sichar
O
RE
VIUDA
Arenos
MI
JA
LUCE NA
GA
RR
MIJARE S
VIU D A
M
O
VA
S
MI
JA
RE
S
AVERAGE BENEFITS
M€/year
51.05
49.93
51.12
51.12
 The best pricing policies performance is similar to the one offered by the SDP
 Equal performance between alternatives in non-drought years
 Improvement obtained by SDP and pricing policies use arises during droughts
6- CONCLUSIONS
 Stochastic programming is a useful tool for estimating optimal policies and MROC time series
under hydrological uncertainty
 Pricing policies defined using MROC data series, after statistical analysis and step building, are
adequate to improve system's global efficiency, being their performance level similar to the one
obtained with SDP
 The suggested framework for pricing policy building can be used to define pricing policies either
under general conditions or drought events.
 Uncertainty is taken into account by the use of stochastic programming
 Equity issues must be assessed using additional economic instruments
AKNOWLEDGEMENTS
This study has been partially funded by the European Union’s Seventh Framework Program (FP7)
ENHANCE (number 308.438)
REFERENCES
ON
RE
 Global MROC probability distribution
plot
 Retrieve MROC values from desired
percentiles
 Summarize all the state values for each
MROC percentile in the form of steps
 Define pricing policies based on those
steps
LE
IJA
Statistical analysis
on combined
MROC values
 Find out the historical system state values
that correspond to each MROC percentile
N
MO
M
SDP
Current
Price 10
Price 11
Price 12
M€/year
63.75
63.06
63.81
63.81
63.81
Scarcity-based
pricing policies
simulation
3- CASE STUDY: Mijares River Basin (Spain)
The river
AVERAGE BENEFITS
SDP
Current
Price 4
Price 5
added one unit of water at
location “L” and time “t”
Modified Case
Simulation (MC)
1940-2009
SIMULATION
MROC values
combination
Base Case Optimization
Shadow values (λL,t) retrieval at the
desired locations “L” and times “t”
 15 pricing policies defined based on the SDP-driven MROC steps
 Pricing policies were evaluated using a hydro-economic simulation model built in MatLab
 Analysis period: 1940-2009, with focus on the 1977-1986 drought
1977-1986
SIMULATION
MROC assessment under optimization
Benefits
BBC
5- RESULTS
Model reassessment?
 Benefits that would have been obtained at one specific location and time if the available water
resources would have been increased by one unit
 Basinwide hydro-economic modeling techniques are required for its assessments
Base Case
Simulation (BC)
 Transform node-related MROC values into
global MROC values with regard to:
Advantages and disadvantages
Supply costs
MROC assessment under simulation
AMAURY
2
TILMANT
Research Institute of Water and Environmental Engineering. Universitat Politècnica de València. 2 Department of Civil and Water Engineering. Université Laval. 3 Contact email: [email protected]
1- INTRODUCTION: The scarcity-price link
MARGINAL
VALUE OF
WATER
MANUEL
1
PULIDO-VELAZQUEZ ,
No Scarcity
 High storage
 Low MROC
Scarcity
 Low storage
 High MROC
1) Griffin, R.C. (2006) “Water Resource Economics: The Analysis of Scarcity, Policies, and Projects.” The MIT Press,
Cambridge, USA. 402 pp.
2) Harou, J.J., Pulido-Velazquez, M., Rosenberg, D.E., Medellín-Azuara, J., Lund, J.R., and Howitt, R.E. (2009). “Hydroeconomic models: Concepts, design, applications, and future prospects.” Journal of Hydrology, 375, 627-643.
3) Pulido-Velazquez, M., Alvarez-Mendiola, E., and Andreu, J. (2013). “Design of efficient water pricing policies
integrating basinwide resource opportunity costs.” Journal of Water Resources Planning and Management 139,
583-592.
4) Rogers, P., de Silva, R., and Bhatia, R. (2002). “Water is an economic good: How to use prices to promote equity,
efficiency and sustainability.” Water Policy 4 1-17.
5) Stedinger, J.R., Sule, B.F., and Loucks, D.P. (1984). “Stochastic Dynamic Programming Models for Reservoir
Operation Optimization.” Water Resources Research, 20(11), 1499-1505.
6) Tilmant, A., Arjoon, D., Marques, G.F. (2014). “Economic Value of Storage in Multireservoir Systems.” Journal of
Water Resources Planning and Management 140 , 375-383
EUROPEAN GEOSCIENCES UNION GENERAL ASSEMBLY 2014
Vienna | Austria | 27 April – 02 May 2014