The impact of water quality as an environmental constraint

Renewable Energy 34 (2009) 655–659
Contents lists available at ScienceDirect
Renewable Energy
journal homepage: www.elsevier.com/locate/renene
The impact of water quality as an environmental constraint on operation
planning of a hydro-thermal power system
A.C.M. Valle a, *, M.A.A. Aguiar b, G. Cruz, Jr. a
a
b
School of Electrical and Computer Engineering, EEEC, Universidade Federal de Goiás, 74605-220 Goiania, Goiás, Brazil
Environmental Sciences Doctorate Program, CIAMB, Universidade Federal de Goiás, Brazil
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 10 December 2007
Accepted 12 May 2008
Available online 30 June 2008
This work presents the development of a model that applies water quality restriction to the long term
planning hydro-thermal power system operation. Taking water quality into account in the long term
planning, guarantees the multiple water usage. The objective is to investigate the sensibility of the long
term planning hydro-thermal power system to the environmental variables focusing water quality.
Among water quality indicators the option was chlorophyll-a and the nutrient phosphorus establishing
a relation with the reservoir storage.
2008 Elsevier Ltd. All rights reserved.
Keywords:
Chlorophyll-a
Hydro-thermal power systems
Energetic planning
1. Introduction
Reservoirs are engineered structures built to benefit a population regarding energy production, recreation, sports, commercial fishing, flood control and water supply. Any event that restricts
these uses causes perceptible impact, along with financial repercussions. A cause of reservoirs unavailability is eutrophication,
or excessive lake plant growth.
In Brazil, according to ANEEL, Agência Nacional de Energia
Elétrica, in September 2003, there were 517 registered operational
power plants, 378 of which were small sized plants – micro and
small power plants. Power plants over 30 MW (139 plants, a total of
69,563 MW) correspond to 98.4% of the country hydroelectricity
capability installed (70,693 MW). In October 2003, 79.09% of the
installed capacity (MW) for electrical energy generation came from
hydropower plants, 18.51% from thermal plants, 2.37% from thermonuclear plants and 0.03% from others [1].
The ideal conditions for electrical energy production can contribute to excessive algae growth, what can be considered one of
the symptoms of eutrophication. According to Imteaz [2], eutrophication is caused by nutrient overload and results in a daily
variation of dissolved oxygen and even unbalanced ecosystems.
High phosphorus concentrations lead to high algae concentration reducing water transparency, which can result in anoxic bottom water and causes financial effects associated with urban water
* Corresponding author. Tel.: þ55 6232096070; fax: þ55 6235211806.
E-mail addresses: [email protected] (A.C.M. Valle), [email protected]
(M.A.A. Aguiar), [email protected] (G. Cruz Jr.).
0960-1481/$ – see front matter 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.renene.2008.05.024
use and recreation [3]. In Brazil advanced eutrophication is characterized by cyanobacteria blooms (potentially toxic) and other
kinds of algae.
The most frequent planning constraint used for phytoplankton
biomass is the amount of chlorophyll-a. The average chlorophylla concentration usually correlates to the eutrophication level, also
a good algae bloom indicator, which can be caused by retention
time increase for energy production. An [4] presents the relation
(chlorophyll-a versus total phosphorus) in log-log regression
models, which in many cases show a quadratic relation.
The objective of this paper is to investigate the sensibility of long
term planning hydro-thermal power system to environmental
variables related to water quality. For this purpose a function that
relates chlorophyll-a, phosphorus and reservoir storage is determined and applied as a restriction for the hydro-thermal power
system planning [5].
2. Materials and methods
2.1. Eutrophication
Brazilian CONAMA 357/2005 legislation provides parameters for
rating and classifying the hydro resources and its uses. For fresh
water, class II, according to CONAMA resolution number 357, dated
March 17, 2005, the following conditions and water quality standard should be observed [6]:
– chlorophyll-a: maximum amount 30.0 g/l;
– total dissolved solids: maximum amount 500.0 mg/l;
656
A.C.M. Valle et al. / Renewable Energy 34 (2009) 655–659
– total phosphorus: maximum amount 0.030 mg/l in lentic
environment;
– nitrate: maximum amount 10.0 mg/l;
– dissolved oxygen: not less than 5 mg/l;
– turbidity: up to 100 (UTN);
– pH: from 6.0 to 9.0;
– biochemical oxygen demand 5 days at 20 C: up to 5 mg/l O2;
and
– cyanobacteria density: maximum amount 50,000 cell/ml or
5 mm3/l.
2.2. Basic eutrophication mechanisms
The main process control variable is solar radiation. Thus, water
body eutrophication can vary according to the water surface geographic location, the solar radiation penetration level at different
depths, the magnitude and nutrient types, and water movement
due to transport and dispersion flow. Aquatic plants widely vary in
the species composition; the impact of each of the mentioned
factors may vary. Some species may require less light and nutrients
for its development than other species.
Tropical countries can have up to 14 h of light available daily,
favoring phytoplankton biomass, which starts to increase as water
temperature rises, as well as light and nutrients in its dissolved
form for phytoplankton use. This is favored by the undesired
presence of different polluting sources in the draining basins.
Sperling [7] stated that it can be estimated if the algae growth in
a lake is being controlled by phosphorus or nitrogen based on the
concentration of nitrogen and phosphorus (N/P). Also, if N/P > 10
the environment would be limited by phosphorus while, if N/P < 10
it would be limited by nitrogen.
According to Salas and Martino [8], the majority of Latin
America tropical lakes is limited by phosphorus. Another aspect is
that even if an external nitrogen input control is made, there are
algae capable of fixating the atmospheric nitrogen. Therefore, they
rather give greater priority to the phosphorus source control when
they intend to control eutrophication in a water body [7].
The main issue of the generation system planning (GSP) is its
complexity, due to the great number of reservoirs and thermal
plants, and the high regulation capacity that requires long study
periods for its planning. The variables are future inflow, function of
the climate conditions and the electricity demand to be supplied.
GSP is also a decision making process based in uncertainties,
where the operator must be provided with adequate statistical
tools to guide him throughout the decision process.
The main objective of the energy (system) operation planning is
minimizing the hydro-thermal system operation costs in a specified period. The problem formulation considering the known inflow and energy demand is shown in Eqs. (1)–(14), in a monthly
analysis.
min
t
pj
T
1
X
"
1
t
t
ð1 þ bÞ
t¼0
t
J G
#
þ
1
ð1 þ bÞ
T
V xT
(1)
subject to
Gt ¼ Dt P t
Pt ¼
J
X
(2)
ptj
(3)
git
(4)
gti git gti
(5)
ptj ¼ kj hmon xtj hjus utj pc qtj
(6)
ptj ptj ptj
(7)
j¼1
Gt ¼
I
X
i¼1
2.3. Hydro-thermal power systems planning
2
Due to the Brazilian generation plant characteristics hydrothermal operation planning is a complex task to achieve. Its objective is to determine a strategy to minimize the system units’
operation cost. The strategy must ensures supply and provide the
energy required in order to guarantee the minimum costs of thermal complementation. This evaluation comprehends the reservoir
optimization and the plant hourly generation taking into account
the environmental and functional restraints.
The Brazilian system is composed by smaller interconnected
systems with reservoirs with high regulation capacity. There is
a dependency between the decisions made in the system planning.
The available hydraulic energy is limited; a decision made in the
present must guarantee the smallest thermal complementation in
the present and assure that the future energy generation is at no
risk. It is a dynamic system; the present is determined by the past
and affects the future.
The most important step of the managing model formulation is
the selection of the objective function and its restrictions and variables. In the water quality management the most common objective function used is the minimization of the costs for
maintaining water quality at a specific level. Water quality is
characterized by the level of variables as dissolved oxygen and algal
biomass [9].
xtþ1
j
¼
xtj
þ
4yt
j
þ
X
3
utk
utj 5Dt
(8)
k˛Uj
utj ¼ qtj þ vtj
(9)
xtj xtj xtj
(10)
utj utj utj
(11)
qtj qtj qtj hliq
(12)
vtj 0
(13)
x0j provided
(14)
A.C.M. Valle et al. / Renewable Energy 34 (2009) 655–659
where
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T – number of time intervals (months);
gti – thermoelectric plant i generation at instant t (MW);
ptj – hydroelectric plant j generation at instant t (MW);
b – future operation cost discount rate (%);
Jt(Gt) – total thermoelectric operation cost (US$/MW);
Dt – electricity demand at instant t (MW);
Gt – total thermoelectric generation at instant t (MW);
Pt – total hydroelectric generation at instant t (MW);
V(xT) – system’s reservoir final state associated cost (US$);
g ti – thermoelectric plant i minimum generation at instant t
(MW);
g tj – thermoelectric plant i maximum generation at instant t
(MW);
ptj – hydroelectric plant j minimum generation at instant t
(MW);
ptj – hydroelectric plant j maximum generation at instant t
(MW);
kj – hydroelectric plant j efficiency;
I – number of thermoelectric plants;
J – number of hydroelectric plants;
x – reservoir storage (hm3)xj – reservoir j minimum storage
(hm3);
xj – reservoir j maximum storage (hm3);
ytj – incremental inflow at reservoir j at instant t (m3/s);
utj – release at reservoir j at instant t (m3/s);
qtj – discharge at reservoir j at instant t (m3/s);
vtj – spillage at reservoir j at instant t (m3/s);
hmon(x) – reservoir level (m);
hjus(u) – tailrace elevation (m);
pc – discharge lost (m);
and Dt – average size of moment t (s).
657
indirectly in the objective function. The restrictions are usually
related to the limitation imposed on the decision or state system
variables [10]. The operation planning presented in Eqs. (1)–(14)
comprehends construction and operation restrictions of the electrical system only.
The relation of water quality and environmental preservation
with the operation planning of hydropower systems is associated
with the maintenance of minimum outflows to guarantee pollution
dilution, minimum storage levels and acceptable dissolved oxygen,
chlorophyll-a and other parameters’ levels regulated by legislation.
Constraints can be applied to reduce abrupt changes in the water
level and to keep the level within limits to prevent ecological
damage either due to low water or flood conditions.
Including water quality restriction in the energetic power system planning is of great importance and guarantees that the water
multiple usages are in focus. Among the many variable constraints
that could be applied, chlorophyll-a was chosen, associated with
the phosphorus amount in the reservoir. The new approach focuses
on the eutrophication process represented by the chlorophylla amount, which is limited by CONAMA legislation.
Its inclusion could be done in two different ways: in the objective function or in the restrictions. Relating the three variables:
chlorophyll-a, phosphorus and reservoir storage, a function was
determined for each reservoir, and two new constraints were added
to hydro-thermal planning problem:
logðchlorophyll aÞ ¼
A0 þ A1 logðphosphorusÞ þ A2 xtþ1
xtj
j
ð15Þ
logðchlorophyll aÞ ¼ logð30 mg=lÞ
(16)
2.4. Data base for analysis
3. Results and discussion
Ilha Solteira, Porto Primavera and Três Irmãos reservoirs were
the locations of this study. Ilha Solteira and Porto Primavera are
located on the Paraná River, and Três Irmãos on the Tietê River. The
limnological monitoring and water quality data were provided by
Companhia Energética do Estado de São Paulo, CESP, exclusively for
the development of this study. The coordinates and energy capacity
of the reservoirs are shown in Table 1.
Some of the data provided for surface, medium and bottom layer
depths were
Statistical analyses of the reservoir storage, chlorophyll-a and
phosphorus using the software MATLAB, determined a function
that expresses these variables correlation. This relation is the environmental restriction to be added in the hydro-thermal operation
planning formulation. A simulator that includes water quality environmental constraints in the operation planning was
implemented.
A sampling site close to the dam was chosen for the study.
Nutrient concentration allied to no adverse conditions that enhance algal development, led to higher levels of chlorophyll-a,
making it the best place to be studied. Closer to the tributaries, in
the rainy season as well as in the dry season, there is a greater water
mixture that reduces the concentration of these elements, altering
water transparency, which restricts algal development.
–
–
–
–
–
–
water temperature ( C);
dissolved oxygen (mg/l);
pH;
conductivity (mS/cm);
turbidity (NTU); and
chlorophyll-a (mg/l);
Table 2
xtj ), surPolynomial coefficients for log(chlorophyll-a) ¼ f(log(phosphorus), xtþ1
j
face values
2.5. Developed activities
There are many ways to consider multiple usage of water in the
reservoir operation. These uses are associated to state, decision and
restriction variables. The state variables are included directly or
Table 1
Study site information
Reservoir
Coordinates
Power capacity
(MW)
Ilha Solteira
Porto Primavera
Três Irmãos
S 20 220 15.600 W 51 210 32.500
S 22 270 22.600 W 52 540 20.800
S 20 400 24.800 W 51 080 47.000
3444.0
1540.0
807.5
Reservoir
Type
Coefficients
A1
A2
Três Irmãos
Rainy
Dry
Generic
1.6419
0.2661
0.4937
0.9885
0.1073
0.1419
0.0090
0.0221
0.0087
Ilha Solteira
Rainy
Dry
Generic
0.0113
0.1811
0.1045
0.1222
0.0036
0.2916
0.0067
0.0196
0.0024
Porto Primavera
Rainy
Dry
Generic
1.8140
0.5105
0.0514
1.590
0.1761
0.1670
0.0340
0.0447
0.0171
A0
658
A.C.M. Valle et al. / Renewable Energy 34 (2009) 655–659
Table 3
xtj ), averPolynomial coefficients for log(chlorophyll-a) ¼ f(log(phosphorus), xtþ1
j
age of the first two layers
Type
Coefficients
A0
Três Irmãos
Ilha Solteira
Porto Primavera
A1
A2
Rainy
Dry
Generic
1.9271
0.3105
0.5946
1.2513
0.1332
0.2337
0.0098
0.0215
0.0106
Rainy
Dry
Generic
0.0310
0.1231
0.0937
0.1300
0.0706
0.2592
0.0072
0.0170
0.0170
Rainy
Dry
Generic
0.8110
0.6588
0.1315
0.7510
0.3337
0.0497
0.0190
0.0195
0.0088
Stored Energy (%)
Reservoir
80
60
40
20
0
Three different equations of chlorophyll-a as a function of
phosphorus and reservoir storage were determined. A relation of
the variables for the whole period, called generic function, one for
the rainy season and another representing the months of the dry
season. After a statistical analysis, it could be observed that the
generic equation is less representative than the ones for dry and
rainy seasons, when adjusting a polynomial to represent the relation among reservoir variables. Therefore, it seemed more accurate to aggregate months with similar hydrological behavior to
adjust these polynomials.
Table 2 presents the A0, A1 and A2 polynomial coefficients for the
surface data and Table 3 presents the coefficients obtained by the
analysis of the average of the first two layers data (surface and
middle). The functions are seasonal (rainy and dry) and generic,
where chlorophyll-a and phosphorus are measured in m/l and the
reservoir storage is in %.
Two different simulations were implemented: (a) analyses using the generic function and (b) analyses where the months were
separated in rainy and dry seasons. For the simulation using the
Paraná River basin, the water quality restriction was applied in Ilha
Solteira and Porto Primavera plants.
Comparing the base case, where no environmental constraints
were applied, with a simulation where the generic equation was
applied to Ilha Solteira, a difference in the stored energy of 14.39%
was observed. When applying the rainy and dry season equations
the difference was of 1.459%. The hydraulic generation showed
smaller differences in both cases. For the generic equation the
difference from the base case was of 0.306% and for the dry and
rainy season equations of 0.2942%.
0
5
10
15
20
25
Figs. 1 and 2 show for a 36-month period the amounts of stored
energy for the generic equation and the dry and rainy equations
applied to both plants, Ilha Solteira and Porto Primavera at the
Paraná River. Figs. 3 and 4 present the energy generation for the
same analyses.
It could be observed that the influence of the environmental
constraint was not significant, given the goals of generated energy
and system stored energy.
Table 4 presents the results of the Paraná River basin simulation,
for Ilha Solteira and Porto Primavera. The simulations refer to the
surface data:
(A)
(B)
(C)
(D)
(E)
(F)
base case, no water quality restriction applied to the plants;
generic polynomial for Ilha Solteira only;
dry and rainy season polynomial for Ilha Solteira only;
generic polynomial for Porto Primavera;
dry and rainy season polynomial for Porto Primavera only;
generic polynomial for both Ilha Solteira and Porto Primavera;
and
(G) dry and rainy season polynomial for both Ilha Solteira and
Porto Primavera.
The equations used in these simulations were obtained using
the surface data (Table 2). An average function was also obtained
(Table 3), based on the average of the equations for both surface and
Base Case
Generic
Hydro Generation (MW)
4000
60
40
20
35
Fig. 2. Stored energy for Paraná River basin, with Ilha Solteira and Porto Primavera,
comparison of the base case and rainy and dry equations.
4500
80
30
Months
Base Case
Generic
100
Stored Energy (%)
Base Case
Dry and Wet
100
3500
3000
2500
2000
1500
1000
500
0
0
5
10
15
20
25
30
35
Months
Fig. 1. Stored energy for Paraná River basin, with Ilha Solteira and Porto Primavera,
comparison of the base case and generic equation.
0
0
5
10
15
20
25
30
35
Months
Fig. 3. Hydraulic energy for Paraná River basin, with Ilha Solteira and Porto Primavera,
comparison of the base case and generic equation.
A.C.M. Valle et al. / Renewable Energy 34 (2009) 655–659
4500
Base Case
Dry and Wet
Hydro Generation (MW)
4000
3500
3000
2500
2000
1500
1000
500
0
0
5
10
15
20
25
30
35
Months
Fig. 4. Hydraulic energy for Paraná River basin, with Ilha Solteira and Porto Primavera,
comparison of the base case and rainy and dry equations.
middle layers, trying to express movement within the water column. It could be observed that there was no significant variation in
the energy generated and the stored energy for simulations using
the surface and middle polynomials.
Simulation in the Tietê River basin, where the Três Irmãos site is
located showed no variation in the variables analyzed and no violation of the chlorophyll-a limit associated to the phosphorus levels.
The methodology applied to chlorophyll-a can be used for other
limnological variables that are regulated by environmental legislations. The inclusion of other variables may bring a significant
variation due to cumulative effects, although in isolation the influence is not relevant.
4. Conclusions
The monitoring programs provide data that effectively contribute to the analysis of the tendency of an ecosystem water
quality. The tendency presented by these analyses allows for correction and prevention measures that minimize the damage caused
by eutrophication.
The hydro-thermal operation planning system does not incorporate, in its restrictions, water quality variables. Inclusion in
the planning of the multiple water usage issue, requires including
other restrictions, besides the ones already applied by the electrical
power sector.
Interventions that can prevent eutrophication in reservoirs are
the maintenance of minimum outflows to guarantee pollution dilution, of minimum volume levels to assure acceptable dissolved
oxygen, chlorophyll-a and other parameters’ levels, and the reduction of abrupt changes in the water level, keeping it within
limits to prevent ecological damage.
Among all variables used for monitoring, chlorophyll-a and the
total phosphorus load are considered of greater relevancy and application [11,12]. Chlorophyll-a was chosen to be applied in the
hydro-thermal operation planning in order to minimize the unwanted effects in water quality. The choice for the model that
Table 4
Results for water quality restriction applied in the Paraná River basin
Results
Case A
Case B
Case C
Case D
Case E
Case F
Case G
R1
R2
R3
2252.96
23.48
NE
2259.85
20.10
600
2259.59
23.14
172
2240.34
22.46
600
2244.78
22.90
303
2240.85
18.88
1150
2251.48
22.58
478
R1: average hydraulic generation (MW); R2: average stored energy (%); R3: number
of chlorophyll-a violations; and NE: not evaluated.
659
relates chlorophyll-a with phosphorus was due to researches
where other professionals determined such a relation for tropical
lakes [11,13–16].
There is no single function describing the variables relation for
all reservoirs. Each reservoir has its own pattern, and a specific
function of chlorophyll-a, phosphorus and reservoir volume is determined that involves monthly, seasonal and annual information.
The planning model employs a monthly analysis, a function of
chlorophyll-a, phosphorus and reservoir volume for each month
satisfies the model requirements. We believe that a monthly function would be more expressive but the amount of data provided was
not enough to establish an accurate statistical relationship.
The seasonal approach to determining the reservoir polynomials was the most expressive representation, taking into account the precipitation level, as the hydrological and limnological
characteristics tend to be similar when clustered into rainy and dry
seasons.
The analyses showed that the effect of the inclusion of an environmental variable, chlorophyll-a as a restriction in the hydropower plants operation planning does not interfere significantly in
the energy generation and in the system stored energy. So no cost
variation per generated MW would be applied, an interesting
conclusion for the electrical power system operators.
These conclusions were based on the use of a single environmental variable. Other variables may be added to the restrictions,
which could lead to cumulative effects, altering the costs of power
per MW produced by the system.
We suggest that future research investigate the inclusion of
other variables in this methodology in order to evaluate if the effect
would be more expressive.
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