Abstract model is required to be developed

Abstract – As the world population
is growing, its water demand is becoming more and more diversified; the
difference between the demand and supply is continuously increasing. The only
solution of this problem is the systematic utilisation of the available water
resources, or by harvesting additional water potential from new water resources
projects and/or by formulating strategies for proper utilization of the
available resources. Considering the fact that most of the River and their
tributaries in India are Seasonal, the Major Steps taken for the remedy of
above mentioned problem is the “Reservoir Projects”.

Development
of monthly release policy for a reservoir is a multistage decision making
process. A model is required to be developed for each individual system of
reservoirs. The essential need for formulation of optimal policy is to determine
of the schedule of releases from a reservoir system, which can maximizes or
minimizes the utilities associated with the release of water.

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Various algorithms have
been applied to optimize the Reservoir operation and maximize the net benefit,
but they have their own limitations, the selection of appropriate model for
deriving reservoir operating policies is difficult and more often there is a
scope for further improvement as the model selection depends on the
characteristics of reservoir considered, on the availability of data, on the
specified objectives and constraints. In the present paper, various
optimization Algorithm will be compared along with their Advantage and
Limitation to find the best suited Algorithm for reservoir operation.

Keywords- Reservoir Operation, Optimization
Algorithm

Introduction
– The essential need for formulation of optimal policy is to determine
of the schedule of releases from a reservoir system, which can maximizes or
minimizes the utilities associated with the release of water.
Applying optimization techniques for reservoir operation is not a new idea.
Various techniques have been applied in an attempt to improve the efficiency of
reservoir(s) operation. These techniques include Linear Programming, Nonlinear
Programming and Dynamic Programming. Recent and more advanced techniques are
Artificial Neural Network, Fuzzy logic, Genetic Algorithm, Evolutionary
Algorithm, Ant Colony Optimization, Particle Swarm Optimization  and Differential Evolution etc.

A
large number of studies are reported by various investigators on optimal
operation of reservoirs, a few of the recent studies are discussed in
chronological order as below.

CRITIQUE

On the basis of study of the literature reviewed
above, it’s clear that optimization in operation of reservoir is one of the
important activities in the field of reservoir operation that aim at an
effective and efficient utilization of water with maximum benefits. Various
algorithms have been applied to optimize the Reservoir operation and maximize
the net benefit, but they have their own Advantages and Disadvantages.

The conventional Linear
Programming has been used mostly for the planning and design problems of single
reservoir systems. Natural processes are rarely linear and solving the problem
by Linear Programming forces approximations, which  may 
lead  to  either, 
approximate  or  sometimes 
even  to  unrealistic solutions. In addition, Linear
Programming yields only point solutions in the policy space and hence it is
unsuitable for the operational problems of reservoirs where decisions are
required to be made successively with the changing state of the system.
Incorporation of inflow stochasticity  
further   increases the
complexity. All the Linear Programming techniques incorporating stochasticity
viz., Stochastic Linear Programming, Chance Constrained Linear Programming,
Reliability Programming have so far been limited to the design problems of
single reservoirs.

Dynamic Programming is
suitable for sequential decision-making process of reservoir operation
problems. Its use is practically restricted to single reservoirs due to the
“curse of dimensionality”. In some cases, Deterministic Dynamic
Programming has been applied to a system of three to four reservoirs but was
found computationally inefficient. The use of Stochastic Dynamic Programming is
restricted to single reservoirs only because of its requirement of discretization
of state and space, the computer storage requirement increases exponentially
with the increase in the number of states (reservoirs).

In Artificial Neural
Network, the neural networks need training to operate. The modeling results
converge to a local minimum. Generalization and over fitting renders inaccuracy
in some cases. The model provides results, which are hard to interpret
occasionally.

Ant  Colony 
Optimization  too  has 
some  disadvantages  like 
the  theoretical analysis is
difficult, sequences of random decisions are present which are not independent,
probability distribution changes with iteration, research is experimental
rather than theoretical,  and time for
convergence is uncertain although convergence is guaranteed.

The simplicity in the
application of Honey Bee Mating optimization (HBMO) is quite an advantage but a
basic disadvantage of the original (HBMO) algorithm is the fact that it may
miss the optimum and provide a near optimum solution in a limited runtime
period.

Particle Swarm Optimization
(PSO) is inspired from the foraging behaviour of birds, because of fast
convergence, fewer parameters setting, and the easiness to implement. Even
though PSO is efficient, it also has some critical problems such as premature
convergence and easily drops into regional optimum. It shares many common
points with Genetic Algorithm (GA). Both algorithms start with a group of a
randomly generated population, both have fitness values to evaluate the
population. Both update the population and search for the optimum with random
techniques. Both systems do not guarantee success.

Differential Evolution
is an improved version of Genetic Algorithms. It relies on mutation rather than
Crossover.  Differential Evolution has
several advantages, it can search randomly, requires only fewer parameters
setting, high performance and applicable to high-dimensional complex
optimization problems. But similar to PSO, DE has several drawbacks including
unstable convergence in the last period and easy to drop into regional optimum.
            The results show the Fuzzy
inference system based reservoir operating rules (FIS-ORs) FIS-ORs to perform
considerably better than the other operating rules when they are recalibrated
every 10 year. The results also suggest that the comparative performances of
the operating rules to be influenced by weighting of the three problem
objectives. Where the weighting is such that the problem is relatively easy, it
is found the FIS-ORs to have no significant advantage.

Chaos Particle Swarm
Optimization- Differential Evolution CPSO-DE algorithm is formed by making a
few alterations in standard PSO. By sample analysis, and comparison with other
algorithms. The calculation results show that CPSO-DE improved the convergence
accuracy of PSO, the ability of global optimization, and increased the
convergence and stability at a certain degree.

 

Conclusion -As
every optimization model has its own limitations, the selection of appropriate
model for derivation of reservoir operating policies is difficult and more often
there is a scope for further improvement as the model selection depends on the
characteristics of the reservoir considered, on the availability of data, on
the specified objectives and constraints. In the present Study, Differential
Evolution algorithm, Partial Swarm Optimization and its Hybrids seems to be
better than the previous methods for the optimal operation of a reservoir. From
the optimization results, general operating policy can be derived.