Jagathy Raj, V P; Jasmin, E A; Imthias Ahamed, T P(IEEE, November 19, 2008)
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Abstract:
Reinforcement Learning (RL) refers to a class of
learning algorithms in which learning system learns which
action to take in different situations by using a scalar
evaluation received from the environment on performing
an action. RL has been successfully applied to many multi
stage decision making problem (MDP) where in each stage
the learning systems decides which action has to be taken.
Economic Dispatch (ED) problem is an important
scheduling problem in power systems, which decides the
amount of generation to be allocated to each generating
unit so that the total cost of generation is minimized
without violating system constraints. In this paper we
formulate economic dispatch problem as a multi stage
decision making problem. In this paper, we also develop
RL based algorithm to solve the ED problem. The
performance of our algorithm is compared with other
recent methods. The main advantage of our method is it
can learn the schedule for all possible demands
simultaneously.
Jagathy Raj, V P; Jasmin, E A; Imthias Ahamed, T P(Elsevier, February 5, 2011)
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Abstract:
This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this
paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then
two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission
losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated
through different representative systems: a three generator system with given generation cost table, IEEE
30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions
and a 20 generator system considering transmission losses. A comparison of the computation times
of different algorithms is also carried out.
Description:
Electrical Power and Energy Systems 33 (2011) 836–845
Jagathy Raj, V P; Jasmin, E A; Imthias Ahamed, T P(ACEEE, May , 2010)
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Abstract:
Unit Commitment Problem (UCP) in power
system refers to the problem of determining the on/ off
status of generating units that minimize the operating cost
during a given time horizon. Since various system and
generation constraints are to be satisfied while finding the
optimum schedule, UCP turns to be a constrained
optimization problem in power system scheduling.
Numerical solutions developed are limited for small systems
and heuristic methodologies find difficulty in handling
stochastic cost functions associated with practical systems.
This paper models Unit Commitment as a multi stage
decision making task and an efficient Reinforcement
Learning solution is formulated considering minimum up
time /down time constraints. The correctness and efficiency
of the developed solutions are verified for standard test
systems
Description:
International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 3, May 2010